DL-lite with attributes and datatypes

We extend the DL-Lite languages by means of attributes and datatypes. Attributes -- a notion borrowed from data models -- associate concrete values from datatypes to abstract objects and in this way complement roles, which describe relationships between abstract objects. The extended languages remain tractable (with a notable exception) even though they contain both existential and (a limited form of) universal quantification. We present complexity results for two most important reasoning problems in DL-Lite: combined complexity of knowledge base satisfiability and data complexity of positive existential query answering

Towards Log-Linear Logics with Concrete Domains

We present $\mathcal{MEL}^{++}$ (M denotes Markov logic networks) an extension of the log-linear description logics $\mathcal{EL}^{++}$-LL with concrete domains, nominals, and instances. We use Markov logic networks (MLNs) in order to find the most probable, classified and coherent $\mathcal{EL}^{++}$ ontology from an $\mathcal{MEL}^{++}$ knowledge base. In particular, we develop a novel way to deal with concrete domains (also known as datatypes) by extending MLN's cutting plane inference (CPI) algorithm.Comment: StarAI201

Query Answering in DL-Lite with Datatypes: A Non-Uniform Approach

Adding datatypes to ontology-mediated queries (OMQs) often makes query answering hard. As a consequence, the use of datatypes in OWL 2 QL has been severely restricted. In this paper we propose a new, non-uniform, way of analyzing the data-complexity of OMQ answering with datatypes. Instead of restricting the ontology language we aim at a classification of the patterns of datatype atoms in OMQs into those that can occur in non-tractable OMQs and those that only occur in tractable OMQs. To this end we establish a close link between OMQ answering with datatypes and constraint satisfaction problems over the datatypes. In a case study we apply this link to prove a P/coNP-dichotomy for OMQs over DL-Lite extended with the datatype (Q,<=). The proof employs a recent dichotomy result by Bodirsky and Kára for temporal constraint satisfaction problems

Towards Analytics Aware Ontology Based Access to Static and Streaming Data (Extended Version)

Real-time analytics that requires integration and aggregation of heterogeneous and distributed streaming and static data is a typical task in many industrial scenarios such as diagnostics of turbines in Siemens. OBDA approach has a great potential to facilitate such tasks; however, it has a number of limitations in dealing with analytics that restrict its use in important industrial applications. Based on our experience with Siemens, we argue that in order to overcome those limitations OBDA should be extended and become analytics, source, and cost aware. In this work we propose such an extension. In particular, we propose an ontology, mapping, and query language for OBDA, where aggregate and other analytical functions are first class citizens. Moreover, we develop query optimisation techniques that allow to efficiently process analytical tasks over static and streaming data. We implement our approach in a system and evaluate our system with Siemens turbine data

A Detailed Comparison of UML and OWL

As models and ontologies assume an increasingly central role in software and information systems engineering, the question of how exactly they compare and how they can sensibly be used together assumes growing importance. However, no study to date has systematically and comprehensively compared the two technology spaces, and a large variety of different bridging and integration ideas have been proposed in recent years without any detailed analysis of whether they are sound or useful. In this paper, we address this problem by providing a detailed and comprehensive comparison of the two technology spaces in terms of their flagship languages – UML and OWL – each a de facto and de jure standard in its respective space. To fully analyze the end user experience, we perform the comparison at two levels – one considering the underlying boundary assumptions and philosophy adopted by each language and the other considering their detailed features. We also consider all relevant auxiliary languages such as OCL. The resulting comparison clarifies the relationship between the two technologies and provides a solid foundation for deciding how to use them together or integrate them

Context Mediation in the Semantic Web: Handling OWL Ontology and Data Disparity through Context Interchange

The COntext INterchange (COIN) strategy is an approach to solving the problem of interoperability of semantically heterogeneous data sources through context mediation. COIN has used its own notation and syntax for representing ontologies. More recently, the OWL Web Ontology Language is becoming established as the W3C recommended ontology language. We propose the use of the COIN strategy to solve context disparity and ontology interoperability problems in the emerging Semantic Web – both at the ontology level and at the data level. In conjunction with this, we propose a version of the COIN ontology model that uses OWL and the emerging rules interchange language, RuleML.Singapore-MIT Alliance (SMA

Mapping relational data model to OWL ontology: knowledge conceptualization in OWL

In this paper, we introduce the issues and solutions of using OWL ontology to model extra restriction on 'Properties' of 'Classes' that are not provided by OWL specifications and to represent associations amongst 'Properties' other than 'Classes'. Two specific types of knowledge that cannot be modeled directly using OWL DL elements are identified and presented. Firstly the data value range constraint for a "DatatypeProperty"; secondly the calculation knowledge representation. Our approach to such issues is to conceptualize the knowledge in OWL and map the conceptualization in an implementation. Examples for each type of the knowledge and their OWL code are provided in detail to demonstrate our approach

Ontology for pixel processing

For all kinds of output devices, such as monitors, printers etc, the most important thing is to show the right information to the user. Pixel is the basic element both on screen and materials printed with. And, as a result pixel processing is the basic technique to make the output correct, precise, and suitable to use on different occasions. Pixel processing solves operations on each pixel of the image, which is for the pixel matrices of that image, so that the image would have different appearance. Ontology is about the exact description of things and their relationships. It is an old study of philosophy from ancient Greece. As the study of artificial intelligence keeps growing, the concept of ontology has been in use more and more in the formalization of knowledge in terms of classes, properties, instances and relations [1]. This paper mainly discusses how to build ontology of pixel processing with OWL. Actually, it is focused on how to describe pixel processing and its functions or operations in an understandable way by computer. With such description, it is possible to improve the development of pixel processing and the sharing of its knowledge both between people and machines. That is from the Natural Language Processing point of view. And also, in the future, it provides a base for intelligent agent to implement pixel processing by understanding such kind of definition and description directly through its knowledge base built up with such ontology. In other words, that may realize the automatic program or program analysis

Fuzzy ontology representation using OWL 2

AbstractThe need to deal with vague information in Semantic Web languages is rising in importance and, thus, calls for a standard way to represent such information. We may address this issue by either extending current Semantic Web languages to cope with vagueness, or by providing a procedure to represent such information within current standard languages and tools. In this work, we follow the latter approach, by identifying the syntactic differences that a fuzzy ontology language has to cope with, and by proposing a concrete methodology to represent fuzzy ontologies using OWL 2 annotation properties. We also report on some prototypical implementations: a plug-in to edit fuzzy ontologies using OWL 2 annotations and some parsers that translate fuzzy ontologies represented using our methodology into the languages supported by some reasoners