Two of the most important threads of work in knowledge representation today are frame-based representation systems (FRS's) and Bayesian networks (BNs). FRS's provide an excellent representation for the organizational structure of large complex domains, but their applicability is limited because of their inability to deal with uncertainty and noise. BNs provide an intuitive and coherent probabilistic representation of our uncertainty, but are very limited in their ability to handle complex structured domains. In this paper, we provide a language that cleanly integrates these approaches, preserving the advantages of both. Our approach allows us to provide natural and compact definitions of probability models for a class, in a way that is local to the class frame. These models can be instantiated for any set of interconnected instances, resulting in a coherent probability distribution over the instance properties. Our language also allows us to represent important types of uncertainty tha...

Bayesian networks provide a modeling language and associated inference algorithm for stochastic domains. They have been successfully applied in a variety of medium-scale applications. However, when faced with a large complex domain, the task of modeling using Bayesian networks begins to resemble the task of programming using logical circuits. In this paper, we describe an object-oriented Bayesian network (OOBN) language, which allows complex domains to be described in terms of inter-related objects. We use a Bayesian network fragment to describe the probabilistic relations between the attributes of an object. These attributes can themselves be objects, providing a natural framework for encoding part-of hierarchies. Classes are used to provide a reusable probabilistic model which can be applied to multiple similar objects. Classes also support inheritance of model fragments from a class to a subclass, allowing the common aspects of related classes to be defined only once. Our language h...

With the development of the semantic web activity, ontologies become widely used to represent the conceptualization of a domain. However, none of the existing ontology languages provides a means to capture uncertainty about the concepts, properties and instances in a domain. Probability theory is a natural choice for dealing with uncertainty. Incorporating probability theory into existing ontology languages will provide these languages additional expressive power to quantify the degree of the overlap or inclusion between two concepts, support probabilistic queries such as finding the most probable concept that a given description belongs to, and make more accurate semantic integration possible. One approach to provide such a probabilistic extension to ontology languages is to use Bayesian networks, a widely used graphic model for knowledge representation under uncertainty. In this paper, we present our on-going research on extending OWL, an ontology language recently proposed by W3C’s Semantic Web Activity. First, the language is augmented to allow additional probabilistic markups, so probabilities can be attached with individual concepts and properties in an OWL ontology. Secondly, a set of translation rules is defined to convert this probabilistically annotated OWL ontology into a Bayesian network. Our probabilistic extension to OWL has clear semantics: the Bayesian network obtained will be associated with a joint probability distribution over the application domain. General Bayesian network inference procedures (e.g., belief propagation or junction tree) can be used to compute P(C | e): the degree of the overlap or inclusion between a concept C and a concept represented by a description e. We also provide a similarity measure that can be used to find the most probable concept that a given description belongs to. 1.

Ontologies play a central role in the development of the semantic web, as they provide precise definitions of shared terms in web resources. One important web ontology language is DAML+OIL; it has a formal semantics and a reasoning support through a mapping to the expressive description logic SHOQ(D) with the addition of inverse roles. In this paper, we present a probabilistic extension of SHOQ(D), called P-SHOQ(D), to allow for dealing with probabilistic ontologies in the semantic web. The description logic P-SHOQ(D) is based on the notion of probabilistic lexicographic entailment from probabilistic default reasoning. It allows to express rich probabilistic knowledge about concepts and instances, as well as default knowledge about concepts. We also present sound and complete reasoning techniques for P-SHOQ(D), which are based on reductions to classical reasoning in SHOQ(D) and to linear programming, and which show in particular that reasoning in P-SHOQ(D) is decidable.

The goal of a mediator system is to provide users a uniform interface to the multitude of informa-tion sources. To translate user queries, given in a mediated schema, to queries on the data sources, mediators rely on explicit mappings between the contents of the data sources and the meanings of the relations in the mediated schema. Thus far, contents of data sources were described qualitatively. In this paper we describe the use of quantitative information in the form of proba-bilistic knowledge in mediator systems. We con-sider several kinds of probabilistic information: information about overlap between collections in the mediated schema, coverage of the information sources, and degrees of overlap between informa-tion sources. We address the problem of ordering accesses to multiple information sources, in order to maximize the likelihood of obtaining answers as early as possible. We describe a declarative for-malism for specifying these kinds of probabilistic information, and we propose algorithms for order-ing the information sources. Finally, we discuss a preliminary experimental evaluation of these al-gorithms on the domain of bibliographic sources available on the WWW. 1

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Concepts in differentiated ontologies inherit definitional structure from concepts in shared ontologies. Shared, inherited structure provides a common ground that supports measures of “description compatibility. ” These algorithms are the primary contribution of this paper. The description-compatibility measures compare concepts to predict semantic compatibility, the probability that an instance of a recommendation will satisfy a request. The description-compatibility measures cross a spectrum regarding their knowledge of the semantics of roles in concept definitions. Some of the measures identify and analyze correspondences among elements of the definitions, and are thus a form of analogical reasoning. We use simulations to evaluate the description-compatibility measures in detail. Description compatibility can be used to rank alternative query translations, and to guide search for capabilities across communities that subscribe to differentiated ontologies. 1.

We review several kinds of previously studied concept similarity measures, and then rephrase them in terms of a simple DL. We discuss the difficulties encountered in trying to generalize these formulations to more complex DLs, and settle on one based on probability/information theory as being the most principled. 1

Description Logics (DLs, for short) allow reasoning about individuals and concepts, i.e. set of individuals with common properties. Typically, DLs are limited to dealing with crisp, well defined concepts. That is, concepts for which the problem whether an individual is an instance of it is a yes/no question. More often than not, the concepts encountered in the real world do not have a precisely defined criteria of membership: we may say that an individual is an instance of a concept only to a certain degree, depending on the individual's properties. Concepts of this kind are rather vague than precise. As fuzzy logic directly deals with the notion of vagueness and imprecision, it o#ers an appealing foundation for a generalisation of DLs to vague concepts. In this paper we present a general fuzzy DL, which combines fuzzy logic with DLs. We define its syntax, semantics and present constraint propagation calculi for reasoning in it. Introduction Description Logics (DLs, for short) provide...

Abstract. In this paper we consider Description Logics (DLs), which are logics for managing structured knowledge, with a well-known fuzzy extension to deal with vague information. While for fuzzy DLs ad-hoc, tableaux-like reasoning procedures have been given in the literature, the topic of this paper is to present a reasoning preserving transformation of fuzzy DLs into classical DLs. This has the considerable practical consequence that reasoning in fuzzy DLs is feasible using already existing DL systems. 1