Natural language processing to build a semantic database - 0 views
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Google obtains information about entities and their relationships to each other from the following sources: CIA World Factbook, Wikipedia / Wikidata (formerly Freebase) Google+ and Google My Business, respectively Structured data (schema.org) Web crawling Licensed data
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Named Entity Analysis and Extraction: This aspect should be familiar to us from the previous papers. It attempts to identify words with a “known” meaning and assign them to classes of entity types. In general, named entities are people, places, and things (nouns). Entities may also contain product names. These are generally the words that trigger a Knowledge Panel. However, words that do not trigger their own Knowledge Panel can also be entities.
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"For this, Google can use the already verified data from (semi-)structured databases like the Knowledge Graph, Wikipedia … as training data to learn to assign unstructured information to existing models or classes and to recognize new patterns. This is where Natural Language Processing in the form of BERT and MUM plays the crucial role. Using Natural Language Processing, Google is able to access a huge range of unstructured information from the entire crawlable world wide web. The final major challenge is validating the accuracy of the information. The solution could be a further development of the Knowledge Vault."