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Overview

Ontologies Contributions from Language Technology Paul Buitelaar DFKI GmbH Language Techology Lab DFKI Competence Center Semantic Web Saarbrücken, Germany. Overview.

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Overview

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  1. Ontologies Contributions from Language TechnologyPaul BuitelaarDFKI GmbHLanguage Techology LabDFKI Competence Center Semantic WebSaarbrücken, Germany

  2. Overview Ontologies and the Semantic WebSemantic Web Intro Ontologies and Knowledge Markup Ontology Development Ontology Lifecycle & Language Technology Language TechnologyLevels of Automatic Linguistic AnalysisOntologies in Multilingual Information Access  A Medical Example: MuchMore Project Semantic Resources in the Medical Domain Demo MuchMore System Language Technology in Annotation and Indexing ConclusionsMuchMore for the Legal Domain…

  3. Semantic Web Semantic Web Services Semantic Web Knowledge Markup Ontologies Intelligent Man-Machine Interface

  4. Ontology-based Knowledge Markup Semantic Metadata • Metadata, e.g. Dublin Core -- Title, Author, etc. • Semantic: Formal Properties of Objects of Class Author Knowledge Markup <xmnls jobs="http://www.jobs.org/daml+oil-jobs-ontology#"> <jobs:systems-analyst> John Smith </jobs:systems-analyst>

  5. Semantic Web Architecture Layered Architecture (Tim Berners-Lee)

  6. Syntax Semantics XML XML Schema NamespacesInterpretation Context Data Types Formalization: Classes (Inheritance), Properties RDF Schema RDF Formalization: Classes, Class Definitions, Properties, Property Types (e.g. Transitivity) OWL (DAML+OIL) Knowledge Markup Languages

  7. Ontologies: Basic Idea • Definition • “… Explicit, Formal Specification of a Shared Conceptualization of aDomain of Interest” T. Gruber Towards principles for the design of ontologies used for knowledge sharing. Int. J. of Human and Computer Studies, 1994 • Purpose • Knowledge Sharing (e.g. between Agents) • Inference (over Sets of Instances) • Related Areas, e.g. • Terminologies, Controlled Vocabulary, Thesauri, Taxonomies, Semantic Lexicons, Wordnets, etc. • Conceptual Models, Schemas, etc.

  8. Ontologies: Applications, e.g. • Semantic Web Services • Interoperability for (Semantic) Web Services • Intelligent Agents • Domain Models for Intelligent Agents • Text Interpretation • Ontology-aware Information Extraction • Multimedia Integration • Ontology-based Alignment of Extracted Objects in Text, Audio, Video • Intelligent Search/Navigation • Ontology-based Indexing in Web-Retrieval

  9. Ontologies: Development • Ontology Editor / KB Management • Most Widely Used: Protégé (Stanford University, Medical Informatics, USA) • Originally for Development and Maintenance of Medical Expert Systems • Other, e.g. • KAON: University of Karlsruhe - AIFB, Germany • WebOde: UPM – Ontology Group, Madrid, Spain • WebOnto: Open University - KMI, UK • Overview at XML.comby Michael Denny: Ontology Building: A Survey of Editing Tools

  10. Class Hierarchy Slot Descriptions http://dmag.upf.es/ontologies/2003/12/ipronto.owl

  11. Ontology Lifecycle Populating Validating Creating Deploying Evolving Maintaining

  12. LT in the Ontology Lifecycle Language Technology (LT) for Ontology: Creating & Evolving Linguistic Analysis to Extract Classes / Relations Classes, Relations/Properties Ontology (Knowledge) Documents (Text) Populating (Knowledge Base Generation) Linguistic Analysis to Extract Instances Instances Language Technology = Automated Linguistic Analysis

  13. Linguistic Analysis: Example The Dell computer with a flat screen had to be rejected because of a failure in the motherboard. flat screen Dell computer has-a reject has-a animate-entity motherboard failure location-of

  14. Part-of-Speech, Morphology Part-of-Speech • e.g.: noun, verb, adjective, preposition, … PoS tag sets may have between 10 and 50 (or more) tags Morphology • Most languages have inflection and declination, e.g.: Singular/Plural computer, computers Present/Past reject, rejected Many languages have also complex (de)composition, e.g.:Flachbildschirm(flat screen) >flach + Bildschirm>flach + Bild + Schirm

  15. Phrases, Terms, Named Entities Semantic Units • Phrases (e.g. nominal - NP, prepositional - PP)NP a flat screen PP with a flat screen NP (recursive) the Dell computer with a flat screena failure in the motherboard Terms (domain-specific phrases)Dell computerDell computer with a flat screen Named Entities (phrases corresponding to dates, names, …) COMPANY Dell COMPANY Dell Computer Corporation PERSON Michael Dell

