A Concept Space Approach to Semantic Exchange

The University of ArizonaManagement Information Systems

A Concept Space Approach toSemantic Exchange

Tobun Dorbin Ng

Dissertation Defense

April 19, 2000


Outline

• Introduction

• Literature Review

• Research Questions & Methodologies

• Concept Space Consultation

• Concept Space Generation

• Conclusions


Objective

• To investigate the use of information technologies that clarify semantic meaning to help users elaborate their information needs by providing their library-specific knowledge during the information seeking process.

Introduction


Knowledge SpacesConcept Spaces

Category Spaces

Distributed, HeterogeneousDatabase Collections

Knowledge Discovery•Concept Association•Cluster Analysis

Search forDocuments

BrowsingClassifications

Query Document Set

Information RetrievalSystems

•Keyword Search•Inverted Index•Summarization•Visualization

Text Image Video

Users

Does a query truly representuser information need?

Can these knowledge sourcesadequately serveusers’ information needs?

Questions& Problems

Introduction


Goal

• To adopt a user-centric and interactive approach to helping users elaborate their information needs with library-specific knowledge and simultaneously gain insight into a library’s offerings related to their information needs.

Introduction


Research Issues

• Interactive Consultation with Knowledge Sources

• Automatic Generation of Semantic-bearing Knowledge Sources from Corresponding Libraries

Introduction


Static Nature of Knowledge in Library Collection

• Characterizing Document Objects

• Characterizing Global Knowledge in Document Collections– Grand Coverage– Knowledge of Knowledge

• Revealing Knowledge in Neighborhood– Contextual Information

Literature Review


Dynamic Nature of User Information Need

• Expressing User Need– Information Need

• Dynamic, not directly observable or symbolized

– Indeterminism– Opportunism– Vocabulary Problem– Recognition with Contextual Information

• Key Word In Context, Relevance Feedback

Literature Review


Perceiving Knowledge

• What is the user’s perspective of knowledge?

• How does a user perceive retrieved or derived knowledge?

• Computing Relevance?

Literature Review


Structure & Context: Aids To Perceive Knowledge

• Structureless and Contextless– Document List

• Structural but Contextless– Dynamic Clustering

• Structural and Contextual– Path to the Knowledge

Literature Review


ResearchQuestions

Knowledge SpacesConcept Spaces

Category Spaces

Distributed, HeterogeneousDatabase Collections

Knowledge Discovery•Concept Association•Cluster Analysis

Search forRelated

Concepts

Search forDocuments

BrowsingClassifications

Context-richQuery

InformationNeed Vocabulary

& Context

Context-coherentDocument Set

Concept ConsultationSystems

Concept Exploration•Branch-and-bound Search•Hopfield Net Activation

Information RetrievalSystems

•Keyword Search•Inverted Index•Summarization•Visualization

Text Image Video

Users

• Can knowledge sources be used to help users express their information needs?

Research Questions & Methodologies


Research Methodologies

• Systems Development Approach

• Experimental Design

Research Questions & Methodologies


Concept Space Consultation

• Algorithmic Concept Exploration

• Large Networks of Knowledge– Man-made Thesauri: LCSH & ACM CRCS– Concept Spaces

• Spreading Activation– Traversing a set of Knowledge Networks

automatically and suggesting a set of most relevant concepts



Research Questions 1&2

• Would the automatic concept exploration process be able to help users identify more relevant concepts?

• Would such a process be able to perform more efficient exploration of a concept space than the conventional manual browsing method?



Research Question 3

• If so, which algorithmic methods - symbolic-based branch-and-bound or neural network-based Hopfield net algorithm - is better in terms of gathering relevant concepts from knowledge sources?



Research Questions 4&5

• Would the concept space consultation process provide a semantic medium to reduce the cognitive demand from users in terms of elaborating information needs?

• Would the concept exploration process be able to help users find more relevant documents?



Two Algorithms forSpreading Activation

• Branch-and-bound Algorithm– Semantic Net Based: “Optimal” Search

• Hopfield Net Algorithm– Neural Net Based: Parallel Relaxation

Search

• Spreading Activation Process– Activation, Weight Computation, Iteration– Stopping Condition



User Evaluation

• 3 Subjects, 6 Tasks, 3 Phases

• Phase 1: Identify subject areas

• Phase 2: Find other topics using spreading activation & manual browsing

• Phase 3: Document evaluation



Findings: Concepts

• Manual browsing achieved higher recall but lower term precision than the algorithmic systems.

