ConceptNet Semantic Relation Ontology

A ConceptNet semantic relation ontology is a semantic relation ontology used by a ConceptNet common sense knowledge base.

• AKA: ConceptNet Relation Ontology.
• Context:
  • It can be based on the Information Extraction using Semantic Mention Patterns, such as:
    • “NP is a part of NP” for the PartOf Relation.
    • “NP is a kind of NP” for the IsA Relation.
    • “NP is made of NP” for the MadeOf Relation.
    • “NP is used for VP” for the UsedFor Relation.
    • “NP can VP” for the CapableOf Relation.
    • “NP wants to VP” for the DesireOf Relation.
    • "You make NP by VP” for the CreatedBy Relation.
    • "An example of NP is NP” for the InstanceOf Relation.
    • “NP is part of NP” for the PartOf Relation.
    • “NP is AP” for the PropertyOf Relation.
    • "The effect of VP is NP|VP” for the EffectOf Relation.
  • …
• Example(s):
  • 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;’)
  • AGENTS (104 000 assertions)
    • (CapableOf ‘dentist’ ‘pull tooth’ ‘f=4;i=0;’)
  • EVENTS (38 000 assertions)
    • (PrerequisiteEventOf ‘read letter’ ‘open envelope’ ‘f=2;i=0;’)
    • (FirstSubeventOf ‘start fire’ ‘light match’ ‘f=2;i=3;’)
    • (SubeventOf ‘play sport’ ‘score goal’ ‘f=2;i=0;’)
    • (LastSubeventOf ‘attend classical concert’ ‘applaud’ ‘f=2;i=1;’)
  • SPATIAL (36 000 assertions)
    • (LocationOf ‘army’ ‘in war’ ‘f=3;i=0;’)
  • CAUSAL (17 000 assertions)
    • (EffectOf ‘view video’ ‘entertainment’ ‘f=2;i=0;’)
    • (DesirousEffectOf ‘sweat’ ‘take shower’ ‘f=3;i=1;’)
  • FUNCTIONAL (115 000 assertions)
    • (UsedFor ‘fireplace’ ‘burn wood’ ‘f=1;i =2;’)
    • (CapableOfReceivingAction ‘drink’ ‘serve’ ‘f =0;i =14;’)
  • AFFECTIVE (34 000 assertions)
    • (MotivationOf ‘play game’ ‘compete’ ‘f =3;i=0;’)
    • (DesireOf ‘person’ ‘not be depressed’ ‘f=2;i=0;’)
• See: Cyc.

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References

2004

• (Liu and Singh, 2004) ⇒ Hugo Liu, and Push Singh. (2004). “ConceptNet — A Practical Commonsense Reasoning Tool-Kit." 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;’)
  • AGENTS (104 000 assertions)
    • (CapableOf ‘dentist’ ‘pull tooth’ ‘f=4;i=0;’)
  • EVENTS (38 000 assertions)
    • (PrerequisiteEventOf ‘read letter’ ‘open envelope’ ‘f=2;i=0;’)
    • (FirstSubeventOf ‘start fire’ ‘light match’ ‘f=2;i=3;’)
    • (SubeventOf ‘play sport’ ‘score goal’ ‘f=2;i=0;’)
    • (LastSubeventOf ‘attend classical concert’ ‘applaud’ ‘f=2;i=1;’)
  • SPATIAL (36 000 assertions)
    • (LocationOf ‘army’ ‘in war’ ‘f=3;i=0;’)
  • CAUSAL (17 000 assertions)
    • (EffectOf ‘view video’ ‘entertainment’ ‘f=2;i=0;’)
    • (DesirousEffectOf ‘sweat’ ‘take shower’ ‘f=3;i=1;’)
  • FUNCTIONAL (115 000 assertions)
    • (UsedFor ‘fireplace’ ‘burn wood’ ‘f=1;i =2;’)
    • (CapableOfReceivingAction ‘drink’ ‘serve’ ‘f =0;i =14;’)
  • AFFECTIVE (34 000 assertions)
    • (MotivationOf ‘play game’ ‘compete’ ‘f =3;i=0;’)
    • (DesireOf ‘person’ ‘not be depressed’ ‘f=2;i=0;’)
  • ConceptNet’s relational ontology was determined quite organically. The original OMCS corpus was built largely through its users filling in the blanks of templates like ‘a hammer is for ...’. Other portions of the OMCS corpus accepted freeform input, but restricted the length of the input so as to encourage pithy phrasing and simple syntax. ConceptNet's choice of relation-types reflect our original choice of templates in OMCS, and also reflect common patterns we observed in the freeform portion of the corpus.

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