Human Plausible Reasoning

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Damankesh, A., F. Oroumchian, and K. F. Shaalan, "Multilingual Information Filtering by Human Plausible Reasoning", Multilingual Information Access Evaluation I, Text Retrieval Experiments, 10th Workshop of the Cross-Language Evaluation Forum (CLEF 2009), vol. 6241, Berlin, Heidelberg, Springer-Verlag , pp. 366–373, 2010. Abstractmulti_lingu_filter.pdf

The theory of Human Plausible Reasoning (HPR) is an attempt by Collins and Michalski to explain how people answer questions when they are uncertain. The theory consists of a set of patterns and a set of inferences which could be applied on those patterns. This paper, investigates the application of HPR theory to the domain of cross language filtering. Our approach combines Natural Language Processing with HPR. The documents and topics are partially represented by automatically extracted concepts, logical terms and logical statements in a language neutral knowledge base. Reasoning provides the evidence of relevance. We have conducted hundreds of experiments especially with the depth of the reasoning, evidence combination and topic selection methods. The results show that HPR contributes to the overall performance by introducing new terms for topics. Also the number of inference paths from a document to a topic is an indication of its relevance.

Damankesh, A., J. Singh, F. Jahedpari, K. Shaalan, and F. Oroumchian, "Using Human Plausible Reasoning as a Framework for Multilingual Information Filtering", CLEF 2009 Workshop, in conjunction with ECDL2009, 13th European Conference on Digital Libraries, Corfu, Greece, 30 September , 2009. Abstractdamankesh-paper-clef2009.pdf

In this paper the application of the theory of Human Plausible Reasoning (HPR) has been investigated in the domain of filtering and cross language information retrieval. The theory of Human Plausible Reasoning first has been introduced by Collins and Michalski on early 1990s; it has been applied to IR since 1995. This work is an extension to those experiments which focuses on building a framework for cross language information retrieval. The system built in these experiments utilizes plausible inferences to infer new, unknown knowledge from existing knowledge to retrieve not only documents which are indexed by the query terms but also those which are plausibly relevant.