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We propose a framework for ranking information based on quality, relevance and importance, and argue that a socio-semantic contextual approach that extends topicality can lead to increased value of information retrieval systems. We use Topic Maps to implement our framework, and discuss procedures for calculating the resource ranking. A fuzzy neural network approach is envisioned to complement the process of manual metadata creation.
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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
Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks
Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks
Topic Maps 2008, Oslo
Subject
Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks
Roy Lachica
The author of FUZZZY
Knowledge is fuzzy!
Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks
Sale in Fisherman’s WorldNorwegian
economy
Fishing
Not all associations are relevant
Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks
Jigsaw puzzle idea of knowledge
6
Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks
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
Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks
Value Matrix
Criteria
Ways ofevaluating
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
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
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
Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks
12
Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks
Agenda
• Goal
• Project Scope
• Problems
• Limitations of Topic Maps
• Information Retrieval
• Proposed Solution
• Partial Implementation on fuzzzy.com
Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks
Getting the right information at the right time and place
(Enhancing Information Retrieval systems)
The Ultimate Goal
14
Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks
Project Scope
• A model based on: Topic Maps
• For use in: Knowledge based systems
• With a: Social collaborative environment
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Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks
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?
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Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks
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
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Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks
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
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Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks
Understanding Information
• Relevance, Importance, Quality and Context are overlapping and ill defined
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Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks
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)
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Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks
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
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Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks
Ontology
Social item
-name
Resource proxy-title-description-published date
Tag item
-preferred name-synonym
Actual resource
1 1 0..*
0..*
Group User
-timezone
Related subject
Tag is category of resource content
Subject
Sub class
Context item
-name-timespan
Task Event ProjectLocation
0..*
0..*
Tag is important for user/group
Location also have a timespan. E.g. The city Oslo did not exist 1100 years ago
**
Tagged with** *
****
**
** *
Sub class Sub class
Authorship
*
*
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Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks
Sample socio-semantic contextual network
Friend of
Memberof group
Userbrowsed
User sent
message
Resource is favorite
for group
Task must be
performed in
Headquarter is located in
Event resultedin problem
Task is done
at event
User has
performed task
User has plannedto perform task
User attendedevent
Born in
Context items Tag items
Social items
Relevant for /in/at
XTM isbased on
Is part
of
Topic ofevent
Resoure containinformation about
RDF is based on
supports
Resource is often used by
Is authored by
Probleminvolves
subject
Resource is important for user
Important subjectfor user
User has
browsed subject
User has Used subject
for tagging
User oftenbrowse
User has browsed place
Often browsedtogether
Often browsed
together
Resource
Proxy
Task ispart of
Subject is
importantfor group
Subject space
RDF
XML
XTM
ResourceProxy
Topic Mapssupports
User
User
Task
Location
Event
ResourceProxy
Project
Group
Topic Maps
Sem Web
Resource
Resource
Resource
Strengthened assoc(important for user)
Strengthed assoc(topic 2 topic relevance)
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Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks
QRI Implementation
-Location (PSI ,val ) [0..n]
-Identity (PSI ,val ) [0..n]-Activity (PSI ,val ) [0..n]
-Time (PSI ,val ) [0..n]
Importance Context (user)
Subject is importantfor user
in context of
-Location (PSI ,val) [ 0..n]
-Identity (PSI ,val) [ 0..n]-Activity (PSI ,val ) [0..n]
-Time (PSI ,val ) [0..n]
Relevance Context (user )
Subject
Quality is not context dependant
Resource
Quality (user )
Subject is relevantfor subject
in context ofResource
proxy
SubjectUser SubjectTopic Maps technology
Non Topic Maps technology
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Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks
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
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Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks
QRI in Resource Ranking
Resources
Topical relevance
subjectsubject
subject
subject
User centered relevance
Relevance Context
Importance Context
Entry subjects
subject subject
user
Importance Context
Automatically created
Manually created
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Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks
User-centered Resource Ranking
Important subjectfor user
(0.5)
Is part of(0.8)
Supports(0.7)
Subjectof event
(0.9)
Attends(0.5)
Contextual topic: Event
supports(0.3)
Is important to user(0.4)
ResourceResource
RDFTopic Maps
Semantic Web UserTopic Maps
conference2008
Semantic interoper
ability
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Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks
Ranking in the Context of a Specific Topic
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Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks
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
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Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks
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
– Context optimalization
– Tuning QRI measures by user
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Quality, Relevance and Importance in Information Retrieval with Fuzzy Semantic Networks
Thank you