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MEIM: A Multi-Source Software Knowledge Entity Extraction Integration Model

Wuqian Lv1, Zhifang Liao1,*, Shengzong Liu2, Yan Zhang3
1 School of Computer Science and Engineering, Central South University, Changsha, 410075, China
2 School of Information Technology and Management, Hunan University of Finance and Economics, Changsha, 410205, China
3 School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, G4 0BA, UK
* Corresponding Author: Zhifang Liao. Email:

Computers, Materials & Continua 2021, 66(1), 1027-1042.

Received 01 July 2020; Accepted 25 July 2020; Issue published 30 October 2020


Entity recognition and extraction are the foundations of knowledge graph construction. Entity data in the field of software engineering come from different platforms and communities, and have different formats. This paper divides multi-source software knowledge entities into unstructured data, semi-structured data and code data. For these different types of data, Bi-directional Long ShortTerm Memory (Bi-LSTM) with Conditional Random Field (CRF), template matching, and abstract syntax tree are used and integrated into a multi-source software knowledge entity extraction integration model (MEIM) to extract software entities. The model can be updated continuously based on user’s feedbacks to improve the accuracy. To deal with the shortage of entity annotation datasets, keyword extraction methods based on Term Frequency–Inverse Document Frequency (TF-IDF), TextRank, and K-Means are applied to annotate tasks. The proposed MEIM model is applied to the Spring Boot framework, which demonstrates good adaptability. The extracted entities are used to construct a knowledge graph, which is applied to association retrieval and association visualization.


Entity extraction; software knowledge graph; software data

Cite This Article

W. Lv, Z. Liao, S. Liu and Y. Zhang, "Meim: a multi-source software knowledge entity extraction integration model," Computers, Materials & Continua, vol. 66, no.1, pp. 1027–1042, 2021.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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