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- W4386066734 abstract "Symbolic representation helps us to represent information in a well-defined rule-driven fashion. Currently, there are several ways to represent Knowledge Graphs in general. However, in this work, we extended the implementation of symbolic representation to model domain-oriented temporal Knowledge Graphs. For symbolic representation, we incorporated Horn rules and SWRL (Semantic Web Rule Language). The presented approach is semi-autonomous: (i) we extracted hand-crafted rules and (ii) we utilized the PSyKE (Platform for Symbolic Knowledge Extraction) package to extract some rules automatically from raw data logs. For domain modeling, we targeted a smart industry environment. To validate the proposed model, we conducted a counterfactual study using Knowledge Graph and network analysis for fact-finding and filtering." @default.
- W4386066734 created "2023-08-23" @default.
- W4386066734 creator A5047751762 @default.
- W4386066734 creator A5075197029 @default.
- W4386066734 date "2023-07-25" @default.
- W4386066734 modified "2023-09-27" @default.
- W4386066734 title "Towards symbolic representation-based modeling of Temporal Knowledge Graphs" @default.
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- W4386066734 doi "https://doi.org/10.1109/smartnets58706.2023.10215541" @default.
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