2004 ConceptNet

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Subject Headings: ConceptNet, Common Sense Knowledge Base, ConceptNet Semantic Relation Ontology, Open Mind Common Sense Project.


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ConceptNet is a freely available commonsense knowledge base and natural-language-processing tool-kit which supports many practical textual-reasoning tasks over real-world documents including topic-gisting, analogy-making, and other context oriented inferences. The knowledge base is a semantic network presently consisting of over 1.6 million assertions of commonsense knowledge encompassing the spatial, physical, social, temporal, and psychological aspects of everyday life. ConceptNet is generated automatically from the 700 000 sentences of the Open Mind Common Sense Project — a World Wide Web based collaboration with over 14000 authors.

1. Introduction

In today’s digital age, text is the primary medium of representing and transmitting information, as evidenced by the pervasiveness of e-mails, instant messages, documents, weblogs, news articles, homepages, and printed materials. …

2. ConceptNet, Cyc, and WordNet


2.2 Structured like WordNet, relationally rich like Cyc

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.

ConceptNet as a context machine

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.

Structure of the ConceptNet knowledge base

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.

Practical commonsense reasoning with the ConceptNet tool-kit

Whereas logic is microscopic, highly granular, well-defined, and static, context is macroscopic, gestalt, heuristic, nd quite dynamic. ConceptNet excels at problems of context because it is more invested in the many ways that commonsense concepts relate to one another, rather than obsessing over the truth conditions of particular assertions. By nuancing network-based reasoning methods such as spreading activation to take advantage of ConceptNet’s relational-ontology, various contextual-commonsense-reasoning tasks can be achieved.


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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2004 ConceptNetHugo Liu
Push Singh
ConceptNet — A Practical Commonsense Reasoning Tool-Kithttp://web.media.mit.edu/~hugo/publications/papers/BTTJ-ConceptNet.pdf2004