Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks

Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks TMRA 2008 4th International Conference on Topic Maps Research and Applications 15 -17 October 2008 Leipzig Germany Dino Karabeg, OMS Group, Department of Informatics, University of Oslo Roy Lachica, Bouvet ASA Sasa Rudan, HeadWare Solutions

Topic Maps 2008, Oslo Subject

Roy Lachica The author of FUZZZY Knowledge is fuzzy!

Sale in Fisherman’s World Norwegian economy Fishing Not all associations are relevant

Jigsaw puzzle idea of knowledge

Building Blocks of a Solution
Accumulate information on usefulness in a value matrix
Use user point of view or scope to estimate usefulness
Estimate usefulness by an algorithm

Value Matrix Criteria Ways of evaluating

Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks TMRA 2008 4th International Conference on Topic Maps Research and Applications 15 -17 October 2008 Leipzig Germany Dino Karabeg, OMS Group, Department of Informatics, University of Oslo Roy Lachica, Bouvet ASA Sasa Rudan, HeadWare Solutions

Agenda
Goal
Project Scope
Problems
Limitations of Topic Maps
Information Retrieval
Proposed Solution
Partial Implementation on fuzzzy.com

The Ultimate Goal Getting the right information at the right time and place (Enhancing Information Retrieval systems)

Project Scope
A model based on: Topic Maps
For use in: Knowledge based systems
With a: Social collaborative environment

The Obstacles (TMRA-07)
Problems with large scale open collaborative Folktologies:
People use different terms/vocabularies
Language evolve over time
People mix different domains and different levels of discourse
People add errors and noise (overlapping, faulty or imprecise)
People have different views of what is important. (user-centric relevance)
People have different views of what things are relevant to each other. (topical relevance)
Topic Map Scopes
The above problems is problematic to solve with TM scopes Users don't share the same world view. Who decides the scopes?

Limitations of Topic Maps
Associations are not weighted: Flat information, no priority
No notion of the user and the context
Problems with representing a nuanced user context with Topic Maps scopes:
Explosion in number of TM constructs/assertions
High demands on computer processing
User context is something else than the domain and therefore would be somewhat misplaced in a Topic Map?
Export / fragment size explodes

Information Retrieval
The basic view in IR:
We have a set of resources and we want to retrieval only the most relevant ones
This view is to simple?
What we actually want is to retrieve the most valuable. The ones with the best quality which is relevant and important in our current context

Understanding Information
Relevance, Importance, Quality and Context are overlapping and ill defined

QRI
Measure for Information Retrival with Topic Maps:
Quality. The intrinsic value of an information resource as judged by an individual. (Unreliable or not understandable info is valueless, even if it may otherwise be highly relevant or important)
Relevance . Relevance is the strength of a relation between two subjects as judged by an individual in a given context. (Different persons with different backgrounds might have different opinions about the appropriateness of relations between concepts)
Importance. Importance reflects the strength of a relation between a user and a subject in a given context. (Context dependant because the perceived importance of a subject changes over time as the background and setting of the individual change)

Assigning QRI
Manually
Quality added by rating resources
Relevance set by rating associations
Topic types for context: e.g. project, event, task, location, group
Importance assigned by setting topics as important (both individual and collective)
Automatic
Quality added when highly ranked users author resources
Relevance added upon simultanous browsing of topics or when following topic associations
Importance (low degree) is set when ever a user browse or use a topic

Ontology

Sample socio-semantic contextual network Strengthened assoc (important for user) Strengthed assoc (topic 2 topic relevance)

QRI Implementation

Context implementation AssocId userId PSI val 234 567 http://xxx 0.8 234 567 http://xx2 0.3 567 321 http://xx3 0.7 .... subject A subject B

QRI in Resource Ranking

User-centered Resource Ranking

Ranking in the Context of a Specific Topic

Partial Implementation on fuzzzy.com
Fuzzzy.com now supports:
Association ranking (relevance without context)
Favorite topics (importance without context)
Recommend bookmarks (Quality)
Socio-Semantic search

Conclusion
Advantages of Topic Maps and neural semantic network approach
Intuitive in the user interface
Semi learning/adaptability
Less work on part of the user
Further work
Prototyping, benchmarking
Constrained Spreading Activation resource calculations
C ontext optimalization
Tuning QRI measures by user

Thank you

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Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks

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Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks Presentation Transcript