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Technologies for (semi-) automatic metadata creation http://gate.ac.uk/ http://nlp.shef.ac.uk/ Diana Maynard University

Technologies for (semi-) automatic metadata creation http://gate.ac.uk/ http://nlp.shef.ac.uk/ Diana Maynard University of Sheffield KnowledgeWeb WP 1.3 meeting, Crete, 14 May 2004. USFD is mainly concerned in this WP with best practices and guidelines for ontology-based web applications

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Technologies for (semi-) automatic metadata creation http://gate.ac.uk/ http://nlp.shef.ac.uk/ Diana Maynard University

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  1. Technologies for (semi-) automatic metadata creation http://gate.ac.uk/http://nlp.shef.ac.uk/ Diana Maynard University of Sheffield KnowledgeWeb WP 1.3 meeting, Crete, 14 May 2004 1

  2. USFD is mainly concerned in this WP with best practices and guidelines for ontology-based web applications State-of-the-art systems and platforms for metadata creation Metadata is created through semantic tagging Metadata can be represented as inline (modification of the original document) or standoff (separate storage from the document) Overview 2

  3. Semi-automatic methods are more reliable, but require human intervention MnM: requires initial human annotation; pre-defined ontology S-CREAM AERODAML Automatic methods less reliable, but suitable for large volumes of text, and offer a dynamic view SemTag: semantic tagging from ontology KIM: semantic tagging and ontology population hTechSight: semantic tagging, ontology population and evolution Semi-automatic v automatic metadata creation 3

  4. MnM S-CREAM Semi-automatic methods 4

  5. Semi-automatic in that it requires initial training by user Uses pre-defined set of concepts in ontology User browses web and manually annotates his chosen pages System learns annotation rules, tests them, and takes over annotation, populating ontologies with the instances found Precision and recall are not perfect, however retraining is possible at any stage MnM 5

  6. Semi-automatic CREAtion of Metadata Uses Onto-O-Mat + Amilcare Trainable for different domains Aligns conceptual markup (which defines relational metadata) provided by e.g. Ont-O-Mat with semantic markup provided by Amilcare S-CREAM 6

  7. Annotated data in S-CREAM 7

  8. Amilcare learns IE rules from pre-annotated data (e.g. using Ont-O-Mat) Uses GATE (ANNIE) for pre-processing + applies rules learnt in training phase to new documents Concepts need to be pre-defined, but system can be trained for new domain Can be tuned towards precision or recall Amilcare 8

  9. SemTag KIM h-Techsight Automatic methods 9

  10. SemTag and KIM both annotate webpages using instances from an ontology Main problem is to disambiguate such instances which occur in multiple parts of the ontology SemTag aims for accuracy of classification, whereas KIM aims more for recall (finding all instances) KIM also uses IE to find new instances not present in ontology SemTag and KIM 10

  11. Automated semantic tagging of large corpora, using TAP ontology (contains 65K instances) Largest scale semantic tagging effort to date Uses concept of Semantic Label Bureau Annotations are stored separately from web pages (standoff markup) Uses corpus-wide statistics to improve quality of tagging, e.g. automated alias discovery Tags can be extracted using a variety of mechanisms, e.g. search for all tags matching a particular object SemTag 11

  12. SemTag Architecture 12

  13. KIM • Uses an ontology (KIMO) with 86K/200K instances • Lookup phase marks instances from the ontology • High ambiguity of instances with the same label (e.g. locations belonging to different countries) • Disambiguation uses an Entity Ranking algorithm, i.e., priority ordering of entities with the same label based on corpus statistics • Lookup is combined with rule-based IE system (from GATE) to recognise new instances of concepts and relations • Special KB enrichment stage where some of these new instances are added to the KB 13

  14. KIM (2) 14

  15. Knowledge management platform for fully automatic metadata creation and ontology population, and semi-automatic ontology evolution, powered by GATE and ToolBox. Data-driven analysis of ontologies enables trends of instances to be monitored Uses GATE to support the instance-based evolution of ontologies in the Chemical Engineering domain. Analysis of unrestricted text to extract instances of concepts from such ontologies Instances populated into a domain-specific ontology and/or exported to an Access / Oracle database h-TechSight KMP 15

  16. 1 Ontology in Employment Web site URL Visualisation of New Instances Analysis of Results DB Evolution of Ontologies 2 3 4 16

  17. Ontology-Based IE for semantic tagging of job adverts, news and reports in chemical engineering domain Semantic tagging used as input for ontological analysis Fundamental to the application is a domain-specific ontology Terminological gazetteer lists are linked to classes in the ontology Rules classify the mentions in the text wrt the domain ontology Annotations output into a database or as an ontology Ontology-based IE in h-TechSight 17

  18. h-Techsight uses rule-based IE system Requires human expert to write rules Accurate on restricted domains with small ontologies Adaptation to a new domain / ontology may require some effort Limitations 18

  19. Tradeoff between semi-automatic and fully automatic systems, dependent on application, corpus size etc Tradeoff between rule-based and ML techniques for IE Tradeoff between dynamic vs static systems Summary 19

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