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A semantic data model in software engineering has various meanings: It is a conceptual data model in which semantic information is included. This means that the model describes the meaning of its instances. Such a semantic data model is an abstraction that defines how the stored symbols (the instance data) relate to the real world. [1]
The semantic link network was systematically studied as a semantic social networking method. Its basic model consists of semantic nodes, semantic links between nodes, and a semantic space that defines the semantics of nodes and links and reasoning rules on semantic links. The systematic theory and model was published in 2004. [20]
Semantic representations (SemR) in meaning–text theory consist primarily of a web-like semantic structure (SemS) which combines with other semantic-level structures (most notably the semantic-communicative structure [SemCommS], [2] which represents what is commonly referred to as "information structure" in other frameworks).
In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]
Conceptual semantics is a framework for semantic analysis developed mainly by Ray Jackendoff in 1976. Its aim is to provide a characterization of the conceptual elements by which a person understands words and sentences, and thus to provide an explanatory semantic representation (title of a Jackendoff 1976 paper).
S. Schema crosswalk; Script theory; Sears Subject Headings; Semantic analysis (knowledge representation) Semantic data model; Semantic interoperability; Semantic knowledge management
In representation learning, knowledge graph embedding (KGE), also referred to as knowledge representation learning (KRL), or multi-relation learning, [1] is a machine learning task of learning a low-dimensional representation of a knowledge graph's entities and relations while preserving their semantic meaning.
Classification technology originally developed for Frame languages is a key enabler of the Semantic Web. [20] [21] The "neats vs. scruffies" divide also emerged in Semantic Web research, culminating in the creation of the Linking Open Data community—their focus was on exposing data on the Web rather than modeling.