________________________________________________________________________________________

________________________________________________________________________________________

Contract No.:

FP7-ICT-247914

Project full title:

MOLTO - Multilingual Online Translation

Deliverable:

D7.3 Patent MT and Retrieval. Final Report.

Security (distribution level):

Public, regular publication

Contractual date of delivery:

M33

Actual date of delivery:

March 2013

Type:

Regular Publication

Status & version:

draft 1.4

Author(s):

Maria Mateva 2 , Meritxell Gonzàlez^1 , Ramona Enache^3 , Cristina España-Bonet^1 , Lluís Màrquez^1 , Borislav Popov^2 , Aarne Ranta^3

Task responsible:

UPC 1

Other contributors:

Ontotext 2 , UGOT^3

ABSTRACT

The present document is the final report of the Patents Case Study. It describes the multilingual patents retrieval prototype, produced in this work-package, and a brief user manual to access the on-line demonstrative application. The “WP7: Patents Case Study” aims to create a prototype for automatic translation and multilingual retrieval of patents. The main highlights achieved in the final prototype, with respect to the previous versions described in the Deliverable 7.1 [MOL12b] and Deliverable 7.2 [MOL12c], are 1) the translation of patent documents semantically annotated, driven by a statistical machine translation system; 2) a new querying approach for (controlled) natural language to SPARQL translation, driven by the Grammatical Framework and the query grammar on the biomedical domain; 3) the integration of a mechanism for query back-off, based on free text search; 4) some updates on the retrieval system to improve the response time of the prototype, and also 5) some updates on the on-line user interface that address usability aspects and additional functionalities.

Contents

1 Introduction

1.1 Motivation

1.2 Related Work

1.3 The MOLTO approach

2 The Patents Case Study Prototype

2.1 Patent corpus and translation

2.1.1 The biomedical patent datasets

2.1.2 Translation of patent documents

2.1.3 Syntactic measures for MT evaluation

2.2 Patents retrieval system, ontologies and semantic annotations

2.2.1 Free text search

2.2.2 Ontologies for the biomedical domain

2.2.3 Semantic annotations extracted from the patents metadata

2.2.4 Retrieval speed optimization

2.2.5 Evaluation of the retrieval system

2.3 GF grammars for generation of SPARQL

2.3.1 The patents query language

2.3.2 Better coverage of the ontology by GF

3 The On-line User Interface for the Patents Retrieval Prototype

3.1 GUI updates

3.1.1 Multilingual annotations

3.1.2 Document source exposed

3.2 NL query examples

3.3 User Scenarios

3.3.1 Query types

3.3.2 Semantic data exploration

3.3.3 Single word query

3.3.4 FTS queries

4 Conclusions and Challenges

A Example Showing the Translation Steps of an Excerpt of Patent

B Ontologies in the Biomedical Domain

C Semantic Annotations - Final Types

D Biomedical Patents - Query Patterns

E Patent Retrieval Databases Roadmap

1 Introduction

This document is the final Report of WP7: “Patents Case Study”. This case study aims to create a prototype (the patents prototype) for automatic translation and retrieval of patents, allowing robust translation of patent abstracts and claims, cross-language retrieval of patent data and multilingual queries.

1.1 Motivation

The five major international patent offices in the world maintain patent databases written mainly in English, French, German, Chinese, Japanese and Korean. In addition, many other smaller offices maintain their databases as well, having documents written in their official languages. These offices and their users have the clear need to exchange the content of their databases and the information related to such inventions. This need has actually promoted the organization of international conferences and competitions related to the field, such as patent classification, retrieval and translation, and the development of systems able to search, access and translate patent contents, either from mono-lingual or cross-lingual databases and make them available to the community, ideally in the user’s language.

1.2 Related Work

Most of the public search interfaces, either from the patent offices as the European Patent Office (EPO[1] ), or independent ones as PatentLens[2] , offer keyword-based search on the title, or multifaceted searching and browsing through the application number, the applicants, inventors and classification codes. Also, systems as Google Patents allow free text search through the original text of the patents.

More recent systems introduced the use of ontologies, which provide a shallow representation of the information space. These light-weight ontologies provide controlled lexicons for the classification of the content. However, few systems take a real advantage of the full potential of an ontological representation. This is the case of the ontology-based retrieval model described in [VFC05]. Their model supports semantic search in document repositories through the exploitation of full-fledged domain ontologies and knowledge bases. As in our system, full documents are returned in response to the user needs. The search system takes advantage of both detailed instance-level knowledge available in the knowledge base, and topic taxonomies for classification.

The BioPatentMiner [MB04] is a more recent system for biomedical information retrieval especially designed to discover relationships among the concepts in the knowledge resources, using a concrete methodology to determine the semantic associations between two resources. The system integrates the patent information from the United States Patent and Trademark Office[3] and creates a Biomedical Semantic Web.

Regarding patent translation systems, the EPO public service, in combination with the Google Patent Translate service, offers automatic translation of abstracts, descriptions and claims excerpts selected by the user to 14 languages.

A different approach is that of the Patent Language Translations Online (PLuTO) project, a dedicated project to patent translation. Its MT framework is a web service whereby users can request translations. The translation engine uses the MaTrEx (Machine Translation Using Examples) system developed at DCU [AFG06]. It is a hybrid data-driven system built following established design patterns, with an extensible framework allowing for the interchange of novel or previously developed modules as it is defined in [TWS10].

1.3 The MOLTO approach

This report gives an overview of the translation and multilingual retrieval system for biomedical patents developed in the MOLTO, and the resources used to this end, with especial focus on the latest developments.

The patents prototype is publicly available at: http://molto-patents.ontotext.com/. It integrates four main components, machine translation, semantic annotation, document retrieval and the on-line user interface. Two different approaches to machine translation have been used. For the massive translation of text, a statistical system has been trained and adapted to translate the text and transfer the semantic annotations into the target languages. The patent documents are semantically enriched and translated using the statistical system. The resulting multilingual documents are used to feed the databases of the retrieval system. On the other hand, a rule-based system is built in order to translate from (controlled) natural language to the semantic query language (SPARQL), in the interface.

The integration of different translation methodologies into the system has been crucial to increase its capabilities and make possible extended features and functionalities, with respect to preliminary version of the system. The preliminary version of the prototype, described in Deliverable 7.1 [MOL12b] had only original patent documents in the databases and the system was only available in English and French. A complete version of the prototype, described in Deliverable 7.2 [MOL12c], included resources also for German, and patent documents translated using the Statistical Machine Translation (SMT) system trained on the domain, and described in Deliverable 5.2 [MOL12a].

The news introduced with respect to previous versions of the prototype are:

1.

A new process for statistical-based translation of patents that allows to transfer the semantic annotations and the original mark-up in the source documents to the target language (Section 2.1).

2.

The updates on the retrieval architecture in order to improve the response time (Section 2.2).

3.

A new querying approach for SPARQL generation based on the grammar – ontology interoperability automation  (Section 2.3).

4.

A new query grammar for the patents domain (Section 2.3).

5.

The new functionalities integrated in the user interface in order to improve the usability of the application, such as the integration of the free-text search as a back-off mechanism for the query language (Section 2.2.1 and Section 3).

2 The Patents Case Study Prototype

The multilingual patents retrieval system (henceforth patents prototype) consists mainly of four modules (see Figure 1). First, the English sections of the original patent documents are semantically annotated using the pipeline especially designed for the ontologies on the domain. The annotated documents are preprocessed, cleaned and marked in order to translate the text using a statistical system trained on the biomedical domain. Both, original and translated texts are then merged into single file in order to feed the retrieval system’s storage. Then, a specific query language has been designed for this use case in order to cope with the semantic annotation types and the relations in the domain ontologies.

The prototype is accessible on-line. It has a web-based graphical interface that allows for querying the system using a controlled natural language. The translation of the user queries, driven by the Grammatical Framework (GF), generates the SPARQL queries directly, as just a translation of the query to another target language. The results of the query are returned from the semantic repository and the document index, and they consist of both an RDF graph and the translated biomedical patent documents.

Figure 1: General architecture of the prototype

2.1 Patent corpus and translation

2.1.1 The biomedical patent datasets

The preparation of the patent corpus and the translation of the documents that feed the prototype databases is part of the work carried at UPC. For the patents case study we have used three different datasets.

The European Patent Office (EPO[4] ) provided some parallel corpus containing the text of 66 patents belonging to the biomedical domain (IPC[5] A61P). This corpus, which only contains the parallel raw text and the identifier of the patent, is being used as the test set of the translation systems developed in WP5.

The statistical machine translation system requires a larger parallel dataset to train the models. For this purpose we used the MAREC corpus which contains not only European patents, but also documents from other international patent offices, dated from 1976 to 2008. Nonetheless, only the documents having text in English, French and German were used, and these were namely the EPO documents. The Deliverable 5.1 [MOL11] gives the description of this collection and the characteristics of the statistical machine translation (SMT) system built with these data.

The EPO provided also a website from where we downloaded 7,705 patent documents, also in the biomedical domain, them all dated from 2010 to 2012. This collection of documents constitutes the dataset used in the patent prototype.

The patent documents follow the normalized XML format defined by the EPO. This format consists of the following sections: the bibliographic data, an abstract, a description, a number of claims and references. The abstract, the description and the claims are always written in one of the three official languages, i.e., English (EN), German (DE) and French (FR), and sometimes they contain also the translation provided by the authors to any of the other two languages or both of them.

