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Asemantic network, orframe network is aknowledge base that representssemantic relations betweenconcepts in a network. This is often used as a form ofknowledge representation. It is adirected orundirected graph consisting ofvertices, which representconcepts, andedges, which representsemantic relations between concepts,[1] mapping or connectingsemantic fields. A semantic network may be instantiated as, for example, agraph database or aconcept map. Typical standardized semantic networks are expressed assemantic triples.
Semantic networks are used innatural language processing applications such assemantic parsing[2] andword-sense disambiguation.[3] Semantic networks can also be used as a method to analyze large texts and identify the main themes and topics (e.g., of social media posts), to reveal biases (e.g., in news coverage), or even to map an entire research field.
Examples of the use of semantic networks in logic, directed acyclic graphs as a mnemonic tool, dates back centuries. The earliest documented use being the Greek philosopher Porphyry's commentary on Aristotle's categories in the third century AD.
In computing history, "Semantic Nets" for the propositional calculus were first implemented forcomputers byRichard H. Richens of the Cambridge Language Research Unit in 1956 as an "interlingua" formachine translation ofnatural languages.[4] Although the importance of this work and the CLRU was only belatedly realized.
Semantic networks were also independently implemented by Robert F. Simmons[5] and Sheldon Klein, using the first order predicate calculus as a base, after being inspired by a demonstration of Victor Yngve. The "line of research was originated by the first President of the Association [Association for Computational Linguistics], Victor Yngve, who in 1960 had published descriptions of algorithms for using a phrase structure grammar to generate syntactically well-formed nonsense sentences. Sheldon Klein and I about 1962-1964 were fascinated by the technique and generalized it to a method for controlling the sense of what was generated by respecting the semantic dependencies of words as they occurred in text."[6] Other researchers, most notablyM. Ross Quillian[7] and others atSystem Development Corporation helped contribute to their work in the early 1960s as part of the SYNTHEX project. It's from these publications at SDC that most modern derivatives of the term "semantic network" cite as their background. Later prominent works were done byAllan M. Collins and Quillian (e.g., Collins and Quillian;[8][9] Collins and Loftus[10] Quillian[11][12][13][14]). Still later in 2006, Hermann Helbig fully describedMultiNet.[15]
In the late 1980s, twoNetherlands universities,Groningen andTwente, jointly began a project calledKnowledge Graphs, which are semantic networks but with the added constraint that edges are restricted to be from a limited set of possible relations, to facilitate algebras on the graph.[16] In the subsequent decades, the distinction between semantic networks andknowledge graphs was blurred.[17][18] In 2012,Google gave their knowledge graph the nameKnowledge Graph.The Semantic Link Network was systematically studied as a social semantics 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.[19] This research direction can trace to the definition of inheritance rules for efficient model retrieval in 1998[20] and the Active Document Framework ADF.[21] Since 2003, research has developed toward social semantic networking.[22] This work is a systematic innovation at the age of the World Wide Web and global social networking rather than an application or simple extension of the Semantic Net (Network). Its purpose and scope are different from that of the Semantic Net (or network).[23] The rules for reasoning and evolution and automatic discovery of implicit links play an important role in the Semantic Link Network.[24][25] Recently it has been developed to support Cyber-Physical-Social Intelligence.[26] It was used for creating a general summarization method.[27] The self-organised Semantic Link Network was integrated with a multi-dimensional category space to form a semantic space to support advanced applications with multi-dimensional abstractions and self-organised semantic links[28][29] It has been verified that Semantic Link Network play an important role in understanding and representation through text summarisation applications.[30][31] Semantic Link Network has been extended from cyberspace to cyber-physical-social space. Competition relation and symbiosis relation as well as their roles in evolving society were studied in the emerging topic: Cyber-Physical-Social Intelligence[32]
More specialized forms of semantic networks has been created for specific use. For example, in 2008, Fawsy Bendeck's PhD thesis formalized theSemantic Similarity Network (SSN) that contains specialized relationships and propagation algorithms to simplify thesemantic similarity representation and calculations.[33]
A semantic network is used when one has knowledge that is best understood as a set of concepts that are related to one another.
