ly have a separate entry for each category; for example, flap would have one entry as a noun and another as a verb. Separate entries are usually also appropriate for each of the senses of a homonym---a word that has more than one unrelated sense even within a single syntactic category; for example, the noun pen would have distinct entries for the senses writing instrument, animal enclosure,andswan. Polysemy--- related or overlapping senses---is a more-complex situation; sometimes the senses may be discrete enough that we can treat them as distinct: for example, window as both opening in wall and glass pane in opening in wall (fall through the window; break the window). But this is not always so; the word open, for example, has many overlapping senses concerning unfolding, expanding, revealing, moving to an open position, making openings in, and so on, and separating them into discrete senses, as the writers of dictionary definitions try to do, is not possible (see also sections 1.2.3 a

In all types of communication, the ability to share information is often hindered because

management

For more than a decade constructing ontologies for legal domains, we, at the Leibniz Center for Law, felt really the need to develop a core ontology for law that would enable us to re-use the common denominator of the various legal domains. In this paper we present two core ontologies for law. The first one was the result of a PhD thesis by [Valente, 1995], called FOLaw. FOLaw specifies functional dependencies between types of knowledge involved in legal reasoning. Despite the fact that FOLaw was the starting point for a number of ontologies and legal reasoning systems in various (European) projects, it is rather an epistemological framework than a (core) ontology. We are not the only ones who easily confound epistemology with ontology. In the paper we present some examples and discuss whether this epistemological promiscuity in (core) ontology development is a serious problem. It is to some extent, as it limits the scope of re-use (if not leading to confusion). Therefore, we started about four years ago the development of a `real' core-ontology for law based upon notions of common sense. The reason for a common-sense foundation is that domain independent concepts of law -- the common denominator -- are still tainted with a strong common-sense flavor. Moreover, domains of law refer to social activities which are generally governed by common-sense notions. This core ontology, called LRI-Core, consists of five major portions (`worlds'): physical, mental and abstract classes; roles and occurrences.

Representing activities and the constraints on their occurrences is an integral aspect of commonsense reasoning, particularly in manufacturing, enterprise modelling, and autonomous agents or robots. In addition to the traditional concerns of knowledge representation and reasoning, the need to integrate software applications in these areas has become increasingly important. However, interoperability is hindered because the applications use different terminology and representations of the domain. These problems arise most acutely for systems that must manage the heterogeneity inherent in various domains and integrate models of different domains into coherent frameworks. For example, such integration occurs in business process reengineering, where enterprise models integrate processes, organizations, goals and customers. Even when applications use the same terminology, they often associate different semantics with the terms. This clash over the meaning of the terms prevents the seamless exchange of information among the applications. Typically, pointto-point translation programs are written to enable communication from one specific application to another. However, as the number of applications has increased and the information has become more complex, it has been more difficult for software developers to provide translators between every pair of applications that must cooperate. What is needed is some way of explicitly specifying the terminology of the applications in an unambiguous fashion. The Process Specification Language (PSL) ([13], [8]) has been designed to facilitate correct and complete exchange of process information among manufacturing systems 1. Included in these applications are scheduling, process modeling, process planning, production planning, simulation, project management, workflow, and business process reengineering. This chapter will give an 1 PSL has been accepted as project ISO 18629 within the International Organisation

Although classical first-order logic is the de facto standard logical foundation for artificial intelligence, the lack of a built-in, semantically grounded capability for reasoning under uncertainty renders it inadequate for many important classes of problems. Probability is the bestunderstood and most widely applied formalism for computational scientific reasoning under uncertainty. Increasingly expressive languages are emerging for which the fundamental logical basis is probability. This paper presents Multi-Entity Bayesian Networks (MEBN), a first-order language for specifying probabilistic knowledge bases as parameterized fragments of Bayesian networks. MEBN fragments (MFrags) can be instantiated and combined to form arbitrarily complex graphical probability models. An MFrag represents probabilistic relationships among a conceptually meaningful group of uncertain hypotheses. Thus, MEBN facilitates representation of knowledge at a natural level of granularity. The semantics of MEBN assigns a probability distribution over interpretations of an associated classical first-order theory on a finite or countably infinite domain. Bayesian inference provides both a proof theory for combining prior knowledge with observations, and a learning theory for refining a representation as evidence accrues. A proof is given that MEBN can represent a probability distribution on interpretations of any finitely axiomatizable first-order theory.

