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Semantic Knowledge Graphing Market Segmented By geographies, types and applications
A knowledge graph is a knowledge base utilised by Google to optimise its search engine results with semantic-search information taken from many sources. It was added to Google’s search engine in 2012 with the initial rollout in its home market, the United States.
The Semantic Knowledge Graph market aims at extracting and presenting the knowledge of a specific domain automatically from a group of documents representative of that domain. The representation encodes the semantic relationship between different words, phrases and concepts such that those relationships can expose new information about the interrelationships between all entities in that domain.
The semantic knowledge graphing market has numerous applications like being used to discover related terms within a domain, explain multiple meanings of a similar phrase, boost semantic search by expanding user queries to related keywords/ phrases, and identifying trending topics among time-series data.
It can also build a recommendation engine based on content, perform data cleansing by scoring each item according to relevance, summarise documents by judging the importance of each phrase and entity within the document and do a predictive analysis of time-series data.
A key driver of the semantic knowledge graphing market is the sheer volume of data available on search engines. According to Internet Live Stats, there are approximately 1.11 billion websites live as of 2016 with hundreds more being added every minute. It can be extremely challenging for website owners to reach their target market or even customers to find the exact data they are looking for.
Semantic knowledge graphs can act as the backbone of any information architecture, enabling entity-centric views on information, data, products, suppliers, employees, locations and research topics. Semantic graphs not only retrieve what is required but also provide the interrelations between the various objects, even if not stated in explicit terms. They thus help in converting unorganized data into useful information.
The second driver of the semantic knowledge graphing market is the occasional personalization of information required. For e.g. - Some drugs might have regulatory aspects, a unique therapeutic character and an entirely different meaning to product managers or salespeople.
At a given time, an individual might only require a certain aspect of information which is relevant in that particular situation. This personalised information processing requires a semantic layer on top of the data layer, particularly when the information is stored in different forms and scattered in several different repositories. Semantic knowledge graph engines can link similar content and documents related to one another in a highly precise manner.
Current semantic knowledge graphs rely on traditional machine learning methods. Thus, their results are not reusable by algorithms and neither can humans easily interpret them. The amount of information being added to the World Wide Web daily is beyond the realm of imagination. Semantic knowledge graphing is not evolving anywhere near as rapidly as required.
China currently has the world’s largest online population, followed by the U.S. which makes these two countries the largest semantic knowledge graphing markets. India is expected to outpace the U.S within this decade and should be a key focus for companies offering semantic knowledge graphs.
Some companies in the semantic knowledge graphing markets are Microsoft Bing’s Satori Knowledge Base, Yandex's Object Answer, LinkedIn's Knowledge Graph and Google’s Knowledge Graph.
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