Requirements and Use Cases ; Report I on the sub-project Smart Content Enrichment

In this technical report, we present the results of the first milestone phase of the Corporate Smart Content sub-project "Smart Content Enrichment". We present analyses of the state of the art in the fields concerning the three working packages defined in the sub-project, which are aspect-oriented ontology development, complex entity recognition, and semantic event pattern mining. We compare the research approaches related to our three research subjects and outline briefly our future work plan

OntoMaven: Maven-based Ontology Development and Management of Distributed Ontology Repositories

In collaborative agile ontology development projects support for modular reuse of ontologies from large existing remote repositories, ontology project life cycle management, and transitive dependency management are important needs. The Apache Maven approach has proven its success in distributed collaborative Software Engineering by its widespread adoption. The contribution of this paper is a new design artifact called OntoMaven. OntoMaven adopts the Maven-based development methodology and adapts its concepts to knowledge engineering for Maven-based ontology development and management of ontology artifacts in distributed ontology repositories.Comment: Pre-print submission to 9th International Workshop on Semantic Web Enabled Software Engineering (SWESE2013). Berlin, Germany, December 2-5, 201

Corporate Smart Content Evaluation

Nowadays, a wide range of information sources are available due to the evolution of web and collection of data. Plenty of these information are consumable and usable by humans but not understandable and processable by machines. Some data may be directly accessible in web pages or via data feeds, but most of the meaningful existing data is hidden within deep web databases and enterprise information systems. Besides the inability to access a wide range of data, manual processing by humans is effortful, error-prone and not contemporary any more. Semantic web technologies deliver capabilities for machine-readable, exchangeable content and metadata for automatic processing of content. The enrichment of heterogeneous data with background knowledge described in ontologies induces re-usability and supports automatic processing of data. The establishment of “Corporate Smart Content” (CSC) - semantically enriched data with high information content with sufficient benefits in economic areas - is the main focus of this study. We describe three actual research areas in the field of CSC concerning scenarios and datasets applicable for corporate applications, algorithms and research. Aspect- oriented Ontology Development advances modular ontology development and partial reuse of existing ontological knowledge. Complex Entity Recognition enhances traditional entity recognition techniques to recognize clusters of related textual information about entities. Semantic Pattern Mining combines semantic web technologies with pattern learning to mine for complex models by attaching background knowledge. This study introduces the afore-mentioned topics by analyzing applicable scenarios with economic and industrial focus, as well as research emphasis. Furthermore, a collection of existing datasets for the given areas of interest is presented and evaluated. The target audience includes researchers and developers of CSC technologies - people interested in semantic web features, ontology development, automation, extracting and mining valuable information in corporate environments. The aim of this study is to provide a comprehensive and broad overview over the three topics, give assistance for decision making in interesting scenarios and choosing practical datasets for evaluating custom problem statements. Detailed descriptions about attributes and metadata of the datasets should serve as starting point for individual ideas and approaches

Provalets: Component-Based Mobile Agents as Microservices for Rule-Based Data Access, Processing and Analytics

Provalets are mobile rule agents for rule-based data access, semantic processing, and inference analytics. They can be dynamically deployed as microservices from Maven repositories into standardized container environments such as OSGi, where they can be used via simple REST calls. The programming model supports rapid prototyping and reuse of Provalets components to build Linked Enterprise Data applications where the sensible corporate data is not transmitted outside the enterprise, but instead the Provalets providing data processing and knowledge inference capabilities are moved closer to the data

Weaving ontology aspects using a catalog of structural ontology design patterns

Modular development of ontologies proves beneficial at different stages of the ontology lifecycle. In our previous work, we proposed aspect-oriented ontology development as a flexible approach to modular ontology development and a-posteriori modularization of existing monolithic ontologies, inspired by aspect-oriented programming and based on so called cross-cutting concerns. Similar to other formalisms for modular ontologies (e.g. E-Connections), aspect-oriented ontologies rely on an extension of the used ontology language. This derivation from the standard in turn requires specially adapted tools in order to use these ontologies in applications. In this paper, we present an approach to the recombination of aspect-oriented ontology modules to standard OWL 2 ontologies by using an aspect-weaving service. The weaving service relies on a preconfigured catalog of structural ontology design patterns. We show that the use of the weaving service yields syntactically correct and semantically complete ontologies while still allowing ontology developers to fully benefit from modular ontology development