Due the characteristics of these documents, they do not constitute an aligned corpus and, in consequence, they cannot be used for training the SMT systems. Instead, we are using these documents to feed the patents retrieval system. To this end, the English sections of the patents are semantically annotated and automatically translated using the process described below, which translates semantically enriched text from the source language (EN) to the target (FR/DE).

From all the documents gathered from the EPO website, up to 4,485 out of the 7,705 documents contain at least one section with abstracts, claims or descriptions written in English. This is the final dataset that we annotated semantically according to concepts derived from several interrelated ontologies of the biomedical and patents domains. Table 1 gives a numerical description of the dataset, i.e., the number of documents having claims, description and abstract sections in English, German and French, respectively. As can be noted, the content of the documents can vary, some of them are bilingual or trilingual, not all of them have descriptions nor abstracts, and the number of claims varies as well.

The complete collection of files is available in the MOLTO svn repository[6] , and it consists of 1) the original patent documents, 2) the English sections of the patent documents semantically annotated, and 3) the automatic translations of claims, abstracts and descriptions into French and German merged into a single file with the original XML schema, defined by the EPO, and markup.

Documents

Claims

Descriptions

Abstracts

English

4,485

62,638

3,832

2,518

German

2,047

32,007

192

80

French

2,011

31,487

130

44

Table 1: Number of sections in the patents prototype dataset

2.1.2 Translation of patent documents

The designed process for patents translation allows for building a translated document having the same XML structure as the original patent and having semantically enriched text. The purpose of this procedure is not only to translate the text, but also to preserve the semantic annotations during the translation process and keep the structure of the chemical compounds (we identified a total number of 1, 097, 243 compound instances in the documents, 243, 823 different lexical entries), given that this is one of the key aspects of the biomedical texts.

The pipeline of the translation process is shown in Figure 2 and the example in Appendix A shows the transformations on the text at each step. The example shows first the original content of a patent document (1) that contains special sections such as an image and sub-indexes.

The first step in the pipeline consists of pre-processing this text in order to get rid of non-relevant marks, such as “¡b¿” and “¡i¿”, because they harm the translation quality, but to preserve other structural markup such as enumeration and heads; encode the text in UTF-8 in order to preserve the symbols in the formulae and the compounds; and annotate the text with the semantic concepts. Example (2) shows the result of this process, which includes the UTF-8 symbols and the information about the PARP instance of the class AnatomicalStructure. Table 2 gives a summary of the semantic classes used and the number of instances found in the corpus; and Appendix E gives several lists of instances for these concepts.

Concept

# Instances

Concept

# Instances

ActiveIngredient

285,192

AnatomicalStructure

2,170,330

Applicant

26,177

DiseaseOrDysfunction

1,218,063

DosageForm

241,279

Drug

160,698

Measurement

1,704,078

PharmaParam

39,330

Receptor

32,885

RouteOfAdministration

99,468

Table 2: List of semantic concepts and number of instances found in the dataset

In the second step, as shown in the diagram, the patent text is extracted from the sections in a structured manner. The resulting text is segmented and tokenized, as required by the translation system. This process is not trivial. On the one side, the characteristics of the domain require a special tokenization process. On the other side, sentences are in general too long and cannot be passed to the translator. The goal is to avoid the arbitrary segmentation of the sentences and improve the translation quality yet following predefined and priorized clues in the text, such as enumerations and paragraph marks. In order to preserve the markup of the text, we make use of the “zone” and “wall” functionalities of the Moses translator [KSF06]. This way, we can maintain the position of the marks in the text while certain degree of word reordering is still allowed during the decoding process. After this step, the structural marks are removed and the remaining text consists of the raw text shown in example (3).

During the third and fourth steps, the raw-marked text is translated using the SMT system and the translated text is post-processed in order to recover the original structure of the document (4), including original formatting, claims enumeration, document source and language metadata and semantic annotations. This process requires the original XML document as shown in the diagram.

Figure 2: Patent document translation pipeline

The patent documents are translated using a variant of the SMT system described in Deliverable 5.2 [MOL12a], which was slightly modified in order to cope with the UTF-8 encoding used in this context. To do so, all the training datasets (train and dev) were converted into UTF-8 and the system was trained again. As a result, the evaluation results of the system, shown in Table 3 and Table 4, varies from the testset reported originally in D5.2. However, one might take into account that the results are not directly comparable to the ones reported in D5.2. First, the tokenization is different which is caused by the new codification and also the new functionalities developed in the evaluation tool to cope with the evaluation needs in MOLTO. And also, because the translations produced by Bing[7] and Google Translate[8] are also different[9] .

System

WER

PER

TER

BLEU

NIST

GTM-2

METEOR-st

ROUGE-S*

ULC

EN -¿ DE

MOLTO-SMT

30.34

22.72

28.69

57.86

9.28

46.98

68.00

63.56

74.80

Bing

51.74

39.84

49.38

35.90

6.73

30.90

52.05

43.83

40.88

Google

31.79

22.85

29.79

62.53

9.77

49.58

70.11

68.39

77.61

EN -¿ FR

MOLTO-SMT

24.96

17.07

23.51

64.70

10.00

49.45

77.19

73.45

74.92

Bing

39.18

29.07

36.45

46.75

8.49

35.76

61.88

59.09

47.42

Google

24.99

17.32

22.96

67.29

10.31

50.16

77.79

77.04

76.70

Table 3: Lexical evaluation of the UTF-8 -  SMT system

System

CP-Oc(*)

CP-Op(*)

CP-STM-9

SP-Op(*)

SP-pNIST-5

ULC

EN -¿ DE

MOLTO-SMT

54.57

60.54

35.99

60.54

7.26

97.86

Bing

39.22

44.10

24.70

44.10

5.88

71.90

Google

57.14

62.46

36.02

62.46

7.10

99.56

EN -¿ FR

MOLTO-SMT

65.99

68.36

54.02

72.29

7.77

97.14

Bing

51.73

53.08

38.37

59.40

6.76

76.98

Google

67.78

69.64

55.81

74.88

8.02

1.00

Table 4: Syntactic evaluation of the UTF-8 - SMT system

The source code for the whole pipeline is available at the MOLTO repository [10]. Processing and translating the whole bunch of documents takes several days if done sequentially. For this reason, the pipeline has been optimized to parallelize the processes when possible, depending on the characteristics of the computation environment and the number of documents. In order to facilitate its use, a main script runs the whole pipeline sequentially for a given set of input files. Furthermore, a simple API[11] has been set up to enable the remote use of the process, which can facilitate the integration with other tools.

2.1.3 Syntactic measures for MT evaluation

The Asiya tool-kit [GM10a], developed at UPC, is an open-source framework for MT evaluation and meta-evaluation. It contains a rich set of evaluation metrics operating at different linguistic level and it uses several similarity measures.

The latest developments in Asiya include the possibility to provide already tokenized text, the integration of the MALT dependency parser [NHN07], for which we obtained models for Catalan, Spanish, English and French, and the integration of the Berkeley Parser [PBTK06, PK07], for which we use a pre-trained model for German. These two new parsers have made possible the development of the syntactic evaluation measures, used for automatic MT evaluation in MOLTO, for French and German, in addition to English. In MOLTO, the Asiya tool-kit needed the development of specific functionalities to cope with the biomedical domain and the MOLTO requirements, such the tokenization of the text and the adjustment of the parsers for the measures described below.

Measures based on shallow parsing (SP) are available for all languages (English, French and German). The SP similarity is based on the lexical overlap according to the part-of-speech. For instance, SP OpNN reflects the proportion of correctly translated singular nouns. The coarser measure SP Op computes the average lexical overlap over all parts of speech. We also use the NIST measure to compute accumulated and individual scores over sequences of (n = 1..5) parts-of-speech (SP NISTip n).

Measures based on constituency parsing (CP) are also available for all languages. They analyze similarities between constituent parse trees associated to automatic and reference translations. Constituent trees are obtained using the [CJ05] parser for English, the Bonsai v3.2 tool for French [CNDA10], and the Berkeley Parser for German. CP STMil measures calculate variants of the syntactic tree matching (STM) measure by Liu and Gildea (2005). For instance, CP STMi5 calculates the proportion of length-5 matching sub-paths. CP Opt computes the lexical overlap according to the part-of-speech ‘t’. And CP Oct computes the lexical overlap according to the phrase constituent type ‘t’.

Measures based on dependency parsing (DPm) are available only for English and French. They capture similarities between dependency trees associated to automatic and reference translations. The pre-trained models for French were obtained from the French Treebank [CCD10] and used in the Bonsai parser, which in turn uses the MALT parser (m). In contrast, the English text is processed with MALT parser [NHN07] and MINIPAR [Lin98].

Finally, the ULC measure is a normalized arithmetic mean of metric scores. Its calculation implies the normalization of heterogeneous scores, some of them not even bounded, into in the range 0, 1. As a consequence, the score constitutes a natural way of building a ranking rather than an overall estimation of the quality. This scheme has proven efficient when used with an appropriate combination of measures, as described in [GM10b].

2.2 Patents retrieval system, ontologies and semantic annotations

The patents retrieval prototype is an overlay of the MOLTO web-based retrieval system, that is based on the KRI[12] prototype [MOL10]. This system, developed and adapted by Ontotext, combines machine translation and multilingual retrieval of patents and has the potential to search through structural knowledge databases (the domain ontologies) and multilingual documents (the biomedical and multilingual patent documents).

