this paper, I will discuss four major topics relating to current research in lexical semantics: methodology, descriptive coverage, adequacy of the representation, and the computational usefulness of representations. In addressing these issues, I will discuss what I think are some of the central problems facing the lexical semantics community, and suggest ways of best approaching these issues. Then, I will provide a method for the decomposition of lexical categories and outline a theory of lexical semantics embodying a notion of cocompositionality and type coercion, as well as several levels of semantic description, where the semantic load is spread more evenly throughout the lexicon. I argue that lexical decomposition is possible if it is performed generatively. Rather than assuming a fixed set of primitives, I will assume a fixed number of generative devices that can be seen as constructing semantic expressions. I develop a theory of Qualia Structure, a representation language for lexical items, which renders much lexical ambiguity in the lexicon unnecessary, while still explaining the systematic polysemy that words carry. Finally, I discuss how individual lexical structures can be integrated into the larger lexical knowledge base through a theory of lexical inheritance. This provides us with the necessary principles of global organization for the lexicon, enabling us to fully integrate our natural language lexicon into a conceptual whole

We present a representation of events and action based on interval temporal logic that is significantly more expressive and more natural than most previous AI approaches. The representation is motivated by work in natural language semantics and discourse, temporal logic, and AI planning and plan recognition. The formal basis of the representation is presented in detail, from the axiomatization of time periods to the relationship between actions and events and their effects. The power of the representation is illustrated by applying it to the axiomatization and solution of several standard problems from the AI literature on action and change. An approach to the frame problem based on explanation closure is shown to be both powerful and natural when combined with our representational framework. We also discuss features of the logic that are beyond the scope of many traditional representations, and describe our approach to difficult problems such as external events and simultaneous action...

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The term ‘ontology ’ has recently acquired a certain currency within the knowledge engineering community, especially in relation to the ARPA knowledge-sharing initiative (see Gruber (to appear), Mars (ed.) 1994, Guarino 1994, Guarino, Carrara and Giaretta 1994, 1994a). The term is used in a number of different senses, however, not all of them clear or mutually compatible. Here

This paper proposes a solution to the computational task of extracting temporal information from simple declarative sentences based on separating temporal analysis into distinct tasks, each of which has access to a selected portion of the temporal input. The ultimate goal is to represent temporal information as explicitly as possible at each stage of analysis in order to provide the appropriate information for the next stage. Because the representations are constructed incrementally, it is important that they should be explicit about what has been derived so far, yet sufficiently noncommittal to avoid conflicting with subsequent processing

The notion of time is ubiquitous in any activity that requires intelligence. In particular, several important notions like change, causality, action are described in terms of time. Therefore, the representation of time and reasoning about time is of crucial importance for many Artificial Intelligence systems. Specifically during the last 10 years, it has been attracting the attention of many AI researchers. In this survey, the results of this work are analysed. Firstly, Temporal Reasoning is defined. Then, the most important representational issues which determine a Temporal Reasoning approach are introduced: the logical form on which the approach is based, the ontology (the units taken as primitives, the temporal relations, the algorithms that have been developed,. . . ) and the concepts related with reasoning about action (the representation of change, causality, action,. . . ). For each issue the different choices in the literature are discussed. 1 Introduction The notion of time i...

Natural language competent embodied cognitive agents should satisfy two requirements. First, they should act in and reason about a changing world, using reasoning in the service of acting and acting in the service of reasoning. Second, they should be able to communicate their beliefs, and report their past, ongoing, and future actions in natural language. This requires a representation of time using a deictic NOW, that models the compositional semantic properties of the English "now". Two problems emerge for an agent that interleaves reasoning and acting in a personal time. The first concerns the representation of plans and reactive rules involving reasoning about "future NOWs". The second emerges when, in the course of reasoning about NOW, the reasoning process itself results in NOW changing. We propose solutions for the two problems and conclude that: (i) for embodied cognitive agents, time is not just the object of reasoning, but is embedded in the reasoning pr...

this paper we propose an analysis and a rearrangement of WordNet's top-level taxonomy of nouns. We briefly review WordNet and identify its main semantic limitations, in the light of the ontology evaluation principles lying at the core of the OntoClean methodology. Then we briefly present a first version of the OntoClean Top (OCT) ontology, and show how WordNet can be aligned with it. The result is a "cleaned-up" WordNet, which is meant to be conceptually more rigorous, cognitively transparent, and efficiently exploitable in several applications

based on theories by both Hornstein (in the spirit of Reichenbach) and Allen, with lexical-semantic information based on an extended version of Jackendoff's theory that includes a verb classification system proposed by Dowry and Vendlet. The model is intended to be extensible to realms outside of the temporal domain (e.g., the spatial domain). The integration of tense and aspect with lexical-semantics is especially critical in machine translation because of the lexical selection process during generation: there is often a number of lexical connective and tense/aspect possibilities that may be produced from a lexical semantic representation, which, as defined in the model presented here, is largely underspecified. The use of tense and aspect information allows the choice of target-language terms to be more finely tuned and the combination of event structures to be more carefully constrained.

This dissertation describes the formal foundations and implementation of a commonsense, mixed-initiative plan reasoning system. By "plan reasoning" I mean the complete range of cognitive tasks that people perform with plans including, for example, plan construction (planning), plan recognition, plan evaluation and comparison, and plan repair (replanning), among other things. "Mixed-initiative" means that several participants can each make contributions to the plan under development through some form of communication. "Commonsense" means that the system represents plans and their constituents at a level that is "natural" to us in the sense that they can be described and discussed in language. In addition, the reasoning that the system performs includes those conclusions that we would take to be sanctioned by common sense, including especially those conclusions that are defeasible given additional knowledge or time spent reasoning. The main theses of this dissertation are the following: ...