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Whenever semantic search and entities are mentioned, we probably immediately think of search engine knowledge graphs and structured data, but natural language understanding Industry Email List is not structured data. However, structured data makes it easier for search engines to understand natural language through disambiguation via distributional Industry Email List similarity because the “society” of a word gives an indication of the topics of the content. Connections between entities and their relationships mapped to a knowledge graph and tied to unique concept identifiers are strong (e.g., schema and structured data).
In addition, some parts of entity understanding are made possible through natural language processing, in the form of entity determination (deciding in a body Industry Email List of text which of two or more entities of the same name is intended), since entity recognition is not Industry Email List automatically unambiguous. The mention of the word "Mozart" in a text could well mean "Mozart", the composer, the café "Mozart", the street "Mozart", and there are a multitude of people and places which bear the same name each other. Most of the web is completely unstructured.
When considering the entire web, even semi-structured Industry Email List data such as semantic headers, bulleted and numbered lists, and tabular data are only a very small part of it. There are a lot of ambiguous text gaps in phrases, sentences, and paragraphs. Natural language processing is all about understanding the unstructured text of sentences, sentences, and Industry Email List paragraphs between all those “things” that are “known to” (the entities). A form of "filling the gaps" in the hot mess between entities. Similarity and relatedness and distributional similarity) contribute to this.