• Manual browsing was also a much more laborious and cognitively demanding process.

• When using the algorithms, subjects reviewed the suggested terms more slowly and treated them more seriously and carefully than when performing manual browsing.



Findings: Documents

• No signification differences (in document recall and precision) were observed between the relevant documents suggested by the algorithms and those generated via the manual browsing process.

• Each approach could contribute to a larger set of relevant documents for users.

• The essential differences were time spent and cognitive effort in both approaches.



Publications

• Chen, H., Lynch, K. J., Basu, K., and Ng, T. D. “Generating, Integrating, and Activating Thesauri for Concept-Based Document Retrieval,” IEEE Expert, Special Series on Artificial Intelligence in Text-Based Information Systems 8(2):25-34 (1993).

• Chen, H. and Ng, T.D. “An Algorithmic Approach to Concept Exploration in a Large Knowledge Network (Automatic Thesaurus Consultation): Symbolic Branch-and-bound Search vs. Connectionist Hopfield Net Activation,” Journal of the American Society for Information Science 3(5): 348-369 (1995).



Concept Space Generation

• Automatic Generation of Large-scale Concept Spaces

• Feasibility and Scalability Issues of Large-scale Concept Space Generation– Domain Knowledge– Computing Resources



Research Question 1

• With regard to computing scalability, would the technique of computer generation of concept spaces be applicable to very large textual databases?



Research Question 2

• With regard to domain specific knowledge scalability, would concept space generation by technology create satisfactory domain-specific concept associations from corresponding textual databases?



Research Question 3

• How does the quality of concept associations in concept space generated from very large textual databases compare with that of a man-made domain-specific thesaurus?



Concept Space Techniques

• Document & Object List Collection

• Object Filtering

• Automatic Indexing

• Co-occurrence Analysis

• Parallel Supercomputing to Laptop Computing

• Large to Small Collections



User Evaluation

• 10 Subjects, 23 Tasks

• Recall & Recognition Phases

• Findings:– Concept space has higher concept recall– INSPEC thesaurus has higher concept

precision– Concept space compliments man-made

thesaurus



Publications

• Chen, H., Schatz, B.R., Ng, T.D., Martinez, J., Kirchhoff, A., and Lin, C. “A Parallel Computing Approach to Creating Engineering Concept Spaces for Semantic Retrieval: The Illinois Digital Library Initiative Project,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Special Section on Digital Libraries: Representation and Retrieval 18(8): 771-782 (1996).

• Chen, H., Martinez, J., Ng, T. D., and Schatz, B. “A Concept Space Approach to Addressing the Vocabulary Problem in Scientific Information Retrieval: An Experiment on the Worm Community Systems,” Journal of the American Society for Information Science 48(1):17-31 (1997).

• Houston, A. L., Chen, H., Hubbard, S. M., Schatz, B. R., Ng, T. D., Sewell, R. R., and Tolle, K. M. “Medical Data Mining on the Internet: Research on a Cancer Information System,” Artificial Intelligence Review13(5/6):437-466 (1999).



Corpuses & Applications• INSPEC, CSQuest

http://ai.bpa.arizona.edu/cgi-bin/mcsquest

• CancerLit, Cancer Space http://ai20.bpa.arizona.edu/cgi-bin/cancerlit/cn

• Webpages, ET-Space http://ai.bpa.arizona.edu/cgi-bin/tng/ETSpace

• GeoRef & Petroleum Abstracts, GIS Space http://ai10.bpa.arizona.edu/gis/

• Law Enforcement, COPLINK Concept Space• DARPA ITO Project Summary Collection

http://ai6.bpa.arizona.edu/cgi-bin/tng/Psum

• CNN News, http://processc.inf.cs.cmu.edu/tng/inf/



Conclusions

• Context-specific Concept Space Consultation

• Concept Space As Semantic Exchange Medium

Conclusions


Lessons Learned

• Both concept space consultation and generation work

• “Strategic” use of knowledge sources

• Concept Space Technique is scalable conceptually and computationally

• Insight to potentially retrieved documents

Conclusions


Future Directions

• Performing Summarization

• Semantic Protocol for Machine Comm.

• Multimedia Concept Association

• Context Analysis with– Metric Clusters: “distance” information– Scalar Clusters: neighboring concepts of

two targeting concepts to compute their similarity

Conclusions