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Knowledge graphs have proven extremely useful in powering diverse applications in semantic search and natural language understanding. Graph4Code is a knowledge graph about program code that can similarly power diverse applications such as program search, code understanding, refactoring, bug detection, and code automation. The graph uses generic techniques to capture the semantics of Python code: the key nodes in the graph are classes, functions and methods in popular Python modules. 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). We make extensive use of named graphs in RDF to make the knowledge graph extensible by the community. We describe a set of generic extraction techniques that we applied to over 1.3M Python files drawn from GitHub, over 2,300 Python modules, as well as 47M forum posts to generate a graph with over 2 billion triples. We also provide a number of initial use cases of the knowledge graph in code assistance, enforcing best practices, debugging and type inference. The graph and all its artifacts are available to the community for use.
As Knowledge Graphs (KGs) continue to gain widespread momentum for use in different domains, storing the relevant KG content and efficiently executing queries over them are becoming increasingly important. A range of Data Management Systems (DMSs) ha
In recent years, the size of big linked data has grown rapidly and this number is still rising. Big linked data and knowledge bases come from different domains such as life sciences, publications, media, social web, and so on. However, with the rapid
The quality assurance of the knowledge graph is a prerequisite for various knowledge-driven applications. We propose KGClean, a novel cleaning framework powered by knowledge graph embedding, to detect and repair the heterogeneous dirty data. In contr
Recent years have witnessed the emergence and flourishing of hierarchical graph pooling neural networks (HGPNNs) which are effective graph representation learning approaches for graph level tasks such as graph classification. However, current HGPNNs
Gremlin is a graph traversal machine and language designed, developed, and distributed by the Apache TinkerPop project. Gremlin, as a graph traversal machine, is composed of three interacting components: a graph $G$, a traversal $Psi$, and a set of t