Semantic Graph Database

A Semantic Graph Database is a knowledge base that is a graph dataset.

• AKA: Concept Network, Semantic Network, Frame Network.
• Context:
  • It can be represented as an edge-weighted graphs where the nodes are concepts and each edge has an associated weight that pertains the semantic similarity between pairs of nodes.
  • It can be created by a Semantic Network Creation Task.
  • It can range from being a Shallow Semantic Similarity Neural Network to being a Deep Semantic Similarity Neural Network.
  • It can range from being an Informal Semantic Network to being a Knowledge Graph.
  • It can be part of Knowledge-Representation System.
  • It can include:
    • a Meronymy Relation.
    • a Subsumption Relation.
    • 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)
    • Antonymy (A denotes the opposite of B)
• Example(s):
  • a Citation Network,
  • a Co-citation Network,
  • a Conceptual Graph,
  • a Definitional Network,
  • an Event Graph,
  • an Executable Network,
  • an Implicational Network,
  • a Learning Network,
  • a Lightweight Ontology,
  • The Semantic Web Network,
  • a Social Network, such as a co-authorship network.
  • WordNet.
  • …
• Counter-Example(s):
  • The Web, because not all relationships are semantic relationships.
  • a Parse Tree, because it is composed of syntactic relations.
  • a Neural Network.
• See: Knowledge Graph, Concept Mapping, Topic Map, Semantic Parsing, Word Sense Disambiguation System, Neural Translation System, Natural Language Processing System, Web Ontology Language.

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References

2019

• (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Semantic_network Retrieved:2019-10-20.
  • 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, mapping or connecting semantic fields.

    Typical standardized semantic networks are expressed as semantic triples.

    Semantic networks are used in natural language processing applications such as semantic parsing and word-sense disambiguation.

2015

• (Sowa, 2015) ⇒ John F. Sowa (2015). “Semantic Networks". Available online at: http://www.jfsowa.com/pubs/semnet.htm Last Updated: 2015-03-02.
  • QUOTE: A semantic network or net is a graph structure for representing knowledge in patterns of interconnected nodes and arcs. Computer implementations of semantic networks were first developed for artificial intelligence and machine translation, but earlier versions have long been used in philosophy, psychology, and linguistics. The Giant Global Graph of the Semantic Web is a large semantic network (Berners-Lee et al. 2001; Hendler & van Harmelen 2008).

    What is common to all semantic networks is a declarative graphic representation that can be used to represent knowledge and support automated systems for reasoning about the knowledge. Some versions are highly informal, but others are formally defined systems of logic. Following are six of the most common kinds of semantic networks:

    • Definitional networks emphasize the subtype or is-a relation between a concept type and a newly defined subtype. The resulting network, also called a generalization or subsumption hierarchy, supports the rule of inheritance for copying properties defined for a supertype to all of its subtypes. Since definitions are true by definition, the information in these networks is often assumed to be necessarily true.
    • Assertional networks are designed to assert propositions. Unlike definitional networks, the information in an assertional network is assumed to be contingently true, unless it is explicitly marked with a modal operator. Some assertional networks have been proposed as models of the conceptual structures underlying natural language semantics.
    • Implicational networks use implication as the primary relation for connecting nodes. They may be used to represent patterns of beliefs, causality, or inferences.
    • Executable networks include some mechanism, such as marker passing or attached procedures, which can perform inferences, pass messages, or search for patterns and associations.
    • Learning networks build or extend their representations by acquiring knowledge from examples. The new knowledge may change the old network by adding and deleting nodes and arcs or by modifying numerical values, called weights, associated with the nodes and arcs.
    • Hybrid networks combine two or more of the previous techniques, either in a single network or in separate, but closely interacting networks.

