Knowledge Graph (KG) Construction Task
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A Knowledge Graph (KG) Construction Task is a knowledge base construction task that produces a knowledge graph.
- Context:
- It can range from being Manual KG Construction, to Semi-Automatic KB Construction, to being Automatic KG Construction.
- It can be supported by a KG Construction System.
- ...
- See: IE from Text, Ontology Construction.
References
2023
- (Cenikj et al., 2023) ⇒ Gjorgjina Cenikj, Lidija Strojnik, Risto Angelski, Nives Ogrinc, Barbara Koroušić Seljak, and Tome Eftimov. (2023). “From Language Models to Large-scale Food and Biomedical Knowledge Graphs.” In: Scientific reports, 13(1).
- QUOTE: ... A Knowledge Graph (KG) is a type of KB, where knowledge is stored in the form of entities characterized by some attributes, and relations connecting the entities. Conventional methods of KG construction can be broadly categorized into manual, and automatic, or semi-automatic methods. The benefits of manual creation and curation approaches are their high precision and reliability11, however, due to the high amount of effort required by domain experts, they also have lower recall rates, poor scalability and time efficiency12. Automatic and semi-automatic KG construction is enabled by text-mining methods, which are able to extract entities and relations which can be structured as a KG. ...
2017
- https://kgtutorial.github.io/
- QUOTE: With the proliferation of large collections of unstructured text, the problem of extracting structured knowledge and integrating it into a coherent knowledge graph has become increasingly important. Applications that rely on structured knowledge representations include digital assistants (Siri, Alexa, Cortana, and Google Now), question answering, summarization, and as well as many downstream autonomous decision-making. Due to its importance, this area has been an active area of research spanning areas of natural language processing, information extraction, information integration, databases, search, and machine learning.
Our goal is to present an accessible and structured overview of the existing approaches to extracting candidate facts from text and incorporating these into a well-formed knowledge graph. Our approach includes identifying the common themes and challenges in the area, and comparing and contrasting the existing approaches on the basis of these aspects. We believe such a unifying framework will provide the necessary tools and perspectives to enable the newcomers to the field to explore, evaluate, and develop novel techniques for automated knowledge graph construction.
- QUOTE: With the proliferation of large collections of unstructured text, the problem of extracting structured knowledge and integrating it into a coherent knowledge graph has become increasingly important. Applications that rely on structured knowledge representations include digital assistants (Siri, Alexa, Cortana, and Google Now), question answering, summarization, and as well as many downstream autonomous decision-making. Due to its importance, this area has been an active area of research spanning areas of natural language processing, information extraction, information integration, databases, search, and machine learning.