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| Learning Concept Hierarchies from Chinese Academic Literature for Domain Ontology Construction |
| Tang Lin1, Guo Chonghui1, Chen Jingfeng1, Sun Leilei2 |
1.Institute of Systems Engineering, Dalian University of Technology, Dalian 116024
2.SKLSDE Lab and BDBC, Beihang University, Beijing 100083 |
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Abstract Constructing domain ontology from academic literature has great significance in promoting discipline development. Taking Chinese academic literature as a data source, this study proposed a semi-automatic method for extracting concept hierarchy. First, a fine-grained universal research framework for constructing hierarchical relations of the domain ontology was proposed. Then, a novel concept representation fusion method was developed, considering concepts semantic features based on deep learning and concept frequency in time series. Combined with an affinity propagation (AP) clustering algorithm, Prim s algorithm, and data from a Web search engine, the ontology concept hierarchy extraction algorithm was proposed via rule-based reasoning (RROCHE). Concept hierarchy relations are learned semi-automatically. The algorithm was then applied to the academic literature on Chinese word segmentation. Numerical experiments examined the feasibility and effectiveness of the proposed methods. The proposed method can also be applied effectively and widely to other domains.
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Received: 25 March 2019
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