TY - GEN
T1 - Ontology-based Document Recommendation System using Topic Modeling
AU - Hu, Yijian
AU - Chang, Shih Yu
AU - Hsu, Kai Rui
AU - Chakraborty, Jaydeep
AU - Bansal, Srividya K.
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/1
Y1 - 2021/1
N2 - 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.
AB - 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.
KW - Data Extraction
KW - Document Similarity
KW - Machine Learning
KW - Topic Modeling
UR - http://www.scopus.com/inward/record.url?scp=85102642655&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85102642655&partnerID=8YFLogxK
U2 - 10.1109/ICSC50631.2021.00088
DO - 10.1109/ICSC50631.2021.00088
M3 - Conference contribution
AN - SCOPUS:85102642655
T3 - Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021
SP - 449
EP - 454
BT - Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Semantic Computing, ICSC 2021
Y2 - 27 January 2021 through 29 January 2021
ER -