  16. Dependency Structure Semantic Structure Dependencies between Predicates and Argumentsthe Dell computer with a flat screen had to be rejectedPRED: reject ARG1: ENTITY ARG2: ‘the Dell computer with a flat screen’‘Logical Form’ :reject(x,y) & animate-entity(x) & computer(y) & … The Dell computer that has been rejected was claimed to have suffered from handling.reject(e1,x1,y1) & animate-entity(x1) & Dell_computer(y1) & claim(e2,x2,e3) & animate-entity(x2) & suffer_from(e3,y1,y2) & handling (y2)

  17. MuchMore Project http://muchmore.dfki.de Demonstration Prototype  Real-Life Medical Scenario for Cross-Lingual Information Retrieval Research & Development  Combined Data- and Knowledge-Driven Performance Evaluation  Performance Comparison of Existing and Novel Methods

  18. Semantic Resources Medical Domain UMLS: Unified Medical Language System Medical MetaThesaurus (only MeSH2001 is used) English, German, Spanish, … 730.000 Concepts 9 Relations (Broader, Narrower,…) Semantic Network 134 Semantic Types 54 Semantic Relations General WordNet (EN), GermaNet (DE), EuroWordNet (“linked”)

  19. C0019682|ENG|P|L0019682|PF|S0048631|HIV|0| C0019682|ENG|S|L0020103|PF|S0049688|HTLV-III|0| C0019682|ENG|S|L0020128|VS|S0049756|Human Immunodeficiency Virus|0| C0019682|ENG|S|L0020128|VWS|S0098727|Virus, Human Immunodeficiency|0| C0019682|FRE|P|L0168651|PF|S0233132|HIV|3| C0019682|FRE|S|L0206547|PF|S0277133|VIRUS IMMUNODEFICIENCE HUMAINE|3| C0019682|GER|P|L0413854|PF|S0538136|HIV|3| C0019682|GER|S|L1261793|PF|S1503739|Humanes T-Zell-lymphotropes Virus Typ III|3| Concept Names: 1.734,706 ENGLISH 1.462,202 GERMAN 66,381 other languages MetaThesaurus, SemNet • Each CUI (Concept Unique Identifier) is mapped to one out of 134 Semantic Types or TUI (Type Unique Identifier) • Clozapine: C0009079  Pharmacologic Substance: T121 • Semantic Types are organized in a Network through 54 Relations • T121|T154|T047

  20. Token (with Part-of-Speech) German: Kreuzbandes English: ligaments Lemma (or Sequence of Lemmas - Decomposition) German: Faserknorpel Faser + Knorpel English: ligament UMLS Concept Code and Semantic Type ligament : C0022745_T030 MeSH Code A2.513 Semantic Relation (over a Pair of UMLS Concepts) C0022745_T030 interconnects C0047693_T065 Annotation & Indexing

  21. UMLS Semantic Network specifies 54 types of relations between 134 semantic types Pharmacologic SubstanceaffectsCell Function Relations are generic and potentially false Therapeutic Proceduremethod_of Occupation,Discipline *discectomymethod_ofhistory Relations are ambiguous Therapeutic ProcedurepreventsNeoplastic Process Therapeutic ProcedurecomplicatesNeoplastic Process Therapeutic ProcedureaffectsNeoplastic Process Therapeutic ProceduretreatsNeoplasticProcess Relations

  22. Discontinuation of heparin is a simple andessential maneuvre, and anticoagulation has tobe continued by alternative drugs. Example

  23. Terms:C0019134Heparin C0005790 Blood coagulation tests C0013227Pharmaceutical preparations Example: Terms/Concepts Discontinuation of heparin is a simple andessential maneuvre, and anticoagulation has tobe continued by alternative drugs.

  24. Example: Relations Discontinuation of heparin is a simple andessential maneuvre, and anticoagulation has tobe continued by alternative drugs. Terms:C0019134Heparin C0005790 Blood coagulation tests C0013227Pharmaceutical preparations • Relations: C0019134 interacts_with C0013227 • C0005790 analyses C0019134 • C0005790 analyses C0013227

  25. Conclusions MuchMore for the Legal Domain… ResourcesLegal Domain Ontology with……Large-scale Terminology for Multiple Languages, or if not available……Large Legal Domain Corpora in Multiple Languages for Term Extraction……and for Relation Extraction if Ontology Needs to be Constructed/Adapted  ToolsLinguistic Analysis (PoS, Morphology, Term Grammars, etc.)……for Multiple Languages……Tuned to the Legal Domain…Information Retrieval Infrastructure, Interface Design, etc.

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