Datalog± Ontology Consolidation

Knowledge bases in the form of ontologies are receiving increasing attention as they allow to clearly represent both the available knowledge, which includes the knowledge in itself and the constraints imposed to it by the domain or the users. In particular, Datalog ± ontologies are attractive because of their property of decidability and the possibility of dealing with the massive amounts of data in real world environments; however, as it is the case with many other ontological languages, their application in collaborative environments often lead to inconsistency related issues. In this paper we introduce the notion of incoherence regarding Datalog± ontologies, in terms of satisfiability of sets of constraints, and show how under specific conditions incoherence leads to inconsistent Datalog ± ontologies. The main contribution of this work is a novel approach to restore both consistency and coherence in Datalog± ontologies. The proposed approach is based on kernel contraction and restoration is performed by the application of incision functions that select formulas to delete. Nevertheless, instead of working over minimal incoherent/inconsistent sets encountered in the ontologies, our operators produce incisions over non-minimal structures called clusters. We present a construction for consolidation operators, along with the properties expected to be satisfied by them. Finally, we establish the relation between the construction and the properties by means of a representation theorem. Although this proposal is presented for Datalog± ontologies consolidation, these operators can be applied to other types of ontological languages, such as Description Logics, making them apt to be used in collaborative environments like the Semantic Web.Fil: Deagustini, Cristhian Ariel David. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Martinez, Maria Vanina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Falappa, Marcelo Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Simari, Guillermo Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentin

RTL Design Quality Checks for Soft IPs

Soft IPs are architectural modules which are delivered in the form of synthesizable RTL level codes written in some HDL (hardware descriptive language) like Verilog or VHDL or System Verilog. They are technology independent and offer high degree of modification flexibility. RTL is the complete abstraction of our design. Since SOC complexity is growing day by day with new technologies and requirement, it will be very much difficult to debug and fix issues after physical level. So to reduce effort and increase efficiency and accuracy it is necessary to fix most of the bugs in RTL level. Also if we are using soft IP, then our bug free IP can be used by third party. So early detection of bugs helps us not to go back to entire design and do all the process again and again. One of the important issue at RTL level of a design is the Clock Domain Crossing (CDC) problem. This is the issue which affects the performance at each and every stage of the design flow. Failure in fixing these issues at the earlier stage makes the design unreliable and design performance collapses. The main issue in real time clock designs are the metastability issue. Although we cannot check or see these issues using our simulator but we have to make preventions at RTL level. This is done by restructuring the design and adding required synchronizers. One more important area of consideration in VLSI design is power consumption. In modern low power designs low power is a key factor. So design consuming less power is preferred over design consuming more power. This decision should be made as early as possible. RTL quality check helps us on this aspect. Using different tools power estimation can be performed at RTL stage which saves lots of efforts in redesigning. This project aims at checking clock domain crossing faults at RTL stage and doing redesign of circuit to eliminate those faults. Also an effort is made to compare quality of two designs in terms of delay, power consumption and area

Detecting and Correcting Conservativity Principle Violations in Ontology-to-Ontology Mappings

In order to enable interoperability between ontology-based systems, ontology matching techniques have been proposed. However, when the generated mappings suffer from logical flaws, their usefulness may be diminished. In this paper we present an approximate method to detect and correct violations to the so-called conservativity principle where novel subsumption entailments between named concepts in one of the input ontologies are considered as unwanted. We show that this is indeed the case in our application domain based on the EU Optique project. Additionally, our extensive evaluation conducted with both the Optique use case and the data sets from the Ontology Alignment Evaluation Initiative (OAEI) suggests that our method is both useful and feasible in practice.Copyright 2014 Springer International Publishing Switzerland. The final publication is available at http://link.springer.com/chapter/10.1007%2F978-3-319-11915-1_