The KRI is the data modeling and knowledge management core of the prototype. It is based on well-known platforms and tools, such as OWLIM[13] . The KRI allows also for building the conceptual models and knowledge bases, needed for developing the query subsystem, yet using the specialized datasets for the biomedical and patents domain, i.e., the patent classification taxonomies, the drugs lexicon, etc. Furthermore, the patents prototype integrates also Apache Solr[14] as means for document snippetting and free text search (FTS).

This section describes the specific actions and new functionalities required to build the KRI retrieval system specialized for the patents use case: biomedical ontologies (Section 2.2.2), semantic annotations (Section 2.2.3) and patent query language (Section 2.3). Next, Section 3 details the user interface for document visualization.

Figure 3: Patents retrieval system architecture with controlled natural language queries and free text search

Since the prototype version described in D7.2 [MOL12c], the patents retrieval system has been improved in a few directions. Figure 3 shows an updated view of the retrieval system (wrt. previous versions).

First, SPARQL generation was delegated completely to GF (Section 2.3). Then, free text search (Section 2.2.1) was added to the system, as a fall-back technology for queries that are not covered by the query language. This functionality uses Apache Solr, that is now also used for document snipetting and results in significantly higher speed response of the system (Section 2.2.4).

Finally, the domestic software products were updated to the last stable versions[15] . For instance, in the development of cultural heritage use case[16] (WP8), we observed the need to use OWLIM 5 in order to make possible the use of federated queries to remote SPARQL end-points. Hence, all the retrieval systems in MOLTO (molto-kri, molto-patents and molto-cultural-heritage) were migrated to OWLIM 5 and Forest 1.4[17] . In the patents use case, this resulted in some small improvements in the user interface, including SPARQL queries syntax colouring. Federated SPARQL queries are also allowed, though not explored in this work package as in WP8.

2.2.1 Free text search

There has been a general observation that the controlled language lacks the extensive coverage of the human speech. Even in a closed domain, such as biomedical patents, a user may want to experiment with diverse queries and get frustrated with no reply to queries that could be considered straightforward for humans.

Fall-back mechanisms to the interpretation of natural language queries can vary. In the patents prototype, we have proposed full text search (FTS) as a feasible solution. The major motivation behind this decision is the fact that our system, apart from the knowledge from a semantic database, provides documents as results. However, this functionality is not a major focus of the work in WP7.

It is worth pointing out that the biomedical lexicons for the GF query grammars (e.g., drugs, active ingredients, etc.) were extracted from the ontologies. Therefore, not all existent drugs and other concepts from the patents were included in the query language. The use of the free text search functionality makes it possible to retrieve also the documents containing such entities.

Furthermore, the Solr index supports multilinguality. The English, French and German version of the documents are indexed separately. In the Solr terminology, each of the documents is indexed into three, very large and customized, text fields, one per language. In our case, these fields are “content_en”, “content_fr” and “content_de”, and the “id” is the actual patent number. The documents (which are initially trilingual), are split via the use of the XML metadata present in the “claims” tags, which was a design decision negotiated between UPC and Ontotext as part of the translation output format. Full text queries and queries for document snippets are executed only on the field of the index, corresponding to the language selected by the user. Hence, the resulting documents always contain the search phrase in the specific language, and so the snippets on the results page. An example of its use is given in Section 3.3.4 that shows the results obtained for the “Zofenopril” drug-name.

2.2.2 Ontologies for the biomedical domain

From the point of view of the semantic data, the patents prototype is based on the Exopatent[18] project developed by Ontotext and Matrixware[19] . It combines several public ontologies and dictionaries to bring up a model for the biomedical patents domain. A full list of the loaded ontologies is given in D7.2 [MOL12c], Appendix A. The conceptual model for the final ontology is shown in Figure 4, where the data model contains the following ontologies: FDA Orange book[20] , PROTON[21] , UMLS[22] , MeSH[23] , SAR[24] and SAFEPat.

Figure 4: Conceptual model for the ontologies on the biomedical patents domain

It is important to stress that when we ask for e.g. the active ingredients of ABSELECT, we return all documents that are related to the active ingredients of this drug (in this specific case, to AMPHOTERICIN B). Intuitively one might expect to have both the drug and the active ingredient in one sentence in a returned document, but we do not keep relations such as “in the same sentence/paragraph” as in our system’s ontology. Rather, we look up for documents which were annotated to “mention” the subject of search, which we find to be AMPHOTERICIN B. Hence, the use of the biomedical ontologies, previously aligned for the purpose, allows us to find the relations between different entities, such as a drug and its active ingredients.

2.2.3 Semantic annotations extracted from the patents metadata

In addition to the concepts listed in Table 2, that belong to the biomedical domain, we have reviewed the XML schema of the EPO patents[25] and decided to select and expose metadata that can be useful from the user’s perspective, i.e., Patent Number, Application Number, Application Date and Publication Date, of a patent. As a result, we created four new semantic annotation types.

We extracted the entities from the documents, and added semantic annotations over them. Next, the new annotation instances were triplified (i.e., converted to RDF) so that they could be requested by SPARQL queries, and hence by natural language queries.

The example below shows the RDF interpretation of the new annotations on the patent number EP1326633B1. It has the application number “01966829”, application date “20010914”, publication date “20110316”, and the patents’s number (split in two parts) “1326633, B1”.

This update allows a patent expert to query the system for a patent with specific patent number or application number, which we believe is a paramount functionality.

<http://moltopatents.ontotext.com/document/EP1326633B1> <http://proton.semanticweb.org/protonkm#mentions> <http://www.semanticweb.org/ontologies/2008/7/Ontology1218740600570.owl#PatentNumber.EP1326633B11> .

<http://www.semanticweb.org/ontologies/2008/7/Ontology1218740600570.owl#PatentNumber.EP1326633B11> <http://www.w3.org/2000/01/rdfschema#label> ”1326633” .

<http://www.semanticweb.org/ontologies/2008/7/Ontology1218740600570.owl#PatentNumber.EP1326633B11> <http://www.w3.org/1999/02/22rdfsyntaxns#type> <http://www.semanticweb.org/ontologies/2008/7/Ontology1218740600570.owl#PatentNumber> .

<http://moltopatents.ontotext.com/document/EP1326633B1> <http://www.semanticweb.org/ontologies/2008/7/Ontology1218740600570.owl#hasPatentNumber> <http://www.semanticweb.org/ontologies/2008/7/Ontology1218740600570.owl#PatentNumber.EP1326633B11> .

<http://moltopatents.ontotext.com/document/EP1326633B1> <http://proton.semanticweb.org/protonkm#mentions> <http://www.semanticweb.org/ontologies/2008/7/Ontology1218740600570.owl#PatentNumber.EP1326633B12> .

<http://www.semanticweb.org/ontologies/2008/7/Ontology1218740600570.owl#PatentNumber.EP1326633B12> <http://www.w3.org/2000/01/rdfschema#label> ”B1” .

<http://www.semanticweb.org/ontologies/2008/7/Ontology1218740600570.owl#PatentNumber.EP1326633B12> <http://www.w3.org/1999/02/22rdfsyntaxns#type> <http://www.semanticweb.org/ontologies/2008/7/Ontology1218740600570.owl#PatentNumber> .

<http://moltopatents.ontotext.com/document/EP1326633B1> <http://www.semanticweb.org/ontologies/2008/7/Ontology1218740600570.owl#hasPatentNumber> <http://www.semanticweb.org/ontologies/2008/7/Ontology1218740600570.owl#PatentNumber.EP1326633B12> .

<http://moltopatents.ontotext.com/document/EP1326633B1> <http://proton.semanticweb.org/protonkm#mentions> <http://proton.semanticweb.org/2006/05/patents#PublicationDate.EP1326633B1> .

<http://proton.semanticweb.org/2006/05/patents#PublicationDate.EP1326633B1> <http://www.w3.org/2000/01/rdfschema#label> ”20110316” .

<http://proton.semanticweb.org/2006/05/patents#PublicationDate.EP1326633B1> <http://www.w3.org/1999/02/22rdfsyntaxns#type> <http://proton.semanticweb.org/2006/05/patents#PublicationDate> .

<http://moltopatents.ontotext.com/document/EP1326633B1> <http://proton.semanticweb.org/2006/05/patents#hasPublicationDate> <http://proton.semanticweb.org/2006/05/patents#PublicationDate.EP1326633B1> .

<http://moltopatents.ontotext.com/document/EP1326633B1> <http://proton.semanticweb.org/protonkm#mentions> <http://www.semanticweb.org/ontologies/2008/7/Ontology1218740600570.owl#ApplicationNumber.EP1326633B1> .

<http://www.semanticweb.org/ontologies/2008/7/Ontology1218740600570.owl#ApplicationNumber.EP1326633B1> <http://www.w3.org/2000/01/rdfschema#label> ”01966829” .

<http://www.semanticweb.org/ontologies/2008/7/Ontology1218740600570.owl#ApplicationNumber.EP1326633B1> <http://www.w3.org/1999/02/22rdfsyntaxns#type> <http://www.semanticweb.org/ontologies/2008/7/Ontology1218740600570.owl#ApplicationNumber> .

<http://moltopatents.ontotext.com/document/EP1326633B1> <http://www.semanticweb.org/ontologies/2008/7/Ontology1218740600570.owl#hasApplicationNumber> <http://www.semanticweb.org/ontologies/2008/7/Ontology1218740600570.owl#ApplicationNumber.EP1326633B1> .

<http://moltopatents.ontotext.com/document/EP1326633B1> <http://proton.semanticweb.org/protonkm#mentions> <http://proton.semanticweb.org/2006/05/patents#ApplicationDate.EP1326633B1> .

