ScheRe: Schema Reshaping for Enhancing Knowledge Graph Construction
Dongzhuoran Zhou, Baifan Zhou, Zhuoxun Zheng, Ahmet Soylu, Ognjen Savković, Egor V. Kostylev, Evgeny Kharlamov · Proceedings of the 31st ACM International Conference on Information & Knowledge Management · 2022
Automatic knowledge graph (KG) construction is widely used for e.g. data integration, question answering and semantic search. There are many approaches of automatic KG construction. Among which, an important approach is to map the raw data to a given domain KG schema, e.g., domain ontology or conceptual graph, and construct the entities and properties according to the domain KG schema. However, the existing approaches to construct KGs are not always efficient enough and the resulting KGs are not sufficiently application and user-friendly. The main challenge arises from the trade-off: the domain KG schema should be domain-generic and knowledge-oriented, to reflect the general domain knowledge rather than data particularities; while a KG schema should be data-oriented, to cover all data features. If the former is directly used for KG construction, this can cause issues like a high load of blank nodes, which are technical nodes in the KGs that represent unknown entities. To this end, we propose our ScheRe system in the demo, which relies on a schema reshaping algorithm and other two semantic modules for enhancing KG construction. The demo attendees will use ScheRe to reshape a domain KG schema to data specific KG schema, build KGs with industrial data, and experience more user-friendly querying.