New paradigm to keyword search: A survey

Laddha, Shilpa; Laddha, Anurag R.; Jawandhiya, Pradip M.

doi:10.1109/icgciot.2015.7380594

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…Semantic-based expansion query is a query expansion method that is carried out by analyzing and understanding the query meaning given by the user to obtain relevant documents. Semantic method implementation offers the ultimate precision rate and recall rate, but it demands a comparatively complex query language [19]. There are several query expansion methods applied in this study:…”

Section: The Proposed Semantic Query Expansion Methodsmentioning

confidence: 99%

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Query Expansion Based on Wikipedia Word Embedding and BabelNet Method for Searching Arabic Documents

Maryamah¹,

Arifin²,

Sarno³

et al. 2019

IJIES

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Document searching using queries that can understand the context can affect the intent and purpose of the user's desire when searching documents. Many studies have been conducted on understanding the context of the query, but differences in terms of language can lead to different methods of context understanding; therefore, methods implemented in the previous studies need to be improved. In this paper, we proposed a query expansion method based on BabelNet search and Word Embedding (BabelNet Embedding). Query expansion method focuses on developing queries based on semantic relationships on queries to understand the context of the query. Candidate queries were developed by finding synonyms, measuring similarity using WordNet, Word Embedding on all articles of Wikipedia, and BableNet Embedding on articles Wikipedia Online. We compared our proposed method with the existing semantic query expansion. Our result provided better result in retrieving relevant document with accuracy of 89% in searching Arabic documents.

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Section: The Proposed Semantic Query Expansion Methodsmentioning

confidence: 99%

“…A number of studies that used the related term expansion method are Wordnet [17][18][19], synonym [20], synonym for finding relevant tweet [21].…”

Section: Related Term Expansion Is a Query Expansionmentioning

confidence: 99%

Query Expansion Based on Wikipedia Word Embedding and BabelNet Method for Searching Arabic Documents

Maryamah¹,

Arifin²,

Sarno³

et al. 2019

IJIES

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“…In general, users enter a number of keywords into the browser, SE execute keyword searches as well as offer appropriate outcome as a result. It is annoying for the average user to recognize the work of AS [16]. It is impossible to recognize the connection between domain-specific terms as well as employ these appropriate terms for improved results [17].…”

Section: Introductionmentioning

confidence: 99%

Development of a Machine Learning model for knowledge acquisition, relationship extraction and discovery in Domain Ontology Engineering

Guruvayur*¹,

Suchithra²

2019

IJRTE

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Creating a fast domain independent ontology through knowledge acquisition is a key problem to be addressed in the domain of knowledge engineering. Updating and validation is impossible without the intervention of domain experts, which is an expensive and tedious process. Thereby, an automatic system to model the ontology has become essential. This manuscript presents a machine learning model based on heterogeneous data from multiple domains including agriculture, health care, food and banking, etc. The proposed model creates a complete domain independent process that helps in populating the ontology automatically by extracting the text from multiple sources by applying natural language processing and various techniques of data extraction. The ontology instances are classified based on the domain. A Jaccord Relationship extraction process and the Neural Network Approval for Automated Theory is used for retrieval of data, automated indexing, mapping and knowledge discovery and rule generation. The results and solutions show the proposed model can automatically and efficiently construct automated Ontology

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“…In general user enters some keywords in the browser, SE perform the search using keywords and provide relevant results as output. It is tedious job for the common person to understand the working of SE [1]. It is not possible to understand the relationship among terms specific to domain and use these relevant terms for better results [2].…”

Section: Introductionmentioning

confidence: 99%

Onto Semantic Tourism Information Retrieval

Laddha¹,

Jawandhiya²

2018

IJET

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Semantic Search is an area of research which focuses on meaning of terms used in user query. Ontology plays significant role to define the concept and the relationship of terms in domain. Since the understanding of concepts is domain specific, Ontology creation is also domain specific. According to this argument, query interpreted in Tourism domain can have different meaning in some other domain. This paper presents a prototype of information retrieval interface using ontology which can save users time by rendering relevant, precise and efficient search results as compared to traditional search interfaces.

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New paradigm to keyword search: A survey

Cited by 8 publications

References 11 publications

Query Expansion Based on Wikipedia Word Embedding and BabelNet Method for Searching Arabic Documents

Query Expansion Based on Wikipedia Word Embedding and BabelNet Method for Searching Arabic Documents

Development of a Machine Learning model for knowledge acquisition, relationship extraction and discovery in Domain Ontology Engineering

Onto Semantic Tourism Information Retrieval

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