<http://proton.semanticweb.org/2006/05/patents#ApplicationDate.EP1326633B1> <http://www.w3.org/2000/01/rdfschema#label> ”20010914”

2.2.4 Retrieval speed optimization

Before we started the final evaluation, we observed that the response time of the retrieval system was too high. After investigation, we found out that the creation of document snippets was the actual bottle neck. The reason was that in the previous version we created snippets for every single document through the GATE[26] library. The process was as follows: each retrieved patent was loaded in the memory, so that it was examined for a semantic annotation that was explicitly mentioned in the RDF result. Having the average size of 1.24 MB per document, and hundreds of annotations per document that had to be observed, the system’s response time reached up to several seconds for the most complex queries. This timing is not acceptable for any commercial retrieval system, so we decided to switch to the Solr snipetting in order to keep the promise for a commercially viable prototype.

The Solr index is much faster, as the document content is indexed preliminary. Yet, we re-indexed the document content, so that each language of a document is observed separately (the indexed texts were taken out of the translated texts using the Apache Tika[27] ).

Our Solr index stores only the patent text, without the semantic metadata. Therefore, the next challenge was to get the snippet by having only the document name and the instance of the annotation. An additional difficulty came from the fact that we deal with three languages, and the annotations have different RDF labels (i.e., texts) for each language.

As a solution, an auxiliary hash map index was created. For each annotation, in each document, the index stores a key following the pattern:

and its value (i.e., the annotation text), as it appears in the patent text for the selected language. The index was used to retrieve the annotation text so that a snippet from the document can be requested directly from Solr by querying the annotation text. These steps improved the overall retrieval speed dramatically - about 5 times (exact speed measures weren’t taken at the beginning). The exact measurements for the new methodology were performed on the updated system via Selenium tests and are discussed in Section 2.2.5.

2.2.5 Evaluation of the retrieval system

We have conducted a basic evaluation of the retrieval system in two directions: system response time and retrieval results for natural language queries.

System response times

We measured the system response times for NL queries executed on the prototype’s user interface (UI), using Selenium[28] and custom scripts. The default test browser was Firefox3; the experiment program was given 3G of memory.

We explored the following test scenario: click on all example queries shown in the home page of the patents prototype website (in English) and then, click on the “Search” button. The time in which the system returned results was measured. For all queries, the average time is presented below.

Attempt

Opening of

Avg. Response Time

Home Page (ms.)

to NL Query (ms.)

Test 1

572.36

72.29

Test 2

444.00

76.64

Test 3

381.86

77.00

Test 4

452.71

74.50

Test 5

472.43

73.36

Table 5: Average system response measures in ms.

Query

Avg. response

time (ms.)

what is the expiration date of the patent for PACERON

74.60

what is the application number for EP2024375B1

73.60

all the patents that mention GABITRIL

77.60

what are the active ingredients of BACLOFEN

69.00

give me all the drugs with the market RX

75.20

all the drugs with the active ingredient ACARBOSE

73.00

all information about AMPICILLIN

84.60

what is the patent with the application number 07252109

72.00

show me the drugs with the therapeutic equivalence code AA

85.20

what is the application date of EP1771422B1

72.20

what is the strength of SELENIUM SULFIDE

72.83

what is the approval date of the patent for REBETOL

71.20

patents that mention SELENIUM SULFIDE and AMPICILLIN

75.20

Table 6: Average system response measures in ms.

NL queries accuracy

We have conducted a large number of tests on the queries to verify whether the documents returned are correct responses to the natural language queries. Some of the results are presented on Table 7. The precision results are the highest possible, as expected for such a system. The reason for this is the lack of any variance in the possible answers, given the control we have over the SPARQL queries. As far as it concerns recall, the documents we return are guaranteed to “mention” a specific object from the ontology. Usually we have its label and define our search query via it. Then, we return all documents that were annotated to contain the specific object. Any possible errors would be due to incorrect entity recognition during the semantic annotation phase, which is not broadly explored in the scope of MOLTO.

Next, the updated query language maps the relations in the ontology much better than in the previous version of the prototype.

Query

Result

Documents returned

patents that mention SELENIUM SULFIDE

all apply

http://molto-patents.ontotext.com/document/EP2384753A1

and AMPICILLIN

http://molto-patents.ontotext.com/document/EP1469876B1

http://molto-patents.ontotext.com/document/EP2068873B1

http://molto-patents.ontotext.com/document/EP1469876B1

what are the patents that mention PHENAZINE

all apply

http://molto-patents.ontotext.com/document/EP2298767A1

and URETHRAL

http://molto-patents.ontotext.com/document/EP2089369B1

what is the application date of EP1771422B1

OK

http://molto-patents.ontotext.com/document/EP1771422B1

what is the strength of SELENIUM SULFIDE

all apply

http://molto-patents.ontotext.com/document/EP2384753A1

http://molto-patents.ontotext.com/document/EP1469876B1

http://molto-patents.ontotext.com/document/EP2343304A1

http://molto-patents.ontotext.com/document/EP2286820A1

http://molto-patents.ontotext.com/document/EP2068873B1

all the drugs with the active ingredient

all OK

50 Documents

ACARBOSE

show me the routes of administration of

all OK

NA

THIAMINE HYDROCHLORIDE

what are the dosage forms of A-HYDROCORT

all OK

NA

Table 7: Correctness of results to NL queries

2.3 GF grammars for generation of SPARQL

For the final version of the patent prototype, we applied for the first time the novel approach of SPARQL generation by GF grammars. Namely, this is a GF translation model able to translate natural language queries into SPARQL, considering SPARQL as yet another “natural” language with its own concrete grammar (see Figure 5).

Figure 5: SPARQL as yet another GF language

In gross, instead of a grammatical representation of a sentence using the Resource Grammar Library[Ran09], the new approach provides a SPARQL representation. Hence, the translation of natural language to SPARQL is fully done by the GF engine. This automation saves lots of additional efforts with respect to providing a mapping from natural language to SPARQL.

In the previous version of the prototype, we used a domain specific language, designed by Ontotext for this purpose. A mapping-rules example of the previous version of the grammar is available at the MOLTO repository svn://molto-project.eu/wp7/molto-patents/natural-language-queries/resources/mapping-rules.

It was obvious that these rules would require human maintenance and updates in order to extend the coverage of the language or cope with any update of the ontologies, as well as the parser for the rules itself. In contrast, the new GF-based SPARQL-grammars approach minimizes the need for this maintenance.

The core grammar for the new presentation is the YAQL (“yet another query language”)[29] , which is developed by UGOT as an extension of WP4. The query grammar for the patents case study is located at svn://molto-project.eu/wp7/query/grammars.

In the Cultural Heritage case study (WP8), the SPARQL generation by GF evolves in a functional representation, which brings the automation to a higher level. Details can be found in D8.3[MOL13].

From the back-end perspective, the system runs a GF process and the SPARQL is generated by a single translation command. The following example

PatentQuery> p -lang=PatentQueryEng -cat=Query

"show me the patents that mention AMPICILLIN" | l -lang=PatentQuerySPARQL

returns the SPARQL query shown below (the $n symbol is used to be an indicator of a new line, which we process in the back-end in order to return a well formed SPARQL query).

PREFIX pkm: <http://proton.semanticweb.org/protonkm#> $n

PREFIX psys: <http://proton.semanticweb.org/protonsys#> $n

CONSTRUCT { ?doc <http://proton.semanticweb.org/protonm#mentions> ?thing . } $n

WHERE { $n

?thing psys:mainLabel " AMPICILLIN " . $n

?doc pkm:mentions ?thing . $n

} ;

2.3.1 The patents query language

Compared to the query language described in D7.2, the current one is improved in terms of coverage and compliance to the patent domain ontology that is behind the information retrieval system. For these reasons, the layered representation that builds on the Query Library, was abandoned in favor of a stand-alone implementation that is faithful to the requirements. In this way, the current grammar is an extension to the grammar described in D7.1, in order to cope with the latest changes in the ontology. In addition, the current grammar provides a concrete syntax corresponding to SPARQL.

Since the grammar is adapted to the domain, the constructors from the abstract syntax describe individual queries. So, the SPARQL mappings are easy to make, since they are written in a gap-filling fashion, by specifying the query with spaces for the arguments. For example, the operation that builds the queries that ask about patents mentioning a certain concept is as follows:

oper mentionP : Str -> {s : Str} = \s1 ->

{s = "PREFIX pkm: <http://proton.semanticweb.org/protonkm#> $n

PREFIX psys: <http://proton.semanticweb.org/protonsys#> $n

CONSTRUCT { ?doc

<http://proton.semanticweb.org/protonm#mentions> ?thing .  } $n

WHERE { $n

?thing psys:mainLabel "++ s1 ++".  $n

?doc pkm:mentions ?thing . $n} ;"} ;

In addition to the direct generation of SPARQL from the patent query grammar, the natural language generation has been optimized for French, rendering more fluent queries. For example, for ”what is the publication date of EP2033641B1”, the French query grammar now returns ”quelle est la date de publication d’ EP2033641B1” instead of ”qu’ est la date de publication d’ EP2033641B1”.

All in all, the current patent query grammar improves over the previous ones (from D7.1 and D7.2) in terms of coverage, quality of the natural language generated and by providing a direct mapping of the natural language queries to SPARQL, which favors scalability and domain adaptation. In addition to this, a large dictionary of terms has been mapped to grammar lexicon and in this way, the grammar provides all the necessary support for the query part of the information retrieval system.

