scholarly journals Computing Repairs of Inconsistent DL-Programs over EL Ontologies

2016 ◽  
Vol 56 ◽  
pp. 463-515 ◽  
Author(s):  
Thomas Eiter ◽  
Michael Fink ◽  
Daria Stepanova
Keyword(s):  
Knowledge Representation ◽  
Description Logic ◽  
Benchmark Problems ◽  
Query Rewriting ◽  
Query Interface ◽  
Rule Based ◽  
Practical Algorithm ◽  
Support Sets ◽  
Answer Sets ◽  
Practical Feasibility

Description Logic (DL) ontologies and non-monotonic rules are two prominent Knowledge Representation (KR) formalisms with complementary features that are essential for various applications. Nonmonotonic Description Logic (DL) programs combine these formalisms thus providing support for rule-based reasoning on top of DL ontologies using a well-defined query interface represented by so-called DL-atoms. Unfortunately, interaction of the rules and the ontology may incur inconsistencies such that a DL-program lacks answer sets (i.e., models), and thus yields no information. This issue is addressed by recently defined repair answer sets, for computing which an effective practical algorithm was proposed for DL-Lite A ontologies that reduces a repair computation to constraint matching based on so-called support sets. However, the algorithm exploits particular features of DL-Lite A and can not be readily applied to repairing DL-programs over other prominent DLs like EL. compared to DL-Lite A , in EL support sets may neither be small nor only few support sets might exist, and completeness of the algorithm may need to be given up when the support information is bounded. We thus provide an approach for computing repairs for DL-programs over EL ontologies based on partial (incomplete) support families. The latter are constructed using datalog query rewriting techniques as well as ontology approximation based on logical difference between EL-terminologies. We show how the maximal size and number of support sets for a given DL-atom can be estimated by analyzing the properties of a support hypergraph, which characterizes a relevant set of TBox axioms needed for query derivation. We present a declarative implementation of the repair approach and experimentally evaluate it on a set of benchmark problems; the promising results witness practical feasibility of our repair approach.

scholarly journals Knowledge Representation of Highly Dynamic Ontologies Using Defeasible Logic

2021 ◽  
Vol 10 (2) ◽  
pp. 948-954
Keyword(s):  
Computational Complexity ◽  
Knowledge Representation ◽  
Knowledge Base ◽  
Description Logic ◽  
Defeasible Logic ◽  
Small Domain ◽  
Automated Support ◽  
Logical Rules

Description logic gives us the ability of reasoning with acceptable computational complexity with retaining the power of expressiveness. The power of description logic can be accompanied by the defeasible logic to manage non-monotonic reasoning. In some domains, we need flexible reasoning and knowledge representation to deal the dynamicity of such domains. In this paper, we present a DL representation for a small domain that describes the connections between different entities in a university publication system to show how could we deal with changeability in domain rules. An automated support can be provided on the basis of defeasible logical rules to represent the typicality in the knowledge base and to solve the conflicts that might happen.

scholarly journals Unifying Class-Based Representation Formalisms

1999 ◽  
Vol 11 ◽  
pp. 199-240 ◽  
Author(s):  
D. Calvanese ◽  
M. Lenzerini ◽  
D. Nardi
Keyword(s):  
Knowledge Representation ◽  
Description Logic ◽  
Description Logics ◽  
Expressive Power ◽  
Finite Model ◽  
Functional Dependencies ◽  
Semantic Data ◽  
Representation Systems ◽  
Frame Systems ◽  
Essential Set

