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An important issue in the newborn discipline of ontological engineering is the construction of libraries of ontologies which are designed for maximum reusability. Van Heijst et. al. suggested that a central part of ontology libraries is the definition of what they called a core ontology, containing elements that are as generic and method-independent as possible. However, their specification of how these core ontologies should be constructed is highly pragmatical, and leaves many problems unresolved. In this article we propose and discuss a number of specific principles for the construction of core ontologies. We demonstrate the advantages of these principles using as an example a core ontology we have built for the domain of law. Several conclusions about the construction of ontology libraries based on core ontologies are drawn. 1 Introduction An important issue in the newborn discipline of ontological engineering is the construction of libraries of ontologies which are desig...

The ontology of law that we present here is one `in between' a completely general top ontology and ontologies of legal domains. It adopts a functional perspective --- that is, the Law is analysed and interpreted through a functional point of view. We propose a number of primitive functions of legal sources and corresponding categories of legal knowledge: normative knowledge, world knowledge, responsibility knowledge, reactive knowledge, creative knowledge and meta-legal knowledge. Together, these functions realize the main function of the legal system: to regulate social behaviour. The

The dynamics model, which is based on L. Talmy’s (1988) theory of force dynamics, characterizes causation as a pattern of forces and a position vector. In contrast to counterfactual and probabilistic models, the dynamics model naturally distinguishes between different cause-related concepts and explains the induction of causal relationships from single observations. Support for the model is provided in experiments in which participants categorized 3-D animations of realistically rendered objects with trajectories that were wholly determined by the force vectors entered into a physics simulator. Experiments 1–3 showed that causal judgments are based on several forces, not just one. Experiment 4 demonstrated that people compute the resultant of forces using a qualitative decision rule. Experiments 5 and 6 showed that a dynamics approach extends to the representation of social causation. Implications for the relationship between causation and time are discussed.

This article presents the results of our effort in specifying the minimal common sense conceptualization used in reasoning about legal causal matters. Coming from the humanistic side of formal ontology, we give priority to the intuitive definitions, rather than to the technical formalization of the contents of our ontology. We therefore introduce all the material in natural language, assuming that a formal language makes a conceptualization clearer only if this is already clear. Section 1 points at some methodological and terminological issues that we believe to be great obstacles to reaching consensus on formal ontology within the AI community. Section 2 proposes a phenomenological approach to formal ontology and a definition of common sense. Sections 3, 4, 5 provide an example of how to apply the proposed approach. Finally, section 6 presents some conclusions and research objectives.

Ontological assumptions are at the very hart of the enterprise of representing knowledge. In AI & Law, these assumptions reflect an underlying view of what law is made of, what legal knowledge is, which knowledge categories play a role in Law and how they interrelate. By and large, however, ontological issues have been rather neglected in AI & Law. The most common ontological view on Law is very simple, and divides legal knowledge into two orthogonal types: rules and cases. In this paper, we will argue extensively in favor of an explicit attention to ontological issues in AI & Law, and put forward this view by proposing a functional ontology of Law. This ontology is basically a set of interconnected primitive categories and subcategories of legal knowledge proposed under a teleological and functional view about the legal system. We will discuss the proposed categories and their interrelations, as well as some of the implications of its use in legal knowledge engineering. 1 ...

Conceptual models, such as the ones used in KADS, have been proposed as important tools for developing knowledge-based systems. These models come usually associated with a domain-independent task such as planning or diagnosis. Such strategy, however, has been criticized for not paying enough attention to the representation and acquisition of domain knowledge. It has been argued that ontologies can provide this missing step. Ontologies are coherent specifications (theories) of the main concepts involved in application domains such as physical systems, medicine or law. Nevertheless, research on these two perspectives have been usually separated. One can find libraries of ontologies (such as the ones developed with ONTOLINGUA) or conceptual models (such as the KADS library), but rarely both. In this article, we show with a specific example that the two ends meet. We propose separately a KADS conceptual model for a central legal task (namely assessment) and an ontology of lega...

The significance of counterfactual thinking in the causal judgment process has been emphasized for nearly two decades, yet no previous research has directly compared the relative effect of thinking counterfactually versus factually on causal judgment. Three experiments examined this comparison by manipulating the task frame used to focus participants’ thinking about a target event. Prior to making judgments about causality, preventability, blame, and control, participants were directed to think about a target actor either in counterfactual terms (what the actor could have done to change the outcome) or in factual terms (what the actor had done that led to the outcome). In each experiment, the effect of counterfactual thinking did not differ reliably from the effect of factual thinking on causal judgment. Implications for research on causal judgment and mental representation are discussed.

Research in open-domain commonsense reasoning has been hindered by the lack of evaluation metrics for judging progress and comparing alternative approaches. Taking inspiration from large-scale question sets used in natural language processing research, we authored one thousand English-language questions that directly assess commonsense causal reasoning, called the Choice Of Plausible Alternatives (COPA) evaluation. Using a forcedchoice format, each question gives a premise and two plausible causes or effects, where the correct choice is the alternative that is more plausible than the other. This paper describes the authoring methodology that we used to develop a validated question set with sufficient breadth to advance open-domain commonsense reasoning research. We discuss the design decisions made during the authoring process, and explain how these decisions will affect the design of high-scoring systems. We also present the performance of multiple baseline approaches that use statistical natural language processing techniques, establishing initial benchmarks for future systems.

Abstract. In this paper we present DIRECT, a system for automatic discovery of responsibility