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2018 | OriginalPaper | Buchkapitel

Semantic Meta-search Using Cohesion Network Analysis

verfasst von : Ionut Daniel Chelcioiu, Dragos Corlatescu, Ionut Cristian Paraschiv, Mihai Dascalu, Stefan Trausan-Matu

Erschienen in: Artificial Intelligence: Methodology, Systems, and Applications

Verlag: Springer International Publishing

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Abstract

Online searching is one of the most frequently performed actions and search engines need to provide relevant results, while maintaining scalability. In this paper we introduce a novel approach grounded in Cohesion Network Analysis in the form of a semantic search engine incorporated in our Hub-Tech platform. Our aim is to help researchers and people unfamiliar with a domain find meaningful articles online, relevant for their project scope. In addition, we integrate state-of-the-art technologies to ensure scalability and low response time, namely SOLR – for data storage and full-text search functionalities – and Akka – for parallel and distributed processing. Preliminary validations denote promising search results, the software being capable to suggest articles in approximately the same way as humans consider them most appropriate – 75% are close results and top 20% are identical to user recommendations. Moreover, Hub-Tech recommended more suitable articles than Google Scholar for our specific task of searching for articles related to a detailed description given as input query (50 + words).

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Vorheriges Kapitel Tractable Classes in Exactly-One-SAT

Nächstes Kapitel A Query Language for Cognitive Maps

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Titel: Semantic Meta-search Using Cohesion Network Analysis
verfasst von: Ionut Daniel Chelcioiu
Dragos Corlatescu
Ionut Cristian Paraschiv
Mihai Dascalu
Stefan Trausan-Matu
Verlag: Springer International Publishing
Buch: Artificial Intelligence: Methodology, Systems, and Applications
Print ISBN: 978-3-319-99343-0

Electronic ISBN: 978-3-319-99344-7

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-99344-7_19

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