Semantic Relation

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A semantic relation is a semantic relationship that is a relation (it maps two or more concepts to the truth set)



References

2009

  • (WordNet, 2009) ⇒ http://wordnetweb.princeton.edu/perl/webwn?s=semantic%20relation
    • a relation between meanings
  • http://en.wiktionary.org/wiki/semantic_relation
    • Any relationship between two or more words based on the meaning of the words
  • http://coral.lili.uni-bielefeld.de/~ttrippel/terminology/node16.html
    • Two basic classes of relations can be distinguished:
      • Coordinating relations: (partial-) synonyms and antonyms (see 2.3.3.5);
      • Subordinating relations: ISA-relation, PARTOF-relation, HASPROP-relation, TYPEOF-relation, CLASSOF-relation, etc. Most frequently used are the ISA- and PARTOF-relations as the most basic and generic relations. For convenience these are not subdivided, which would be possible otherwise.
    • ISA-relation in terminology are called generic relations. According to Van Eynde (1999, forthcoming), they cover a wide range of categories which are used in other frameworks, such as inheritance, implication and inclusion. It is the most frequent relation resulting from subdividing concepts, called taxonomies in lexical semantics (Cruse, 1986, see). If every x is a y, or if every x is a type of y, the relation of x and y is an ISA-relation. Example: A compound is a wordtex2html_wrap_inline3239 (EAGLET, 1997-99, see). HASPROP-relations are closely related to ISA-relations, stating that x has the properties of y. This relation rarely appears in terminologies. PARTOF-relations are often called mereonomies, a parts-pieces relation. There are also superordinates and subordinates: The part is superordinate to the piece. Mereonymic relations can be classified as x is part of y. Example: Compounding is part of word formation (EAGLET, 1997-99, see).
  • http://www.db.dk/bh/Lifeboat_KO/CONCEPTS/semantic_relations.htm (by Birger Hjørland)
    • Semantic relations (meaning relations): In the narrow sense are semantic relations relations between concepts or meanings.
    • Relations between concepts, senses or meanings should not be confused with relations between the terms, words, expressions or signs that are used to express the concepts. It is, however, common to mix both of these kinds of relations under the heading "semantic relations" (i.e., Cruse, 1986; Lyons, 1977; Malmkjær, 1995 & Murphy, 2003), why synonyms, homonyms etc. are considered under the label "semantic relations" in in a broader meaning of this term.
    • Some important kinds of semantic relations are:
      • Active relation: A semantic relation between two concepts, one of which expresses the performance of an operation or process affecting the other.
      • Antonymy (A is the opposite of B; e.g. cold is the opposite of warm)
      • Associative relation: A relation which is defined psychologically: that (some) people associate concepts (A is mentally associated with B by somebody). Often are associative relations just unspecified relations.
      • Causal relation: A is the cause of B. For example: Scurvy is caused by lack of vitamin C.
      • Homonym. Two concepts, A and B, are expressed by the same symbol. Example: Both a financial institution and a edge of a river are expressed by the word bank (the word has two senses).
      • Hyponymous relationships ("is a" relation or hyponym-hyperonym), generic relation, genus-species relation: a hierarchical subordinate relation. (A is kind of B; A is subordinate to B; A is narrower than B; B is broader than A). The "is a" relation denotes what class an object is a member of. For example, "CAR - is a - VEHICLE" and "CHICKEN - is a - BIRD". It can be thought of as being a shorthand for "is a type of". When all the relationships in a system are "is a", is the system a taxonomy. The "generic of" option allows you to indicate all the particular types (species, hyponyms) of a concept. The "specific of" option allows you to indicate the common genus (hypernym) of all the particular types.
      • Instance-of relation. (“instance”, example relation) designates the semantic relations between a general concept and individual instances of that concept. A is an example of B. Example: Copenhagen is an instance of the general concept 'capital'.
      • Locative relation: A semantic relation in which a concept indicates a location of a thing designated by another concept. A is located in B; example: Minorities in Denmark.
      • Meronymy, partitive relation (part-whole relation): a relationship between the whole and its parts (A is part of B) A meronym is the name of a constituent part of, the substance of, or a member of something. Meronymy is opposite to holonymy (B has A as part of itself). (A is narrower than B; B is broader than A).
      • Passive relation: A semantic relation between two concepts, one of which is affected by or subjected to an operation or process expressed by the other.
      • Paradigmatic relation. Wellisch (2000, p. 50): “A semantic relation between two concepts, that is considered to be either fixed by nature, self-evident, or established by convention. Examples: mother / child; fat /obesity; a state /its capital city”.
      • Polysemy: A polysemous (or polysemantic) word is a word that has several sub-senses which are related with one another. (A1, A2 and A3 shares the same expression)
      • Possessive: a relation between a possessor and what is possessed.
      • Related term. A term that is semantically related to another term. In thesauri are related terms often coded RT and used for other kinds of semantic relations than synonymity (USE; UF), homonymity (separated by paranthetical qualifier), generic relations and partitative relations (BT; NT). Related terms may, for example express antagonistic relations, active/passive relations, causal relations, locative relations, paradigmatic relations.
      • Synonymy (A denotes the same as B; A is equivalent with B).
      • Temporal relation: A semantic relation in which a concept indicates a time or period of an event designated by another concept. Example: Second World War, 1939-1945.
      • Troponymy is defined in WordNet 2 as: the semantic relation of being a manner of does something (or sense 2: "the place names of a region or a language considered collectively").

