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[In this chapter, we introduce the basic concept of a knowledge graph (KG). While a knowledge graph seems to be a very simple way of representing information, it turns out to be quite powerful and is almost a lingua franca of sorts between humans and machines. On the one hand, knowledge graphs,...
[Extracting information from both Web and natural language documents is the central step in knowledge graph construction, since it is the first line of attack in going from a corpus that is not machine-understandable or queryable to a semi-structured corpus that can be queried and reasoned over....
[When knowledge graphs are constructed over a large corpus, many documents of which are independently describing the same entity in the real world, multiple nodes in the KG have to be resolved algorithmically into a single entity. The problem of devising automatic algorithms that decide when two...
[With the possible exception of good data collection and ontology design, information extraction and entity resolution are the two most important data-driven steps in a domain-specific knowledge graph construction pipeline. Yet, it is very rarely the case that the story ends there. Once...
[Knowledge graphs have continued to proliferate in various communities, including academia, government and industry. In this chapter, we describe some of the knowledge graph ecosystems that have emerged and matured in recent years. Chief among these ecosystems is the Web of Linked Data, which...
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