A Survey of Semantic Similarity Methods for Ontology Based Information Retrieval

Saruladha, K.; Aghila, G.; Raj, Sajina

doi:10.1109/icmlc.2010.63

Cited by 37 publications

(24 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These can be categorized as edge-based approaches (evaluating the distance separating two concepts in the reference semantic network, e.g., [48,66]) and node-based approaches (estimating concept information contents using corpus based statistics, e.g., [53,69]). In short, while semantic similarity measures have been thoroughly investigated in the literature [16,74,98], nonetheless, efficiently integrating such techniques within XML document/grammar comparison remains an open issue yet to be investigated. To sum up, considering the semantic factor in XML similarity computations would clearly amend comparison results, and is central in most application scenarios discussed in Section 4, namely in XML querying, classification, clustering, and the selective dissemination/filtering of semantically related XML data.…”

Section: Xml Semantic-based Similaritymentioning

confidence: 99%

“…In this context, a vast arsenal of methods to determine the semantic similarity between concepts in a knowledge base (semantic network) has been developed in the fields of Information Retrieval and Natural Language Processing [16,74,98]. These can be categorized as edge-based approaches (evaluating the distance separating two concepts in the reference semantic network, e.g., [48,66]) and node-based approaches (estimating concept information contents using corpus based statistics, e.g., [53,69]).…”

Section: Xml Semantic-based Similaritymentioning

confidence: 99%

See 1 more Smart Citation

XML document-grammar comparison: related problems and applications

Tekli

Chbeir

Traina

et al. 2011

Open Computer Science

View full text Add to dashboard Cite

10.2478/s13537-011-0005-1International audienceXML document comparison is becoming an ever more popular research issue due to the increasingly abundant use of XML. Likewise, a growing interest fosters the development of XML grammar matching and comparison, due to the proliferation of heterogeneous XML data sources, particularly on the Web. Nonetheless, the process of comparing XML documents with XML grammars, i.e., XML document and grammar similarity evaluation, has not yet received the attention it deserves. In this paper, we provide an overview on existing research related to XML document/grammar comparison, presenting the background and discussing the various techniques related to the problem. We also discuss some prominent application domains, ranging over document classification and clustering, document transformation, grammar evolution, selective dissemination of XML information, XML querying, as well as alert filtering in intrusion detection systems and Web Services matching and communications

show abstract

Section: Xml Semantic-based Similaritymentioning

confidence: 99%

Section: Xml Semantic-based Similaritymentioning

confidence: 99%

XML document-grammar comparison: related problems and applications

Tekli

Chbeir

Traina

et al. 2011

Open Computer Science

View full text Add to dashboard Cite

show abstract

“…They can therefore be used to improve classic models, e.g., synonyms will no longer be considered as totally different words. As an example, semantic measures have been successfully used in the design of ontology-based information retrieval systems and for query expansion, e.g., (Hliaoutakis, 2005;Hliaoutakis et al, 2006;Varelas et al, 2005;Baziz et al, 2007;Saruladha et al, 2010b;Sy et al, 2012).…”

Section: Other Applicationsmentioning

confidence: 99%

Semantic Similarity from Natural Language and Ontology Analysis

Harispe

Ranwez

Janaqi

et al. 2015

Synthesis Lectures on Human Language Technologies

166

102

View full text Add to dashboard Cite

Artificial Intelligence federates numerous scientific fields in the aim of developing machines able to assist human operators performing complex treatments -most of which demand high cognitive skills (e.g. learning or decision processes). Central to this quest is to give machines the ability to estimate the likeness or similarity between things in the way human beings estimate the similarity between stimuli.In this context, this book focuses on semantic measures: approaches designed for comparing semantic entities such as units of language, e.g. words, sentences, or concepts and instances defined into knowledge bases. The aim of these measures is to assess the similarity or relatedness of such semantic entities by taking into account their semantics, i.e. their meaning -intuitively, the words tea and coffee, which both refer to stimulating beverage, will be estimated to be more semantically similar than the words toffee (confection) and coffee, despite that the last pair has a higher syntactic similarity. The two state-of-theart approaches for estimating and quantifying semantic similarities/relatedness of semantic entities are presented in detail: the first one relies on corpora analysis and is based on Natural Language Processing techniques and semantic models while the second is based on more or less formal, computer-readable and workable forms of knowledge such as semantic networks, thesaurus or ontologies.Semantic measures are widely used today to compare units of language, concepts, instances, or even resources indexed by them (e.g., documents, genes). They are central elements of a large variety of Natural Language Processing applications and knowledge-based treatments, and have therefore naturally been subject to intensive and interdisciplinary research efforts during last decades. Beyond a simple inventory and categorization of existing measures, the aim of this monograph is to convey novices as well as researchers of these domains towards a better understanding of semantic similarity estimation and more generally semantic measures. To this end, we propose an in-depth characterisation of existing proposals by discussing their features, the assumptions on which they are based and empirical results regarding their performance in particular applications. By answering these questions and by providing a detailed discussion on the foundations of semantic measures, our aim is to give the reader key knowledge required to: (i) select the more relevant methods according to a particular usage context, (ii) understand the challenges offered to this field of study, (iii) distinguish room of improvements for state-of-the-art approaches and (iv) stimulate creativity towards the development of new approaches. In this aim, several definitions, theoretical and practical details, as well as concrete applications are presented.keywords: Semantic similarity, semantic relatedness, semantic measures, distributional measures, domain ontology, knowledge-based semantic measure.iii iv

show abstract

“…When a new information retrieval system is going to be build, several questions arises related to the semantic similarity matching function to be used. In [46] authors discussed the survey of different similarity measuring methods used to compare and find very similar concepts of an ontology.…”

Section: Semantic Retrieval Of Documentsmentioning

confidence: 99%

Semantic Retrieval Approach for Web Documents

Harb¹,

Fouad²,

Nagdy³

2011

IJACSA

View full text Add to dashboard Cite

Abstract-Because of explosive growth of resources in the internet, the information retrieval technology has become particularly important. However the current retrieval methods are essentially based on the full text matching of keywords approach lacking of semantic information and can't understand the user's query intent very well. These methods return a large number of irrelevant information, and are unable to meet the user's request. Systems have been established so far failed to overcome fully the limitations of search based on keywords. Such systems are built from variations of classic models that represent information by keywords. Using Semantic Web is a way to increase the precision of information retrieval systems. In this paper, we propose the semantic information retrieval approach to extract the information from the web documents in certain domain (jaundice diseases) by collecting the domain relevant documents using focused crawler based on domain ontology, and using similar semantic content that is matched with a given user's query. Semantic retrieval approach aims to discover semantically similar terms in documents and query terms using WordNet.

show abstract

A Survey of Semantic Similarity Methods for Ontology Based Information Retrieval

Cited by 37 publications

References 5 publications

XML document-grammar comparison: related problems and applications

XML document-grammar comparison: related problems and applications

Semantic Similarity from Natural Language and Ontology Analysis

Semantic Retrieval Approach for Web Documents

Contact Info

Product

Resources

About