Ontoprise GmbH

Karlsruhe, Germany

Ontoprise GmbH

Karlsruhe, Germany
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Janssen F.,TU Darmstadt | Fallahi F.,Ontoprise GmbH | Noessner J.,University of Mannheim | Paulheim H.,TU Darmstadt
CEUR Workshop Proceedings | Year: 2012

Ontology matching approaches have mostly worked on the schema level so far. With the advent of Linked Open Data and the availability of a massive amount of instance information, instance-based approaches become possible. This position paper discusses approaches and challenges for using those instances as input for machine learning algorithms, with a focus on rule learning algorithms, as a means for ontology matching.

Gmez-Prez J.M.,Intelligent Software Components ISOCO S.A. | Erdmann M.,Ontoprise GmbH | Greaves M.,Vulcan Inc. | Corcho O.,Technical University of Madrid | Benjamins R.,Telefnica ID
International Journal of Human Computer Studies | Year: 2010

The development of knowledge-based systems is usually approached through the combined skills of software and knowledge engineers (SEs and KEs, respectively) and of subject matter experts (SMEs). One of the most critical steps in this task aims at transferring knowledge from SMEs' expertise to formal, machine-readable representations, which allow systems to reason with such knowledge. However, this process is costly and error prone. Alleviating such knowledge acquisition bottleneck requires enabling SMEs with the means to produce the target knowledge representations, minimizing the intervention of KEs. This is especially difficult in the case of complex knowledge types like processes. The analysis of scientific domains like Biology, Chemistry, and Physics uncovers: (i) that process knowledge is the single most frequent type of knowledge occurring in such domains and (ii) specific solutions need to be devised in order to allow SMEs to represent it in a computational form. We present a framework and computer system for the acquisition and representation of process knowledge in scientific domains by SMEs. We propose methods and techniques to enable SMEs to acquire process knowledge from the domains, to formally represent it, and to reason about it. We have developed an abstract process metamodel and a library of problem solving methods (PSMs), which support these tasks, respectively providing the terminology for SME-tailored process diagrams and an abstract formalization of the strategies needed for reasoning about processes. We have implemented this approach as part of the DarkMatter system and formally evaluated it in the context of the intermediate evaluation of Project Halo, an initiative aiming at the creation of question answering systems by SMEs. © 2010 Elsevier Ltd. All rights reserved.

Gomez-Perez J.M.,OCO S.A. | Erdmann M.,Ontoprise GmbH | Greaves M.,Vulcan Inc. | Corcho O.,Technical University of Madrid
IEEE Transactions on Knowledge and Data Engineering | Year: 2013

Enabling Subject Matter Experts (SMEs) to formulate knowledge without the intervention of Knowledge Engineers (KEs) requires providing SMEs with methods and tools that abstract the underlying knowledge representation and allow them to focus on modeling activities. Bridging the gap between SME-authored models and their representation is challenging, especially in the case of complex knowledge types like processes, where aspects like frame management, data, and control flow need to be addressed. In this paper, we describe how SME-authored process models can be provided with an operational semantics and grounded in a knowledge representation language like F-logic to support process-related reasoning. The main results of this work include a formalism for process representation and a mechanism for automatically translating process diagrams into executable code following such formalism. From all the process models authored by SMEs during evaluation 82 percent were well formed, all of which executed correctly. Additionally, the two optimizations applied to the code generation mechanism produced a performance improvement at reasoning time of 25 and 30 percent with respect to the base case, respectively. © 1989-2012 IEEE.

Becker C.,MediaEvent Services GmbH and Co. KG | Bizer C.,Free University of Berlin | Erdmann M.,Ontoprise GmbH | Greaves M.,Vulcan Inc.
CEUR Workshop Proceedings | Year: 2010

In this paper, we present a project which extends a SMW+ semantic wiki with a Linked Data Integration Framework that performs Web data access, vocabulary mapping, identity resolution, and quality evaluation of Linked Data. As a result, a large collection of neurogenomicsrelevant data from the Web can be flexibly transformed into a unified ontology, allowing unified querying, navigation, and visualization; as well as support for wiki-style collaboration, crowdsourcing, and commentary on chosen data sets.

