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Ad hoc retrieval via entity linking and semantic similarity.

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Title: Ad hoc retrieval via entity linking and semantic similarity.
Authors: Ensan, Faezeh1 ensan@um.ac.ir, Du, Weichang2
Source: Knowledge & Information Systems. Mar2019, Vol. 58 Issue 3, p551-583. 33p.
Subjects: Information retrieval, Querying (Computer science), Semantics, Information storage & retrieval systems, Information resources management
Abstract: Semantic search has emerged as a possible way for addressing the challenges of traditional keyword-based retrieval systems such as the vocabulary gap between the query and document spaces. In this paper, we propose a novel semantic retrieval framework that uses semantic entity linking systems for forming a graph representation of documents and queries, where nodes represent concepts extracted from documents and edges represent semantic relatedness between those concepts. The core of our proposed work is a semantic-enabled language model that estimates the probability of generating query concepts given values assigned to document concepts. The semantic retrieval framework also provides basis for interpolating keyword-based retrieval systems with the semantic-enabled language model. We conduct comprehensive experiments over several Trec document collections and analyze the performance of different configurations of the framework across multiple retrieval measures. Our experimental results show that the proposed semantic retrieval model has a synergistic impact on the results obtained through the state-of-the-art keyword-based systems, and the consideration of semantic information can complement and enhance the performance of such retrieval models. [ABSTRACT FROM AUTHOR]
Copyright of Knowledge & Information Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Semantic search has emerged as a possible way for addressing the challenges of traditional keyword-based retrieval systems such as the vocabulary gap between the query and document spaces. In this paper, we propose a novel semantic retrieval framework that uses semantic entity linking systems for forming a graph representation of documents and queries, where nodes represent concepts extracted from documents and edges represent semantic relatedness between those concepts. The core of our proposed work is a semantic-enabled language model that estimates the probability of generating query concepts given values assigned to document concepts. The semantic retrieval framework also provides basis for interpolating keyword-based retrieval systems with the semantic-enabled language model. We conduct comprehensive experiments over several Trec document collections and analyze the performance of different configurations of the framework across multiple retrieval measures. Our experimental results show that the proposed semantic retrieval model has a synergistic impact on the results obtained through the state-of-the-art keyword-based systems, and the consideration of semantic information can complement and enhance the performance of such retrieval models. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Knowledge & Information Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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