Construction of Knowledge Graphs: State and Challenges
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866910588779626496 |
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| author | Hofer, Marvin Obraczka, Daniel Saeedi, Alieh Köpcke, Hanna Rahm, Erhard |
| author_facet | Hofer, Marvin Obraczka, Daniel Saeedi, Alieh Köpcke, Hanna Rahm, Erhard |
| contents | With knowledge graphs (KGs) at the center of numerous applications such as recommender systems and question answering, the need for generalized pipelines to construct and continuously update such KGs is increasing. While the individual steps that are necessary to create KGs from unstructured (e.g. text) and structured data sources (e.g. databases) are mostly well-researched for their one-shot execution, their adoption for incremental KG updates and the interplay of the individual steps have hardly been investigated in a systematic manner so far. In this work, we first discuss the main graph models for KGs and introduce the major requirement for future KG construction pipelines. Next, we provide an overview of the necessary steps to build high-quality KGs, including cross-cutting topics such as metadata management, ontology development, and quality assurance. We then evaluate the state of the art of KG construction w.r.t the introduced requirements for specific popular KGs as well as some recent tools and strategies for KG construction. Finally, we identify areas in need of further research and improvement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_11509 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Construction of Knowledge Graphs: State and Challenges Hofer, Marvin Obraczka, Daniel Saeedi, Alieh Köpcke, Hanna Rahm, Erhard Artificial Intelligence Databases Machine Learning With knowledge graphs (KGs) at the center of numerous applications such as recommender systems and question answering, the need for generalized pipelines to construct and continuously update such KGs is increasing. While the individual steps that are necessary to create KGs from unstructured (e.g. text) and structured data sources (e.g. databases) are mostly well-researched for their one-shot execution, their adoption for incremental KG updates and the interplay of the individual steps have hardly been investigated in a systematic manner so far. In this work, we first discuss the main graph models for KGs and introduce the major requirement for future KG construction pipelines. Next, we provide an overview of the necessary steps to build high-quality KGs, including cross-cutting topics such as metadata management, ontology development, and quality assurance. We then evaluate the state of the art of KG construction w.r.t the introduced requirements for specific popular KGs as well as some recent tools and strategies for KG construction. Finally, we identify areas in need of further research and improvement. |
| title | Construction of Knowledge Graphs: State and Challenges |
| topic | Artificial Intelligence Databases Machine Learning |
| url | https://arxiv.org/abs/2302.11509 |