Ontology-based Document Recommendation System using Topic Modeling

Yijian Hu, Shih Yu Chang, Kai Rui Hsu, Jaydeep Chakraborty, Srividya K. Bansal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

For now, most search engines have limitations on finding the most suitable results from documents at a semantic level. This paper aims to provide users with more accurate document search results not only at a syntactic level but also on a semantic level. For example, when a user searches 'coffee' on Amazon, does the user only want coffee? Coffee is a kind of functional drink, the user may also want to know other functional drinks such as tea or Redbull. Coffee helps people stay awake, the user may just want something to help him/her stay awake or focused. In this project, document data from a question-and-answer website called Stack Exchange is analyzed and compared by using the Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) topic modeling algorithm. After completing topic modeling, using an ontology built with Protégé, data is further processed at a semantic level. We utilize the ontology rules and instances to optimize the search results.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages449-454
Number of pages6
ISBN (Electronic)9781728188997
DOIs
StatePublished - Jan 2021
Externally publishedYes
Event15th IEEE International Conference on Semantic Computing, ICSC 2021 - Virtual, Laguna Hills, United States
Duration: Jan 27 2021Jan 29 2021

Publication series

NameProceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021

Conference

Conference15th IEEE International Conference on Semantic Computing, ICSC 2021
Country/TerritoryUnited States
CityVirtual, Laguna Hills
Period1/27/211/29/21

Keywords

  • Data Extraction
  • Document Similarity
  • Machine Learning
  • Topic Modeling

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Decision Sciences (miscellaneous)

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