The notion of class is ubiquitous in computer science and is central in many formalisms for the representation of structured knowledge used both in knowledge representation and in databases. In this paper we study the basic issues underlying such representation formalisms and single out both their common characteristics and their distinguishing features. Such investigation leads us to propose a unifying framework in which we are able to capture the fundamental aspects of several representation languages used in different contexts. The proposed formalism is expressed in the style of description logics, which have been introduced in knowledge representation as a means to provide a semantically well-founded basis for the structural aspects of knowledge representation systems. The description logic considered in this paper is a subset of first order logic with nice computational characteristics. It is quite expressive and features a novel combination of constructs that has not been studied before. The distinguishing constructs are number restrictions, which generalize existence and functional dependencies, inverse roles, which allow one to refer to the inverse of a relationship, and possibly cyclic assertions, which are necessary for capturing real world domains. We are able to show that it is precisely such combination of constructs that makes our logic powerful enough to model the essential set of features for defining class structures that are common to frame systems, object-oriented database languages, and semantic data models. As a consequence of the established correspondences, several significant extensions of each of the above formalisms become available. The high expressiveness of the logic we propose and the need for capturing the reasoning in different contexts forces us to distinguish between unrestricted and finite model reasoning. A notable feature of our proposal is that reasoning in both cases is decidable. We argue that, by virtue of the high expressive power and of the associated reasoning capabilities on both unrestricted and finite models, our logic provides a common core for class-based representation formalisms.

Developing Rule-Based Applications for the Web

2011 ◽  
pp. 456-477 ◽  
Author(s):  
Vassilis Papataxiarhis ◽  
Vassileios Tsetsos ◽  
Isambo Karali ◽  
Panagiotis Stamatopoulos
Keyword(s):  
Knowledge Representation ◽  
Comparative Study ◽  
Logic Programming ◽  
Web Applications ◽  
Distributed Applications ◽  
Rule Based ◽  
The Web

Embedding rules into Web applications, and distributed applications in general, seems to constitute a significant task in order to accommodate desired expressivity features in such environments. Various methodologies and reasoning modules have been proposed to manage rules and knowledge on the Web. The main objective of the chapter is to survey related work in this area and discuss relevant theories, methodologies and tools that can be used to develop rule-based applications for the Web. The chapter deals with both ways that have been formally defined for modeling a domain of interest: the first based on standard logics while the second one stemmed from the logic programming perspective. Furthermore, a comparative study that evaluates the reasoning engines and the various knowledge representation methodologies, focusing on rules, is presented.

scholarly journals “Virtual Doctor” Management Technique in the Diagnosis of ENT Diseases

2019 ◽  
Vol 15 (09) ◽  
pp. 88
Author(s):  
Arshi Naim ◽  
Mohammad Faiz Khan ◽  
Mohammad Rashid Hussain ◽  
Nawsher Khan
Keyword(s):  
Artificial Intelligence ◽  
Expert System ◽  
Knowledge Representation ◽  
Daily Practice ◽  
Management Technique ◽  
Rule Based ◽  
Rule Based System ◽  
Related Disease ◽  
Effectiveness And Efficiency ◽  
Early Diagnose

<p>This research project is about the managing of an ENT ( Ear Nose Throat) diagnosis expert system through the virtual doctor that can assist physicians in diagnosing ENT related diseases. ENT problems can distress hearing, speaking, learning and many other significant behaviors and untreated ENT diseases can be serious. Therefore, early diagnose of ENT diseases is fundamental. This study is qualitative in nature where we have used the concept of Virtual Doctor under artificial intelligence (AI) based expert system, already designed to assist physicians in the diagnosis of ENT related disease in the absence of ENT experts. This system can reduce the excess created due to the busy schedules of ENT experts and enhance the effectiveness and efficiency of healthcare system. Virtual Doctor for ENT diagnosis uses rule based system for knowledge representation and has sub-systems which can enhance the physician’s ability in reaching a diagnosis decision with assurance. In this paper, we described the management system for application of Virtual Doctor for the diagnosis of  ENT related diseases which can be used by physicians in their daily practice.</p>

scholarly journals Vicious Circle Principle and Logic Programs with Aggregates

2014 ◽  
Vol 14 (4-5) ◽  
pp. 587-601 ◽  
Author(s):  
MICHAEL GELFOND ◽  
YUANLIN ZHANG
Keyword(s):  
Knowledge Representation ◽  
Vicious Circle ◽  
Logic Programs ◽  
Vicious Circle Principle ◽  
Knowledge Representation Language ◽  
Representation Language ◽  
Answer Sets ◽  
Mathematical Semantics

AbstractThe paper presents a knowledge representation language $\mathcal{A}log$ which extends ASP with aggregates. The goal is to have a language based on simple syntax and clear intuitive and mathematical semantics. We give some properties of $\mathcal{A}log$, an algorithm for computing its answer sets, and comparison with other approaches.

