Exploring Semantic Hierarchies to Improve Resolution Theorem Proving on Ontologies

A resolution-theorem-prover (RTP) evaluates the validity (truthfulness) of conjectures against a set of axioms in a knowledge base. When given a conjecture, an RTP attempts to resolve the negated conjecture with axioms from the knowledge base until the prover nds a contradiction. If the RTP nds a contradiction between the axioms and a negated conjecture, the conjecture is proven. The order in which the axioms within the knowledge-base are evaluated signicantly impacts the runtime of the program, as the search-space increases exponentially with the number of axioms. Ontologies, knowledge bases with semantic (and predominantly hierarchical) structures, describe objects and their relationships to other objects. For example, a \u27Sedan\u27 class might exist in a sample ontology with \u27Automobile\u27 as a parent class and \u27Minivan\u27 as a sibling class. Currently, hierarchical structures within an ontology are not taken into account when evaluating the relevance of each axiom. Instead, each predicate is automatically assigned a weight based on a heuristic measure (such as the number of terms or the frequency of predicates relevant to the conjecture) and axioms with higher weights are evaluated rst. My research aims to intelligently select relevant axioms within a knowledge-base given a structured relationship between predicates. I have used semantic hierarchies passed to a weighting function to assign weights to each predicate. The research aims to design heuristics based upon the semantics of the predicates, rather than solely the syntax of the statements. I developed weighting functions based upon various parameters relevant to the ontological structure of predicates contained in the ontology, such as the size and depth of a hierarchy based upon the structure. The functions I have designed calculate weights for each predicate and thus each axiom in attempts to select relevant axioms when proving a theorem. I have conducted an experimental study to determine if my methods show any improvements over current reasoning methods. Results for the experiments conducted show promising results for generating weights based on semantic hierarchies and encourage further research

OWL-POLAR : A Framework for Semantic Policy Representation and Reasoning

Peer reviewedPreprin

Context-Aware Modeling Using Semantic Web and Z Notation

Surveys in user context modeling have shown that the semantic web is one of the promising approach to represent and structure the contextual information captured from user’s surrounding environment in a context-aware application. A benefit of using semantic web language is that it enables application to reason user contextual information in order to get the knowledge of user’s behavior. However, regarding its notation format, semantic web is suitable for implementation level or to be consumed by application run-time. Context-aware application is a part of distributed computing system. In distributed computing system, the language used for specification should be distinguished from the implementation / run-time purpose. This is known as separation of modeling language. Regarding the context-aware application, for those who are concerned with specification of context modeling, the language that is used for specification should also be distinguished from the implementation one. This thesis aims at proposing the use of formal specification technique to develop a generic context ontology model of user’s behavior at the Computer and Information Sciences Department, Universiti Teknologi PETRONAS. Initially, the context ontology was written in OWL semantic web language. The further process is mapping onto a formal specification language, i.e. onto Z notation. As a result, specification of context ontology and its consistency checking have been developed and verified beyond the semantic web language environment. An inconsistency of context model has been detected during the verification of Z model, which cannot be revealed by current OWL DL reasoner. The context-aware designers might benefit from the formal specification of context ontology, where the designers could fully use formal verification technique to check the correctness of context ontology. Thus, the modeling approach in this thesis has shown that it could complement the context ontology development process, where the checking and refinement are performed beyond the semantic web reasone

Accessible reasoning with diagrams: From cognition to automation

High-tech systems are ubiquitous and often safety and se- curity critical: reasoning about their correctness is paramount. Thus, precise modelling and formal reasoning are necessary in order to convey knowledge unambiguously and accurately. Whilst mathematical mod- elling adds great rigour, it is opaque to many stakeholders which leads to errors in data handling, delays in product release, for example. This is a major motivation for the development of diagrammatic approaches to formalisation and reasoning about models of knowledge. In this paper, we present an interactive theorem prover, called iCon, for a highly expressive diagrammatic logic that is capable of modelling OWL 2 ontologies and, thus, has practical relevance. Significantly, this work is the first to design diagrammatic inference rules using insights into what humans find accessible. Specifically, we conducted an experiment about relative cognitive benefits of primitive (small step) and derived (big step) inferences, and use the results to guide the implementation of inference rules in iCon

