Abstract
The notion of semantic information granulation is explored to estimate the information specificity or generality of documents. Basically, a document is considered more specific than another document if it contains more cohesive domain-specific terminologies than that of the other one. We believe that the dimension of semantic granularity is an important supplement to the existing similarity-based and popularity-based measures for building effective document ranking functions. The main contributions of this paper is the illustration of the design and development of a fuzzy ontology based granular information retrieval (IR) system to improve the effectiveness of IR decision making for various domains. Based on the notion of semantic information granulation, a novel computational model is developed to estimate the semantic granularity of documents; these documents can then be ranked according to the information seekers' specific semantic granularity requirements. One main component of the proposed computatio...
Highlights
Classical similarity-based document ranking functions 22,23, and the recent popularity-based ranking algorithms 16 have been applied to developed information retrieval (IR) systems such as Internet search engines
To objectively estimate the semantic granularity of a document, it is possible to refer to a domain ontology such as the Medical Subject Headings (MeSH)a to determine if a document contains specific terminologies or general concepts
By exploiting the granular computing methodology, we design and develop a novel granular IR system to enhance document retrieval decision making for specific domains
Summary
Classical similarity-based document ranking functions 22,23, and the recent popularity-based ranking algorithms 16 have been applied to developed IR systems such as Internet search engines. When researchers refer to information granularity in the discipline of granular computing, they often mean the ”structural granularity”, that is, the structural abstractions of information items such as the coarse level of a document containing the finer levels of sections, chapters, paragraphs, sentences, and so on. To objectively estimate the semantic granularity of a document, it is possible to refer to a domain ontology such as the Medical Subject Headings (MeSH)a to determine if a document contains specific terminologies or general concepts. One of the main contributions of this paper is the development of a computational model for granular information retrieval Such a computational model supports the objective estimation of the granularity of documents by consulting domain ontology, and it can deal with the subjectivity in information granularity by taking into account the individual user’s granularity requirement. We offer concluding remarks and describe future directions of our research work
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