2013

• (Guzzi et al.,2013) ⇒ Pietro Hiram Guzzi, Pierangelo Veltri, and Mario Cannataro (2013). "Thresholding of Semantic Similarity Networks Using a Spectral Graph-Based Technique". In: Proceedings of the Second International Workshop on New Frontiers in Mining Complex Patterns (NFMCP 2013/ECML-PKDD 2013).
  • QUOTE: SSNs are edge-weighted graphs where the nodes are concepts (e.g. proteins) and each edge has an associated weight that represents the semantic similarity among related pairs of nodes(...)

2008a

• (Dextre Clarke et al., 2008) ⇒ Stella Dextre Clarke, Alan Gilchrist, Ron Davies and Leonard Will. (2008). “Glossary of Terms Relating to Thesauri and Other Forms of Structured Vocabulary for Information Retrieval." Willpower Information
  • semantic network
    • actual or virtual graphical representation of concepts and the relationships between them
    • A semantic network is a way of representing an ontology. The vertices of the network represent concepts and the edges represent semantic relationships between them. The vertices are sometimes called "nodes", which are not to be confused with the node labels of a thesaurus or a faceted classification.

2008b

• (Hendler & Harmelen, 2008) ⇒ Jim Hendler, and Frankvan Harmelen. (2008). “The Semantic Web: Webizing Knowledge Representation.” In: "Handbook of Knowledge Representation" (Edited by F. van Harmelen, V. Lifschitz and B. Porter). Foundations of Artificial Intelligence - Elsevier Journal, (2008). doi:10.1016/S1574-6526(07)03021-0

2007

• (Obitko, 2007) ⇒ Marek Obitko. (2007). “Translations between Ontologies in Multi-Agent Systems", Ph.D. dissertation, Faculty of Electrical Engineering, Czech Technical University in Prague. http://www.obitko.com/tutorials/ontologies-semantic-web/semantic-networks.html
  • Semantic network (also called concept network) is a graph, where vertices represent concepts and where edges represent relations between concepts. Semantic network at the level of ontology expresses vocabulary that is helpful especially for human, but that still can be usable for machine processing. The relations between concepts that are used in semantic networks are as follows:
    • synonym - concept A expresses the same thing as concept B
    • antonym - concept A expresses the opposite of concept B
    • meronym, holonym - part-of and has-part relation between concepts
    • hyponym, hypernym - inclusion of semantic range between concepts in both directions

Semantic nets were created as an attempt to express interlingua, a common language that would be used for translation between various natural languages. A typical example is WordNet that describes relations between English words and defines the words using natural language. Parts of WordNet were translated to other languages and the links between various languages exist and can be used as the base for translation.

Topic Maps are (syntactically) standardized form of semantic networks. They allow using topics (concepts), associations (relations) between concepts (including specifying role of topic in the association), and occurrences (resources relevant to topic, in fact instances of topic). Topics, associations and occurrences are used to create ontology of a domain, and a particular topic map then uses them to expresses state of affairs in the domain.

2001

• (Berners-Lee et al., 2001) ⇒ Tim Berners-Lee, James Hendler, Ora Lassila. (2001). “The Semantic Web.” In: Scientific American, 284(5).

1984

• (Sowa, 1984) ⇒ John F. Sowa. (1984). “Conceptual Structures: Information Processing in Mind and Machine." Addison Wesley.

1975

• (Woods, 1975) ⇒ W. A. Woods. (1975). “What's in a Link: Foundations for Semantic Networks." BOLT BERANEK AND NEWMAN INC CAMBRIDGE MASS
  • CITED BY ~809 http://scholar.google.com/scholar?cites=15187150237776112981
  • ABSTRACT: The paper is concerned with the theoretical underpinnings for semantic network representations. It is concerned specifically with understanding the semantics of the semantic network structures themselves, i.e., with what the notations and structures used in a semantic network can mean, and with interpretations of what these links mean that will be logically adequate to the job of representing knowledge. It focuses on several issues: the meaning of 'semantics', the need for explicit understanding of the intended meanings for various types of arcs and links, the need for careful thought in choosing conventions for representing facts as assemblages of arcs and nodes, and several specific difficult problems in knowledge representation - especially problems of relative clauses and quantification.

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