7 Named entity disambiguation
This chapter covers
- Combining named entity disambiguation with knowledge graph technologies
- Building a knowledge graph from multiple sources
- Performing advanced analysis
Natural language processing (NLP) techniques play a critical role in the automatic construction of knowledge graphs (KGs) from unstructured data. A key task in this process is named entity recognition (NER), which identifies mentions of relevant named entities in raw text. NER assigns these entities to predefined categories such as people, organizations, locations, or diseases. Although NER is an important component in building KGs, it doesn’t give us a precise understanding of text in our application domain.
7.1 From recognition to disambiguation
Imagine developing an intelligent advisory system (IAS) to support the activities of stakeholders in the healthcare field. A critical attribute of such IASs is interactivity, which is the ability to exchange information with humans through multiple interactions. Features which enable this exchange include the following:
- Detecting meaningful entities in natural language
- Retrieving information about these entities from different knowledge sources
NER inference can’t provide these features. For example, consider the following paragraph from a weekly bulletin released by the European Centre for Disease Prevention and Control (ECDC) [1]: