Semantic similarity

About: Semantic similarity is a research topic. Over the lifetime, 14605 publications have been published within this topic receiving 364659 citations. The topic is also known as: semantic relatedness.

A hybrid approach for searching in the semantic web

Abstract: This paper presents a search architecture that combines classical search techniques with spread activation techniques applied to a semantic model of a given domain. Given an ontology, weights are assigned to links based on certain properties of the ontology, so that they measure the strength of the relation. Spread activation techniques are used to find related concepts in the ontology given an initial set of concepts and corresponding initial activation values. These initial values are obtained from the results of classical search applied to the data associated with the concepts in the ontology. Two test cases were implemented, with very positive results. It was also observed that the proposed hybrid spread activation, combining the symbolic and the sub-symbolic approaches, achieved better results when compared to each of the approaches alone.

Similarity measures for short segments of text

Abstract: Measuring the similarity between documents and queries has been extensively studied in information retrieval However, there are a growing number of tasks that require computing the similarity between two very short segments of text These tasks include query reformulation, sponsored search, and image retrieval Standard text similarity measures perform poorly on such tasks because of data sparseness and the lack of context In this work, we study this problem from an information retrieval perspective, focusing on text representations and similarity measures We examine a range of similarity measures, including purely lexical measures, stemming, and language modeling-based measures We formally evaluate and analyze the methods on a query-query similarity task using 363,822 queries from a web search log Our analysis provides insights into the strengths and weaknesses of each method, including important tradeoffs between effectiveness and efficiency

Are non-semantic morphological effects incompatible with a distributed connectionist approach to lexical processing?

Abstract: On a distributed connectionist approach, morphology reflects a learned sensitivity to the systematic relationships among the surface forms of words and their meanings. Performance on lexical tasks should thus exhibit graded effects of both semantic and formal similarity. Although there is evidence for such effects, there are also demonstrations of morphological effects in the absence of semantic similarity (when formal similarity is controlled) in morphologically rich languages like Hebrew. Such findings are typically interpreted as being problematic for the connectionist account. To evaluate whether this interpretation is valid, we carried out simulations in which a set of morphologically related words varying in semantic transparency were embedded in either a morphologically rich or impoverished artificial language. We found that morphological priming increased with degree of semantic transparency in both languages. Critically, priming extended to semantically opaque items in the morphologically rich la...

The human phenotype ontology.

Abstract: A standardized, controlled vocabulary allows phenotypic information to be described in an unambiguous fashion in medical publications and databases. The Human Phenotype Ontology (HPO) is being developed in an effort to provide such a vocabulary. The use of an ontology to capture phenotypic information allows the use of computational algorithms that exploit semantic similarity between related phenotypic abnormalities to define phenotypic similarity metrics, which can be used to perform database searches for clinical diagnostics or as a basis for incorporating the human phenome into large-scale computational analysis of gene expression patterns and other cellular phenomena associated with human disease. The HPO is freely available at http://www.human-phenotype-ontology.org.