Inferring query models by computing information flow
Proceedings of the eleventh international conference on Information and knowledge management
Larks: Dynamic Matchmaking Among Heterogeneous Software Agents in Cyberspace
Autonomous Agents and Multi-Agent Systems
Geometry and Meaning
A Practical Logic of Cognitive Systems, Volume 2: The Reach of Abduction: Insight and Trial
A Practical Logic of Cognitive Systems, Volume 2: The Reach of Abduction: Insight and Trial
Semantically Annotating a Web Service
IEEE Internet Computing
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We argue that web service discovery technology should help the user navigate a complex problem space by providing suggestions for services which they may not be able to formulate themselves as (s)he lacks the epistemic resources to do so. Free text documents in service environments provide an untapped source of information for augmenting the epistemic state of the user and hence their ability to search effectively for services. A quantitative approach to semantic knowledge representation is adopted in the form of semantic space models computed from these free text documents. Knowledge of the user’s agenda is promoted by associational inferences computed from the semantic space. The inferences are suggestive and aim to promote human abductive reasoning to guide the user from fuzzy search goals into a better understanding of the problem space surrounding the given agenda. Experimental results are discussed based on a complex and realistic planning activity.