2008

2004

  • (Liu & Singh, 2004) ⇒ Hugo Liu, and Push Singh. (2004). "ConceptNet—A Practical Commonsense Reasoning Tool-Kit." In: BT Technology Journal, Springer.
    • Secondly, we extend WordNet's repertoire of semantic relations from the triplet of synonym, is-a, and part-of, to a present repertoire of twenty semantic relations including, for example, EffectOf (causality), SubeventOf (event hierarchy), CapableOf (agent’s ability), PropertyOf, LocationOf, and MotivationOf (affect). Some further intuition for this relational ontology is given in the next section of the paper. Although ConceptNet increases the number and variety of semantic relations, engineering complexity is not necessarily increased.
    • Like WordNet, ConceptNet’s semantic network is amenable to context-friendly reasoning methods such as spreading activation [9] (think — activation radiating outward from an origin node) and graph traversal. However, since ConceptNet’s nodes and relational ontology are more richly descriptive of everyday commonsense than WordNet’s, better contextual commonsense inferences can be achieved, and require only simple improvements to spreading activation.
    • The ConceptNet knowledge base is formed by the linking together of 1.6 million assertions (1.25 million of which are klines) into a semantic network of over 300 000 nodes. The present relational ontology consists of twenty relation-types.
    • Figure 2 is a treemap of the ConceptNet relational ontology, showing the relative amounts of knowledge falling under each relation-type. Table 1 gives a concrete example of each relation-type.
    • Table 1 ConceptNet’s twenty relation-types are illustrated by examples from actual ConceptNet data. The relation-types are grouped into various thematics. f counts the number of times a fact is uttered in the OMCS corpus. i counts how many times an assertion was inferred during the ‘relaxation’ phase.
    • K-LINES (1.25 million assertions)
      • (ConceptuallyRelatedTo ‘bad breath’ ‘mint’ ‘f=4;i=0;’)
      • (ThematicKLine ‘wedding dress’ ‘veil’ ‘f=9;i=0;’)
      • (SuperThematicKLine ‘western civilisation’ ‘civilisation’ ‘f=0;i=12;’)
    • THINGS (52 000 assertions)
      • (IsA ‘horse’ ‘mammal’ ‘f=17;i=3;’)
      • (PropertyOf ‘fire’ ‘dangerous’ ‘f=17;i=1;’)
      • (PartOf ‘butterfly’ ‘wing’ ‘f=5;i=1;’)
      • (MadeOf ‘bacon’ ‘pig’ ‘f=3;i=0;’)
      • (DefinedAs ‘meat’ ‘flesh of animal’ ‘f=2;i=1;’)
      • ...
  • (Moldovan & al, 2004) ⇒ Dan Moldovan, Adriana Badulescu, Marta Tatu, Daniel Antohe, and Roxana Girju. (2004). "Models for the Semantic Classification of Noun Phrases." In: Proceedings of HLT/NAACL, Computational Lexical Semantics workshop.
    • 1 POSSESSION an animate entity possesses (owns) another entity; (family estate; the girl has a new car.), (Vanderwende 1994)
    • 2 KINSHIP an animated entity related by blood, marriage, adoption or strong affinity to another animated entity; (Mary’s daughter; my sister); (Levi 1979)
    • 3 PROPERTY/ characteristic or quality of an entity/event/state; (red rose; The thunderstorm was awful.); (Levi 1979) ATTRIBUTE-HOLDER
    • 4 AGENT the doer or instigator of the action denoted by the predicate; (employee protest; parental approval; The king banished the general.); (Baker, Fillmore, and Lowe 1998)