Decision Retrieval and Storage Enabled Through Description Logic

2007 ◽  
Author(s):  
Gregory M. Mocko ◽  
David W. Rosen ◽  
Farrokh Mistree
Keyword(s):  
Knowledge Representation ◽  
Product Development ◽  
Cantilever Beam ◽  
Description Logic ◽  
Decision Models ◽  
Design Decision ◽  
Engineering Knowledge ◽  
Query Concepts ◽  
And Storage

The problem addressed in the paper is how to represent the knowledge associated with design decision models to enable storage, retrieval, and reuse. The paper concerns the representations and reasoning mechanisms needed to construct decision models of relevance to engineered product development. Specifically, AL[E][N] description logic is proposed as a formalism for modeling engineering knowledge and for enabling retrieval and reuse of archived models. Classification hierarchies are constructed using subsumption in DL. Retrieval of archived models is supported using subsumption and query concepts. In our methodology, design decision models are constructed using the base vocabulary and reuse is supported through reasoning and retrieval capabilities. Application of the knowledge representation for the design of a cantilever beam is demonstrated.

scholarly journals Enhancing Lazy Grounding with Lazy Normalization in Answer-Set Programming

2019 ◽  
Vol 33 ◽  
pp. 2694-2702
Author(s):  
Jori Bomanson ◽  
Tomi Janhunen ◽  
Antonius Weinzierl
Keyword(s):  
Asymptotic Behavior ◽  
Knowledge Representation ◽  
Answer Set Programming ◽  
Expressive Power ◽  
Rule Based ◽  
On Demand ◽  
Space And Time ◽  
Different Types ◽  
Representation Formalism ◽  
Answer Set

Answer-Set Programming (ASP) is an expressive rule-based knowledge-representation formalism. Lazy grounding is a solving technique that avoids the well-known grounding bottleneck of traditional ASP evaluation but is restricted to normal rules, severely limiting its expressive power. In this work, we introduce a framework to handle aggregates by normalizing them on demand during lazy grounding, hence relieving the restrictions of lazy grounding significantly. We term our approach as lazy normalization and demonstrate its feasibility for different types of aggregates. Asymptotic behavior is analyzed and correctness of the presented lazy normalizations is shown. Benchmark results indicate that lazy normalization can bring up-to exponential gains in space and time as well as enable ASP to be used in new application areas.

scholarly journals Enhancing the Efficiency of a Decision Support System through the Clustering of Complex Rule-Based Knowledge Bases and Modification of the Inference Algorithm

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Agnieszka Nowak-Brzezińska
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Knowledge Representation ◽  
Knowledge Base ◽  
Support System ◽  
Inference Algorithm ◽  
Rule Based ◽  
New Knowledge ◽  
The Impact ◽  
Base Structure

Decision support systems founded on rule-based knowledge representation should be equipped with rule management mechanisms. Effective exploration of new knowledge in every domain of human life requires new algorithms of knowledge organization and a thorough search of the created data structures. In this work, the author introduces an optimization of both the knowledge base structure and the inference algorithm. Hence, a new, hierarchically organized knowledge base structure is proposed as it draws on the cluster analysis method and a new forward-chaining inference algorithm which searches only the so-called representatives of rule clusters. Making use of the similarity approach, the algorithm tries to discover new facts (new knowledge) from rules and facts already known. The author defines and analyses four various representative generation methods for rule clusters. Experimental results contain the analysis of the impact of the proposed methods on the efficiency of a decision support system with such knowledge representation. In order to do this, four representative generation methods and various types of clustering parameters (similarity measure, clustering methods, etc.) were examined. As can be seen, the proposed modification of both the structure of knowledge base and the inference algorithm has yielded satisfactory results.

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