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Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web

This approach utilizes ontology-centered personalized web pages to present knowledge extracted from the web. It includes dynamic generation, continuous updating, and the use of metaphors to enhance understanding.

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Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web

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  1. Ontology-Centered Personalized Presentation ofKnowledge Extracted from the Web Ralitsa Angelova

  2. Goal and Novelty Goal: • Keep the holistic character in the domain's ontology and induce it in the learner's mind. Novelty: • Dynamic generation of the domain structure of personalized web pages • Possibility of continuous updating the content of the generated pages • Usage of metaphors for enhancing understanding

  3. Ontology In context of knowledge and knowledge representation sharing, ontology means specification of conceptualization. • Description of the concepts and relationships that can exist for an agent or community of agents • Specification for making ontological commitments with respect to the theory specified by the ontology (Newell,1982) Every knowledge based system is committed to specific conceptualization, representing in an abstract way the world it has to overview

  4. How learning can be facilitated? Main techniques: • Induce the sense of whole in learner's mind • Usage of metaphors - trees, stacks, pointers

  5. Problems Faced problems: • The huge amount of information may lead to confusion (surfing) • Metaphors are hard to be tacked by a foreign speakers ("sustain a loss") • Change in the information scenario in general Solutions: • Find information using intelligent (multi) agent systems Letizia, crawlers (i.e. Northern Lite) • Perform a personalized metaphor presentation according to the learner KB model • JIT -Just In Time vs. Once For Ever principles semantic networks

  6. Approach The approach combines: • Search for information using agents • Text mining techniques • Learner modelling • Personalized web page generation Result: The semantic network of the concepts from the domain ontology is mapped on a network of personalized web pages which are automatically generated. Implemented in INCO Copernicus project Larflast - LeARning Foreign LAnguage Specific Terminology http://www-it.fmi.uni-sofia.bg/larflast

  7. Metaphor Identification, Annotation and Usage Provide: • Better understanding of a concept (Lakoff and Johnson 1980). • Such inside can not be obtained in KB approaches centered on taxonomic ontologies Example: "Stocks are very sensitive creatures" (NYSE) Ontology centered KB System: • Securities • Capital • Asset • Possession

  8. Metaphor usage effects Significance of metaphors related to the significance of the source concept in our lives. Projection of: • Attributes • Relations • Scenes • Etc.

  9. Classification of source concepts • Resources • Instruments • Physical objects • Humans • Actions • Processes Lakoff and Johnson • Orientational • Structural • Ontological Example: Organism concept - healthy, sufferance Pillar (for buildings) - reliable

  10. Annotation of metaphors XML annotation • For each concept a set of attributes is defined • For each attribute a set of values is included Example: Organism concept < metaph what="stock" how="organism" why="reactivity"> Stocks are very sensitive creatures </metaph>

  11. Metaphor processing in Style STYLE - Scientific Terminology Learning Environment • Gathering relevant texts form the web • Identification (acquisition) of metaphors in the selected texts and their XML markup • Personalized usage of the metaphors

  12. Metaphor identification and web page generation

  13. Information acquisition, annotation and usage in Larflast

  14. XML annotation <?xml version="1.0"?> <corpus subject="finance"> <articol nr="11" type="educational" URL="http://www.nyse.com/about/education/invest/17214.html"> <text> Stocks are very sensitive creatures. They react to all kinds of influences, large and small, . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . </text> </articol> <articol nr="12" type="educational" URL="http://www.nyse.com/about/education/investworld/17311.html"> <titlu> Raising Capital to Succeed</titlu> <text> A company faced with growing pains may choose one of several ways to raise needed capital. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . </text> </articol> </corpus>

  15. Identification and annotation

  16. Concept Editor

  17. Generated, personalized Web page including metaphors offering semantics to terms

  18. Larflast architecture - acquisition of information from the Web

  19. From information to knowledge

  20. Usage of knowledge, page generation, on-line authoring of available XML Templates

  21. Ontology centered Presentation on the Web Novelty of the approach • Attempt to transform the construction and the delivery of knowledge into dynamic process, continuously updates by the incoming information on the domain (from the Web) and the learner (derived from the Learner Model) • In other systems: the domain model is build once for all, the user model is developed in correspondence with the domain model. Thus, the structure of user model is decided once for all, at define time.

  22. Learner Model Includes: • Correct, erroneous and incomplete learner's beliefs • Misunderstandings • Misconceptions about concepts Ways to infer the learner model: • Stating from the analyses of the results at test time • As the path followed by the learner during browsing (Letizia)

  23. Hypertext Conceptualization and understanding in learning process are facilitated from the web "hyperspace and concepts" Hypertext: • "Conceptual Framework for Augmenting Human Intellect" Douglas Engelbart • "... a system for massively parallel creative work and study ... to the betterment of human understanding." Theodor Nelson

  24. Ontology presentation • The conceptual map of the considered domain (the ontology) should be filtered according to the Learner Model • Build the web pages network from the relevant concepts and their relations • Explicit relations • "is -a" • "part -of" • "agent" • "instrument" • Implicit relations • "similar"

  25. Result • The ontology • The network of generated web pages • The conceptual map on learner's mind They all have the same (semantic network like) structure. The holistic character of the knowledge body in learner's mind is assured!

  26. Example

  27. Conclusion • The structure of the generated web pages is very precise and easy to understand framework • All generated pages are coherent • Easily updated • Ontology-centered • Personalized Facilitates the learning process and improves learner's results.

  28. Discussion

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