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Semantic Based Entity Retrieval and Disambiguation System for Twitter Streams

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Title: Semantic Based Entity Retrieval and Disambiguation System for Twitter Streams
Language: English
Authors: Kumar, Narayanasamy Senthil (ORCID 0000-0002-2951-5740), Dinakaran, Muruganantham (ORCID 0000-0003-3326-868X)
Source: Knowledge Management & E-Learning. Jun 2019 11(2):262-280.
Availability: Laboratory of Knowledge Management & E-Learning. Web site: http://www.kmel-journal.org/ojs/index.php/online-publication
Peer Reviewed: Y
Page Count: 20
Publication Date: 2019
Document Type: Journal Articles
Reports - Descriptive
Descriptors: Semantics, Social Media, Language Usage, Nouns, Phrase Structure, Social Networks, Barriers, Information Seeking, Information Sources, Online Searching, Computational Linguistics, Foreign Countries
Geographic Terms: India
ISSN: 2073-7904
Abstract: Social media networks have evolved as a large repository of short documents and gives the greater challenges to effectively retrieve the content out of it. Many factors were involved in this process such as restricted length of a content, informal use of language (i.e., slangs, abbreviations, styles, etc.) and low contextualization of the user generated content. To meet out the above stated problems, latest studies on context-based information searching have been developed and built on adding semantics to the user generated content into the existing knowledge base. And also, earlier, bag-of-concepts has been used to link the potential noun phrases into existing knowledge sources. Thus, in this paper, we have effectively utilized the relationships among the concepts and equivalence prevailing in the related concepts of the selected named entities by deriving the potential meaning of entities and find the semantic similarity between the named entities with three other potential sources of references (DBpedia, Anchor Texts and Twitter Trends).
Abstractor: As Provided
Entry Date: 2020
Accession Number: EJ1245687
Database: ERIC
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  Data: Social media networks have evolved as a large repository of short documents and gives the greater challenges to effectively retrieve the content out of it. Many factors were involved in this process such as restricted length of a content, informal use of language (i.e., slangs, abbreviations, styles, etc.) and low contextualization of the user generated content. To meet out the above stated problems, latest studies on context-based information searching have been developed and built on adding semantics to the user generated content into the existing knowledge base. And also, earlier, bag-of-concepts has been used to link the potential noun phrases into existing knowledge sources. Thus, in this paper, we have effectively utilized the relationships among the concepts and equivalence prevailing in the related concepts of the selected named entities by deriving the potential meaning of entities and find the semantic similarity between the named entities with three other potential sources of references (DBpedia, Anchor Texts and Twitter Trends).
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