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Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

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Page 1: Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

Ontology-Centered Personalized Presentation ofKnowledge Extracted from the Web

Ralitsa Angelova

Page 2: Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

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

Page 3: Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

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

Page 4: Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

How learning can be facilitated?

Main techniques:

Induce the sense of whole in learner's mind

Usage of metaphors - trees, stacks, pointers

Page 5: Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

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 systemsLetizia, 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

Page 6: Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

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

Page 7: Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

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

Page 8: Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

Metaphor usage effects

Significance of metaphors related to the significance of the source concept in our lives.

Projection of:

Attributes Relations Scenes Etc.

Page 9: Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

Classification of source concepts

Resources Instruments Physical objects Humans Actions Processes

Lakoff and Johnson Orientational Structural OntologicalExample: Organism concept - healthy, sufferance Pillar (for buildings) - reliable

Page 10: Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

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>

Page 11: Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

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

Page 12: Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

Metaphor identification and web page generation

Page 13: Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

Information acquisition, annotation and usage in Larflast

Page 14: Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

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 toraise needed capital.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .</text></articol></corpus>

Page 15: Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

Identification and annotation

Page 16: Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

Concept Editor

Page 17: Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

Generated, personalized Web page including metaphors offering semantics to terms

Page 18: Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

Larflast architecture - acquisition of information from the Web

Page 19: Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

From information to knowledge

Page 20: Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

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

Page 21: Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

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.

Page 22: Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

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)

Page 23: Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

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

Page 24: Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

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"

Page 25: Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

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!

Page 26: Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

Example

Page 27: Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

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.

Page 28: Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web Ralitsa Angelova

Discussion