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

at the 14th Conference of the Spanish Association for Artificial Intelligence (CAEPIA 2011)

Technical Report TR-2011/1, Department of Languages and Computation. University of Almeria November 2011. Joaquín Cañadas, Grzegorz J. Nalepa, Joachim Baumeister (Editors)The seventh workshop on Knowledge Engineering and Software Engineering (KESE7) was held at the Conference of the Spanish Association for Artificial Intelligence (CAEPIA-2011) in La Laguna (Tenerife), Spain, and brought together researchers and practitioners from both fields of software engineering and artificial intelligence. The intention was to give ample space for exchanging latest research results as well as knowledge about practical experience.University of Almería, Almería, Spain. AGH University of Science and Technology, Kraków, Poland. University of Würzburg, Würzburg, Germany

Validation Framework for RDF-based Constraint Languages

In this thesis, a validation framework is introduced that enables to consistently execute RDF-based constraint languages on RDF data and to formulate constraints of any type. The framework reduces the representation of constraints to the absolute minimum, is based on formal logics, consists of a small lightweight vocabulary, and ensures consistency regarding validation results and enables constraint transformations for each constraint type across RDF-based constraint languages

Automatic Geospatial Data Conflation Using Semantic Web Technologies

Duplicate geospatial data collections and maintenance are an extensive problem across Australia government organisations. This research examines how Semantic Web technologies can be used to automate the geospatial data conflation process. The research presents a new approach where generation of OWL ontologies based on output data models and presenting geospatial data as RDF triples serve as the basis for the solution and SWRL rules serve as the core to automate the geospatial data conflation processes

Real Time Reasoning in OWL2 for GDPR Compliance

This paper shows how knowledge representation and reasoning techniques can be used to support organizations in complying with the GDPR, that is, the new European data protection regulation. This work is carried out in a European H2020 project called SPECIAL. Data usage policies, the consent of data subjects, and selected fragments of the GDPR are encoded in a fragment of OWL2 called PL (policy language); compliance checking and policy validation are reduced to subsumption checking and concept consistency checking. This work proposes a satisfactory tradeoff between the expressiveness requirements on PL posed by the GDPR, and the scalability requirements that arise from the use cases provided by SPECIAL's industrial partners. Real-time compliance checking is achieved by means of a specialized reasoner, called PLR, that leverages knowledge compilation and structural subsumption techniques. The performance of a prototype implementation of PLR is analyzed through systematic experiments, and compared with the performance of other important reasoners. Moreover, we show how PL and PLR can be extended to support richer ontologies, by means of import-by-query techniques. PL and its integration with OWL2's profiles constitute new tractable fragments of OWL2. We prove also some negative results, concerning the intractability of unrestricted reasoning in PL, and the limitations posed on ontology import

Linked data authority records for Irish place names

Linked Data technologies are increasingly being implemented to enhance cataloguing workflows in libraries, archives and museums. We review current best practice in library cataloguing, how Linked Data is used to link collections and provide consistency in indexing, and briefly describe the relationship between Linked Data, library data models and descriptive standards. As an example we look at the Logainm.ie dataset, an online database holding the authoritative hierarchical list of Irish and English language place names in Ireland. This paper describes the process of creating the new Linked Logainm dataset, including the transformation of the data from XML to RDF and the generation of links to external geographic datasets like DBpedia and the Faceted Application of Subject Terminology. This dataset was then used to enhance the National Library of Ireland's metadata MARCXML metadata records for its Longfield maps collection. We also describe the potential benefits of Linked Data for libraries, focusing on the use of the Linked Logainm dataset and its future potential for Irish heritage institutions

An ontology-based secure design framework for graph-based databases

Graph-based databases are concerned with performance and flexibility. Most of the existing approaches used to design secure NoSQL databases are limited to the final implementation stage, and do not involve the design of security and access control issues at higher abstraction levels. Ensuring security and access control for Graph-based databases is difficult, as each approach differs significantly depending on the technology employed. In this paper, we propose the first technology-ascetic framework with which to design secure Graph-based databases. Our proposal raises the abstraction level by using ontologies to simultaneously model database and security requirements together. This is supported by the TITAN framework, which facilitates the way in which both aspects are dealt with. The great advantages of our approach are, therefore, that it: allows database designers to focus on the simultaneous protection of security and data while ignoring the implementation details; facilitates the secure design and rapid migration of security rules by deriving specific security measures for each underlying technology, and enables database designers to employ ontology reasoning in order to verify whether the security rules are consistent. We show the applicability of our proposal by applying it to a case study based on a hospital data access control.This work has been developed within the AETHER-UA (PID2020-112540RB-C43), AETHER-UMA (PID2020-112540RB-C41) and AETHER-UCLM (PID2020-112540RB-C42), ALBA (TED2021-130355B-C31, TED2021-130355B-C33), PRESECREL (PID2021-124502OB-C42) projects funded by the “Ministerio de Ciencia e Innovación”, Andalusian PAIDI program with grant (P18-RT-2799) and the BALLADER Project (PROMETEO/2021/088) funded by the “Consellería de Innovación, Universidades, Ciencia Sociedad Digital”, Generalitat Valenciana

Knowledge Patterns for the Web: extraction, tranformation and reuse

This thesis aims at investigating methods and software architectures for discovering what are the typical and frequently occurring structures used for organizing knowledge in the Web. We identify these structures as Knowledge Patterns (KPs). KP discovery needs to address two main research problems: the heterogeneity of sources, formats and semantics in the Web (i.e., the knowledge soup problem) and the difficulty to draw relevant boundary around data that allows to capture the meaningful knowledge with respect to a certain context (i.e., the knowledge boundary problem). Hence, we introduce two methods that provide different solutions to these two problems by tackling KP discovery from two different perspectives: (i) the transformation of KP-like artifacts to KPs formalized as OWL2 ontologies; (ii) the bottom-up extraction of KPs by analyzing how data are organized in Linked Data. The two methods address the knowledge soup and boundary problems in different ways. The first method provides a solution to the two aforementioned problems that is based on a purely syntactic transformation step of the original source to RDF followed by a refactoring step whose aim is to add semantics to RDF by select meaningful RDF triples. The second method allows to draw boundaries around RDF in Linked Data by analyzing type paths. A type path is a possible route through an RDF that takes into account the types associated to the nodes of a path. Then we present K~ore, a software architecture conceived to be the basis for developing KP discovery systems and designed according to two software architectural styles, i.e, the Component-based and REST. Finally we provide an example of reuse of KP based on Aemoo, an exploratory search tool which exploits KPs for performing entity summarization