Most semantic networks are cognitively based. They also consist of arcs and nodes which can be organized into a taxonomic hierarchy. Semantic networks contributed ideas ofspreading activation,inheritance, and nodes as proto-objects.
The following code shows an example of a semantic network in theLisp programming language using anassociation list.
(setq*database*'((canary(is-abird)(coloryellow)(sizesmall))(penguin(is-abird)(movementswim))(bird(is-avertebrate)(has-partwings)(reproductionegg-laying))))
To extract all the information about the "canary" type, one would use theassoc function with a key of "canary".[34]
An example of a semantic network isWordNet, alexical database ofEnglish. It groups English words into sets of synonyms calledsynsets, provides short, general definitions, and records the various semantic relations between these synonym sets. Some of the most common semantic relations defined aremeronymy (A is a meronym of B if A is part of B),holonymy (B is a holonym of A if B contains A),hyponymy (ortroponymy) (A is subordinate of B; A is kind of B),hypernymy (A is superordinate of B),synonymy (A denotes the same as B) andantonymy (A denotes the opposite of B).
WordNet properties have been studied from anetwork theory perspective and compared to other semantic networks created fromRoget's Thesaurus andword association tasks. From this perspective the three of them are asmall world structure.[35]
It is also possible to represent logical descriptions using semantic networks such as theexistential graphs ofCharles Sanders Peirce or the relatedconceptual graphs ofJohn F. Sowa.[1] These have expressive power equal to or exceeding standardfirst-order predicate logic. Unlike WordNet or other lexical or browsing networks, semantic networks using these representations can be used for reliable automated logical deduction. Some automated reasoners exploit the graph-theoretic features of the networks during processing.
Other examples of semantic networks are Gellish models.Gellish English with itsGellish English dictionary, is aformal language that is defined as a network of relations between concepts and names of concepts. Gellish English is a formal subset of natural English, just as Gellish Dutch is a formal subset of Dutch, whereas multiple languages share the same concepts. Other Gellish networks consist of knowledge models and information models that are expressed in the Gellish language. A Gellish network is a network of (binary) relations between things. Each relation in the network is an expression of a fact that is classified by a relation type. Each relation type itself is a concept that is defined in the Gellish language dictionary. Each related thing is either a concept or an individual thing that is classified by a concept. The definitions of concepts are created in the form of definition models (definition networks) that together form a Gellish Dictionary. A Gellish network can be documented in a Gellish database and is computer interpretable.
SciCrunch is a collaboratively edited knowledge base for scientific resources. It provides unambiguous identifiers (Research Resource IDentifiers or RRIDs) for software, lab tools etc. and it also provides options to create links between RRIDs and from communities.
Another example of semantic networks, based oncategory theory, isologs. Here each type is an object, representing a set of things, and each arrow is a morphism, representing a function.Commutative diagrams also are prescribed to constrain the semantics.
In the social sciences people sometimes use the term semantic network to refer toco-occurrence networks.[36]
There are also elaborate types of semantic networks connected with corresponding sets of software tools used forlexicalknowledge engineering, like the Semantic Network Processing System (SNePS) of Stuart C. Shapiro[37] or theMultiNet paradigm of Hermann Helbig,[38] especially suited for the semantic representation of natural language expressions and used in severalNLP applications.
Semantic networks are used in specialized information retrieval tasks, such asplagiarism detection. They provide information on hierarchical relations in order to employsemantic compression to reduce language diversity and enable the system to match word meanings, independently from sets of words used.
The Knowledge Graph proposed by Google in 2012 is actually an application of semantic network in search engine.
Modeling multi-relational data like semantic networks in low-dimensional spaces through forms ofembedding has benefits in expressing entity relationships as well as extracting relations from mediums like text. There are many approaches to learning these embeddings, notably using Bayesian clustering frameworks or energy-based frameworks, and more recently, TransE[39] (NIPS 2013). Applications of embedding knowledge base data includeSocial network analysis andRelationship extraction.
The first semantic network for computers was Nude, created by R. H. Richens of the Cambridge Language Research Unit in 1956 as an interlingua for machine translation of natural languages.
usage [of the term 'knowledge graph'] has evolved
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