A survey of large-scale reasoning on the Web of data

As more and more data is being generated by sensor networks, social media and organizations, the Webinterlinking this wealth of information becomes more complex. This is particularly true for the so-calledWeb of Data, in which data is semantically enriched and interlinked using ontologies. In this large anduncoordinated environment, reasoning can be used to check the consistency of the data and of asso-ciated ontologies, or to infer logical consequences which, in turn, can be used to obtain new insightsfrom the data. However, reasoning approaches need to be scalable in order to enable reasoning over theentire Web of Data. To address this problem, several high-performance reasoning systems, whichmainly implement distributed or parallel algorithms, have been proposed in the last few years. Thesesystems differ significantly; for instance in terms of reasoning expressivity, computational propertiessuch as completeness, or reasoning objectives. In order to provide afirst complete overview of thefield,this paper reports a systematic review of such scalable reasoning approaches over various ontologicallanguages, reporting details about the methods and over the conducted experiments. We highlight theshortcomings of these approaches and discuss some of the open problems related to performing scalablereasoning

SPETA: Social pervasive e-tourism advisor

Tourism is one of the major sources of income for many countries. Therefore, providing efficient, real-time service for tourists is a crucial competitive asset which needs to be enhanced using major technological advances. The current research has the objective of integrating technological innovation into an information system, in order to build a better user experience for the tourist. The principal strength of the approach is the fusion of context-aware pervasive systems, GIS systems, social networks and semantics. This paper presents the SPETA system, which uses knowledge of the user’s current location, preferences, as well as a history of past locations, in order to provide the type of recommender services that tourists expect from a real tour guide.This work is supported by the Spanish Ministry of Industry, Tourism, and Commerce under the GODO project (FIT-340000-2007-134), under the PIBES project of the Spanish Committee of Education and Science (TEC2006-12365-C02-01) and under the MID-CBR project of the Spanish Committee of Education and Science (TIN2006-15140-C03-02).Publicad

Proceedings of the IJCAI-09 Workshop on Nonmonotonic Reasoning, Action and Change

Copyright in each article is held by the authors. Please contact the authors directly for permission to reprint or use this material in any form for any purpose.The biennial workshop on Nonmonotonic Reasoning, Action and Change (NRAC) has an active and loyal community. Since its inception in 1995, the workshop has been held seven times in conjunction with IJCAI, and has experienced growing success. We hope to build on this success again this eighth year with an interesting and fruitful day of discussion. The areas of reasoning about action, non-monotonic reasoning and belief revision are among the most active research areas in Knowledge Representation, with rich inter-connections and practical applications including robotics, agentsystems, commonsense reasoning and the semantic web. This workshop provides a unique opportunity for researchers from all three fields to be brought together at a single forum with the prime objectives of communicating important recent advances in each field and the exchange of ideas. As these fundamental areas mature it is vital that researchers maintain a dialog through which they can cooperatively explore common links. The goal of this workshop is to work against the natural tendency of such rapidly advancing fields to drift apart into isolated islands of specialization. This year, we have accepted ten papers authored by a diverse international community. Each paper has been subject to careful peer review on the basis of innovation, significance and relevance to NRAC. The high quality selection of work could not have been achieved without the invaluable help of the international Program Committee. A highlight of the workshop will be our invited speaker Professor Hector Geffner from ICREA and UPF in Barcelona, Spain, discussing representation and inference in modern planning. Hector Geffner is a world leader in planning, reasoning, and knowledge representation; in addition to his many important publications, he is a Fellow of the AAAI, an associate editor of the Journal of Artificial Intelligence Research and won an ACM Distinguished Dissertation Award in 1990