An Ontology-Based Labeling of Influential Topics Using Topic Network Analysis
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초록
In this paper, we present an ontology-based approach to labeling influential topics of scientific articles. First, tolook for influential topics from scientific article, topic modeling is performed, and then social network analysisis applied to the selected topic models. Abstracts of research papers related to data mining published over the20 years from 1995 to 2015 are collected and analyzed in this research. Second, to interpret and to explainselected influential topics, the UniDM ontology is constructed from Wikipedia and serves as concepthierarchies of topic models. Our experimental results show that the subjects of data management and queriesare identified in the most interrelated topic among other topics, which is followed by that of recommendersystems and text mining. Also, the subjects of recommender systems and context-aware systems belong to themost influential topic, and the subject of k-nearest neighbor classifier belongs to the closest topic to other topics. The proposed framework provides a general model for interpreting topics in topic models, which plays animportant role in overcoming ambiguous and arbitrary interpretation of topics in topic modeling.
Abstract
In this paper, we present an ontology-based approach to labeling influential topics of scientific articles. First, tolook for influential topics from scientific article, topic modeling is performed, and then social network analysisis applied to the selected topic models. Abstracts of research papers related to data mining published over the20 years from 1995 to 2015 are collected and analyzed in this research. Second, to interpret and to explainselected influential topics, the UniDM ontology is constructed from Wikipedia and serves as concepthierarchies of topic models. Our experimental results show that the subjects of data management and queriesare identified in the most interrelated topic among other topics, which is followed by that of recommendersystems and text mining. Also, the subjects of recommender systems and context-aware systems belong to themost influential topic, and the subject of k-nearest neighbor classifier belongs to the closest topic to other topics. The proposed framework provides a general model for interpreting topics in topic models, which plays animportant role in overcoming ambiguous and arbitrary interpretation of topics in topic modeling.
참고문헌 (23)
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