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Natural language processing (NLP) is a 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.
A semantic network, or frame network is a knowledge base that represents semantic relations between concepts in a network. This is often used as a form of knowledge representation. It is a directed or undirected graph consisting of vertices, which represent concepts, and edges, which represent semantic relations between concepts, mapping or connecting semantic fields. A semantic network may be instantiated as, for example, a graph database or a concept map.
The Semantic Web is an extension of the World Wide Web through standards set by the World Wide Web Consortium (W3C). The goal of the Semantic Web is to make Internet data machine-readable. To enable the encoding of semantics with the data, technologies such as Resource Description Framework (RDF) and Web Ontology Language (OWL) are used. These technologies are used to formally represent metadata. For example, ontology can describe concepts, relationships between entities, and categories of things. These embedded semantics offer significant advantages such as reasoning over data and operating with heterogeneous data sources.
Computational semiotics is an interdisciplinary field that applies, conducts, and draws on research in logic, mathematics, the theory and practice of computation, formal and natural language studies, the cognitive sciences generally, and semiotics proper. The term encompasses both the application of semiotics to computer hardware and software design and, conversely, the use of computation for performing semiotic analysis. The former focuses on what semiotics can bring to computation; the latter on what computation can bring to semiotics.
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".
The semantic gap characterizes the difference between two descriptions of an object by different linguistic representations, for instance languages or symbols. According to Andreas Hein, the semantic gap can be defined as "the difference in meaning between constructs formed within different representation systems". In computer science, the concept is relevant whenever ordinary human activities, observations, and tasks are transferred into a computational representation.
The ultimate goal of semantic technology is to help machines understand data. To enable the encoding of semantics with the data, well-known technologies are RDF and OWL. These technologies formally represent the meaning involved in information. For example, ontology can describe concepts, relationships between things, and categories of things. These embedded semantics with the data offer significant advantages such as reasoning over data and dealing with heterogeneous data sources.
Computational semantics is the study of how to automate the process of constructing and reasoning with meaning representations of natural language expressions. It consequently plays an important role in natural language processing and computational linguistics.
SemEval is an ongoing series of evaluations of computational semantic analysis systems; it evolved from the Senseval word sense evaluation series. The evaluations are intended to explore the nature of meaning in language. While meaning is intuitive to humans, transferring those intuitions to computational analysis has proved elusive.
The following outline is provided as an overview of and topical guide to natural language processing:
Semantic queries allow for queries and analytics of associative and contextual nature. Semantic queries enable the retrieval of both explicitly and implicitly derived information based on syntactic, semantic and structural information contained in data. They are designed to deliver precise results or to answer more fuzzy and wide open questions through pattern matching and digital reasoning.
This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence, its sub-disciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision.
Semantic parsing is the task of converting a natural language utterance to a logical form: a machine-understandable representation of its meaning. Semantic parsing can thus be understood as extracting the precise meaning of an utterance. Applications of semantic parsing include machine translation, question answering, ontology induction, automated reasoning, and code generation. The phrase was first used in the 1970s by Yorick Wilks as the basis for machine translation programs working with only semantic representations.
A semantic decomposition is an algorithm that breaks down the meanings of phrases or concepts into less complex concepts. The result of a semantic decomposition is a representation of meaning. This representation can be used for tasks, such as those related to artificial intelligence or machine learning. Semantic decomposition is common in natural language processing applications.
Paola Velardi is a Full Professor of computer science at Sapienza University in Rome, Italy. She is an Italian scientist born in Rome, on April 24, 1955. Her research encompasses natural language processing, machine learning, business intelligence and semantic web, web information extraction in particular. Velardi is one of the hundred female scientists included in the database "100esperte.it". This online, open database champions the recognition of top-rated female scientists in the Science, Technology, Engineering and Mathematics (STEM) area.
Jiebo Luo is a Chinese-American computer scientist, Professor of Computer Science at the University of Rochester and Distinguished Researcher with Goergen Institute for Data Science. He is interested in artificial intelligence, data science and computer vision.
A knowledge graph is a knowledge base that uses a graph-structured data model or topology to integrate data. Knowledge graphs are often used to store interlinked descriptions of entities – objects, events, situations or abstract concepts – with free-form semantics.
References
- ↑ Zhao, Jun; Harmelen, Frank; Tang, Jie; Han, Xianpei; Wang, Quan; Li, Xianyong, eds. (2019). Knowledge Graph and Semantic Computing. Knowledge Computing and Language Understanding: Third China Conference, CCKS 2018, Tianjin, China, August 14–17, 2018, Revised Selected Papers. Communications in Computer and Information Science. Springer Singapore. ISBN 978-981-13-3145-9.