2.3.2 Better coverage of the ontology by GF

In D7.2, we pointed that the natural language defined by the query grammar suffered from ambiguities and inconsistencies. We reduced these to a minimum, and also extended the grammar with several new and more precise natural language queries. We added eight grammar lexicons (i.e., the dictionaries shown in Table 8), which allowed for larger flexibility on the query language itself. These correspond to that annotation types whose instances were of interest.

Annotation (Entity) type

Examples

Dictionary size

DRUG

FLUONID, HEXADROL

5,529 entries

ACTIVE_INGREDIENT

BUDESONIDE, MORPHINE SULFATE

1,803 entries

ROUTE_OF_ADMINISTRATION

DENTAL, CARDIAC

53 entries

APPLICANT

ASTRAZENECA, BAYER PHARMS

1,822 entries

APPLICATION_NUMBER

10184202, 09814154

4,609 entries

PATENT_NUMBER

EP2219477B1, EP2382981A3

4,609 entries

TE_CODE

AA, AB, BX

17 entries

MARKET

DISCN, OTC, RX

3 entries

Table 8: Dictionary types for the data GF grammars

The extension of the query language introduced new useful queries. For example, we added the following queries:

what are the drugs with the active ingredient ACTIVE_INGREDIENT

what are the drugs with the therapeutic equivalence code TE_CODE

what is the application date of PATENT

what is the application number for PATENT

what are the patents that mention X

what are the patents that mention Y and Z

where X, Y and Z are one of the above types {DRUG, ACTIVE_INGREDIENT, APPLICANT, ROUTE_OF_ADMINISTRATION}.

A selected list of lexical items for the ontology classes can be seen in Appendix E. The complete list of concept instances (i.e., drugs, active ingredients, patent numbers, etc.) is available at svn://molto-project.eu/wp7/resources/lexicons.

Our observation is that the more the grammar approximates the RDF-predicate relation’s names, the easier and the more precise SPARQL queries can be introduced.

3 The On-line User Interface for the Patents Retrieval Prototype

The patents prototype can be accessed on-line at:

http://molto-patents.ontotext.com.

The general workflow of the application is shown in Figure 3 (Section 2.2). We combined a number of diverse technologies to come up with a multilingual search engine for semantically annotated patents.

The interface allows for querying the system in the EPO official languages, i.e., English, German and French. The queries are written using the controlled natural language defined by the GF query grammar for patents described in the previous sections. The GF engine is used to parse the user’s query to abstract syntax and to translate it to SPARQL syntax (as described in Section 2.3). Then, the SPARQL query is executed against the semantic repository. As a result, the user receives a subset of documents with snippets and RDF facts. Finally, the user interface displays a browsable list of domain concepts and documents that allows for navigating the ontology and inspecting the patent documents.

For those cases in which the user’s query cannot be parsed by GF, we have enabled the FTS (described in Section 2.2.1) that uses Solr’s indexing and returns to the user only document snippets that contain the keywords in search.

Regarding the graphical user interface, some deficiencies were detected in the previous version of the prototype, these being the main ones:

- inaccurate possible queries,

- insufficient coverage of the ontology relations by the query language,

- document snippets in English only,

- semantic annotations in English only, and

- slow response time of the application.

Our goal for the final prototype was to overcome these issues and come up with an online application that is commercially viable. We believe we have made significant improvements on the points listed above. The results are discussed in the next sections.

3.1 GUI updates

Among the updates towards the improvement of the user experience, we count the perceivable speed-up due to Solr snippetting and a couple of other minor GUI speed-ups. For details, see Section 2.2. Furthermore, we made the following improvements to the interface.

3.1.1 Multilingual annotations

Unlike the previous version of the prototype, in the final version the semantic annotations are transmitted to the translated sections of the patent documents. The process that lead to this advancement is described in Section 2.1. From semantic data’s perspective, this is a huge step forward. Figure 6 shows an excerpt of French text that has been automatically annotated.

This step sets up the grounding for further research on how well the annotations are transferred and whether machine translation could be used to enrich semantic annotations with labels in different languages.

Figure 6: Annotations transmitted to French

3.1.2 Document source exposed

Another minor improvement is the exposure of the ”Document Source” of a patent text. This makes clear the origin of the text in the chosen language, i.e., original or translated by the automatic systems. Currently, the two possible options are “EPO” vs. “MOLTO-SMTv1”, as indicated in Figure 7.

Figure 7: Patent document source

3.2 NL query examples

The controlled language of the prototype was extended and improved. As a result, we have new queries that cover the ontology much better (the most common ontology relations are covered by the GF grammars). Some of the new queries are essential for the usability of the prototype. For example, it is now possible to search a patent by its number, or by its application number. We also introduced series of queries like give me all the drugs with the market RX , where we collect drugs with common characteristics (e.g. market, active ingredient, therapeutic equivalence (TE) code, dosage form, etc).

Table 9 presents several query examples taken from the UI that the user can use to query the system. As in previous versions, the interface has an auto-complete functionality that can predict and suggest the applicable lexical types and entries (e.g., DRUG, TE_CODE, and so on) for each possible query. The table shows also the number of results that are currently returned (NA stands for “inapplicable result type for this particular query”).

Query

# of documents

# of RDF

returned

triples

all the patents that mention GABITRIL

6

6

what is the expiration date of the patent for PACERONE

NA

4

what is the application number for EP2024375B1

NA

1

what are the active ingredients of BACLOFEN

*43

2,262

give me all the drugs with the market RX

*12

1,421,934

all the drugs with the active ingredient ACARBOSE

*50

1,905

all information about AMPICILLIN

*91

308

what is the patent with the application number 07252109

1

1

show me the drugs with the therapeutic equivalence code AA

*86

769,172

what is the application date of EP1771422B1

NA

1

patents that mention SELENIUM SULFIDE and AMPICILLIN

3

12

* out of the top 100 RDF facts

Table 9: Query examples with results counts

3.3 User Scenarios

In this subsection we list some interesting user scenarios that are new to the system (or were not mentioned previously).

3.3.1 Query types

Among the wide variety of new queries added to the query language, the most powerful one is the and-query, meaning a query of the type:

show me all patents that mention X and Y

where X and Y have one of the types: Drug, Applicant, ActiveIngredient, and RouteOfAdministration (see Table 8 for details on the number of entities of each of the four types, and Appendix E for a list of instances).

The and-query allows a user to search for two different entities of the types mentioned above, which offers to the user the possibility to build up to  42 million different queries.

Figure 8 shows an example of an and-query and the results returned: the RDF triples and three documents. We also demonstrate that the two semantically annotated instances are located in the documents. Figure 9 shows the occurrences in patent number EP2068873B1.

Figure 8: Results of the and-query: patents that mention SELENIUM SULFIDE and AMPICILLIN

Figure 9: EP2068873B1 - a patent document retrieved for the query patents that mention SELENIUM SULFIDE and AMPICILLIN

3.3.2 Semantic data exploration

The semantic annotations[30] are metadata over an existing excerpt of a text document. Each semantic annotation has these two properties: a class (from the top-level of the ontology) to which it belongs to, and a unique URI that represents it (i.e., an instance). Other metadata is possible but optional.

Since the patents prototype is based on the KRI project[31] , it facilitates the exploration of semantic data. We have provided the ability to follow the related semantic data about the instance by clicking on the annotation’s highlights (i.e., a browsable semantic graph). The semantic data gives richer and valuable information to the user. Let us explore the following use case to show it.

A user searches for the patents that mention the drug ONDANSETRON and opens a document that contains the drug, e.g. EP2086643B1. The user selects drugs from the “Semantic Annotations” menu (the check list menu bar in the right of the web page). The “Check all” radio button shows all the semantic annotation types.

Next, the user searches for the specific occurrence of ONDANSETRON (in the current version of the prototype one should use the browser’s search to find the exact occurrence of a string in the patent).

Once done, the user will be navigated to a paragraph that discusses about treatment and prevention of bulimia. The disease is semantically annotated. When the user selects diseaseordysfunction, the mention of bulimia is highlighted (see Figure 10). The user can then click on the annotation’s highlight and get redirected to a new page that shows all occurrences of the bulimia’s instance. It is the subject in triples from the ontology loaded on the prototype.

Figure 10: bulimia as subject in the RDF triples

Another interesting view is at the Object tab. It shows the RDF facts in which the same instance is an object. This usually gives the list of documents that “mention”[32] the entity. In the specific case we get the list of documents that have bulimia annotated in them, as in Figure 11. Hence, users are able to explore lots of linked information related to their search.

Figure 11: bulimia as object in the RDF triples

3.3.3 Single word query

It was observed that a novel user of the system may get frustrated because the lack of single-word queries, e.g., a drug name or a patent number. Hence, we added single-word queries in the GF query grammar, so that a user can directly ask for a patent number, a drug, an active ingredient, etc. An example is shown in Figure 12. The input box integrates also a predictive text mechanism for single substances and patent numbers. The latter saves both time and the possibility of spelling mistakes, which otherwise would have brought the user to free text search (see Section3.3.4).

Unfortunately, it is impossible to have in the ontology all the drugs that are mentioned in the patent documents. There will always be a chance of a new drug in a so far unseen document. In the system described so far, it was not possible to query for entries that were not in the data lexicons extracted from the domain ontology. As a workaround to this issue we added free text search functionality to the system.

Figure 12: Single drug search (by GF)

3.3.4 FTS queries

This version of the prototype supports free text search in the three languages (English, French and German). It serves as a fall-back mechanism for queries that the GF engine cannot parse and also for drugs names and chemical substances, compounds, etc., that are not in the ontology (and hence neither in the GF query grammar).

