Exploiting Natural Language for Knowledge Representation and Reasoning

As the title suggests, the panel will address such questions as, “What syntactic/semantic (or other) resources does NL potentially offer that could be profitably exploited for knowledge representation and reasoning? What are the prospective benefits? How can they be realized? What's the down side?”. The intention is that some sense of the state of the art, difficulties, tradeoffs, and the most promising directions for further work should emerge from the presentations and discussion. Some specific resources suggested in advance as possible topics for discussion are: • generalized quantifiers • predicate and sentence nominalization • predicate and sentence modification • probabilistic qualification • collections, substances, kinds, properties • events, situations, times, locations, actions, plans • aspectual classes (Aktionsarten) • more generally, commonsense ontologies • donkey anaphora and generics • “natural” forms of inference • language (especially narratives) as a guide to “warranted nonmonotonic inference” • feature logics (aimed at NL syntax) as general KRs • indexicality, context • vagueness as a useful feature • ambiguity as a useful feature • theory of communication, knowledge, collaboration One can consider the potential benefits of such resources collectively or one by one. Let us imagine that we have a formalized representation that is natural-language-like, incorporating many of the above features. One potential benefit would be the improved opportunity for “knowledge bootstrapping” via natural language, since the necessary transformations in going from NL input to the internal representation would then be minimal. Moreover, since we tend to have good intuitions about what follows from what (either deductively or defeasibly) when considering examples formulated in natural language, a language-like representation might facilitate the formulation of effective deductive and nondeductive inference mechanisms. Another significant benefit might be the comprehensibility of a representation parasitic on language, from the perspective of users of the KR who need to peruse, extend, and debug sizable knowledge bases. Against such an across-the-board strategy one might argue that it would make inference intractable or at least “gappy” in unforseeable ways. Even unquatified predicate logic is intractable in a worst-case sense (if P ≠ NP). Keeping in mind that the goals of AI are performance goals (or, goals of explicating human performance), we must ensure that our KR's lend themselves to fast, reliable inference. So we should move away from general language-like expressiveness towards collections of tractable, specialized sublanguages. There are replies to this objection, but one can also remain agnostic about the across-the-board strategy and consider benefits obtainable by exploiting some of the above resources. Knowledge bootstrapping, formulation of inference rules, and knowledge base browsing may become significantly easier even with some seemingly minor, “cosmetic” changes to a representation, bringing it superficially closer to NL without increase in expressive power. The expectation is that some panelists will be able to point to specific gains already obtained or eventually expected from language-inspired approaches to various representational issues: NL-like quantification might promote readability and effective inference; ordinary ways of talking about properties, propositions, actions, and plans (often involving phrasings that reify or nominalize these) might provide the key to a workable analysis of attitudes and actions; ontological categories derived from language (including collections, substances, time, space, events, and specific “natural taxonomies”) should facilitate the encoding of at least the “commonsense core” of many applications; the form taken by generics in NL might suggest ways of formalizing defeasible generalizations; indexicality in NL (e.g., as exemplified by words like ‘now’, ‘here’, or ‘I’) might suggest effective ways of “situated reasoning”, for instance by a planner; the ways in which language allows for vagueness and ambiguity might suggest ways of dealing effectively with imprecise or underspecified information; the conventions underlying cooperative dialogs might suggest formal protocols for arriving at mutual knowledge; the feature logics that have evolved for describing the syntactic structure of natural languages show signs of being apt for world-description as well; and so on. No doubt there are caveats and unresolved difficulties. Language may perhaps mislead, where the devices it employs are peculiar to its communicative function and its one-dimensional acoustic form; and it may not lead to any one place, judging from the variety of theories that have been proposed for many of the linguistic phenomena mentioned above. But it is surely an invaluable source of representational ideas that should be heeded by the KR&R community.