A Toolkit for Generating Code Knowledge Graphs
Open accessAbstract
Knowledge graphs have been proven extremely useful in powering diverse applications in semantic search and natural language understanding. In this work, we present GraphGen4Code, a toolkit to build code knowledge graphs that can similarly power various applications such as program search, code understanding, bug detection, and code automation. GraphGen4Code uses generic techniques to capture code semantics with the key nodes in the graph representing classes, functions and methods. Edges indicate function usage (e.g., how data flows through function calls, as derived from program analysis of real code), and documentation about functions (e.g., code documentation, usage documentation, or forum discussions such as StackOverflow). Our toolkit uses named graphs in RDF to model graphs per program, or can output graphs as JSON. We show the scalability of the toolkit by applying it to 1.3 million Python files drawn from GitHub, 2,300 Python modules, and 47 million forum posts. This results in an integrated code graph with over 2 billion triples. We make the toolkit to build such graphs as well as the sample extraction of the 2 billion triples graph publicly available to the community for use.
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CodeBERT: A Pre-Trained Model for Programming and Natural Languages
Feng, Z., Guo, D., Tang, D., Duan, N., Feng, X., Gong, M., Shou, L., Qin, B., Liu, T., Jiang, D., et al. (2020) · arXiv · doi:10.48550/arxiv.2002.08155 · Cited by 243 · see citing works
A Semantic Parsing and Reasoning-Based Approach to Knowledge Base Question Answering
Abdelaziz, I., Ravishankar, S., Kapanipathi, P., Roukos, S., & Gray, A. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i18.17988 · Cited by 26 · cited 1× · see citing works
“These knowledge graphs have provided significant advantages in a number of different application areas, such as semantic parsing [20], recommendation systems [13], information retrieval [14], and question answering [1, 34, 36].”
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- Authors
- Abdelaziz, I., Dolby, J., McCusker, J., & Srinivas, K.
- Affiliations
- IBM (United States) (US)
- Rensselaer Polytechnic Institute (US)
- Venue
- Proceedings of the 11th Knowledge Capture Conference
- Publisher
- ACM
- Published
- 2021-12-02
- Pages
- 137-144
- OpenAlex
- W3203324567
- Cited by
- 46
- References
- 2
- Citations by year
- 2026: 9 · 2025: 11 · 2024: 8 · 2023: 6 · 2022: 9 · 2021: 3
- FWCI
- 5.43
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International Conference on Knowledge Capture (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- TIB Data Manager
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Physical Sciences › Computer Science › Software › Model-Driven Software Engineering Techniques
- Keywords
- program semantics, call graph, data flow, code documentation, program search, program comprehension, bug detection, Python code
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