Situated modeling of epistemic puzzles

Ankara : The Department of Computer Engineering and Information Science and the Institute of Engineering and Science of Bilkent Univ., 1994.Thesis (Master's) -- -Bilkent University, 1994.Includes bibliographical references leaves 67-72Situation theory is a mathematical theory of meaning introduced by Jon Barwise and John Perry. It has evoked great theoretical and practical interest and motivated the framework of a few ‘computational’ systems. PROSIT is the pioneering work in this direction. Unfortunately, there is a lack of reallife applications on these systems and this study is a preliminary attempt to remedy this deficiency. Here, we examine how much PROSIT reflects situationtheoretic concepts and solve a group of epistemic puzzles, using the constructs provided by this programming language.Ersan, MuratM.S

Reasoning-Supported Quality Assurance for Knowledge Bases

The increasing application of ontology reuse and automated knowledge acquisition tools in ontology engineering brings about a shift of development efforts from knowledge modeling towards quality assurance. Despite the high practical importance, there has been a substantial lack of support for ensuring semantic accuracy and conciseness. In this thesis, we make a significant step forward in ontology engineering by developing a support for two such essential quality assurance activities

Minimizing conservativity violations in ontology alignments: algorithms and evaluation

In order to enable interoperability between ontology-based systems, ontology matching techniques have been proposed. However, when the generated mappings lead to undesired logical consequences, their usefulness may be diminished. In this paper, we present an approach to detect and minimize the violations of the so-called conservativity principle where novel subsumption entailments between named concepts in one of the input ontologies are considered as unwanted. The practical applicability of the proposed approach is experimentally demonstrated on the datasets from the Ontology Alignment Evaluation Initiative

Qualitative Process Analysis : Theoretical Requirements and Practical Implementation in Naval Domain

Understanding complex behaviours is an essential component of everyday life, integrated into daily routines as well as specialised research. To handle the increasing amount of data available from (logistic) dynamic scenarios, analysis of the behaviour of agents in a given environment is becoming more automated and thus requires reliable new analytical methods. This thesis seeks to improve analysis of observed data in dynamic scenarios by developing a new model for transforming sparse behavioural observations into realistic explanations of agent behaviours, with the goal of testing that model in a real-world maritime navigation scenario

Efficient Maximum A-Posteriori Inference in Markov Logic and Application in Description Logics

Maximum a-posteriori (MAP) query in statistical relational models computes the most probable world given evidence and further knowledge about the domain. It is arguably one of the most important types of computational problems, since it is also used as a subroutine in weight learning algorithms. In this thesis, we discuss an improved inference algorithm and an application for MAP queries. We focus on Markov logic (ML) as statistical relational formalism. Markov logic combines Markov networks with first-order logic by attaching weights to first-order formulas. For inference, we improve existing work which translates MAP queries to integer linear programs (ILP). The motivation is that existing ILP solvers are very stable and fast and are able to precisely estimate the quality of an intermediate solution. In our work, we focus on improving the translation process such that we result in ILPs having fewer variables and fewer constraints. Our main contribution is the Cutting Plane Aggregation (CPA) approach which leverages symmetries in ML networks and parallelizes MAP inference. Additionally, we integrate the cutting plane inference (Riedel 2008) algorithm which significantly reduces the number of groundings by solving multiple smaller ILPs instead of one large ILP. We present the new Markov logic engine RockIt which outperforms state-of-the-art engines in standard Markov logic benchmarks. Afterwards, we apply the MAP query to description logics. Description logics (DL) are knowledge representation formalisms whose expressivity is higher than propositional logic but lower than first-order logic. The most popular DLs have been standardized in the ontology language OWL and are an elementary component in the Semantic Web. We combine Markov logic, which essentially follows the semantic of a log-linear model, with description logics to log-linear description logics. In log-linear description logic weights can be attached to any description logic axiom. Furthermore, we introduce a new query type which computes the most-probable 'coherent' world. Possible applications of log-linear description logics are mainly located in the area of ontology learning and data integration. With our novel log-linear description logic reasoner ELog, we experimentally show that more expressivity increases quality and that the solutions of optimal solving strategies have higher quality than the solutions of approximate solving strategies