Information Retrieval Based on Conceptual Distance in Is‐a Hierarchies

Lee, Woo Jung; Kim, Myoung Ho; Lee, Yoon Joon

doi:10.1108/eb026913

Cited by 198 publications

(13 citation statements)

References 17 publications

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“…Roughly, these relations denote domain, generalization, specialization and composition links. This approach could be generalized to all ontology having in its structure a semantic network as defined by [7]: broadly a set of concepts and semantic relations between these concepts. …”

Section: Resultsmentioning

confidence: 99%

“…proposed [11], [12], [13], [7] for disambiguating words in text. In our case, the finality is not mainly Word Sense Disambiguation (WSD) -for a complete survey of WSD, we refer the interested reader to the Senseval home page (http://www.senseval.org) -, however, we got inspired by the Pederson's approach described in [13] which uses an adapted Lesk [14] algorithm based on gloss overlaps between concepts (WordNet Synsets) for computing semantic similarity between concepts-senses.…”

Section: The Use Of Semantics In Information Retrievalmentioning

confidence: 99%

“…We propose to build a semantic network for representing the semantic content of a document. The semantic network to which we refer here is one as defined by [7]: "semantic network is broadly described as any representation interlinking nodes with arcs, where the nodes are concepts and the links are various kinds of relationships between concepts". The resulted scored concepts could be used in indexing and then searching phases of an Information Retrieval System (IRS).…”

Section: Introductionmentioning

confidence: 99%

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Semantic cores for representing documents in IR

Baziz

Boughanem

Aussenac-Gilles

et al. 2005

Proceedings of the 2005 ACM Symposium on Applied Computing

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This paper deals with the use of ontologies for Information Retrieval.Roughly, the proposed approach consists in identifying important concepts in documents using two criterions, co-occurrence and semantic relatedness and then disambiguating them via an external general purpose ontology, namely WordNet. Matching the ontology and a document results in a set of scored concept-senses (nodes) with weighted links. This representation, called semantic core of a document best reveals the semantic content of the document. We regard our approach, of which the first evaluation results are encouraging, as a short but strong step toward the long term goal of Intelligent Indexing and Semantic Retrieval.

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Section: Resultsmentioning

confidence: 99%

Section: The Use Of Semantics In Information Retrievalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Semantic cores for representing documents in IR

Baziz

Boughanem

Aussenac-Gilles

et al. 2005

Proceedings of the 2005 ACM Symposium on Applied Computing

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“…Most simply, two sets of GO terms can be compared by head to head matching where the similarity can be determined by the number of common annotations from both the sets [47]. Based on the GO hierarchy, the similarity of two GO terms can be defined as the minimum path length between them on the GO DAG [47,48]. A better alternative to the minimum path is to consider the Lowest Common Ancestor (LCA) for a pair of GO terms in the hierarchy, for which the Information Content (IC) is computed [49-51].…”

Section: Introductionmentioning

confidence: 99%

Quantification of protein group coherence and pathway assignment using functional association

Chitale

Palakodety

2011

BMC Bioinformatics

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BackgroundGenomics and proteomics experiments produce a large amount of data that are awaiting functional elucidation. An important step in analyzing such data is to identify functional units, which consist of proteins that play coherent roles to carry out the function. Importantly, functional coherence is not identical with functional similarity. For example, proteins in the same pathway may not share the same Gene Ontology (GO) terms, but they work in a coordinated fashion so that the aimed function can be performed. Thus, simply applying existing functional similarity measures might not be the best solution to identify functional units in omics data.ResultsWe have designed two scores for quantifying the functional coherence by considering association of GO terms observed in two biological contexts, co-occurrences in protein annotations and co-mentions in literature in the PubMed database. The counted co-occurrences of GO terms were normalized in a similar fashion as the statistical amino acid contact potential is computed in the protein structure prediction field. We demonstrate that the developed scores can identify functionally coherent protein sets, i.e. proteins in the same pathways, co-localized proteins, and protein complexes, with statistically significant score values showing a better accuracy than existing functional similarity scores. The scores are also capable of detecting protein pairs that interact with each other. It is further shown that the functional coherence scores can accurately assign proteins to their respective pathways.ConclusionWe have developed two scores which quantify the functional coherence of sets of proteins. The scores reflect the actual associations of GO terms observed either in protein annotations or in literature. It has been shown that they have the ability to accurately distinguish biologically relevant groups of proteins from random ones as well as a good discriminative power for detecting interacting pairs of proteins. The scores were further successfully applied for assigning proteins to pathways.

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“…302 Hybrid methods: These approaches combine the information 303 gathering from ontology and statistical analysis results from 304 the corpus like in [28]. Also there is a recent survey in [26] 305 about these studies. The study in [22] is also used WordNet to build a semantic 369 proximity matrix based on Omiotis [23], which is a where j 0 is the gram or kernel matrix of the corpus in BOW repre-384 sentation and k is the decay factor.…”

mentioning

confidence: 98%