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    Semantic Network: Fundamentals and Applications
    Semantic Network: Fundamentals and Applications
    Semantic Network: Fundamentals and Applications
    Ebook98 pages1 hour

    Semantic Network: Fundamentals and Applications

    By Fouad Sabry

    ()

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    About this ebook

    What Is Semantic Network


    A knowledge base that depicts the semantic relations that exist between concepts in a network is known as a semantic network, also known as a frame network. This is a form of knowledge representation that is frequently put to use. It can be either directed or undirected and consists of vertices, which represent concepts, and edges, which reflect semantic relations between concepts, mapping or linking semantic fields. Vertices are used to represent concepts. Edges represent semantic interactions. A semantic network can be "instantiated" in a variety of different ways, such as a concept map or a graph database. Semantic triples are the typical way that typical standardized semantic networks are expressed.


    How You Will Benefit


    (I) Insights, and validations about the following topics:


    Chapter 1: Semantic Network


    Chapter 2: Knowledge Representation and Reasoning


    Chapter 3: Semantic Web


    Chapter 4: Ontology (Computer Science)


    Chapter 5: John F. Sowa


    Chapter 6: Conceptual Graph


    Chapter 7: Semantic Similarity


    Chapter 8: Semantic Research


    Chapter 9: Semantic Data Model


    Chapter 10: Knowledge Graph


    (II) Answering the public top questions about semantic network.


    (III) Real world examples for the usage of semantic network in many fields.


    (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of semantic network' technologies.


    Who This Book Is For


    Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of semantic network.

    LanguageEnglish
    PublisherOne Billion Knowledgeable
    Release dateJun 26, 2023
    Semantic Network: Fundamentals and Applications

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      Book preview

      Semantic Network - Fouad Sabry

      Chapter 1: Semantic network

      A semantic network, also known as a frame network, is a kind of knowledge base that uses a network to describe the semantic connections between ideas. This is a common method of representing information. 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. Instances of semantic networks include concept maps and graph databases. The most common representation for a semantic network is a set of triples.

      Natural language processing technologies like semantic parsing rely on semantic networks.

      Semantic networks, or directed acyclic graphs, have been used as a mnemonic device in logic for millennia. The Greek philosopher Porphyry's third-century AD commentary on Aristotle's categories is the oldest known usage of the term.

      Richard H. Richens of the Cambridge Language Research Unit created the first computer implementation of the propositional calculus using Semantic Nets in 1956 as a interlingua for machine translation of natural languages.

      Robert F. Simmons independently deployed a semantic network. Google's knowledge graph was dubbed Knowledge Graph in 2012.

      As a means of semantic social networking, the semantic link network has been the subject of systematic research. Semantic nodes, linkages between nodes, and a space defining the semantics of nodes, links, and reasoning rules on semantic links make up its fundamental paradigm. Publication of the comprehensive theory and model occurred in 2004.

      When your expertise is best represented as a web of interconnected ideas, a semantic network is the way to go.

      The majority of semantic webs have a mental foundation. Additionally, they are made up of arcs and nodes that may be arranged in a hierarchical structure. Concepts of activation propagation, inheritance, and nodes as proto-objects were introduced by semantic networks.

      Constructing semantic networks, sometimes called co-occurrence networks, requires the identification of keywords in the text, the calculation of co-occurrence frequencies, and the analysis of the networks to discover significant words and themes.

      Here is some code demonstrating a simple semantic network implemented in Lisp by use of 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))))

      Using the assoc function with the key canary, one can get all data associated with the canary category.

      WordNet, an English lexical database, is a good illustration of a semantic network. It does this by categorizing English words into synsets, defining them in broad terms, and keeping track of the many semantic relationships between synsets. These include meronymy (where A is a meronym of B if A is included in B), holonymy (where B is a holonym of A if B includes A), hyponymy (or troponymy) (where A is subordinate to B; A is a kind of B), hypernymy (where A is superordinate to B), synonymy (where A denotes the same as B), and antonymy (where A denotes the opposite of (A denotes the opposite of B).

      Comparing WordNet to other semantic networks built using Roget's Thesaurus and word association tasks has been the focus of research on WordNet's characteristics from the standpoint of network theory. From this vantage point, the three of them make up a miniature version of the outside world.

      Semantic networks, like Charles Sanders Peirce's existential graphs or John F. Sowa's related conceptual graphs, may also be used to describe logical descriptions. The expressiveness of these is on par with or even better than that of traditional first-order predicate logic. Semantic networks based on these representations are superior than WordNet and similar lexical and browsing networks because they may be utilized for trustworthy automated logic deduction. During processing, certain automated reasoners take use of the networks' graph-theoretic properties.

      Gellish models are another kind of semantic network. The Gellish English dictionary provides a formal definition of the language as a network of associations between ideas and their respective names. Similar to how Gellish Dutch is a formal subset of Dutch, Gellish English is a formal subset of natural English, although the same ideas may be expressed in many other languages. Additional Gellish networks are made out of Gellish-language-expressed knowledge and information models. A Gellish network is a set of interconnections between entities with just two possible states. Each link in the network represents an instance of some fact, and these facts are categorized by the different connection types. The Gellish language dictionary defines each relation type as a separate term. Concepts and things that may be placed into a certain concept's category are the two types of linked things. In order to define ideas, a Gellish Dictionary is built up using definition models (definition networks). In order to have a computer understand a Gellish network, it must be recorded in a Gellish database.

      SciCrunch is a database of scientific articles curated by a group of scientists. Research Resource IDentifiers (RRIDs) are unique identifiers for research resources including programs, lab equipment, and other resources.

      Ologs is another kind of semantic network that borrows heavily from category theory. Each of them is an object representing some collection of objects, and the connections between them are morphisms. In addition, commutative diagrams are required to impose restrictions on the semantics.

      Semantic networks are often used as a synonym for co-occurrence networks in the social sciences.

      Several natural language processing (NLP) applications make use of the Semantic Network Processing System (SNePS) developed by Stuart C. Shapiro, which is particularly well-suited for the semantic representation of natural language expressions.

      Plagiarism detection and other niche information retrieval tasks benefit from the usage of semantic networks. They aid in matching word meanings without regard to lexical context by providing data on hierarchical relations that may be compressed using semantics.

      Google's 2012 Knowledge Graph proposal is a search engine implementation

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