Knowledge graph based entity selection framework for ad-hoc retrieval
Recent entity-based retrieval models utilizing knowledge bases have shown significant improvement in ad-hoc retrieval. However, a lack of coherence between candidate entities can lead to query intent drift at retrieval time. To address this issue, we present an entity selection algorithm that utiliz...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Web Semantics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1570826824000349 |
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| author | Pankaj Singh Plaban Kumar Bhowmick |
| author_facet | Pankaj Singh Plaban Kumar Bhowmick |
| author_sort | Pankaj Singh |
| collection | DOAJ |
| description | Recent entity-based retrieval models utilizing knowledge bases have shown significant improvement in ad-hoc retrieval. However, a lack of coherence between candidate entities can lead to query intent drift at retrieval time. To address this issue, we present an entity selection algorithm that utilizes a graph clustering framework to discover the semantics between entities and encompass the query with highly coherent entities accumulated from different resources, including knowledge bases, and pseudo-relevance feedback documents. Through this work, we propose: (1) An entity acquisition strategy to systematically acquire coherent entities for query expansion. (2) We propose a graph representation of entities to capture the coherence between entities where nodes correspond to the entities and edges represent semantic relatedness between entities. (3) We propose two different entity ranking approaches to select candidate entities based on the coherence with query entities and other coherent entities. A set of experiments on five TREC collections: ClueWeb09B, ClueWeb12B, Robust04, GOV2, and MS-Marco dataset under document retrieval task were conducted to verify the proposed algorithm’s performance. The reported results indicated that the proposed methodology outperforms existing state-of-the-art retrieval approaches in terms of MAP, NDCG, and P@20. The code and relevant data are available in https://github.com/pankajkashyap65/KnowledgeGraph. |
| format | Article |
| id | doaj-art-aac190f1408b4a66879ed7b46ccdcea0 |
| institution | Kabale University |
| issn | 1570-8268 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Web Semantics |
| spelling | doaj-art-aac190f1408b4a66879ed7b46ccdcea02025-01-12T05:24:30ZengElsevierWeb Semantics1570-82682025-01-0184100848Knowledge graph based entity selection framework for ad-hoc retrievalPankaj Singh0Plaban Kumar Bhowmick1Department of Artificial Intelligence & Machine Learning, Symbiosis Institute of Technology, Pune, 412115, Maharastra, IndiaDepartment of Artificial Intelligence, Indian Institute of Technology, Kharagpur, 721302, West Bengal, India; Corresponding author.Recent entity-based retrieval models utilizing knowledge bases have shown significant improvement in ad-hoc retrieval. However, a lack of coherence between candidate entities can lead to query intent drift at retrieval time. To address this issue, we present an entity selection algorithm that utilizes a graph clustering framework to discover the semantics between entities and encompass the query with highly coherent entities accumulated from different resources, including knowledge bases, and pseudo-relevance feedback documents. Through this work, we propose: (1) An entity acquisition strategy to systematically acquire coherent entities for query expansion. (2) We propose a graph representation of entities to capture the coherence between entities where nodes correspond to the entities and edges represent semantic relatedness between entities. (3) We propose two different entity ranking approaches to select candidate entities based on the coherence with query entities and other coherent entities. A set of experiments on five TREC collections: ClueWeb09B, ClueWeb12B, Robust04, GOV2, and MS-Marco dataset under document retrieval task were conducted to verify the proposed algorithm’s performance. The reported results indicated that the proposed methodology outperforms existing state-of-the-art retrieval approaches in terms of MAP, NDCG, and P@20. The code and relevant data are available in https://github.com/pankajkashyap65/KnowledgeGraph.http://www.sciencedirect.com/science/article/pii/S1570826824000349Information retrievalEntity-based retrievalQuery expansionKnowledge graphPseudo-relevance feedback |
| spellingShingle | Pankaj Singh Plaban Kumar Bhowmick Knowledge graph based entity selection framework for ad-hoc retrieval Web Semantics Information retrieval Entity-based retrieval Query expansion Knowledge graph Pseudo-relevance feedback |
| title | Knowledge graph based entity selection framework for ad-hoc retrieval |
| title_full | Knowledge graph based entity selection framework for ad-hoc retrieval |
| title_fullStr | Knowledge graph based entity selection framework for ad-hoc retrieval |
| title_full_unstemmed | Knowledge graph based entity selection framework for ad-hoc retrieval |
| title_short | Knowledge graph based entity selection framework for ad-hoc retrieval |
| title_sort | knowledge graph based entity selection framework for ad hoc retrieval |
| topic | Information retrieval Entity-based retrieval Query expansion Knowledge graph Pseudo-relevance feedback |
| url | http://www.sciencedirect.com/science/article/pii/S1570826824000349 |
| work_keys_str_mv | AT pankajsingh knowledgegraphbasedentityselectionframeworkforadhocretrieval AT plabankumarbhowmick knowledgegraphbasedentityselectionframeworkforadhocretrieval |