Figure 13 shows a free text search for the medicine Zofenopril that occurs in patents, but is not part of the ontology. The system returned 37 documents with sentence or paragraph snippets in the language of the user. The snippets explore the occurrences of the search phrase in the document.

Figure 13: Zofenopril - free text search query

4 Conclusions and Challenges

The patents case study sets up the grounds where to put together several technologies in order to come up with a useful platform for multilingual patent retrieval system. The main challenges addressed in the patent prototype are a) to translate semantically enriched patent documents, including the original markup, b) to design the mechanisms to enable the multilingual indexing and retrieval of the patents, c) to define and develop a query language and the query grammar for the system and d) to set up an on-line application for retrieval of patent document that serves as a testbed of our work.

The patents prototype combines two different approaches for machine translation, semantic annotations and retrieval techniques. The translation of the patent documents is mainly based on statistical machine translation techniques, although certain hybridization with rule-based systems (the GF) improves the quality of the translation, as discussed in WP5. One of the challenges in this task was to come up with a mechanism to translate the semantics of the source texts to the target languages. What remains as a future challenge is the use of these annotations to still increase either the accuracy of the annotations or the quality of the translations.

The GF has been proved an efficient way of generating the SPARQL queries, as if it was “Yet Another Query Language”. In other words, it allows to translate a natural language query from the user’s language to SPARQL, which makes the system accessible to a broader community rather than just skilled users. This automation facilitates also the interoperability between the query grammar and the ontologies and speeds up the development and maintenance of the querying subsystem.

Finally, the patent prototype is not comparable with the interfaces exposed by the European Patent Office, namely because they were conceived for different purposes. Nonetheless, the MOLTO patents prototype demonstrates that a patents retrieval system that addresses multilingualism by means of automatic translation techniques is commercially viable. This topic will be further discussed as part of WP10.

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[TWS10]    John Tinsley, Andy Way, and Páraic Sheridan. PLuTO: MT for Online Patent Translation. In Proceedings of the 9th Conferences of the Association for Machine Translation in the Americas (AMTA 2010), 2010.

[VFC05]    David Vallet, Miriam Fernández, and Pablo Castells. An ontology-based information retrieval model. In In ESWC, pages 455–470. Springer, 2005.

A Example Showing the Translation Steps of an Excerpt of Patent

The excerpt in the following example belongs to the first claim-text of the English section for claims of the patent number EP1330442B1.

1.

The original text extracted from the patent number EP1330442B1.

The use of a compound of the formula:

<chemistry id=”chem0028” num=”0028”><img id=”ib0030” file=”imgb0030.tif” wi=”55” he=”37” imgcontent=”chem” imgformat=”tif”/>

</chemistry>

or isomers i.e. geometric, optical, entianomeric, diasteriomeric, epimeric, stereoisomeric, tautomeric, conformational, or anomeric forms, salts, solvates and chemically protected forms thereof, in the preparation of a medicament for inhibiting the activity of PARP, wherein:

<claimtext>A and B together represent a fused aromatic ring, optionally substituted with one or more substituent groups selected from halo, nitro, hydroxy, ether, thiol, thioether, amino, C<sub>17</sub> alkyl, C<sub>320</sub> heterocyclyl and C<sub>520</sub> aryl;

</claimtext>

<claimtext>R<sub>C</sub> is CH<sub>2</sub>R<sub>L</sub>, where R<sub>L</sub> is a C<sub>520</sub> aryl group, optionally substituted with one or more substituent groups selected from C<sub>17</sub> alkyl, C<sub>520</sub> aryl, C<sub>320</sub> heterocyclyl, halo, hydroxy, ether, nitro, cyano, acyl, carboxy, ester, amido, amino, sulfonamido, acylamido, ureido, acyloxy, thiol, thioether, sulfoxide and sulfone; and

</claimtext>

<claimtext>R<sub>N</sub> is hydrogen.</claimtext>

2.

Annotated text with UTF-8 encoding.

The use of a compound of the formula:

<chemistry id=”chem0028” num=”0028”><img id=”ib0030” wi=”55” imgformat=”tif” file=”imgb0030.tif” imgcontent=”chem” he=”37”/>

</chemistry>

or isomers i.e. geometric, optical, entianomeric, diasteriomeric, epimeric, stereoisomeric, tautomeric, conformational, or anomeric forms, salts, solvates and chemically protected forms thereof, in the preparation of a medicament for inhibiting the activity of

<AnatomicalStructure inst=”umls/id/C1538577” class=”semanticnetwork/id/T017”>PARP

</AnatomicalStructure>

, wherein:

<claimtext>A and B together represent a fused aromatic ring, optionally substituted with one or more substituent groups selected from halo, nitro, hydroxy, ether, thiol, thioether, amino, C 17 alkyl, C 320 heterocyclyl and C 520 aryl;

</claimtext>

<claimtext>R C is CH 2R L , where R L is a C 520 aryl group, optionally substituted with one or more substituent groups selected from C 17 alkyl, C 520 aryl, C 320 heterocyclyl, halo, hydroxy, ether, nitro, cyano, acyl, carboxy, ester, amido, amino, sulfonamido, acylamido, ureido, acyloxy, thiol, thioether, sulfoxide and sulfone; and

</claimtext>

<claimtext>R N is hydrogen.</claimtext>

3.

Marked text ready for translation.

The use of a compound of the formula : _w_ <wall/> _z_ <zone> or isomers i.e. geometric , optical, entianomeric , diasteriomeric , epimeric , stereoisomeric , tautomeric , conformational, or anomeric forms , salts, solvates and chemically protected forms thereof , in the preparation of a medicament for inhibiting the activity of _f_ </zone> _z_ <zone> PARP _f_ </zone>  _z_ <zone> , wherein : _f_ </zone> _z_ <zone> A and B together represent a fused aromatic ring , optionally substituted with one or more substituent groups selected from halo, nitro , hydroxy, ether, thiol, thioether, amino, C 17 alkyl, C 320 heterocyclyl and C 520 aryl; _f_ </zone> _z_ <zone> R C is CH_2R L , where R L is a C 520 aryl group , optionally substituted with one or more substituent groups selected from C 17 alkyl, C 520 aryl, C 320 heterocyclyl, halo, hydroxy, ether, nitro , cyano, acyl, carboxy, ester , amido , amino, sulfonamido, acylamido, ureido , acyloxy, thiol, thioether, sulfoxide and sulfone; and _f_ </zone> _z_ <zone> R N is hydrogen .

4.

Translated patent text with the marks for reconstruction.

Verwendung einer Verbindung der Formel : _f_ _w_ _z_ oder Isomere , d.h. geometrischen , optical, entianomeric , diastereomere , epimeric , stereoisomeren , tautomeren , conformational, oder anomeren bildet , salts, Solvate und chemisch geschützte Formen davon , bei der Herstellung eines Arzneimittels zur Hemmung der Aktivität von _f_ _z_ PARP _f_ _z_ , wobei : _f_ _z_ A und B gemeinsam für einen kondensierten aromatischen Ring , gegebenenfalls substituiert mit einem oder mehreren Substituentengruppen , ausgewählt aus halo, Nitro , hydroxy, ether, thiol, thioether, amino, C 17 alkyl, C 320 Heterocyclyl und C 520 aryl; _f_ _z_ R C CH_2R L , wobei R ein C 520 Arylgruppe , gegebenenfalls substituiert mit einem oder mehreren Substituentengruppen , ausgewählt aus C 17 alkyl, C 520 aryl, C 320 heterocyclyl, halo, hydroxy, ether, Nitro , cyano, acyl, carboxy, , Amido amino, sulfonamido, acylamido, Ureido , acyloxy, thiol, thioether, Sulfoxid und sulfone; und _f_ _z_ N R Wasserstoff ist .

5.

Translated patent text reconstructed using the original document EP1330442B1.

Verwendung einer Verbindung der Formel :

<chemistry id=”chem0028” num=”0028”>

<img file=”imgb0030.tif” he=”37” id=”ib0030” imgcontent=”chem” imgformat=”tif” wi=”55”></img>

</chemistry>

oder Isomere , d.h. geometrischen , optical, entianomeric , diastereomere , epimeric , stereoisomeren , tautomeren , conformational, oder anomeren bildet , salts, Solvate und chemisch geschützte Formen davon , bei der Herstellung eines Arzneimittels zur Hemmung der Aktivität von

<AnatomicalStructure class=”semanticnetwork/id/T017” inst=”umls/id/C1538577”>   PARP

</AnatomicalStructure>

, wobei :

<claimtext>   A und B gemeinsam für einen kondensierten aromatischen Ring , gegebenenfalls substituiert mit einem oder mehreren Substituentengruppen , ausgewählt aus halo, Nitro , hydroxy, ether, thiol, thioether, amino, C 17 alkyl, C 320 Heterocyclyl und C 520 aryl;

</claimtext>

<claimtext>   R C CH_2R L , wobei R ein C 520 Arylgruppe , gegebenenfalls substituiert mit einem oder mehreren Substituentengruppen , ausgewählt aus C 17 alkyl, C 520 aryl, C 320 heterocyclyl, halo, hydroxy, ether, Nitro , cyano, acyl, carboxy, , Amido amino, sulfonamido, acylamido, Ureido , acyloxy, thiol, thioether, Sulfoxid und sulfone; und

</claimtext>

<claimtext>   N R Wasserstoff ist. </claimtext>

B Ontologies in the Biomedical Domain

Several ontologies from the biomedical domain that are aligned for the patent retrieval prototype. This is the complete list of all datasets that are loaded in the semantic repository, and the description of their content.

kb/FDA/FDA_classonly_2.owl:

FDA Products naive ontology created and aligned with a basic upper level ontology PROTON; (The diagram is already attached to previous deliverable).

