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WP8: User Centred Applications

WP8: User Centred Applications. Enrico Motta, Marta Sabou, Vanessa Lopez, Laurian Gridinoc, Lucia Specia Knowledge Media Institute The Open University Milton Keynes, UK. WP8 Goals and Tasks. Objective:

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WP8: User Centred Applications

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  1. WP8: User Centred Applications Enrico Motta, Marta Sabou, Vanessa Lopez, Laurian Gridinoc, Lucia Specia Knowledge Media InstituteThe Open UniversityMilton Keynes, UK

  2. WP8 Goals and Tasks • Objective: • To provide and evaluate concrete applications of OK to support user tasks on the Web, such as knowledge retrieval and ontology-assisted browsing. • Tasks: • T8.1. Semantic Browsing • Evolve Magpie so that it does not rely on design time ontology selection • T8.2. Ontology Based Query Answering • Evolve AquaLog towards domain independent QA • Evaluating the value of OK Technology • Compare standard and OK-enabled versions of both systems

  3. Outline • Vision: • “Open” is core to novel Semantic Web applications • Novel technical challenges arise • Building novel applications within OpenKnowledge: • New methods: • Dynamic ontology mapping • Providing more semantic data: • Folksonomy enrichment

  4. The SW gets BIGGER The Semantic Web registered a 300% growth in 2004 alone, thus outpacing the growth of the Web itself. Lee, J., Goodwin, R. (2004) The Semantic Webscape: a View of the Semantic Web. IBM Research Report.

  5. Access Gateways exist

  6. Example1: AquaLog 1. NL Question 2. Linguistic interpretation 3. Ontology based interpretation 4. Answer

  7. Example1: AquaLog • Limited to the domain and data provided by a single ontology

  8. PowerAqua: QA on the 'open' Semantic Web • Cross domain QA: Selects and combines relevant • information from multiple ontologies: • automatically locate ontologies • map user terminology to ontologies • integrate info from different ontologies (mapping)

  9. Example2: Magpie NL Question Ontology concepts Instances highlighted according to their type

  10. Example2: Magpie • Limited to the domain and data provided by a single ontology

  11. PowerMagpie: Semantic browsing on the 'open' SW • Open semantic browsing: Dynamically • selects and combines relevant • information from multiple ontologies: • automatically locate ontologies • integrate info from different ontologies (mapping)

  12. New Tools are OPEN • … with respect to the topic domain • Instead of deciding the domain at design time • Let the user decide the domain of interest at run-time • Thus: Lower the cost of user participation • … with respect to the explored data • Instead of “hard-wiring” one knowledge sources at design time - smart databases • Dynamically select and make use of multiple, heterogeneous knowledge sources: • Online available ontologies/semantic data • Non-semantic data, e.g., folksonomies • Thus: Lower the cost of data integration

  13. Key Paradigm Shift • Source of Intelligence: • Early Semantic Web tools: • A function of sophisticated, task-centric problem solving • New Tools: • A side-effect of size and heterogeneity • (Collective Intelligence) Invited talks and papers: Motta, E., Sabou, M. "Next Generation Semantic Web Applications". ASWC’06. Motta, E., Sabou, M. "Language Technologies and the Evolution of the Semantic Web". LREC’06

  14. Ontology Modularization Current work focuses on user-mediated ontology selection Current work assumes user involvement Dynamic Ontology Mapping • Current work: • design-time mapping of complete ontologies • assumptions on the domain and structure of the ontologies What is needed? Dynamic Ontology Selection

  15. Outline • Vision: • “Open” is core to novel Semantic Web applications • Novel technical challenges arise • Building novel applications within OpenKnowledge: • New methods: • Dynamic ontology matching • Providing more semantic data: • Folksonomy enrichment

  16. Achievements – at a glance • Ontology Matching • Two dynamic ontology matching algorithms • Run-time matching of knowledge structures • No assumptions on domain, structure etc. • Core to our tools and to the OK infrastructure • Defined, implemented, documented, partially tested • PowerMap – part of PowerAqua • MatchMiner - matching by using the Semantic Web as background knowledge • Acquiring semantic data • A Hybrid Algorithm for learning relations from text • Semantic enrichment of folksonomies by exploring online ontologies

  17. PowerMap: core of PowerAqua PowerMap • 1. Ontology identification • Syntactic mapping Keywords • 2. Extracting (clusters of) triples • Semantic mapping Ontology Triples 3. Filtering triples • Lopez, V., Sabou, M., Motta, E. "Mapping the Real Semantic Web on the Fly". ISWC’06. • Reported in deliverables D3.1. and D4.1.

