philip.resnikfleast.sun.com This paper presents a new measure of semantic similarity in an IS-A taxonomy, based on the notion of information content. Experimental evaluation suggests that the measure performs encouragingly well (a correlation of r = 0.79 with a benchmark set of human similarity judgments, with an upper bound of r = 0.90 for human subjects performing the same task), and significantly better than the traditional edge counting approach (r = 0.66). 1

This article presents a measure of semantic similarityinanis-a taxonomy based on the notion of shared information content. Experimental evaluation against a benchmark set of human similarity judgments demonstrates that the measure performs better than the traditional edge-counting approach. The article presents algorithms that take advantage of taxonomic similarity in resolving syntactic and semantic ambiguity, along with experimental results demonstrating their e#ectiveness. 1. Introduction Evaluating semantic relatedness using network representations is a problem with a long history in arti#cial intelligence and psychology, dating back to the spreading activation approach of Quillian #1968# and Collins and Loftus #1975#. Semantic similarity represents a special case of semantic relatedness: for example, cars and gasoline would seem to be more closely related than, say, cars and bicycles, but the latter pair are certainly more similar. Rada et al. #Rada, Mili, Bicknell, & Blett...

Word groupings useful for language processing tasks are increasingly available, as thesauri appear on-line, and as distributional word clustering techniques improve. However, for many tasks, one is interested in relationships among word senses, not words. This paper presents a method for automatic sense disambiguation of nouns appearing within sets of related nouns -- the kind of data one finds in on-line thesauri, or as the output of distributional clustering algorithms. Disambiguation is performed with respect to WordNet senses, which are fairly fine-grained; however, the method also permits the assiment of higher-level WordNet categories rather than sense labels. The method is illustrated primarily by example, though results of a more rigorous evaluation are also presented.

xiaohua.zhou @ drexel.edu Abstract. Recent research shows that ontology as background knowledge can improve document clustering quality with its concept hierarchy knowledge. Previous studies take term semantic similarity as an important measure to incorporate domain knowledge into clustering process such as clustering initialization and term re-weighting. However, not many studies have been focused on how different types of term similarity measures affect the clustering performance for a certain domain. In this paper, we conduct a comparative study on how different semantic similarity measures of term including path based similarity measure, information content based similarity measure and feature based similarity measure affect document clustering. We evaluate term re-weighting as an important method to integrate domain ontology to clustering process. Meanwhile, we apply k-means clustering on one real-world text dataset, our own corpus generated from PubMed. Experiment results on 8 different semantic measures have shown that: (1) there is no a certain type of similarity measures that significantly outperforms the others; (2) Several similarity measures have rather more stable performance than the others; (3) term re-weighting has positive effects on medical document clustering, but might not be significant when documents are short of terms.

This paper presents a new measure of semantic similarity in an is-a taxonomy, based on the notion of information content. Experimental evaluation suggests that the measure performs encouragingly well (a correlation of r = 0:79 with a benchmark set of human similarity judgments, with an upper bound of r = 0:90 for human subjects performing the same task), and significantly better than the traditional edge counting approach (r = 0:66). 1 Introduction Evaluating semantic relatedness using network representations is a problem with a long history in artificial intelligence and psychology, dating back to the spreading activation approach of Quillian [ 1968 ] and Collins and Loftus [ 1975 ] . Semantic similarity represents a special case of semantic relatedness: for example, cars and gasoline would seem to be more closely related than, say, cars and bicycles, but the latter pair are certainly more similar. Rada et al. [ 1989 ] suggest that the assessment of similarity in semantic n...

Abstract: The semantic web relies on ontologies representing domains through their main concepts and the relations between them. Such a domain knowledge is the keystone to represent the semantic contents of web resources and services in metadata associated to them. These metadata then enable us to search for information based on the semantics of web resources rather than their syntactic forms. However, in the context of the semantic web there are many possibilities of executing queries that would not retrieve any resource. The viewpoints of the designers of ontologies, of the designers of annotations and of the users performing a Web search may not completely match. The user may not completely share or understand the viewpoints of the designers and this mismatch may lead to missed answers. Approximate query processing is then of prime importance for efficiently searching the Semantic Web. In this paper we present the Corese ontology-based search engine we have developped to handle RDF(S) and OWL Lite metadata. We present its theoretical foundation, its query language, and we stress its ability to process approximate queries. Key-words: