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Knowledge Bases as Complex Systems (particularly Semantics and Structure)
The Web of Data has become so complex, dynamic, contextualised and large that it starts to exhibit complex systems’ behaviour: the interactions between individual data items influence the global structure in an emergent way. Classical Knowledge Representation has no answer on how to give formal semantics to such complex systems, and on how to define which new knowledge should be automatically derived and which one not.
Traditional accounts of the meaning of knowledge graphs are based on model-theoretic semantics. This describes the denotation of symbols, the meaning of logical connectives, and of logical entailment. But there is more to it: the WoD graph has a power-law degree distribution, is a single connected component, that the connectivity of the graph depends on a small number of nodes, and that its structure varies between aggregation levels. In short: the Web of Data has the making of a Complex System.
Important fundamental and practical AI questions follow: Do such properties contribute to the semantics of the knowledge graph? Is the meaning of densely connected nodes more "important" or more "certain"? Is the meaning of a node dependent on the cluster ("context") in which it appears? How does the graph structure affect algorithms for storing, querying and analysing the knowledge graph? Research to answer those questions is driven by a common theme: to study Knowledge Bases as Complex Systems.
The Web of Data has become so complex, dynamic, contextualised and large that it starts to exhibit complex systems’ behaviour: the interactions between individual data items influence the global structure in an emergent way. Classical Knowledge Representation has no answer on how to give formal semantics to such complex systems, and on how to define which new knowledge should be automatically derived and which one not.
Traditional accounts of the meaning of knowledge graphs are based on model-theoretic semantics. This describes the denotation of symbols, the meaning of logical connectives, and of logical entailment. But there is more to it: the WoD graph has a power-law degree distribution, is a single connected component, that the connectivity of the graph depends on a small number of nodes, and that its structure varies between aggregation levels. In short: the Web of Data has the making of a Complex System.
Important fundamental and practical AI questions follow: Do such properties contribute to the semantics of the knowledge graph? Is the meaning of densely connected nodes more "important" or more "certain"? Is the meaning of a node dependent on the cluster ("context") in which it appears? How does the graph structure affect algorithms for storing, querying and analysing the knowledge graph? Research to answer those questions is driven by a common theme: to study Knowledge Bases as Complex Systems.
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Proceedings of the 12th Knowledge Capture Conference 2023pp.70-74, (2023)
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Delaram Javdani Rikhtehgar,Shenghui Wang, Hester Huitema, Julia Alvares,Stefan Schlobach,Carolien Rieffe,Dirk Heylen
2023 ADJUNCT PROCEEDINGS OF THE 31ST ACM CONFERENCE ON USER MODELING, ADAPTATION AND PERSONALIZATION, UMAP 2023pp.379-387, (2023)
ESWC (Satellite Events)pp.54-58, (2023)
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Proceedings of the 12th Knowledge Capture Conference 2023pp.75-82, (2023)
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Annelore Jellemijn Pieters,Stefan Schlobach
International Conference on Health Information Science (HIS)pp.76-87, (2022)
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