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After OWL: defacto standards for semantic technologies (or: what do you get for €40m

After OWL: defacto standards for semantic technologies (or: what do you get for €40m EU research money?) http://gate.ac.uk/ http://nlp.shef.ac.uk/

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After OWL: defacto standards for semantic technologies (or: what do you get for €40m

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  1. After OWL: defacto standards for semantic technologies (or: what do you get for €40m EU research money?) http://gate.ac.uk/http://nlp.shef.ac.uk/ Hamish Cunningham,Kalina Bontcheva, Valentin Tablan, Diana Maynard,Wim Peters, Niraj Aswani, Milena Yankova, Yaoyong Li, Akshay Java, Michael Dowman ILASH workshop, March 2004

  2. Context: increasing use of “semantic” technology in IT the role(s) of human language technology substantial investment in the next phase of semantic web research Semantic Web: moving on from formal standards Acronym soup: GATE: HLT API 4 SDK SW & KT An application: Ontology-Based IE in KIM Issues in API design, next steps Structure of the talk 2(24)

  3. Gartner, December 2002: taxonomic and hierachical knowledge mapping and indexing will be prevalent in almost all information-rich applications through 2012 more than 95% of human-to-computer information input will involve textual language A contradiction: to deal with the information deluge we need formal knowledge in semantics-based systems our information spaces are in informal and ambiguous natural language The challenge: to reconcile these two phenomena The Knowledge Economy and Human Language 3(24)

  4. HLT: Closing the Loop KEY MNLG: Multilingual Natural Language GenerationOIE: Ontology-aware Information ExtractionAIE: Adaptive IECLIE: Controlled Language IE (M)NLG Semantic Web; Semantic Grid;Semantic Web Services Formal Knowledge(ontologies andinstance bases) HumanLanguage OIE (A)IE ControlledLanguage CLIE 4(24)

  5. 6th framework IP project Duration: 36 months from 1/1/4, €12.5m http://sekt.semanticweb.org/ Improve automation of ontology and metadata generation Develop highly-scalable solutions Research sound inferencing despite inconsistent models Develop semantic knowledge access tools Develop methodology for deployment SEKT: Semantic Knowledge Technology 5(24)

  6. 20th Century audio-visual media is rapidly disappearing Preservation and restoration are high cost The costs must be justified by increased access “Metadata”: descriptive information about content PrestoSpace (€9m IP, 40 months from 02/04): rich metadata and semantic access cross-lingual access syndicated delivery repurposeable content PrestoSpace (20th Century Rot) 6(24)

  7. “Building the European Research Area” in KM through collaboration with related IP and NoE projects in this area for a coordinated impact strategy SEKT, DIP, KnowledgeWeb – SDK cluster:http://sdk.semanticweb.org/ Other related projects: AceMedia IP (semantic knowledge systems) PrestoSpace IP (cultural heritage / digital libraries) BRICKS IP (cultural heritage / digital libraries) Total EU/6FP investment in semantic tech. research €40m: potential to influence the emergence of defacto standards The “SDK” research cluster 7(24)

  8. Computer scientists love standards, so we have many For any given problem there are usually 3 “standards” OWL is no exception: Lite, DL, Full There are good reasons, but cf. RDF(S) implementation history: applications will of necessity mix and match If we can achieve standard practice and libraries in applications we will have made a next step and will promote takeup (Pathological) example: TCP/IP vs. OSI Next step for Semantics tech: from formal to defacto standards? 8(24)

  9. What sorts of software do we need? Ontology and metadata management: storage; versionning; caching, inferencing; etc. (below) Human language technology components and services (not monolithic systems, not unproven research prototypes) The role of measurement in scaling and robustness: in HLT this means MUC, TREC, ACE, TIDES, ... Here’s one we baked earlier.... HLT API 4 SDK SW & KT 9(24)

  10. Eight years old, with the largest user constituency of its type An architecture A macro-level organisational picture for LE software systems. A framework For programmers, GATE is an object-oriented class library that implements the architecture. A development environment For language engineers, computational linguists et al, a graphical development environment. Some free components... ...and wrappers for other people's components Tools for: evaluation; visualise/edit; persistence; IR; IE; dialogue; ontologies; etc. Free software (LGPL). Download at http://gate.ac.uk/download/ GATE (the Volkswagen Beetle of Language Processing) is: 10(24)

  11. GATE team projects. Past: Conceptual indexing: MUMIS: automatic semantic indices for sports video MUSE, cross-genre entitiy finder HSL, Health-and-safety IE Old Bailey: collaboration with HRI on 17th century court reports Multiflora: plant taxonomy text analysis for biodiversity research e-science EMILLE: S. Asian language corpus ACE/ TIDES: Arabic, Chinese NE JHU summer w/s on semtagging Present: Advanced Knowledge Technologies: €12m UK five site collaborative project ETCSL: Sumerian digital library MiAKT: medical informatics / AKT SEKT: Semantic Knowledge Tech PrestoSpace: AV Preservation KnowledgeWeb; h-TechSight GATE users = significant proportion of community. A small sample: the American National Corpus project the Perseus Digital Library project, Tufts University, US Longman Pearson publishing, UK Merck KgAa, Germany Canon Europe, UK Knight Ridder, US BBN (leading HLT research lab), US SMEs: Melandra, SG-MediaStyle, ... Imperial College, London, the University of Manchester, UMIST, the University of Karlsruhe, Vassar College, the University of Southern California and a large number of other UK, US and EU Universities UK and EU projects inc. MyGrid, CLEF, dotkom, AMITIES, CubReporter, Poesia... Critical mass: 000s people 00s sites 11(24)