FDA_to_KIM.nt:

mapping between FDA_classonly_2.owl and proton classes.

FDA_products.nt:

triples extracted with gazetteers from FDA Orange Book.

measure-unit-classes.owl:

measurement units ontology

measure-unit-instances.owl:

instances of measurements.

unit-main-labels.nt:

labels of the measurement units.

proton-measure.owl:

mapping between proton and measurements ontology.

proton-patents.owl:

patent structure ontology.

skos.rdf:

SKOS ontology(Simple Knowledge Organization System).

umls-semnet-proton.nt:

UMLS general concepts.

pathologic-functions.nt:

UMLS pathologic functions instances.

anatomical-structures.nt:

UMLS anatomical structures instances.

umls-gpcr-proteins.nt:

UMLS G-proteins coupled receptors instances.

pharma-params-instances.nt:

FDA pharmaceutical parameters.

owl.rdfs:

specifies in RDF Schema format the built-in classes and properties that together form the basis of the RDF/XML syntax of OWL Full, OWL DL and OWL Lite.

protonsys.n3;protontop.n3;protonext.n3;protonkm.n3:

the Proton upper level ontology.

annotated_docs_tripples.n3:

triples from the new annotations in the patent documents.

C Semantic Annotations - Final Types

Hereby we provide the list of semantic annotations that were kept for the final version of the prototype.

Annotation

Semantics

ActiveIngredient

The active ingredients recognized by the FDA. Patent and generic

FDA-approved drugs may have the same active ingredients.

http://www.semanticweb.org/ontologies/2008/7/Ontology1218740600570.owl\#ActiveIngredient

AnatomicalStructure

A normal or pathological part of the anatomy or structural

organization of an organism.

http://linkedlifedata.com/resource/semanticnetwork/id/T017

Applicant

This is the company who has applied to the FDA for approval.

Usually synonymous with ”manufacturer.”

http://www.semanticweb.org/ontologies/2008/7/Ontology1218740600570.owl\#Applicant

ApplicantionDate

EPO patent application date(extracted from the patent’s metadata).

http://proton.semanticweb.org/2006/05/patents\#ApplicationDate

ApplicantionNumber

EPO patent application number(extracted from the patent’s metadata).

http://www.semanticweb.org/ontologies/2008/7/Ontology1218740600570.owl\#ApplicationNumber

DiseaseOrDysfunction

A disordered process, activity, or state of the organism as a whole, of a body

system or systems, or of multiple organs or tissues. Included here are normal

responses to a negative stimulus as well as patholologic conditions or states that

are less specific than a disease. Pathologic functions frequently have systemic effects.

http://linkedlifedata.com/resource/semanticnetwork/id/T046

DosageForm

This is form in which the approved strength can be administered. Approved drugs

will be specific to a strenght and dosage form, although one approval may

include several dosage/strength/route of administration combinations.

http://www.semanticweb.org/ontologies/2008/7/Ontology1218740600570.owl\#DosageForm

Drug

Usually the commercial name in the case of a trade drug.

If generic, may be only the active ingredient.

http://www.semanticweb.org/ontologies/2008/7/Ontology1218740600570.owl\#FDA\_DrugName

Measurement

General type for variety of measurements.

http://proton.semanticweb.org/2006/05/measure

PatentNumber

EPO patent number(extracted from the patent’s metadata).

http://www.semanticweb.org/ontologies/2008/7/Ontology1218740600570.owl\#PatentNumber

PharmaParam

A pharmacological parameter indicating a level or boundary.

http://proton.semanticweb.org/2006/05/measure\#PharmaParam

PublicationDate

EPO patent publication date(extracted from the patent’s metadata).

http://proton.semanticweb.org/2006/05/patents\#PublicationDate

Receptor

A specific structure or site on the cell surface or within its cytoplasm

that recognizes and binds with other specific molecules. These include the

proteins on the surface of an immunocompetent cell that binds with antigens,

or proteins found on the surface molecules that bind with hormones or

neurotransmitters and react with other molecules that respond in a specific way.

http://linkedlifedata.com/resource/semanticnetwork/id/T192

RouteOf

The method by which a drug is taken, i.e. oral vs. injection.

Administration

http://www.semanticweb.org/ontologies/2008/7/

Ontology1218740600570.owl\#RouteOfAdministration

Table 10: Semantic annotation types for the final prototype

D Biomedical Patents - Query Patterns

The patents query language covers the set of query topics shown in Table 11. The grammar was augmented in order to cover better the relations in the ontologies. Details about it are given in Section 2.3. Below is the list of all generic queries that can be asked to the system.

information about a drug

drugs that are compounds

active ingredients of a drug

drug preparations

dosage forms of a drug

the name of a drug

route of administration of a drug

methods in the patent

dosage form of a drug

use of patent

patent number

use of drug

the expiration of a patent

strength of a drug

patent use codes

claims from a date that mention a given drug

patent application number

claims about a given drug authored by somebody

applicant for a patent

approval date of a patent

Table 11: The biomedical patent query topics

what is the information about ACTIVE_INGREDIENT

what are the patent numbers

what is the approval date of the patent for DRUG

what is the patent with the application number APP_NUMBER

what is the information about APPLICANT

what is the application date of PATENT

what is the application number for PATENT

what is the approval date of the applicant APPLICANT

what is the strength of drugs that contain CHEMICAL_SUBSTANCE

what are the drugs with the active ingredient ACTIVE_INGREDIENT

what are the active ingredients of DRUG

what are the drugs that mention APPLICANT

what are the dosage forms of DRUG

what is the information about DRUG

what are the drugs with the market MARKET

what are the names of DRUG

what are the drugs with the route of administration ROUTE_OF_ADMINISTRATION

what is the strength of DRUG

what are the drugs with the therapeutic equivalence code TE_CODE

what is the therapeutic equivalence code of DRUG

what is the expiration date of the patent for DRUG

what are the markets of DRUG

PATENT

what is the expiration date of the application number APP_NUMBER

what is the expiration date of PATENT

what is the information about ROUTE_OF_ADMINISTRATION

what are the patents that mention APPLICANT

what are the patents that mention DRUG

what are the patents that mention ACTIVE_INGREDIENT

what are the patents that mention ROUTE_OF_ADMINISTRATION

what are the patents that mention APPLICANT and APPLICANT

what are the patents that mention APPLICANT and DRUG

what are the patents that mention APPLICANT and ACTIVE_INGREDIENT

what are the patents that mention APPLICANT and ROUTE_OF_ADMINISTRATION

what are the patents that mention DRUG and APPLICANT

what are the patents that mention DRUG and DRUG

what are the patents that mention DRUG and ACTIVE_INGREDIENT

what are the patents that mention DRUG and ROUTE_OF_ADMINISTRATION

what are the patents that mention ACTIVE_INGREDIENT and APPLICANT

what are the patents that mention ACTIVE_INGREDIENT and DRUG

what are the patents that mention ACTIVE_INGREDIENT and ACTIVE_INGREDIENT

what are the patents that mention ACTIVE_INGREDIENT and ROUTE_OF_ADMINISTRATION

what are the patents that mention ROUTE_OF_ADMINISTRATION and APPLICANT

what are the patents that mention ROUTE_OF_ADMINISTRATION and DRUG

what are the patents that mention ROUTE_OF_ADMINISTRATION and ACTIVE_INGREDIENT

what are the patents that mention ROUTE_OF_ADMINISTRATION and ROUTE_OF_ADMINISTRATION

Each of them has several possible verbalizations, for example for show me the information about AMPICILLIN these are:

what is the information about AMPICILLIN

information about AMPICILLIN

the information about AMPICILLIN

give me the information about AMPICILLIN

show me the information about AMPICILLIN

what information do you have about AMPICILLIN

what information can I get about AMPICILLIN

all information about AMPICILLIN

all about AMPICILLIN

AMPICILLIN

The French versions of the above queries are:

quelle est l’ information à propos de AMPICILLIN

information à propos de AMPICILLIN

l’ information à propos de AMPICILLIN

montre l’ information à propos de AMPICILLIN

quelle information avez à propos de AMPICILLIN vous

quelle information peux obtenir à propos de AMPICILLIN je

toute de l’ information à propos de AMPICILLIN

tout à propos de AMPICILLIN

AMPICILLIN

E Patent Retrieval Databases Roadmap

A complete list of the patent retrieval databases was given in the previous Deliverable D7.2. For the readability of this document, we have included this appendix with a shorter selection of them. They constitute the concepts contained in the ontologies that can be also found in the patent documents indexed in the system.

Drug

#docs

Drug

#docs

ACETIC ACID

1829

PREDNISONE

318

SODIUM CHLORIDE

1434

IBUPROFEN

317

TALC

1366

AMPICILLIN

295

INSULIN

1042

INDOMETHACIN

279

SODIUM BICARBONATE

819

MITOXANTRONE

272

PENICILLIN

765

MERCAPTOPURINE

257

MAGNESIUM SULFATE

619

CYTARABINE

243

AMMONIUM CHLORIDE

597

PREDNISOLONE

234

DIMETHYL SULFOXIDE

525

NAPROXEN

231

STERILE WATER

509

ESTRADIOL

229

ADENOSINE

499

LOVASTATIN

227

CYCLOPHOSPHAMIDE

490

SIMVASTATIN

226

CISPLATIN

449

IFOSFAMIDE

225

DEXAMETHASONE

442

HYDROXYUREA

222

FLUOROURACIL

439

POTASSIUM CHLORIDE

222

ETOPOSIDE

404

FOLIC ACID

219

PACLITAXEL

379

VITAMIN D

207

TAXOL

360

TAXOTERE

205

MITOMYCIN

355

TESTOSTERONE

203

CARBOPLATIN

354

FLUTAMIDE

201

Table 12: Drugs most mentioned in patents.

