Knowledge Extraction
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Knowledge Extraction
Knowledge extraction is the creation of knowledge from structured (relational databases, XML) and unstructured (text, documents, images) sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing. Although it is methodically similar to information extraction ( NLP) and ETL (data warehouse), the main criterion is that the extraction result goes beyond the creation of structured information or the transformation into a relational schema. It requires either the reuse of existing formal knowledge (reusing identifiers or ontologies) or the generation of a schema based on the source data. The RDB2RDF W3C group is currently standardizing a language for extraction of resource description frameworks (RDF) from relational databases. Another popular example for knowledge extraction is the transformation of Wikipedia into structured data and also the mapping to existing knowledge ...
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Knowledge Discovery
Knowledge extraction is the creation of Knowledge representation and reasoning, knowledge from structured (relational databases, XML) and unstructured (text (literary theory), text, documents, images) sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must Knowledge representation and reasoning, represent knowledge in a manner that facilitates inferencing. Although it is methodically similar to information extraction (Natural language processing, NLP) and extract, transform, load, ETL (data warehouse), the main criterion is that the extraction result goes beyond the creation of structured information or the transformation into a Database schema, relational schema. It requires either the reuse of existing Knowledge representation and reasoning, formal knowledge (reusing identifiers or ontologies) or the generation of a schema based on the source data. The RDB2RDF W3C group is currently standardizing a language for extraction of Re ...
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Knowledge Representation And Reasoning
Knowledge representation and reasoning (KRR, KR&R, KR²) is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as Computer-aided diagnosis, diagnosing a medical condition or natural language user interface, having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge in order to design Formalism (mathematics), formalisms that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of ''reasoning'', such as the application of rules or the relations of Set theory, sets and subsets. Examples of knowledge representation formalisms include Semantic network, semantic nets, systems architecture, Frame (artificial intelligence), frames, rules, and Ontology (information science), ont ...
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DBpedia Spotlight
DBpedia (from "DB" for "database") is a project aiming to extract structured content from the information created in the Wikipedia project. This structured information is made available on the World Wide Web. DBpedia allows users to semantically query relationships and properties of Wikipedia resources, including links to other related datasets. In 2008, Tim Berners-Lee described DBpedia as one of the most famous parts of the decentralized Linked Data effort. Background The project was started by people at the Free University of Berlin and Leipzig University''DBpedia: A Nucleus for a Web of Open Data'', available a in collaboration with OpenLink Software, and is now maintained by people at the University of Mannheim and Leipzig University. The first publicly available dataset was published in 2007. The data is made available under free licences (CC-BY-SA), allowing others to reuse the dataset; it doesn't however use an open data license to waive the sui generis database ri ...
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picture info

Tim Berners-Lee
Sir Timothy John Berners-Lee (born 8 June 1955), also known as TimBL, is an English computer scientist best known as the inventor of the World Wide Web. He is a Professorial Fellow of Computer Science at the University of Oxford and a professor at the Massachusetts Institute of Technology (MIT). Berners-Lee proposed an information management system on 12 March 1989, then implemented the first successful communication between a Hypertext Transfer Protocol (HTTP) client and server via the Internet in mid-November. Berners-Lee is the director of the World Wide Web Consortium (W3C), which oversees the continued development of the Web. He co-founded (with his then wife-to-be Rosemary Leith) the World Wide Web Foundation. He is a senior researcher and holder of the 3Com founder's chair at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). He is a director of the Web Science Research Initiative (WSRI) and a member of the advisory board of the MIT Center for Co ...
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Domain Knowledge
Domain knowledge is knowledge of a specific, specialized discipline or field, in contrast to general (or domain-independent) knowledge. The term is often used in reference to a more general discipline—for example, in describing a software engineer who has general knowledge of computer programming as well as domain knowledge about developing programs for a particular industry. People with domain knowledge are often regarded as specialists or experts in their field. Knowledge capture In software engineering, ''domain knowledge'' is knowledge about the environment in which the target system operates, for example, software agents. Domain knowledge usually must be learned from software users in the domain (as domain specialists/experts), rather than from software developers. It may include user workflows, data pipelines, business policies, configurations and constraints and is crucial in the development of a software application. Expert's domain knowledge (frequently informal and il ...
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Ontology (information Science)
In computer science and information science, an ontology encompasses a representation, formal naming, and definition of the categories, properties, and relations between the concepts, data, and entities that substantiate one, many, or all domains of discourse. More simply, an ontology is a way of showing the properties of a subject area and how they are related, by defining a set of concepts and categories that represent the subject. Every academic discipline or field creates ontologies to limit complexity and organize data into information and knowledge. Each uses ontological assumptions to frame explicit theories, research and applications. New ontologies may improve problem solving within that domain. Translating research papers within every field is a problem made easier when experts from different countries maintain a controlled vocabulary of jargon between each of their languages. For instance, the definition and ontology of economics is a primary concern in Marxist econo ...
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Virtuoso Universal Server
Virtuoso Universal Server is a middleware and database engine hybrid that combines the functionality of a traditional relational database management system (RDBMS), object–relational database (ORDBMS), virtual database, RDF, XML, free-text, web application server and file server functionality in a single system. Rather than have dedicated servers for each of the aforementioned functionality realms, Virtuoso is a "universal server"; it enables a single multithreaded server process that implements multiple protocols. The free and open source edition of Virtuoso Universal Server is also known as OpenLink Virtuoso. The software has been developed by OpenLink Software with Kingsley Uyi Idehen and Orri Erling as the chief software architects. Database structure Core database engine Virtuoso provides an extended object–relational model, which combines the flexibility of relational access with inheritance, run time data typing, late binding, and identity-based access. Vir ...
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YAGO (Ontology)
YAGO (Yet Another Great Ontology) is an open source knowledge base developed at the Max Planck Institute for Computer Science in Saarbrücken. It is automatically extracted from Wikipedia and other sources. As of 2019, YAGO3 has knowledge of more than 10 million entities and contains more than 120 million facts about these entities. The information in YAGO is extracted from Wikipedia (e.g., categories, redirects, infoboxes), WordNet (e.g., synsets, hyponymy), and GeoNames. The accuracy of YAGO was manually evaluated to be above 95% on a sample of facts. To integrate it to the linked data cloud, YAGO has been linked to the DBpedia ontology and to the SUMO ontology. YAGO3 is provided in Turtle and tsv formats. Dumps of the whole database are available, as well as thematic and specialized dumps. It can also be queried through various online browsers and through a SPARQL endpoint hosted by OpenLink Software. The source code of YAGO3 is available on GitHub. YAGO has been used in th ...
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FOAF (software)
FOAF (an acronym of friend of a friend) is a machine-readable ontology describing persons, their activities and their relations to other people and objects. Anyone can use FOAF to describe themselves. FOAF allows groups of people to describe social networks without the need for a centralised database. FOAF is a descriptive vocabulary expressed using the Resource Description Framework (RDF) and the Web Ontology Language (OWL). Computers may use these FOAF profiles to find, for example, all people living in Europe, or to list all people both you and a friend of yours know. This is accomplished by defining relationships between people. Each profile has a unique identifier (such as the person's e-mail addresses, international telephone number, Facebook account name, a Jabber ID, or a URI of the homepage or weblog of the person), which is used when defining these relationships. The FOAF project, which defines and extends the vocabulary of a FOAF profile, was started in 2000 by Libby ...
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