Existing text analysis techniques can identify entities within documents. For example, in the sentence \u201cHaruhiko Kuroda, governor of Bank of Japan, announced 0.1 percent growth,\u201d \u2018Haruhiko Kuroda\u2019 and \u2018Bank of Japan\u2019 are both entities, and they are \u2018tagged\u2019 as such using specialised markup language. These tags are simply a way of highlighting that the text has some significance; it remains with the human user to understand what the tags mean.<\/p>\n
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Where semantics gets more interesting is in the representation and analysis of the relationships<\/em> between these entities. Using the same example, the system is able to create a formal, machine-readable relationship between Haruhiko Kuroda, his role as the governor, and the Bank of Japan.<\/p>\n In order for this to happen, the pre-existing environment must be defined. In order for the system to understand that \u2018governor\u2019 is a \u2018job\u2019 which exists within the entity of \u2018Bank of Japan\u2019, a rule must exist which states this as an abstraction. This is called an ontology.<\/p>\n Think of an ontology as the rule-book: it describes the world in which the source material exists. If semantic technology was used in the context of pharmaceuticals, the ontology would be full of information about classifications of diseases, disorders, body systems and their relationships to each other. If the same technology was used in the context of the football World Cup, the ontology would contain information about footballers, managers, teams and the relationships between those entities.<\/p>\n What happens when we put this all together? We can begin to infer relationships between entities in a system that have not been directly linked by human action.<\/p>\n An example: a visitor arrives on the website of a newspaper and would like information about bank governors in Asia. Semantic technology allows the website to return a much more sophisticated set of results from the initial search query. Because the system has an understanding of the relationships defining bank governors generally (via the the ontology), it is able to leverage the entire database of published text content in a more sophisticated way, capturing relationships that would have been overlooked by computer analysis alone. The result is that the user is provided with content more closely aligned to what they are already reading.<\/p>\n Read the sentence and answer the question: \u201cWhat is a \u2018Haruhiko Kuroda\u2019?\u201d As a human the answer is obvious. He is several things: human, male, and a governor of the Bank of Japan. This is the type of analytical thought process, this ability to assign traits to entities and then use these traits to infer relationships between new entities, that has so far eluded computer systems. The technology allows the inference of relationships that are not specifically stated within the source material: because the system knows that Haruhiko Kuroda is governor of Bank of Japan, it is able to infer that he works with other employees of the Bank of Japan, that he lives in Tokyo, which is in Japan, which is a set of islands in the Pacific.<\/p>\n Companies such as the BBC, which Ontotext has worked with, are sitting on more text data than they have ever experienced before. This is hardly unique to the publishing industry, either. According to Eric Schmidt, former Google CEO and executive chairman of Alphabet, every two days we create as much information as was generated from the dawn of civilisation up until 2003 – and he said that in 2010<\/a>. Five years later and businesses of all sizes are waking up to this fact – they must invest in the infrastructure to fully take advantage of their own data. You may not be aware of it, but you are already using semantic technology every day. Take Google search as an example: when you input a search term, for example \u2018Bulgaria\u2019, two columns appear. On the left are the actual search results, and on the right are semantic search results: information about the country\u2019s flag, capital, currency and other information that is pulled from various sources based on semantic inference.<\/p>\n Written by\u00a0Jarred McGinnis, UK managing consultant at Ontotext<\/em><\/strong><\/em><\/p>\n","protected":false},"excerpt":{"rendered":" Most buzzwords circulating right now describe very attention-grabbing products: virtual reality headsets, smart watches, internet-connected toasters. Big Data is the prime example of this: but how ‘big’ is enterprise Big Data?<\/p>\n","protected":false},"author":7,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[156,656,3424,1297,3425],"tags":[],"class_list":["post-17376","post","type-post","status-publish","format-standard","hentry","category-big-data","category-database","category-linked-data","category-opinion","category-semantic-technology"],"_links":{"self":[{"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/posts\/17376","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/comments?post=17376"}],"version-history":[{"count":1,"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/posts\/17376\/revisions"}],"predecessor-version":[{"id":17377,"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/posts\/17376\/revisions\/17377"}],"wp:attachment":[{"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/media?parent=17376"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/categories?post=17376"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/tags?post=17376"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"1](\"http:\/\/www.businesscloudnews.com\/files\/2015\/09\/1-tagging.jpg\")
<\/a>Once tagged, entities can then be recognised and have information from various sources associated with them. Groundbreaking? Not really. It\u2019s easy to tag content such that the system knows that \u201cHaruhiko Kuroda\u201d is a type of \u2018person\u2019, however this still requires human input.<\/p>\n<\/a><\/p>\n<\/a><\/p>\n<\/a><\/p>\n