Multi-Term Semantic Context Elicitation from Collaborative Networks

Paolo Mengoni, Alfredo Milani, Yuanxi Li

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

In this work we present an innovative approach to the semantic context elicitation among a set of terms. Topic and context elicitation using semantic features can be applied to query expansion, natural language processing, and multimedia retrieval. Different techniques rely on web objects to extract information considering the direct semantic relationship between the observed objects. In our approach we explore the Wikipedia collaborative network to extract the pairwise semantic chains. The terms that constitute all the pairwise chains will define the context in which the set of terms are immersed. Traversal, graph and Steiner tree analysis are evaluated by experts. Results are encouraging and experts agree that the Steiner tree analysis conveys additional semantic information about the relationship among the words in the context.

Original languageEnglish
Title of host publicationProceedings - 2018 1st IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages234-238
Number of pages5
ISBN (Electronic)9781538695555
ISBN (Print)9781538695562
DOIs
Publication statusPublished - Sep 2018
Event1st IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2018 - Laguna Hills, United States
Duration: 26 Sep 201828 Sep 2018

Publication series

NameProceedings - IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE

Conference

Conference1st IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2018
Country/TerritoryUnited States
CityLaguna Hills
Period26/09/1828/09/18

Scopus Subject Areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications

User-Defined Keywords

  • Heuristic search
  • Random walk
  • Semantic context
  • Steiner trees
  • Wikipedia

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