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Semantic Analytics
Semantic analytics, also termed ''semantic relatedness'', is the use of ontologies to analyze content in web resources. This field of research combines text analytics and Semantic Web technologies like RDF. Semantic analytics measures the relatedness of different ontological concepts. Some academic research groups that have active project in this area include Kno.e.sis Center at Wright State University among others. History An important milestone in the beginning of semantic analytics occurred in 1996, although the historical progression of these algorithms is largely subjective. In his seminal study publication, Philip Resnik established that computers have the capacity to emulate human judgement. Spanning the publications of multiple journals, improvements to the accuracy of general semantic analytic computations all claimed to revolutionize the field. However, the lack of a standard terminology throughout the late 1990s was the cause of much miscommunication. This prompted B ...
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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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Natural Language Processing
Natural language processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves. Challenges in natural language processing frequently involve speech recognition, natural-language understanding, and natural-language generation. History Natural language processing has its roots in the 1950s. Already in 1950, Alan Turing published an article titled "Computing Machinery and Intelligence" which proposed what is now called the Turing test as a criterion of intelligence, t ...
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Semantic Similarity
Semantic similarity is a metric defined over a set of documents or terms, where the idea of distance between items is based on the likeness of their meaning or semantic content as opposed to lexicographical similarity. These are mathematical tools used to estimate the strength of the semantic relationship between units of language, concepts or instances, through a numerical description obtained according to the comparison of information supporting their meaning or describing their nature. The term semantic similarity is often confused with semantic relatedness. Semantic relatedness includes any relation between two terms, while semantic similarity only includes "is a" relations. For example, "car" is similar to "bus", but is also related to "road" and "driving". Computationally, semantic similarity can be estimated by defining a topological similarity, by using ontologies to define the distance between terms/concepts. For example, a naive metric for the comparison of concepts order ...
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Relationship Extraction
A relationship extraction task requires the detection and classification of semantic relationship mentions within a set of artifacts, typically from text or XML documents. The task is very similar to that of information extraction (IE), but IE additionally requires the removal of repeated relations (other) and generally refers to the extraction of many different relationships. Concept and applications The concept of relationship extraction was first introduced during the 7th Message Understanding Conference in 1998. Relationship extraction involves the identification of relations between entities and it usually focuses on the extraction of binary relations. Application domains where relationship extraction is useful include gene-disease relationships, protein-protein interaction etc. Current relationship extraction studies use machine learning technologies, which approach relationship extraction as a classification problem. Never-Ending Language Learning is a semantic ma ...
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Semantic Scholar
Semantic Scholar is an artificial intelligence–powered research tool for scientific literature developed at the Allen Institute for AI and publicly released in November 2015. It uses advances in natural language processing to provide summaries for scholarly papers. The Semantic Scholar team is actively researching the use of artificial-intelligence in natural language processing, machine learning, Human-Computer interaction, and information retrieval. Semantic Scholar began as a database surrounding the topics of computer science, geoscience, and neuroscience. However, in 2017 the system began including biomedical literature in its corpus. As of September 2022, they now include over 200 million publications from all fields of science. Technology Semantic Scholar provides a one-sentence summary of scientific literature. One of its aims was to address the challenge of reading numerous titles and lengthy abstracts on mobile devices. It also seeks to ensure that the three mill ...
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Knowledge Management
Knowledge management (KM) is the collection of methods relating to creating, sharing, using and managing the knowledge and information of an organization. It refers to a multidisciplinary approach to achieve organisational objectives by making the best use of knowledge. An established List of academic disciplines, discipline since 1991, KM includes courses taught in the fields of business administration, information systems, management, Library science, library, and information science. Other fields may contribute to KM research, including information and media, computer science, public health and policy, public policy. Several universities offer dedicated master's degrees in knowledge management. Many large companies, public institutions, and non-profit organisations have resources dedicated to internal KM efforts, often as a part of their strategic management, business strategy, information technology, IT, or human resource management departments. Several consulting companies ...
