What is Semantic Network?
Semantic networks are knowledge structures that map the connections between ideas and show how they are connected to one another. In order to mine data, link ideas, and highlight connections, semantic networks use AI programming. Because of this feature, businesses may improve their customer service by facilitating enhanced product searches for their clientele. It may also improve the efficiency with which advertising and sales teams target potential customers.
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Using a network to describe the semantic connections between ideas, a semantic network (sometimes called a “frame network”) may be used as a knowledge repository. It’s a common way of representing information and understanding. It is a mapping or connection of semantic fields, represented as a directed or undirected graph with concepts as vertices and semantic interactions between concepts as edges. Semantic networks may take several forms, including idea maps and graph databases. Semantic triples are a common representation for conventional semantic networks. Several NLP tasks, including semantic parsing and word-sense disambiguation, make use of semantic networks. Additionally, semantic networks may be used to show biases in news coverage, to map an entire study area, or to simply evaluate and identify the key themes, elements, and subjects in very long texts (such as social media posts).
Semantic networks can improve several sectors, including sales, marketing, retail, and healthcare, but typically this domain operates in the background of company operations and doesn’t touch employees’ everyday lives. Semantic networking is being used by new technologies like Microsoft’s Office Graph to link similar ideas in the workplace. Emails may be mined for meeting materials, workers with relevant expertise can be contacted, and external data can be compiled.
History
Semantic networks (also known as directed acyclic graphs) have been used as a mnemonic device in logic for millennia. The term was first used in writing in the third century AD, in a commentary by the Greek philosopher Porphyry on Aristotle’s categories.
Although the significance of Richard H. Richens’s work and the Cambridge Language Research Unit (CLRU) was not fully appreciated until much later, he was the first to implement “Semantic Nets” for the propositional calculus for computers in 1956 as a “interlingua” for machine translation of natural languages. After being inspired by Victor Yngve’s presentation, Robert F. Simmons and Sheldon Klein separately developed an implementation of semantic networks based on the first order predicate calculus. The “Yngve, the first president of the Association for Computational Linguistics, began this line of inquiry in 1960 when he presented a description of algorithms for employing a phrase structure grammar to construct syntactically well-formed nonsensical sentences. Around 1962–1964, Sheldon Klein and I were intrigued by the idea and broadened it into a means of influencing the meaning of output by attending to the contextual meaning of words.” In the early 1960s, they worked on the SYNTHEX project with other researchers, the most notable of whom were M. Ross Quillian and his colleagues at System Development Corporation. Most contemporary iterations of the phrase “semantic network” trace their origins to these SDC articles. Allan M. Collins and Quillian produced further influential works (e.g., Collins and Quillian; Collins and Loftus Quillian). Hermann Helbig finished describing MultiNet in 2006.
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Basics of Semantic Networks
- Use a semantic network when your information is best represented as a web of interconnected ideas.
- The majority of semantic webs are founded on some kind of mental process. Additionally, they are made up of arcs and nodes that may be arranged in a hierarchical structure. The concepts of activation propagation, inheritance, and nodes as proto-objects were all introduced by semantic networks.
- Semantic networks, sometimes called co-occurrence networks, may be built by extracting keywords from a text, computing the frequencies of co-occurrences, and evaluating the resulting networks to discover significant terms and thematic clusters.
Software Tools
Extensive semantic networks, such as Stuart C. Shapiro’s Semantic Network Processing System (SNePS) or Hermann Helbig’s MultiNet paradigm, are well-suited for the semantic representation of natural language expressions and are used in a number of NLP applications. Plagiarism detection is only one of the many information-finding applications that benefit from the usage of semantic networks. They aid the system in matching word meanings regardless of the sets of words employed by providing information on hierarchical relations for the purpose of using semantic compression to decrease linguistic variety.
Google’s 2012 Knowledge Graph proposal is a search engine implementation of a semantic network.
The advantages of modelling multi-relational data, such as semantic networks, in low-dimensional spaces through embedding lie in their ability to describe entity connections and to extract relations from media like text. Bayesian clustering frameworks, energy-based frameworks, and, more recently, TransE are just a few of the various methods that may be used to learn such embeddings (NIPS 2013). Examples of uses for embedded knowledge base data include social network analysis and relationship extraction.
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