Active Ingredient

#docs

Active Ingredient

#docs

CALCIUM

2573

COPPER

722

ALCOHOL

2391

LACTIC ACID

698

AMINO ACIDS

1777

SODIUM SULFATE

679

SODIUM CHLORIDE

1434

GLYCERIN

662

TALC

1366

CALCIUM CARBONATE

660

MANNITOL

1362

BIOTIN

621

CITRIC ACID

1208

MAGNESIUM SULFATE

619

SORBITOL

1206

AMMONIUM CHLORIDE

597

GLYCINE

1160

SODIUM ACETATE

586

SULFUR

1033

DIMETHYL SULFOXIDE

525

PHOSPHORIC ACID

1018

SODIUM CITRATE

508

TYROSINE

979

ADENOSINE

499

GLUTAMINE

955

CYCLOPHOSPHAMIDE

490

PROTEASE

903

SODIUM PHOSPHATE

480

SODIUM CARBONATE

878

CISPLATIN

449

TARTARIC ACID

821

DEXAMETHASONE

442

SODIUM BICARBONATE

819

FLUOROURACIL

439

DEXTROSE

804

ETOPOSIDE

404

ASCORBIC ACID

802

ASPIRIN

384

UREA

774

PACLITAXEL

379

Table 13: Active ingredients most mentioned in patents.

Drug

Administration

#docs

Drug

Administration

#docs

SODIUM CHLORIDE

INJECTION

1220

PACLITAXEL

INJECTION

312

INSULIN

INJECTION

802

MITOMYCIN

INJECTION

310

SODIUM BICARBONATE

INJECTION

630

CARBOPLATIN

INJECTION

299

PENICILLIN

ORAL

594

TAXOL

INJECTION

295

MAGNESIUM SULFATE

INJECTION

500

IBUPROFEN

ORAL

291

AMMONIUM CHLORIDE

INJECTION

470

PREDNISONE

ORAL

282

CYCLOPHOSPHAMIDE

ORAL

439

FLUOROURACIL

TOPICAL

274

CYCLOPHOSPHAMIDE

INJECTION

420

INDOMETHACIN

ORAL

257

ADENOSINE

INJECTION

399

MITOXANTRONE

INJECTION

249

DEXAMETHASONE

ORAL

388

MERCAPTOPURINE

ORAL

237

CISPLATIN

INJECTION

385

CYTARABINE

INJECTION

220

FLUOROURACIL

INJECTION

381

NAPROXEN

ORAL

219

DEXAMETHASONE

INJECTION

380

AMPICILLIN

ORAL

217

ETOPOSIDE

ORAL

357

PREDNISOLONE

ORAL

211

ETOPOSIDE

INJECTION

353

LOVASTATIN

ORAL

210

Table 14: Drug names and routes of administration that are most mentioned in documents.

Drug

Dosage Form

#docs

Drug

Dosage Form

#docs

TALC

POWDER

1052

CYCLOPHOSPHAMIDE

TABLET

222

SODIUM CHLORIDE

INJECTABLE

722

CISPLATIN

INJECTABLE

216

DIMETHYL SULFOXIDE

SOLUTION

521

ADENOSINE

INJECTABLE

215

STERILE WATER

LIQUID

465

INDOMETHACIN

SUSPENSION

212

DEXAMETHASONE

SOLUTION

432

ETOPOSIDE

INJECTABLE

206

INSULIN

INJECTABLE

423

TAXOL

INJECTABLE

192

FLUOROURACIL

SOLUTION

418

CYCLOSPORINE

SOLUTION

190

SODIUM BICARBONATE

INJECTABLE

370

PACLITAXEL

INJECTABLE

182

PREDNISONE

SOLUTION

309

NAPROXEN

SUSPENSION

178

MAGNESIUM SULFATE

INJECTABLE

260

CARBOPLATIN

INJECTABLE

172

AMMONIUM CHLORIDE

INJECTABLE

253

DEXAMETHASONE

TABLET

168

IBUPROFEN

SUSPENSION

248

MITOMYCIN

INJECTABLE

166

CYCLOPHOSPHAMIDE

INJECTABLE

243

ETOPOSIDE

CAPSULE

164

FLUOROURACIL

INJECTABLE

232

MITOXANTRONE

INJECTABLE

159

DEXAMETHASONE

INJECTABLE

230

BALANCED SALT

SOLUTION

145

Table 15: Drug names and dosage forms that are most mentioned in documents.

Drug

Active Ingredient

#docs

ABELCET

AMPHOTERICIN B

2

ABILIFY

ARIPIPRAZOLE

8

ABRAXANE

PACLITAXEL

28

ACARBOSE

ACARBOSE

152

ACCOLATE

ZAFIRLUKAST

7

ACCUPRIL

QUINAPRIL HYDROCHLORIDE

3

ACCUTANE

ISOTRETINOIN

5

ACEBUTOLOL HYDROCHLORIDE

ACEBUTOLOL HYDROCHLORIDE

7

ACEON

PERINDOPRIL ERBUMINE

5

ACETAMINOPHEN

ACETAMINOPHEN

161

ACETAZOLAMIDE

ACETAZOLAMIDE

50

ACETOHEXAMIDE

ACETOHEXAMIDE

53

ACETYLCYSTEINE

ACETYLCYSTEINE

61

ACTH

CORTICOTROPIN

11

ACTOS

PIOGLITAZONE HYDROCHLORIDE

4

ACYCLOVIR

ACYCLOVIR

109

ACYCLOVIR

ACYCLOVIR SODIUM

2

ACYCLOVIR SODIUM

ACYCLOVIR SODIUM

2

ADAGEN

PEGADEMASE BOVINE

3

ADALAT

NIFEDIPINE

4

ADENOSINE

ADENOSINE

499

ADRUCIL

FLUOROURACIL

9

ADVICOR

NIACIN

8

ADVICOR

LOVASTATIN

10

ADVIL

IBUPROFEN

3

AEROBID

FLUNISOLIDE

2

AEROLATE SR

THEOPHYLLINE

1

AGENERASE

AMPRENAVIR

15

Table 16: Drug names, the active ingredients of which are mentioned in documents.

1http://worldwide.espacenet.com/

2http://www.patentlens.ne

3http://www.uspto.gov

4http://www.epo.org/

5http://www.wipo.int/classifications/ipc/en/

6svn://molto-project.eu/patents-corpora/EPO-www-patents/

7http://www.bing.com/translator

8http://translate.google.com/

9Bing and Google update their systems periodically. These translations are dated April 2013.

10The source files can be found in svn://molto-project.eu/patents-corpora/corpora-parser.tgz along with the documents used in this prototype.

11http://falkor.lsi.upc.edu/MOLTO , {credentials molto/onlinepatenttranslation}

12http://molto.ontotext.com/

13OWLIM (http://www.ontotext.com/owlim) is a commercial RDF database management system, developed by Ontotext.

14http://lucene.apache.org/solr/

15with respect to the time of update - December, 2012

16http://museum.ontotext.com/

17Forest is a web-based framework for management of RDF datastores and semantically annotated documents, developed by Ontotext.

18http://exopatent.ontotext.com/

19Matrixware was a former participant in the MOLTO project.

20http://www.accessdata.fda.gov/scripts/cder/ob/default.cfm and http://www.accessdata.fda.gov/scripts/cder/ob/default.cfm

21http://www.ontotext.com/proton-ontology

22http://krono.act.uji.es/people/Ernesto/UMLS\_SN\_OWL

23http://www.nlm.nih.gov/mesh/

24Search and Rescue operations

25http://docs.epoline.org/ebd/doc/ep-patent-document-v1-4.dtd

26http://gate.ac.uk/family/embedded.html

27http://tika.apache.org/

28http://docs.seleniumhq.org/http://docs.seleniumhq.org/

29svn://molto-project.eu/wp4/YAQL

30http://www.ontotext.com/kim/semantic-annotation

31http://molto.ontotext.com

32http://proton.semanticweb.org/protonm\#mentions

Table of Contents

MOL12b

MOL12c

Introduction

Motivation

Related Work

The MOLTO approach

The Patents Case Study Prototype

Patent corpus and translation

The biomedical patent datasets

Translation of patent documents

Syntactic measures for MT evaluation

Patents retrieval system, ontologies and semantic annotations

Free text search

Ontologies for the biomedical domain

Semantic annotations extracted from the patents metadata

Retrieval speed optimization

Evaluation of the retrieval system

GF grammars for generation of SPARQL

The patents query language

Better coverage of the ontology by GF

The On-line User Interface for the Patents Retrieval Prototype

GUI updates

Multilingual annotations

Document source exposed

NL query examples

User Scenarios

Query types

Semantic data exploration

Single word query

FTS queries

Conclusions and Challenges

Example Showing the Translation Steps of an Excerpt of Patent

Ontologies in the Biomedical Domain

Semantic Annotations - Final Types

Biomedical Patents - Query Patterns

Patent Retrieval Databases Roadmap

1

2

VFC05

MB04

3

AFG06

TWS10

MOL12a

4

5

MOL11

6

KSF06

7