Abstract
Knowledge graphs are an increasingly popular choice for integrating heterogeneous data that have been collected independently from multiple sources. In many scenarios, including research and industry collaborations, the source data can be provided at different granularities, that is at more detailed and independently at more summarized levels, each with its own ontology. In such scenarios it is often important to find justifications at a more detailed data level for the data at a more summarized level, that is to find correct provenance of the existing summarized data. To address this previously unexplored challenge, in this paper we present a framework for enabling provenance-aware knowledge-graph integration and querying for such hierarchical data. We introduce the proposed domain-independent algorithms, outline their implementation, and report experimental results for two real-life application domains. The findings suggest that our proposed framework can be effective and efficient in enabling provenance-aware summarization querying across hierarchical knowledge-graph data.
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This work has been supported by the National Science Foundation under Grant No. CBET-2019435.
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Hou, PY., Ao, J., Schatz, K., Gulyuk, A.V., Yingling, Y.G., Chirkova, R. (2023). Provenance-Aware Data Integration and Summarization Querying for Knowledge Graphs. In: Delir Haghighi, P., et al. Information Integration and Web Intelligence. iiWAS 2023. Lecture Notes in Computer Science, vol 14416. Springer, Cham. https://doi.org/10.1007/978-3-031-48316-5_29
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