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Domain-Specific Knowledge Graph Construction for Semantic Analysis

Part of the Lecture Notes in Computer Science book series (LNISA,volume 12124)


Knowledge graphs are widely used for systematic representation of real-world data. Large-scale, general purpose knowledge graphs, having millions of facts, have been constructed through automated techniques from publicly available datasets such as Wikipedia. However, these knowledge graphs are typically incomplete and often fail to correctly capture the semantics of the data. This holds true particularly for domain-specific data, where the generic techniques for automated knowledge graph creation often fail due to several challenges, such as lack of training data, semantic ambiguities and absence of representative ontologies. The focus of this thesis is on automated knowledge graph construction for the cultural heritage domain. The goal is to tackle the research challenges encountered during the creation of an ontology and a knowledge graph from digitized collections of cultural heritage data. This paper identifies the specific research problems for these tasks and presents a methodology and approach for a solution, along with preliminary results.


  • Knowledge graphs
  • Ontology learning
  • Cultural heritage

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I am thankful to my advisor Ralf Krestel for his feedback and Felix Naumann and Fabian Suchanek for their valuable comments. I would also like to thank Elena Demidova for guidance and suggestions during revisions.

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Jain, N. (2020). Domain-Specific Knowledge Graph Construction for Semantic Analysis. In: , et al. The Semantic Web: ESWC 2020 Satellite Events. ESWC 2020. Lecture Notes in Computer Science(), vol 12124. Springer, Cham.

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