Knowledge representation and reasoning

About: Knowledge representation and reasoning is a research topic. Over the lifetime, 20078 publications have been published within this topic receiving 446310 citations. The topic is also known as: KR & KR².

SPOOK: a system for probabilistic object-oriented knowledge representation

Abstract: In previous work, we pointed out the limitations of standard Bayesian networks as a modeling framework for large, complex domains. We proposed a new, richly structured modeling language, Object-oriented Bayesian Networks, that we argued would be able to deal with such domains. However, it turns out that OOBNs are not expressive enough to model many interesting aspects of complex domains: the existence of specific named objects, arbitrary relations between objects, and uncertainty over domain structure. These aspects are crucial in real-world domains such as battlefield awareness. In this paper, we present SPOOK, an implemented system that addresses these limitations. SPOOK implements a more expressive language that allows it to represent the battlespace domain naturally and compactly. We present a new inference algorithm that utilizes the model structure in a fundamental way, and show empirically that it achieves orders of magnitude speedup over existing approaches.

On the Completeness of First-Order Knowledge Compilation for Lifted Probabilistic Inference

Abstract: Probabilistic logics are receiving a lot of attention today because of their expressive power for knowledge representation and learning. However, this expressivity is detrimental to the tractability of inference, when done at the propositional level. To solve this problem, various lifted inference algorithms have been proposed that reason at the first-order level, about groups of objects as a whole. Despite the existence of various lifted inference approaches, there are currently no completeness results about these algorithms. The key contribution of this paper is that we introduce a formal definition of lifted inference that allows us to reason about the completeness of lifted inference algorithms relative to a particular class of probabilistic models. We then show how to obtain a completeness result using a first-order knowledge compilation approach for theories of formulae containing up to two logical variables.

A practically implementable and tractable delegation logic

Abstract: We address the goal of making Delegation Logic (DL) into a practically implementable and tractable trust management system. DL (N. Li et al., 1999) is a logic based knowledge representation (i.e., language) for authorization in large scale, open, distributed systems. DL inferencing is computationally intractable and highly impractical to implement. We introduce a new version of Delegation Logic that remedies these difficulties. To achieve this, we impose a syntactic restriction and redefine the semantics somewhat. We show that, for this revised version of DL, inferencing is computationally tractable under the same commonly met restrictions for which Ordinary Logic Programs (OLP) inferencing is tractable (e.g., Datalog and bounded number of logical variables per rule). We give an implementation architecture for this version of DL; it uses a delegation compiler from DL to OLP and can modularly exploit a variety of existing OLP inference engines. As proof of concept, we have implemented a large expressive subset of this version of DL, using this architecture.

Justification Based Explanation in Ontologies

Abstract: The Web Ontology Language, OWL, is the latest standard in logic based ontology languages. It is built upon the foundations of highly expressive Description Logics, which are fragments of First Order Logic. These logical foundations mean that it is possible to compute what is entailed by an OWL ontology. The reasons for entailments can range from fairly simple localised reasons through to highly non-obvious reasons. In both cases, without tool support that provides explanations for entailments, it can be very difficult or impossible to understand why an entailment holds. In the OWL world, justifications, which are minimal entailing subsets of ontologies, have emerged as the dominant form of explanation.This thesis investigates justification based explanation techniques. The core of the thesis is devoted to defining and analysing Laconic and Precise Justifications. These are fine-grained justifications whose axioms do not contain any superfluous parts. Optimised algorithms for computing these justifications are presented, and an extensive empirical investigation shows that these algorithms perform well on state of the art, large and expressive bio-medical ontologies. The investigation also highlights the prevalence of superfluity in real ontologies, along with the related phenomena of justification masking. The practicality of computing Laconic Justifications coupled with the prevalence of non-laconic justifications in the wild indicates that Laconic and Precise justifications are likely to be useful in practice.The work presented in this thesis should be of interest to researchers in the area of knowledge representation and reasoning, and developers of reasoners and ontology editors, who wish to incorporate explanation generation techniques into their systems.