Abstract. Development of high throughput data acquisition technologies, together with advances in computing, and communications have resulted in an explosive growth in the number, size, and diversity of potentially useful information sources. This has resulted in unprecedented opportunities in data-driven knowledge acquisition and decision-making in a number of emerging increasingly data-rich application domains such as bioinformatics, environmental informatics, enterprise informatics, and social informatics (among others). However, the massive size, semantic heterogeneity, autonomy, and distributed nature of the data repositories present significant hurdles in acquiring useful knowledge from the available data. This paper introduces some of the algorithmic and statistical problems that arise in such a setting, describes algorithms for learning classifiers from distributed data that offer rigorous performance guarantees (relative to their centralized or batch counterparts). It also describes how this approach can be extended to work with autonomous, and hence, inevitably semantically heterogeneous data sources, by making explicit, the ontologies (attributes and relationships between attributes) associated with the data sources and reconciling the semantic differences among the data sources from a user’s point of view. This allows user or context-dependent exploration of semantically heterogeneous data sources. The resulting algorithms have been implemented in INDUS- an open source software package for collaborative discovery from autonomous, semantically heterogeneous, distributed data sources. 1

This paper proposes a similarity measure to compare cases represented by labeled graphs. We first define an expressive model of directed labeled graph, allowing multiple labels on vertices and edges.

Uncertainty is a fundamental and irreducible aspect of our knowledge about the world. Probability is the most well-understood and widely applied logic for computational scientific reasoning under uncertainty. As theory and practice advance, general-purpose languages are beginning to emerge for which the fundamental logical basis is probability. However, such languages have lacked a logical foundation that fully integrates classical first-order logic with probability theory. This paper presents such an integrated logical foundation. A formal specification is presented for multi-entity Bayesian networks (MEBN), a knowledge representation language based on directed graphical probability models. A proof is given that a probability distribution over interpretations of any consistent, finitely axiomatizable first-order theory can be defined using MEBN. A semantics based on random variables provides a logically coherent foundation for open world reasoning and a means of analyzing tradeoffs between accuracy and computation cost. Furthermore, the underlying Bayesian logic is inherently open, having the ability to absorb new facts about the world, incorporate them into existing theories, and/or modify theories in the light of evidence. Bayesian inference provides both a proof theory for combining prior knowledge with observations, and a learning theory for refining a representation as evidence accrues. The results of this paper provide a logical foundation for the rapidly evolving literature on first-order Bayesian knowledge representation, and point the way toward Bayesian languages suitable for general-purpose knowledge representation and computing. Because first-order Bayesian logic contains classical first-order logic as a deterministic subset, it is a natural candidate as a universal representation for integrating domain ontologies expressed in languages based on classical first-order logic or subsets thereof.

This paper analyzes the ontological requirements for representing biology knowledge, and identifies several areas where current knowledge representation (KR) paradigms need to be extended. We focus on the representation of experimental materials and methods, and the reasoning task of intelligent information retrieval; however, the ontological issues we raise apply to biology (and experimental sciences) in general. We have identified two important concept types in molecular biology that cause problems for standard knowledge models: 1) complex substances such as mixtures and nucleic acid sequences; 2) transformations (such as biochemical reactions) that convert one substance into another. We describe these problems, propose solutions for some of them, and give examples of the need for such knowledge representations in intelligent information retrieval. 1. Introduction Current research aimed at the development of knowledge sharing technology [Gruber 1993, Lehman 1995] is based on the fo...