VerbNet syntactic frames account for over 84% exact matches to the frames found in PropBank. ^ To evaluate VerbNet's syntactic coverage it has been mapped to the Proposition Bank. This detailed level of representation also provides a suitable pivot representation for generation in other natural languages, i.e., a form of interlingua. In order to support the animation of the actions, PARs have to make explicit many details that are often underspecified in the language. ^ One of VerbNet's primary applications has been as a basis for Parameterized Action Representations (PARs), which are used to animate the actions of virtual human agents in a simulated 3D environment. Each class in the hierarchy is characterized extensionally by its set of verbs, and intensionally by syntactic frames and semantic predicates and a list of typical verb arguments. Classes are hierarchically organized to ensure that all their members have common semantic and syntactic properties. ^ In order to address this gap, we created VerbNet, a verb lexicon compatible with Word-Net but with explicitly stated syntactic and semantic information, using Levin verb classes to systematically construct lexical entries. Dorr's LCS lexicon attempts to address these limitations, but does not provide broad coverage of syntactic frames or different senses or links to actual instances in corpora. WordNet does not provide a comprehensive account of possible syntactic frames and predicate argument structures associated with individual verb senses and ComLex provides syntactic frames but ignores sense distinctions.
James Pustejovsky's Generative Lexicon has concentrated on nouns rather than verbs. Overall, this provides a means for semantic integration from heterogeneous sources under a single schema and opens up possibilities to draw on natural language processing techniques for querying and data mining.ĭespite the proliferation of approaches to lexicon development, the field of natural language processing has yet to develop a clear consensus on guidelines for computational verb lexicons, which has severely limited their utility in information processing applications. This article presents FrameBase, a wide-coverage knowledge base schema that uses linguistic frames to represent and query n-ary relations from other knowledge bases, providing multiple levels of granularity connected via logical entailment.
They also increase semantic heterogeneity, making it impossible to query the data concisely and without prior knowledge of each individual source. While n-ary relations can be converted to triples in a number of ways, unfortunately, the structurally different choices made in different knowledge sources significantly impede our ability to connect them. However, many facts about the world involve more than two entities. Large-scale knowledge graphs such as those in the Linked Open Data cloud are typically stored as subject-predicate-object triples.
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