  12. Architectural principles • Non-prescriptive, theory neutral (strength and weakness) • Re-use, interoperation, not reimplementation (e.g. diverse XML support, integration of Protégé, Jena, Weka, interoperation with SCHUG in MUMIS) • (Almost) everything is a component, and component sets are user-extendable • (Almost) all operations are available both from API and GUI • Why does this matter? It means that GATE works well with other tools, embeds easily, and achieves robustness through focus (API requirements) 12(24)

  13. CREOLE: a Collection of REusable Objects for Language Engineering: GATE components: modified Java Beans with XML configuration The minimal component = 10 lines of Java, 10 lines of XML, 1 URL Why bother? Allows the system to load arbitrary language processing components All the world’s a Java Bean.... 13(24)

  14. OBIE ANNIE … ADiff DocVR OntolVR ... Application Layer IDE GUI Layer (VRs) XMLDocumentFormat email PDF docs RTF docs HTML docs XML docs Corpus Document HTMLDocumentFormat DocumentContent AnnotationSet PDFDocumentFormat Annotation TRs NE POS Co-ref FeatureMap TEs … … Processing Layer (PRs) Corpus Layer (LRs) DocumentFormatLayer (LRs) … XML Oracle PostgreSql .ser DataStore Layer WebServices GATE APIs Onto-logy ProtégéOnto-logy Word- net Gaz-etteers ... Language Resource Layer (LRs) • NOTES (2) • eg: Protégé LR & VR both wrapped in Res. (bean) API • ontology repositories and inference are the same: KAON + Sesame + Orenge + ? • NOTES • everything is a replaceable bean • all communication via fixed APIs • low coupling, high modularity, high extensibility 14(24)

  15. OGSA, WMSO in the web services layer? Eclipse: less code for us, more services for users? (A free OWL/UML drawing tool, for example) ISO TC37/SC4: JNLE special; LIRICS consortium Issues (1): a common HLT API 15(24)

  16. Bulgaria London XYZ UK API Application: Ontology-based IE XYZ was establishedon 03 November 1978 in London. It opened a plant in Bulgaria in … Ontology & KB Location Company HQ partOf City Country type type HQ type type establOn partOf “03/11/1978” 16(24)

  17. Classes, instances & metadata … Entity Person Job-title president G.Brown minister chancellor … “Gordon Brown met George Bush during his two day visit. <metadata> <DOC-ID>http://… 1.html</DOC-ID> <Annotation> <s_offset> 0 </s_offset> <e_offset> 12 </e_offset> <string>Gordon Brown</string> <class>…#Person</class> <inst>…#Person12345</inst> </Annotation> <Annotation> <s_offset> 18 </s_offset> <e_offset> 32 </e_offset> <string>George Bush</string> <class>…#Person</class> <inst>…#Person67890</inst> </Annotation> </metadata> Classes+instances before Bush Classes+instances after 17(24)

  18. OBIE in KIM • An ontology (KIMO) and 200K instances KB • High ambiguity of instances with the same label – uses disambiguation step • Lookup phase marks mentions from the ontology • Combined with GATE-based IE system to recognise new instances of concepts and relations • KB enrichment stage where some of these new instances are added to the KB • Disambiguation uses an Entity Ranking algorithm, i.e., priority ordering of entities with the same label based on corpus statistics (e.g., Paris) 18(24) Popov et al. KIM. ISWC’03

  19. OBIE in KIM (2) 19(24) Popov et al. KIM. ISWC’03

  20. KIM demo... • Continue to exploit the pluggability and community effects of GATE (and Sesame, Lucene, ...) • SWAN: Semantic Web Annotator at DERI/Galway • Syndication • Social networking • Evaluation (below) Next steps in OBIE 20(24)

  21. (The “P” in OLP) Challenge:Evaluating Richer NE Tagging • Need for new metrics when evaluating hierarchy/ontology-based NE tagging • Need to take into account distance in the hierarchy • Tagging a company as a charity is less wrong than tagging it as a person 21(24)

  22. Detection of entities and events, given a target ontology of the domain. Disambiguation of the entities and events from the documents with respect to instances in the given ontology. For example, measuring whether the IE correctly disambiguated “Cambridge” in the text to the correct instance: Cambridge, UK vs Cambridge, MA. Decision when a new instance needs to be added to the ontology, because the text contains a new instance, that does not already exist in the ontology. SW IE Evaluation tasks 22(24)

  23. Two design approaches: the “richest set of features” approachpool experience, cover all the bases, be relevant to very many users (“top-down”) the “highest common factors” approachanalyse software, pick common features, create plugability layer (“bottom-up”) Both useful; can be combined Approach B. has some key advantages: leads to quicker version 1.0 minimises arguments (criteria: feature exists in several sys, not is “good”) Problems: features present several places but not all – “operation not supported”? new work not prefigured in version 1.0 – roadmaps, placeholders Issues (2): a common OMM API 23(24)

  24. Tutorial on HLT for the Semantic Web at European Semantic Web Symposium:http://www.esws2004.org/ These slides: http://gate.ac.uk/sale/talks/ilash-semweb-mar2004.ppt More information: http://gate.ac.uk/http://nlp.shef.ac.uk/ The end 24(24)

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