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Cortana (virtual Assistant)
Cortana is a virtual assistant developed by Microsoft that uses the Bing search engine to perform tasks such as setting reminders and answering questions for the user. Cortana is currently available in English, Portuguese, French, German, Italian, Spanish, Chinese, and Japanese language editions, depending on the software platform and region in which it is used. Microsoft began reducing the prevalence of Cortana and converting it from an assistant into different software integrations in 2019. It was split from the Windows 10 search bar in April 2019. In January 2020, the Cortana mobile app was removed from certain markets, and on March 31, 2021, the Cortana mobile app was shut down globally. History Cortana was demonstrated for the first time at the Microsoft Build developer conference in San Francisco in April 2014. It was launched as a key ingredient of Microsoft's planned "makeover" of future operating systems for Windows Phone and Windows. It is named after Cortana, ...
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Google Assistant
Google Assistant is a virtual assistant software application developed by Google that is primarily available on mobile and home automation devices. Based on artificial intelligence, Google Assistant can engage in two-way conversations, unlike the company's previous virtual assistant, Google Now. Google Assistant debuted in May 2016 as part of Google's messaging app Allo, and its voice-activated speaker Google Home. After a period of exclusivity on the Pixel and Pixel XL smartphones, it was deployed on other Android devices starting in February 2017, including third-party smartphones and Android Wear (now Wear OS), and was released as a standalone app on the iOS operating system in May 2017. Alongside the announcement of a software development kit in April 2017, Assistant has been further extended to support a large variety of devices, including cars and third-party smart home appliances. The functionality of the Assistant can also be enhanced by third-party developers. User ...
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Amazon Alexa
Amazon Alexa, also known simply as Alexa, is a virtual assistant technology largely based on a Polish speech synthesiser named Ivona, bought by Amazon in 2013. It was first used in the Amazon Echo smart speaker and the Echo Dot, Echo Studio and Amazon Tap speakers developed by Amazon Lab126. It is capable of voice interaction, music playback, making to-do lists, setting alarms, streaming podcasts, playing audiobooks, and providing weather, traffic, sports, and other real-time information, such as news. Alexa can also control several smart devices using itself as a home automation system. Users are able to extend the Alexa capabilities by installing "skills" (additional functionality developed by third-party vendors, in other settings more commonly called apps) such as weather programs and audio features. It uses automatic speech recognition, natural language processing, and other forms of weak AI to perform these tasks. Most devices with Alexa allow users to activate the devi ...
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Knowledge Base
A knowledge base (KB) is a technology used to store complex structured and unstructured information used by a computer system. The initial use of the term was in connection with expert systems, which were the first knowledge-based systems. Original usage of the term The original use of the term knowledge base was to describe one of the two sub-systems of an expert system. A knowledge-based system consists of a knowledge-base representing facts about the world and ways of reasoning about those facts to deduce new facts or highlight inconsistencies. Properties The term "knowledge-base" was coined to distinguish this form of knowledge store from the more common and widely used term ''database''. During the 1970s, virtually all large management information systems stored their data in some type of hierarchical or relational database. At this point in the history of information technology, the distinction between a database and a knowledge-base was clear and unambiguous. A databas ...
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Web Resource
A web resource is any identifiable resource (digital, physical, or abstract) present on or connected to the World Wide Web.
by Tim Berners-Lee
Resources are identified using s (URI).RFC 1738 Uniform Resource Locators (URL) In the Semantic Web, web resources and their semantic properties are described using the Resource Description ...
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Entity Linking
In natural language processing, entity linking, also referred to as named-entity linking (NEL), named-entity disambiguation (NED), named-entity recognition and disambiguation (NERD) or named-entity normalization (NEN) is the task of assigning a unique identity to entities (such as famous individuals, locations, or companies) mentioned in text. For example, given the sentence ''"Paris is the capital of France"'', the idea is to determine that ''"Paris"'' refers to the city of Paris and not to Paris Hilton or any other entity that could be referred to as ''"Paris"''. Entity linking is different from named-entity recognition (NER) in that NER identifies the occurrence of a named entity in text but it does not identify which specific entity it is (see Differences from other techniques). Introduction In entity linking, words of interest (names of persons, locations and companies) are mapped from an input text to corresponding unique entities in a target knowledge base. Words of inte ...
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