    • 5 TEMPORAL time associated with an event; (5-o’clock tea; winter training; the store opens at 9 am), includes DURATION (Navigli and Velardi 2003),
    • 6 DEPICTION- an event/action/entity depicting another event/action/entity; (A picture of my niece.), DEPICTED
    • 7 PART-WHOLE an entity/event/state is part of another entity/event/state (door knob; door of the car), (MERONYMY) (Levi 1979), (Dolan et al. 1993),
    • 8 HYPERNYMY an entity/event/state is a subclass of another; (daisy flower; Virginia state; large company, such as Microsoft) (IS-A) (Levi 1979), (Dolan et al. 1993)
    • 9 ENTAIL an event/state is a logical consequence of another; (snoring entails sleeping)
    • 10 CAUSE an event/state makes another event/state to take place; (malaria mosquitoes; to die of hunger; The earthquake generated a Tsunami), (Levi 1979)
    • 11 MAKE/PRODUCE an animated entity creates or manufactures another entity; (honey bees; nuclear power plant; GM makes cars) (Levi 1979)
    • 12 INSTRUMENT an entity used in an event/action as instrument; (pump drainage; the hammer broke the box) (Levi 1979)
    • 13 LOCATION/SPACE spatial relation between two entities or between an event and an entity; includes DIRECTION; (field mouse; street show; I left the keys in the car), (Levi 1979), (Dolan et al. 1993)
    • 14 PURPOSE a state/action intended to result from a another state/event; (migraine drug; wine glass; rescue mission; He was quiet in order not to disturb her.) (Navigli and Velardi 2003)
    • 15 SOURCE/FROM place where an entity comes from; (olive oil; I got it from China) (Levi 1979)
    • 16 TOPIC an object is a topic of another object; (weather report; construction plan; article about terrorism); (Rosario and Hearst 2001)
    • 17 MANNER a way in which an event is performed or takes place; (hard-working immigrants; enjoy immensely; he died of cancer); (Blaheta and Charniak 2000)
    • 18 MEANS the means by which an event is performed or takes place; (bus service; I go to school by bus.) (Quirk et al.1985)
    • 19 ACCOMPANIMENT one/more entities accompanying another entity involved in an event; (meeting with friends; She came with us) (Quirk et al.1985)
    • 20 EXPERIENCER an animated entity experiencing a state/feeling; (Mary was in a state of panic.); (Sowa 1994)
    • 21 RECIPIENT an animated entity for which an event is performed; (The eggs are for you) ; includes BENEFICIARY; (Sowa 1994)
    • 22 FREQUENCY number of occurrences of an event; (bi-annual meeting; I take the bus every day); (Sowa 1994)
    • 23 INFLUENCE an entity/event that affects other entity/event; (drug-affected families; The war has an impact on the economy.);
    • 24 ASSOCIATED WITH an entity/event/state that is in an (undefined) relation with another entity/event/state; (Jazz-associated company;)
    • 25 MEASURE an entity expressing quantity of another entity/event; (cup of sugar; 70-km distance; centennial rite; The jacket cost $60.)
    • 26 SYNONYMY a word/concept that means the same or nearly the same as another word/concept; (NAME) (Marry is called Minnie); (Sowa 1994)
    • 27 ANTONYMY a word/concept that is the opposite of another word/concept; (empty is the opposite of full); (Sowa 1994)
    • 28 PROBABILITY OF the quality/state of being probable; likelihood
    • ...

2002

2001

1995

1973


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