Heymans S.,Vienna University of Technology | Korf R.,Ontoprise GmbH | Erdmann M.,Ontoprise GmbH | Puhrer J.,Vienna University of Technology | Eiter T.,Vienna University of Technology
Proceedings - 2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010 | Year: 2010

In W3C's Rule Interchange Format (RIF), F-Logic rules have received considerable attention as a major logical rule formalism, while combinations of rules with Description Logic (DL) ontologies in RIF, let alone with F-Logic rules, are far less developed. To mend this, we first present F-Logic# knowledge bases, a framework based on the semantics of the well-investigated dl-programs, that provides a loose coupling approach to integrating F-Logic rules and DL ontologies by allowing rules to query the ontology using external atoms. We investigate the semantical properties of this framework and define a stratified fragment that allows for fast reasoning - a necessity on a Web with large amounts of data. We then shape F-Logic# as a RIF dialect, setting it firmly in a Web context and providing an expressive combination of F-Logic rules with DL ontologies in RIF. Finally, we show how to extend the F-Logic rule engine OntoBroker towards reasoning with F-Logic#, enabling as such a first commercial implementation for loosely-coupled ontologies and rules. © 2010 IEEE.

Zendoia J.,Mechanical Design Research | Zapp M.,Fraunhofer Institute for Manufacturing Engineering and Automation | Agyapong-Kodua K.,University of Nottingham | Lohse N.,University of Nottingham | Singh M.,Ontoprise GmbH
International Journal of Computer Integrated Manufacturing | Year: 2013

Design methods applied at the concept development stage of a design process help the derivation of alternative design solutions so that, based on product requirements and design specifications, selected design options can be evaluated. How this is achieved is important because design decisions contribute largely to the cost and manufacturability of products. The main scenario of the European machine tool (MT) industry is a small- or medium-sized enterprise designing and producing small series of dedicated MTs competing on a global market and working in close collaboration with suppliers and machine end users. In this scenario, MT manufacturers need close collaboration with their component suppliers and end users to develop effective design solutions. The article identifies some aspects of 'collaborative design methods' that have been applied successfully in the aerospace and automotive industries and recommends a 'knowledge-centred' approach with the potential to capture, transfer and share knowledge at the different life phases of the MT, but currently applied mainly at the conceptual design stage. The proposed methodology relies on a flexible knowledge-based 'co-design environment'. The methodology will enhance knowledge capitalisation, allow early virtual assessment of design decisions and reduce re-design cycles. © 2013 Taylor & Francis.

Gomez-Perez J.M.,ISOCO S.A. | Corcho O.,Technical University of Madrid | Erdmann M.,Ontoprise GmbH
CEUR Workshop Proceedings | Year: 2010

Enabling Subject Matter Experts (SMEs) to formulate knowledge without the intervention of Knowledge Engineers (KEs) requires providing SMEs with methods and tools that abstract the underlying knowledge representation, allowing SMEs to focus on the modeling activities. However, automatically bridging the gap between SME-authored models and their internal representation is not an easy task, especially in the case of complex knowledge types like processes, where aspects like frame management, data, and control flow need to be addressed. In this paper, we present a process representation formalism and method for automatically grounding SME-authored process models in the form of process diagrams into a particular representation language, supporting process representation and reasoning.

Kiss E.M.,Ontoprise GmbH | Brockmans S.,Ontoprise GmbH | Angele J.,Ontoprise GmbH
Cognitive Technologies | Year: 2014

The Texo use case in the THESEUS research program offers technology and solutions for service marketplaces addressing in particular small and medium-sized enterprises (SMEs), which profit most from a standardized and open platform with out-of-the-box functionality. ontoprise, a leading company in semantic technologies since 1999, contributed the Texo semantic infrastructure as a collaborative development and runtime environment for the Service Ontology. By providing the Texo partners with a continually improved semantic infrastructure, we learned about the chances and challenges of using semantic technologies in the Internet of Services. As an illustrative example for the advantages of these semantic technologies in a B2B context, we present a proof of concept for an ontology-based recommendation system, which relies on the semantic infrastructure, and includes enhanced data integration support. © Springer International Publishing Switzerland 2014.

Angele J.,Ontoprise GmbH
Semantic Web | Year: 2014

OntoBroker provides a comprehensive, scalable and high-performance Semantic Web middleware. It supports all of the W3C Semantic Web recommendations for ontology languages and query languages. It is an ontology repository that includes a high performance deductive reasoning engine. Especially reasoning with rules is a major unique selling point for ontoprise. OntoBroker integrates a connector framework which makes it easy to connect a multitude of data sources like databases, web services etc. Thus it combines structured and unstructured data in one framework, OntoBroker is easy to extend and to integrate into existing IT landscapes and applications as it offers a variety of open interfaces. OntoBroker is also closely connected to ontoprise's ontology modeling environment OntoStudio which is the development environment for handling ontologies, mappings to information sources, rules, generating queries, creating business intelligence reports etc. At many customers OntoBroker serves as a common semantic layer which is accessed by various applications and integrates different information sources. OntoBroker is the run-time environment for industrial solutions like SemanticGuide, SemanticXpress, and SemanticIntegrator. As part of those meanwhile thousands of installations are in productive use. © 2014-IOS Press and the authors. All rights reserved.

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