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Inference and Access Control for Big Data
Published in Bhavani Thuraisngham, Murat Kantarcioglu, Latifur Khan, Secure Data Science, 2022
Bhavani Thuraisngham, Murat Kantarcioglu, Latifur Khan
More recently, the concept of knowledge graphs for representing and reasoning about knowledge has become very popular. As stated in [FENS2020], “Since its inception by Google, Knowledge Graph has become a term that is recently ubiquitously used yet does not have a well-established definition.” The knowledge graphs have essentially evolved from the semantic networks and conceptual graphs of the 1970s and 1980s. They integrate the semantic networks and conceptual graphs with the more recent work in semantic web and ontologies. These knowledge graphs capture the knowledge and reason about the knowledge using various reasoning techniques built into them.
When Big Data and Data Science Prefigured Ambient Intelligence
Published in Kuan-Ching Li, Beniamino DiMartino, Laurence T. Yang, Qingchen Zhang, Smart Data, 2019
Thanks to semantic databases for generic knowledge (DBPedia), geographical knowledge (Geonames, CIA world facts book), named entities extracted from DBPedia (GlobalAtlas, YAGO), medical knowledge (Bioportal) or media (Google Knowledge Graph), the Semantic Web enables to link the huge and multilingual volume of text stored on the Web in order to provide a common structure to unstructured data [24]. Such a structure is queried thanks to SPARQL, a W3C protocol and query language for RDF stores. So we can retrieve pictures, songs and videos qualified by labels/tags tied by conceptual relations through the social and semantic Web. The Open Archive Initiative6 (OAI) also provides a protocol for databases harvesting through the Web based on the Dublin Core metadata.7
When TV Meets the Web: Toward Personalized Digital Media
Published in Spyrou Evaggelos, Iakovidis Dimitris, Mylonas Phivos, Semantic Multimedia Analysis and Processing, 2017
Dorothea Tsatsou, Matei Mancas, Jaroslav Kuchař, Lyndon Nixon, Miroslav Vacura, Julien Leroy, François Rocca, Vasileios Mezaris
Freebase [140] is a public collection of community-contributed interlinked data, or as the community itself describes it “an entity graph of people, places and things.” The Freebase ontologies are again user-generated and edited, consisting of semi-structured information in the form of folksonomies. It was recently employed by the Google Knowledge Graph [375] to expand Google search results about such entities with related information.
An intelligent approach for mining knowledge graphs of online news
Published in International Journal of Computers and Applications, 2022
Kumar Abhishek, Vaibhav Pratihar, Shishir Kumar Shandilya, Sanju Tiwari, Vinay Kumar Ranjan, Sudhakar Tripathi
Whenever knowledge about a particular domain is formulated into a structured format, the resulting structure is termed as a knowledge base. These structures can be used for easy retrieval of knowledge or updating other knowledge bases. Several existing Knowledge Graphs are explored in [13,14] to categorize various parameters of existing Knowledge Graphs. Project NELL [15] was one such research project, which aimed to create a system that extracts facts from billions of web pages. Some predefined ontology was used to generate relations from a massive corpus along with a multi-web paradigm. This kind of work is termed Distant Supervision [16]. Similar projects such as YAGO [17], DBpedia [18], Google Knowledge Vault [19], which is now Google Knowledge Graph, are also serving the purpose of knowledge bases where the fetched information from the web was being stored. DBpedia is a project for the extraction of structured knowledge from Wikipedia articles. Google’s Knowledge Graph [1] is currently the back end of Google Search and Google Assistant. The project was aimed to move from a web of strings to the web of things.