Semantic network
A semantic network may be instantiated as, for example, a graph database or a concept map.
[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] 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.
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.
[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.
[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.
Semantic networks contributed to the ideas of spreading activation, inheritance, and nodes as proto-objects.
[35] In the field of linguistics, semantic networks represent how the human mind handles associated concepts.
These thematic relationships are common within semantic networks and are notable results in free association tests.
The effect of priming on a semantic network linking can be seen through the speed of the reaction time to the word.
The following code shows an example of a semantic network in the Lisp programming language using an association list.
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).
[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.
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.
Applications of embedding knowledge base data include Social network analysis and Relationship extraction.
