Improved concept-based query expansion using Wikipedia

Yuvarani, M.; Iyengar, N. Ch. S. N.; Kannan, A.

doi:10.1504/ijcnds.2013.054833

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…The next well known query expansion approach which use global analysis is called concept-based query expansion (Fonseca et al, 2005;Qiu & Frei, 1993;Yuvarani et al, 2013;J. Zhang et al, 2009).…”

Section: Global Analysismentioning

confidence: 99%

“…Wikipedia is the world's biggest encyclopaedia of entities. (Aggarwal & Buitelaar, 2012;Al-Shboul & Myaeng, 2014;Azad & Deepak, 2019a;Wu & Weld, 2008;Yuvarani et al, 2013)Wikipedia has well-structured and quickly expanding material, as well as a wide range of expertise and highly reliable content. WordNet is made up of a network of synsets, which are groupings of synonyms connected by a set of relevant attributes.…”

Section: Web Knowledge Analysis Qementioning

confidence: 99%

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Methods and Datasets for Feature-based query refinement collection

Tamannaee

2024

Preprint

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With the massive and fast-growing amount of information on the Web, maintaining the effectiveness of Information Retrieval (IR) is a real challenge. The system in charge of online search must be able to search through billions of documents stored on millions of devices (Manning Christopher D et al., 2010). Traditional information retrieval systems try to sort out the input queries by mostly emphasizing on lexical similarity and exact term matching between query and documents using frequency-based methods. In other words, the relevancy of a query to a document is viewed based on the closeness of the distribution of words in a candidate document to the query. Since the lexical content of the optimal response is not usually known to the user, the user formulates a query with vocabulary that may have minimal overlap with the vocabulary appearing in its optimal document. Low overlap between query and document vocabulary is called term mismatch which emerges in retrieval results as poor recall performance. The term mismatch problem also has been referred to as lexical gap or lexical chasm with query on one side of the gap and documents on the other side. IR systems use different techniques to bridge the lexical chasm and solve the term mismatch problem. Many different query refinement techniques have already been developed. Given the user query, each refinement technique outputs a modified version of user’s query that can be used as an arch over the lexical gap from the query side to the document side.

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Section: Global Analysismentioning

confidence: 99%

Section: Web Knowledge Analysis Qementioning

confidence: 99%

Methods and Datasets for Feature-based query refinement collection

Tamannaee

2024

Preprint

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“…Query reformulation through the combination of multiple information sources has a better effectiveness compared to the use a single information source [10]. Diverse sources such as Wikipedia [11] were found to be beneficial for document retrieval.…”

Section: Related Workmentioning

confidence: 99%

Health Query Expansion based on Graph Matching between DBpedia and UMLS

2021

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Information Retrieval (IR) in the medical domain is considered as a challenging task for many reasons. Short health queries tend to lack information on user's intent, and the target corpus may not have sufficient information for Relevance Feedbacks. And even, if the user obtains relevant documents to his/her queries, it is difficult for him/her to understand the technical terms. In contrast, in this paper, we propose an approach for health queries reformulation based on graph matching between two external linked data sources: DBpedia and Unified Medical Language System (UMLS). DBpedia has a broad coverage of topics and less noise compared to Wikipedia articles, and UMLS is specific to the medical domain. We also introduced the degree centrality to measure the graph connectivity and to select the most efficient candidate terms for query expansion. Experimental results on MEDLINE collection using Okapi BM25 as a retrieval model showed that our approach outperformed related methods, and the two sources achieved very good retrieval results. They helped in the diversification of the retrieved documents and the improvement of the recall.

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“…Social knowledge sources usually require knowledge analysis methods to extract knowledge from these sources, while formal knowledge sources like ontologies and knowledge graphs can provide an understanding of the relationships between concepts directly. As the largest single knowledge source Wikipedia is used in many query recommendation methods, such as the search direction discovery method proposed in the research by Yuvarani et al [ 23 ]. The Linked Data project connects many formal knowledge sources, including Wikipedia, and thus has also been used for query recommendations [ 24 ].…”

Section: Related Workmentioning

confidence: 99%

Recommending Queries by Extracting Thematic Experiences from Complex Search Tasks

Zhao

Zhang

et al. 2018

Entropy

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Since complex search tasks are usually divided into subtasks, providing subtask-oriented query recommendations is an effective way to support complex search tasks. Currently, most subtask-oriented query recommendation methods extract subtasks from plain form search logs consisting of only queries and clicks, providing limited clues to identify subtasks. Meanwhile, for several decades, the Computer Human Interface (CHI)/Human Computer Interaction (HCI) communities have been working on new complex search tools for the purpose of supporting rich user interactions beyond just queries and clicks, and thus providing rich form search logs with more clues for subtask identification. In this paper, we researched the provision of subtask-oriented query recommendations by extracting thematic experiences from the rich form search logs of complex search tasks logged in a proposed visual data structure. We introduce the tree structure of the visual data structure and propose a visual-based subtask identification method based on the visual data structure. We then introduce a personalized PageRank-based method to recommend queries by ranking nodes on the network from the identified subtasks. We evaluated the proposed methods in experiments consisting of informative and tentative search tasks.

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Improved concept-based query expansion using Wikipedia

Cited by 4 publications

References 7 publications

Methods and Datasets for Feature-based query refinement collection

Methods and Datasets for Feature-based query refinement collection

Health Query Expansion based on Graph Matching between DBpedia and UMLS

Recommending Queries by Extracting Thematic Experiences from Complex Search Tasks

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