Wordnet and Ontology Based Query Expansion for Semantic Information Retrieval in Sports Domain

Devi, M. Uma; Gandhi, G. Meera

doi:10.3844/jcssp.2015.361.371

Cited by 19 publications

(7 citation statements)

References 20 publications

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“…Although the number of queries of ten can be questionably small, in the case of ontology-based IR such a number is sufficient as also exhibited by the work of Devi and Gandhi (2014), Kara et al (2012) and Vallet et al (2005).…”

Section: Resultsmentioning

confidence: 99%

Semantic text-based image retrieval with multi-modality ontology and DBpedia

Aspura

Noah

2017

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Purpose The purpose of this study is to reduce the semantic distance by proposing a model for integrating indexes of textual and visual features via a multi-modality ontology and the use of DBpedia to improve the comprehensiveness of the ontology to enhance semantic retrieval. Design/methodology/approach A multi-modality ontology-based approach was developed to integrate high-level concepts and low-level features, as well as integrate the ontology base with DBpedia to enrich the knowledge resource. A complete ontology model was also developed to represent the domain of sport news, with image caption keywords and image features. Precision and recall were used as metrics to evaluate the effectiveness of the multi-modality approach, and the outputs were compared with those obtained using a single-modality approach (i.e. textual ontology and visual ontology). Findings The results based on ten queries show a superior performance of the multi-modality ontology-based IMR system integrated with DBpedia in retrieving correct images in accordance with user queries. The system achieved 100 per cent precision for six of the queries and greater than 80 per cent precision for the other four queries. The text-based system only achieved 100 per cent precision for one query; all other queries yielded precision rates less than 0.500. Research limitations/implications This study only focused on BBC Sport News collection in the year 2009. Practical implications The paper includes implications for the development of ontology-based retrieval on image collection. Originality value This study demonstrates the strength of using a multi-modality ontology integrated with DBpedia for image retrieval to overcome the deficiencies of text-based and ontology-based systems. The result validates semantic text-based with multi-modality ontology and DBpedia as a useful model to reduce the semantic distance.

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Section: Resultsmentioning

confidence: 99%

Semantic text-based image retrieval with multi-modality ontology and DBpedia

Aspura

Noah

2017

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“…The method classifies the genes towards various groups. Similarly, the classification of sports document retrieval is handled with semantic ontology with word net [21]. Kara, Soner, et al [22] discussed the indexing of documents according to the value of semantic measures.…”

Section: Literature Reviewmentioning

confidence: 99%

Semantic Conceptual Relational Similarity Based Web Document Clustering for Efficient Information Retrieval Using Semantic Ontology

2021

KSII TIIS

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In the modern rapid growing web era, the scope of web publication is about accessing the web resources. Due to the increased size of web, the search engines face many challenges, in indexing the web pages as well as producing result to the user query. Methodologies discussed in literatures towards clustering web documents suffer in producing higher clustering accuracy. Problem is mitigated using, the proposed scheme, Semantic Conceptual Relational Similarity (SCRS) based clustering algorithm which, considers the relationship of any document in two ways, to measure the similarity. One is with the number of semantic relations of any document class covered by the input document and the second is the number of conceptual relation the input document covers towards any document class. With a given data set Ds, the method estimates the SCRS measure for each document Di towards available class of documents. As a result, a class with maximum SCRS is identified and the document is indexed on the selected class. The SCRS measure is measured according to the semantic relevancy of input document towards each document of any class. Similarly, the input query has been measured for Query Relational Semantic Score (QRSS) towards each class of documents. Based on the value of QRSS measure, the document class is identified, retrieved and ranked based on the QRSS measure to produce final population. In both the way, the semantic measures are estimated based on the concepts available in semantic ontology. The proposed method had risen efficient result in indexing as well as search efficiency also has been improved.

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“…Prototype results of the proposed methodology demonstrated improvement for web queries in terms of the relevance percentage. Recently, Devi and Gandhi [79] proposed a semantic query expansion model for the sports domain. The main steps of the proposed QE algorithm are as follows: (1) parse the search query, (2) extract synonyms from WordNet, (3) expand the query with words from a sports ontology, and (4) retrieve the results using the Google search API.…”

Section: Mixed-mode Approachesmentioning

confidence: 99%

A Taxonomy and Survey of Semantic Approaches for Query Expansion

et al. 2019

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Conventional approaches to query expansion (QE) rely on the integration of an unstructured corpus and probabilistic rules for the extraction of candidate expansion terms. These methods do not consider search query semantics, thereby resulting in ineffective retrieval of information. The semantic approaches for QE overcome this limitation, whereby a search query is expanded with meaningful terms that accord with user information needs. This paper surveys recent approaches to semantic QE that employ different models and strategies and leverages various knowledge structures. We organize these approaches into a taxonomy that includes linguistic methods, ontology-based methods, and mixed-mode methods. We also discuss the strengths and limitations of each type of semantic QE method. In addition, we evaluate various semantic QE approaches in terms of knowledge structure utilization, corpus collection, baseline model adaptation, and retrieval performance. Finally, future directions in exploiting personalized social information and multiple ontologies for semantic QE are suggested. INDEX TERMS Information retrieval, morphological expansion, ontology, semantic query expansion.

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Wordnet and Ontology Based Query Expansion for Semantic Information Retrieval in Sports Domain

Cited by 19 publications

References 20 publications

Semantic text-based image retrieval with multi-modality ontology and DBpedia

Semantic text-based image retrieval with multi-modality ontology and DBpedia

Semantic Conceptual Relational Similarity Based Web Document Clustering for Efficient Information Retrieval Using Semantic Ontology

A Taxonomy and Survey of Semantic Approaches for Query Expansion

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