Semantic network
It has been suggested that Semantic computing be merged into this article. (Discuss) Proposed since April 2024. |
Part of a series on | ||||
Network science | ||||
---|---|---|---|---|
Network types | ||||
|
||||
Graphs | ||||
|
||||
Models | ||||
|
||||
| ||||
Information mapping |
---|
Topics and fields |
Node–link approaches |
|
See also |
A semantic network, or frame network is a
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
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
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:
. 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
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 properties have been studied from a
Other examples
It is also possible to represent logical descriptions using semantic networks such as the
Other examples of semantic networks are
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
In the social sciences people sometimes use the term semantic network to refer to
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
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.
See also
- Abstract semantic graph
- Chunking (psychology)
- CmapTools
- Concept map
- Formal semantics (natural language)
- Network diagram
- Ontology (information science)
- Repertory grid
- Semantic lexicon
- Semantic similarity network
- Semantic neural network
- SemEval – an ongoing series of evaluations of computational semantic analysis systems
- Sparse distributed memory
- Taxonomy (general)
- Unified Medical Language System (UMLS)
- Word-sense disambiguation (WSD)
- Resource Description Framework
Other examples
- Cognition Network Technology
- Lexipedia
- OpenCog
- Open Mind Common Sense (OMCS)
- Schema.org
- SNOMED CT
- Universal Networking Language (UNL)
- Wikidata
- Freebase
References
- ^ a b John F. Sowa (1987). "Semantic Networks". In Stuart C Shapiro (ed.). Encyclopedia of Artificial Intelligence. Archived from the original on 8 October 2018. Retrieved 29 April 2008.
- ^ Poon, Hoifung, and Pedro Domingos. "Unsupervised semantic parsing Archived 7 February 2019 at the Wayback Machine." Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1-Volume 1. Association for Computational Linguistics, 2009.
- ^ Sussna, Michael. "Word sense disambiguation for free-text indexing using a massive semantic network Archived 21 September 2021 at the Wayback Machine." Proceedings of the second international conference on Information and knowledge management. ACM, 1993.
- ISBN 9780367636524. Archivedfrom the original on 5 December 2021. Retrieved 5 December 2021.
- OCLC 26391254.
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.
- ^ Robert F. Simmons (1963). "Synthetic language behavior". Data Processing Management. 5 (12): 11–18.
- ^ Simmons, "Themes From 1972" Archived 1 September 2019 at the Wayback Machine, ACL Anthology, 1982
- ^ Quillian, R. A notation for representing conceptual information: An application to semantics and mechanical English para- phrasing. SP-1395, System Development Corporation, Santa Monica, 1963.
- .
- .
- S2CID 14217893.
- ^ Quillian, M. R. (1967). Word concepts: A theory and simulation of some basic semantic capabilities. Behavioral Science, 12(5), 410–430.
- ^ Quillian, M. R. (1968). Semantic memory. Semantic information processing, 227–270.
- S2CID 15304609.
- ^ Quillian, R. Semantic Memory. Unpublished doctoral dissertation, Carnegie Institute of Technology, 1966.
- ISBN 978-3540244615. Archived(PDF) from the original on 30 August 2017. Retrieved 19 March 2018.
- ISBN 978-0444883940. Archived from the original(PDF) on 11 August 2016. Note from the cited chapter: The author's name P. James is a pseudonym for the group of researchers that took part in or are still taking part in the project "Knowledge Graphs", that was started as a joint project of the universities of Groningen and Twente in the Netherlands. Alstein, de By, Edens, and Miltenburg were students that contributed to the project. In the course of the project, members of the project group have been R.R. Bakker, H. van den Berg, C. Hoede, M.A.W. Houtsma, H.J. Smit, F.N. Stokman, P.H. de Vries, and M. Willems.
- ISBN 9783319250076.
- ^ McCusker, James P.; Chastain, Katherine (April 2016). "What is a Knowledge Graph?". authorea.com. Archived from the original on 17 June 2021. Retrieved 15 June 2016.
usage [of the term 'knowledge graph'] has evolved
- ^ H. Zhuge, Knowledge Grid, World Scientific Publishing Co. 2004.
- ^ H. Zhuge, Inheritance rules for flexible model retrieval. Decision Support Systems 22(4)(1998)379–390
- ^ H. Zhuge, Active e-document framework ADF: model and tool. Information & Management 41(1): 87–97 (2003)
- ^ H.Zhuge and L.Zheng, Ranking Semantic-linked Network, WWW 2003
- ^ H.Zhuge, The Semantic Link Network, in The Knowledge Grid: Toward Cyber-Physical Society, World Scientific Publishing Co. 2012.
- ^ H. Zhuge, L. Zheng, N. Zhang and X. Li, An automatic semantic relationships discovery approach. WWW 2004: 278–279.
- ^ H. Zhuge, Communities and Emerging Semantics in Semantic Link Network: Discovery and Learning, IEEE Transactions on Knowledge and Data Engineering, 21(6)(2009)785–799.
- ^ H.Zhuge, Semantic linking through spaces for cyber-physical-socio intelligence: A methodology, Artificial Intelligence, 175(2011)988–1019.
- ^ H. Zhuge, Multi-Dimensional Summarization in Cyber-Physical Society, Morgan Kaufmann, 2016.
- ^ H. Zhuge, The Web Resource Space Model, Springer, 2008.
- ^ H.Zhuge and Y.Xing, Probabilistic Resource Space Model for Managing Resources in Cyber-Physical Society, IEEE Transactions on Service Computing, 5(3)(2012)404–421.
- .
- .
- ^ H. Zhuge, Cyber-Physical-Social Intelligence on Human-Machine-Nature Symbiosis, Springer, 2020.
- OCLC 501314022.
- ISBN 9780367636524. Archivedfrom the original on 5 December 2021. Retrieved 5 December 2021.
- ^ Swigger, Kathleen. "Semantic.ppt". Archived from the original on 10 May 2013. Retrieved 23 March 2011.
- S2CID 6000627.
- ^ Wouter Van Atteveldt (2008). Semantic Network Analysis: Techniques for Extracting, Representing, and Querying Media Content (PDF). BookSurge Publishing. Archived (PDF) from the original on 28 November 2021. Retrieved 28 November 2021.
- from the original on 5 December 2021. Retrieved 5 December 2021.
- ISBN 9780367636524. Archivedfrom the original on 5 December 2021. Retrieved 5 December 2021.
- ^ "Stuart C. Shapiro". Archived from the original on 27 August 2006. Retrieved 29 August 2006.
- ^ "Hermann Helbig". Archived from the original on 4 May 2006. Retrieved 14 March 2006.
- ^ Bordes, Antoine; Usunier, Nicolas; Garcia-Duran, Alberto; Weston, Jason; Yakhnenko, Oksana (2013), Burges, C. J. C.; Bottou, L.; Welling, M.; Ghahramani, Z. (eds.), "Translating Embeddings for Modeling Multi-relational Data" (PDF), Advances in Neural Information Processing Systems 26, Curran Associates, Inc., pp. 2787–2795, archived (PDF) from the original on 20 December 2018, retrieved 29 November 2018
Further reading
- Allen, J. and A. Frisch (1982). "What's in a Semantic Network". In: Proceedings of the 20th. annual meeting of ACL, Toronto, pp. 19–27.
- John F. Sowa, Alexander Borgida (1991). Principles of Semantic Networks: Explorations in the Representation of Knowledge.
- Segev, E. (Ed.) (2022). Semantic Network Analysis in Social Sciences. New York: Routledge.
External links
- "Semantic Networks" by John F. Sowa
- "Semantic Link Network" by Hai Zhuge