  18. MatchMiner • rely on online ontologies (Semantic Web) to derive mappings • ontologies are dynamically discovered and combined • does not require any a priori knowledge about the domain • returns semantic relations as mappings Semantic Web rel A B • M. Sabou, M. d’Aquin, E. Motta, “Using the Semantic Web as Background Knowledge in Ontology • Mapping", Ontology Mapping Workshop, ISWC’06. – Best Paper Award • Reported in Deliverable D4.1.

  19. Large Scale Evaluation Matching AGROVOC (16k terms) and NALT(41k terms) (derived from 180 different ontologies) Evaluation: 1600 mappings, two teams Average precision: 70% (comparable/better than standard) M. Sabou, M. d’Aquin, W.R. van Hage, E. Motta, “Improving Ontology Matching by Dynamically Exploring Online Knowledge", submitted for review, 2007.

  20. takenWith photograph camera digital Semantic Folksonomy Enrichment Tags NLP/Clustering {camera, digital, photograph} {damage, flooding, hurricane, katrina, Louisiana} Clusters Find and combine Online ontologies Ontology L.Specia, E. Motta, "Integrating Folksonomies with the Semantic Web", submitted for review, 2007.

  21. Examples

  22. Examples

  23. Summary • The growing SW allows opening up applications • With respect to their domain • And the exploited data sources • Novel (dynamic) methods are required for: • Ontology selection, matching and modularization • Dynamic and approximate ontology matching: • Is core to both our applications and the OK framework • We provided two novel algorithms for this topic • Folksonomy enrichment • Is a way to get more data for our tools • We provided an algorithm based on ontology matching

  24. Next Steps • Finalize the prototypes: • PowerAqua (M18) • Integrate PowerMap within PowerAqua • Make use of the semantically enriched folksonomies • Semantic Browser (M18) • Combine ontology selection, matching and modularization techniques • Evaluate our applications (M24, M36): • When based on mainstream SW technology • Extended to take advantage of the OK infrastructure

  25. Thank you!

  26. Vision Papers • Motta, E., Sabou, M. (2006). "Next Generation Semantic Web Applications". ASWC. • Motta, E., Sabou, M. (2006). "Language Technologies and the Evolution of the Semantic Web". LREC 2006 • Motta, E. (2006). "Knowledge Publishing and Access on the Semantic Web: A Socio-Technological Analysis". IEEE Intelligent Systems, Vol.21, 3, (88-90). • V. Lopez, E. Motta and V. Uren (2006) “PowerAqua: Fishing the Semantic Web”, ESWC’06.

  27. Ontology Matching • Lopez, V., Sabou, M., Motta, E. (2006). "Mapping the real semantic web on the fly". ISWC. • Sabou, M., D'Aquin, M., Motta, E. (2006). "Using the semantic web as background knowledge for ontology mapping". ISWC 2006 Workshop on Ontology Mapping. • M. Sabou, M. d’Aquin, W.R. van Hage (2007), E. Motta, “Improving Ontology Matching by Dynamically Exploring Online Knowledge", submitted for review.

  28. Relation Learning/Folksonomy Enrichment • L. Specia, E. Motta (2006): “A hybrid approach for relation extraction aimed to semantic annotations”. 7th Flexible Query Answering Systems (FQAS). • L. Specia, E. Motta (2006): “A hybrid approach for extracting semantic relations from texts”. Workshop on Ontology Learning and Population (OLP2) • L.Specia, E. Motta (2007), "Integrating Folksonomies with the Semantic Web", submitted for review, 2007

  29. Related NeOn papers • Ontology Selection • Sabou, M., Lopez, V., Motta, E. (2006). "Ontology Selection for the Real Semantic Web: How to Cover the Queen’s Birthday Dinner?". EKAW 2006 • Ontology Modularization • D'Aquin, M., Sabou, M., Motta, E. (2006). "Modularization: A key for the dynamic selection of relevant knowledge components". ISWC 2006 Workshop on Ontology Modularization

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