Fundamental properties of aboutness (poster abstract)
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A Default Logic Based Framework for Context-Dependent Reasoning with Lexical Knowledge
Journal of Intelligent Information Systems
Application of aboutness to functional benchmarking in information retrieval
ACM Transactions on Information Systems (TOIS)
Logic and uncertainty in information retrieval
Lectures on information retrieval
Merging structured text using temporal knowledge
Data & Knowledge Engineering
Logical fusion rules for merging structured news reports
Data & Knowledge Engineering
Logic and Uncertainty in Information Retrieval
ESSIR '00 Proceedings of the Third European Summer-School on Lectures on Information Retrieval-Revised Lectures
Towards a belief-revision-based adaptive and context-sensitive information retrieval system
ACM Transactions on Information Systems (TOIS)
Formalizing retrieval goal change by prioritized abduction
IHI'04 Proceedings of the 2004 international conference on Intuitive Human Interfaces for Organizing and Accessing Intellectual Assets
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There is a need to develop more intelligent means for handling text in applications such as information retrieval, information filtering, and message classification. This raises the need for mechanisms for ascertaining what an item of text is about. Even though natural language processing offers the best results, it is not always viable. A less accurate, but more viable alternative, is to reason with keywords in the text. Unfortunately, classical reasoning is often inadequate for determining from some keywords what a text is about. In particular, it does not allow context-dependent interpretation of keywords. So for example, if some text has the keyword "oil", it is usually also about "minerals", though with exceptions such as when it has the keyword "cooking". To address this kind of problem, we consider a model of ``aboutness'' based on default logic.