GRISP: A Massive Multilingual Terminological Database for Scientific and Technical Domains

1. GRISP: A Massive Multilingual Terminological Databasefor Scientific and Technical Domains • Patrice Lopez and Laurent Romary • INRIA & HUB – IDSL • patrice_lopez@hotmail.comlaurent.romary@inria.fr

2. Overview • GRISP (Generic Research Insight in Scientific and technical Publications) • Multiple scientific and technical fields • Multilingual (en, fr, de) • Built from the compilation of open resources • Sound conceptual model • Mapping across a variety of domains • Use of structural constraints • Machine learning techniques for controlling the fusion process • Our sources: MeSH, UMLS, Specialist Lexicon, Gene Ontology, ChEBI, WordNet, WOLF, SUMO, IPC, Wikipedia • Result: several millions terms, concepts, semantic relations and definitions.

3. Why are we doing all this? • Terminology is the main vehicle by which technical and scientific units of knowledge are represented and conveyed (30-80%; Ahmad, 1996) • Application to a large collection of multilingual and multi-domain patent documents • Two underlying considerations: • Cost of manually maintained terminological resources • Cf. Biosis, IATE, TermScience • Khayari et al., 2006: Modeling the heterogeneity of resources • A lot of available resources online, based on heterogeneous organizational principles • Underlying vision: Integrating knowledge engineering into current state of the art information retrieval and classification systems

4. Merging terminological resources • Related to the fusion of ontologies • Ontologies are usually relatively small in size • Semi-automatic methods: McGuinness et al., 2000 • Fully automatic method • Madhavan et al., 2001: exploit structural and linguistic matching • Doan et al., 2001: Machine learning techniques (concepts and properties) • Gal et al., 2005: fuzzy logic methods • Existing work on merging classification systems • Wang et al., 2008: Merging of subject headers in Digital Libraries • Automatic merging techniques for heterogeneous terminologies has not been yet investigated • Much richer linguistic content • No formal organization of concepts • Do not model facts or assertions

5. A quick reminder • Terminological resources • Approximation of lexical semantics in specialized fields • Based on a concept to term (onomasiological) model • Naturally multilingual (term grouping according to languages) • Existing standards • ISO 704: editorial principles for building up a terminological resource • ISO 16642: Abstract model for representing terminological databases • Romary, 2001 • ISO 30042: A concrete XML syntax (TBX) • Note: terminology standards do not standardize terminologies!

6. Target terminological model • Multiple languages • Multiple terms • Variants, abbreviation, inflexions • Multiple descriptions • E.g. multiple definitions, complementing each other • Additional information: illustrations, formulae, etc. • Basic conceptual relations • Local metadata • Provides management information attached to the various terminological description levels (e.g. origin, validation level, register) • Allows the creation of views (e.g. all MeSH entries; cf. Khayari et al., 2006) • And yes, ISO 16642 (TMF) can all this! • Main issue: identifying the relevant data category in the various source terminologies

7. TMF model 1 TMF model 2 TMF model 2 TMF model 2 Target model Merging terminologies,merging models

8. Terminological Entry Terminological Entry Terminological Entry Terminological Entry Ontological relations, definition Dialectal information, definition Grammatical information, register, … definition Metadata (sources, revisions) Language Section Language Section Language Section Language Section Language Section Language Section Language Section Language Section Term Section Term Section Term Section Term Section Term Section Term Section Term Section Term Section Term Section Term Section Term Section Term Section Term Section Term Section Term Section Term Section TMF in a nutshell Terminological Data Collection (TDC) + any kind of local metadata (origin, certainty, accessibility)

9. Data category mapping TMF model 1 TMF model 2 TMF model 2 TMF model 2 Target model Merging terminologies,merging models /definition/ /definition/

10. Identifying domains • Theoretical background • Non-ambiguity of a term within a domain • E.g. 129 domains in MESH • GRISP • Set of 76 reference domains (see table 1) • Scientific and technical domains of Wordnet Domains (Magnini and Cavaglià, 2000) • Organised as a hierarchy • Manual mapping from resource specific domains to our reference set

11. Merging concepts • Identification of common concepts across terminological sources – core principles • Baseline: same term + same domain = same concept • Difficulties: Conflicting domain mapping, high polysemy of term variants and incorrectly positioned concepts (e.g. Wikipedia) • Wrongly merged concepts • Lost in precision for concept description • Revised: same preferred term + same domain = same concept • Source conformance rule: separated concepts in a given source cannot be further merged (by transitivity) • Not applied to Wordnet, IPC and Wikipedia • Smoothing down the rules: using machine learning techniques

12. Concept merging as a machine learning process Concept pool Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Features Merging decision SVM (Support Vector Machine) and MLP (Multi-Layer Perceptron) binary classification models

13. Training process • Training features • (f1-2) sources (e.g. S1=“MeSH”, S2=“Wikipedia”) • (f3) Number of common domains between the two concepts • (f4) Number of same source-specific categorizations • (f5) Boolean indicating if both preferred terms are identical • (f6) Boolean indicating if both preferred terms are identical after stemming • (f7) Ratio of identical terms given all terms • (f8) Similarity measure of the definition texts, after stemming and based on negative KL divergence • (f9) Number of domains of the merged concept • (f10) Number of words of the longest common terms • Training data • Wikipedia – MeSH mapping • Pascal database (INIST)

14. Result overview • Observations: • Small number of actual merges (cf. product names, chemical and medical entities) • Merging relevant for frequently used concepts • Overall content: • 596,865 definitions • 1,321,988 source specific categorizations of concepts • 20,000 acronyms • 14,268 chemical formulas and • 12,375 chemical structure identifiers.

15. Evaluation • Random subset of 10% of the merging examples extracted from Wikipedia/MeSH mappings and from the PASCAL terminology • Merging Rule 2 produces almost perfect merging but with a very low coverage • Rule 1 extends the coverage at the price of a relatively high rate of merging error • Machine Learning approaches further extend the coverage while maintaining a high precision

16. GRISP browser: radial engine rendering rendering rendering

18. Application: Patatras • PATATRAS (PATent and Article Tracking, Retrieval and AnalysiS) • Context: CLEF-IP competition • Prior art search task (EPO documents) • 1,9 million documents in English, French and German (more than 3 billion words) • Ranked first for all subtasks of the evaluation track among 14 participants (Roda et al., 2009) • Conceptual indexing of the CLEF-IP corpus • Development of a term annotator based on GRISP • Term variant matching after POS + lemmatization • Concept disambiguation based on IPC classes of the documents • 1.1 million different terms identified • 176 million annotations

19. Results: Patatras • Significant accuracy improvements for CLEF-IP • Combination of a word-based and concept-based ranked results with a regression model Based on 10,000 queries

20. Epilogue • Online tool • Contact: patrice_lopez@hotmail.com • Free resource • Based on the freely available subset of resources • Constant evolution • Maintenance according to evolution of our sources • Addition of further sources