Hybrid Intelligence for Knowledge Graph Construction

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Knowledge Graphs (KG) [11] have risen to be one the most powerful mechanismsto represent knowledge and have been shown to play an important role for manyapplications, including chatbot development, data integration, and semanticsearch [17].However, most data sources are generally still represented via heterogeneousnon-graph data structures, schemes, and formats [7]. The conversion of thesesources into KG views still necessitates a considerable effort from both KGengineers and domain experts when completing each of the tasks in the pipeline,proving this to be a time consuming and often complicated process [1].While advances have been made in terms of supporting KG engineers in theirwork, "existing techniques offer [...] solutions, each covering a specific aspect ofKG construction, but automatically orchestrating [...] the whole constructionprocess remains the challenge" [12]. This challenge motivates our research.Therefore, our objectives are (i) enriching the theoretical understanding ofKG construction in the context of data integration processes and automation,(ii) extending the application domain of AI to the orchestration of KG construction tasks into pipelines and (iii) supporting the community of practitioners bysupplying them with novel methods for KG construction pipeline design, generation and automation.We propose to undertake this by first researching and modelling KG construction pipelines to discover and represent general features and operations.Then, basing ourselves on the actions of the KG engineer understood as a goal oriented agent, we will propose the design of a hybrid intelligent problem-solvingagent as a possible solution to the automatic orchestration of these data integration pipelines, focusing on structured and semi-structured sources.