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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,^[1] mapping or connecting semantic fields. A semantic network may be instantiated as, for example, a graph database or a concept map. Typical standardized semantic networks are expressed as semantic triples.

Semantic networks are used in neurolinguistics and natural language processing applications such as semantic parsing^[2] and word-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.^[4]

History

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 for computers by Richard H. Richens of the Cambridge Language Research Unit in 1956 as an "interlingua" for machine translation of natural languages,^[5] although the importance of this work and the Cambridge Language Research Unit was only belatedly realized.

Semantic networks were also independently implemented by Robert F. Simmons^[6] 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 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."^[7] Other researchers, most notably M. Ross Quillian^[8] and others at System Development Corporation helped contribute to their work in the early 1960s as part of the SYNTHEX project. It's these publications at System Development Corporation that most modern derivatives of the term "semantic network" cite as their background. Later prominent works were done by Allan M. Collins and Quillian (e.g., Collins and Quillian;^[9][10] Collins and Loftus^[11] Quillian^{[12][13][14][15]}). Still later in 2006, Hermann Helbig fully described MultiNet.^[16]

In the late 1980s, two universities in the Netherlands, Groningen and Twente, jointly began a project called Knowledge 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.^[17] In the subsequent decades, the distinction between semantic networks and knowledge graphs was blurred.^[18][19] In 2012, Google gave their knowledge graph the name Knowledge Graph.

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] This research direction can trace to the definition of inheritance rules for efficient model retrieval in 1998^[21] and the Active Document Framework ADF.^[22] Since 2003, research has developed toward social semantic networking.^[23] 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).^[24] The rules for reasoning and evolution and automatic discovery of implicit links play an important role in the Semantic Link Network.^[25][26] Recently it has been developed to support Cyber-Physical-Social Intelligence.^[27] It was used for creating a general summarization method.^[28] 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^[29][30] It has been verified that Semantic Link Network play an important role in understanding and representation through text summarisation applications.^[31][32] 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^[33]

More specialized forms of semantic networks has been created for specific use. For example, in 2008, Fawsy Bendeck's PhD thesis formalized the Semantic Similarity Network (SSN) that contains specialized relationships and propagation algorithms to simplify the semantic similarity representation and calculations.^[34]

Basics of semantic networks

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 consist of arcs (spokes) and nodes (hubs) which can be organized into a taxonomic hierarchy. Different semantic networks can also be connected by bridge nodes. Semantic networks contributed to the ideas of spreading activation, inheritance, and nodes as proto-objects.

One process of constructing semantic networks, known also as co-occurrence networks, includes identifying keywords in the text, calculating the frequencies of co-occurrences, and analyzing the networks to find central words and clusters of themes in the network.^[35]

In linguistics

In the field of linguistics, semantic networks represent how the human mind handles associated concepts. Typically, concepts in a semantic network can have one of two different relationships: either semantic or associative.

If semantic in relation, the two concepts are linked by any of the following semantic relationships: synonymy, antonymy, hypernymy, hyponymy, holonymy, meronymy, or metonymy, or polysemy. These are not the only semantic relationships, but some of the most common.

If associative in relation, the two concepts are linked based on their frequency to occur together. These associations are accidental, meaning that nothing about their individual meanings requires them to be associated with one another, only that they typically are. Examples of this would be pig and farm, pig and trough, or pig and mud. While nothing about the meaning of pig forces it to be associated with farms, as pigs can be wild, the fact that pigs are so frequently found on farms creates an accidental associated relationship. These thematic relationships are common within semantic networks and are notable results in free association tests.

As the initial word is given, activation of the most closely related concepts begin, spreading outward to the lesser associated concepts. An example of this would be the initial word pig prompting mammal, then animal, and then breathes. This example shows that taxonomic relationships are inherent within semantic networks. The most closely related concepts typically share semantic features, which are determinants of semantic similarity scores. Words with higher similarity scores are more closely related, thus have higher probability of being a close word in the semantic network.

These relationships can be suggested into the brain through priming, where previous examples of the same relationship are shown before the target word is shown. The effect of priming on a semantic network linking can be seen through the speed of the reaction time to the word. Priming can help to reveal the structure of a semantic network and which words are most closely associated with the original word.

Disruption of a semantic network can lead to a semantic deficit, not the same as semantic dementia.

In the Brain

There exists physical manifestation of semantic relationships in the brain as well. Category-specific semantic circuits show that words belonging to different categories are processed in circuits differently located throughout the brain. For example, the semantic circuits for a word associated with the face or mouth (such as lick) is located in a different place of the brain than a word associated with the leg or foot (such as kick). This is a primary result of a 2013 study published by Friedemann Pulvermüller. These semantic circuits are directly tied to their sensorimotor areas of the brain. This is known as embodied semantics, a subtopic of embodied language processing.

If brain damage occurs, the normal processing of semantic networks could be disrupted, leading to preference into what kind of relationships dominate the semantic network in the mind.

Examples

In Lisp

The following code shows an example of a semantic network in the Lisp programming language using an association list.

(setq *database*
'((canary  (is-a bird)
           (color yellow)
           (size small))
  (penguin (is-a bird)
           (movement swim))
  (bird    (is-a vertebrate)
           (has-part wings)
           (reproduction egg-laying))))

To extract all the information about the "canary" type, one would use the assoc function with a key of "canary".^[36]

WordNet

An example of a semantic network is WordNet, a lexical database of English. It groups English words into sets of synonyms called synsets, provides short, general definitions, and records the various semantic relations between these synonym sets. Some of the most common semantic relations defined are meronymy (A is a meronym of B if A is part of B), holonymy (B is a holonym of A if B contains A), hyponymy (or troponymy) (A is subordinate of B; A is kind of B), hypernymy (A is superordinate of B), synonymy (A denotes the same as B) and antonymy (A denotes the opposite of B).

WordNet properties have been studied from a network theory perspective and compared to other semantic networks created from Roget's Thesaurus and word association tasks. From this perspective the three of them are a small world structure.^[37]

Other examples

It is also possible to represent logical descriptions using semantic networks such as the existential graphs of Charles Sanders Peirce or the related conceptual graphs of John F. Sowa.^[1] These have expressive power equal to or exceeding standard first-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 its Gellish English dictionary, is a formal 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 on category theory, is ologs. 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 to co-occurrence networks.^[38][39] The basic idea is that words that co-occur in a unit of text, e.g. a sentence, are semantically related to one another. Ties based on co-occurrence can then be used to construct semantic networks. This process includes identifying keywords in the text, constructing co-occurrence networks, and analyzing the networks to find central words and clusters of themes in the network. It is a particularly useful method to analyze large text and big data.^[40]

Software tools

There are also elaborate types of semantic networks connected with corresponding sets of software tools used for lexical knowledge engineering, like the Semantic Network Processing System (SNePS) of Stuart C. Shapiro^[41] or the MultiNet paradigm of Hermann Helbig,^[42] especially suited for the semantic representation of natural language expressions and used in several NLP applications.

Semantic networks are used in specialized information retrieval tasks, such as plagiarism detection. They provide information on hierarchical relations in order to employ semantic 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 of embedding 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^[43] (NIPS 2013). Applications of embedding knowledge base data include Social network analysis and Relationship extraction.