Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/83091
Title: HDSKG: Harvesting domain specific knowledge graph from content of webpages
Authors: Zhao, Xuejiao
Xing, Zhenchang
Kabir, Muhammad Ashad
Sawada, Naoya
Li, Jing
Lin, Shang-Wei
Keywords: Knowledge graph
Structural information extraction
Issue Date: 2017
Source: Zhao, X., Xing, Z., Kabir, M. A., Sawada, N., Li, J., & Lin, S.-W. (2017). HDSKG: Harvesting domain specific knowledge graph from content of webpages. 2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER), 56-67.
Abstract: Knowledge graph is useful for many different domains like search result ranking, recommendation, exploratory search, etc. It integrates structural information of concepts across multiple information sources, and links these concepts together. The extraction of domain specific relation triples (subject, verb phrase, object) is one of the important techniques for domain specific knowledge graph construction. In this research, an automatic method named HDSKG is proposed to discover domain specific concepts and their relation triples from the content of webpages. We incorporate the dependency parser with rule-based method to chunk the relations triple candidates, then we extract advanced features of these candidate relation triples to estimate the domain relevance by a machine learning algorithm. For the evaluation of our method, we apply HDSKG to Stack Overflow (a Q&A website about computer programming). As a result, we construct a knowledge graph of software engineering domain with 35279 relation triples, 44800 concepts, and 9660 unique verb phrases. The experimental results show that both the precision and recall of HDSKG (0.78 and 0.7 respectively) is much higher than the openIE (0.11 and 0.6 respectively). The performance is particularly efficient in the case of complex sentences. Further more, with the self-training technique we used in the classifier, HDSKG can be applied to other domain easily with less training data.
URI: https://hdl.handle.net/10356/83091
http://hdl.handle.net/10220/42426
ISBN: 978-1-5090-5501-2
DOI: 10.1109/SANER.2017.7884609
Rights: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [https://doi.org/10.1109/SANER.2017.7884609].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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