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

Drawing OWL 2 ontologies with Eddy the editor

In this paper we introduce Eddy, a new open-source tool for the graphical editing of OWL~2 ontologies. Eddy is specifically designed for creating ontologies in Graphol, a completely visual ontology language that is equivalent to OWL~2. Thus, in Eddy ontologies are easily drawn as diagrams, rather than written as sets of formulas, as commonly happens in popular ontology design and engineering environments. This makes Eddy particularly suited for usage by people who are more familiar with diagramatic languages for conceptual modeling rather than with typical ontology formalisms, as is often required in non-academic and industrial contexts. Eddy provides intuitive functionalities for specifying Graphol diagrams, guarantees their syntactic correctness, and allows for exporting them in standard OWL 2 syntax. A user evaluation study we conducted shows that Eddy is perceived as an easy and intuitive tool for ontology specification

Ontology Mediated Information Extraction with MASTRO SYSTEM-T

In several data-centric application domains, the need arises to extract valuable information from unstructured text documents. The recent paradigm of Ontology Mediated Information Extraction (OMIE) faces this problem by taking into account the knowledge expressed by a domain ontology, and reasoning over it to improve the quality of extracted data. MASTRO SYSTEM-T is a novel tool for OMIE, developed by Sapienza University and IBM Almaden Research. In this work, we demonstrate its usage for information extraction over real-world financial text documents from the U.S. EDGAR system

Semantic technologies for the production and publication of open data in ACI - Automobile club d’Italia

Semantic technologies combine knowledge representation techniques with artificial intelligence in order to achieve a more effective management of enterprise knowledge bases, thanks to the separation of the conceptual level of the applications from the logical and physical ones, and to the automatic reasoning services they deploy for data access and control. In this context, Ontology-based Data Management (OBDM) [3] has consolidated itself as a paradigm for data integration and governance, based on a three-tier architecture: the ontology, the data sources, and the mappings, which declaratively link the ontology predicates to the data in the sources. In this talk1 we present a joint project by Sapienza University of Rome, the Automobile Club d’Italia (ACI), and OKKAM S.r.l.2, a spinoff of the University of Trento. The objectives of the project were the definition of an ontology of ACI’s Public Vehicle Register (PRA) and car tax domains, the development of an OBDM system to access the data through such ontology, and the creation of a web portal for the publication of ACI’s car parc data in Linked Open format

Towards a Framework for Ontology-Based Data Access: Materialization of OWL Ontologies From Relational Databases

The landscape of information systems applications is comprised of legacy components that many times rely on heterogeneous data sources, proprietary data formats, and a low-level knowledge representation. Only seasoned programmers who maintain those systems may interpret these data. Ontology-based Data Access is a novel approach for developing information systems where an ontology defines a high-level global schema of already existing data sources and provides a vocabulary for user queries. In this work, we report on the construction of a system that exports such data, represented as a legacy relational database, as an OWL ontology in accordance to the W3C Direct Mapping specification. We discuss several case studies that show how the proposed application works.X Workshop Innovación en Sistemas de Software (WISS)Red de Universidades con Carreras en Informática (RedUNCI

Ontop: answering SPARQL queries over relational databases

We present Ontop, an open-source Ontology-Based Data Access (OBDA) system that allows for querying relational data sources through a conceptual representation of the domain of interest, provided in terms of an ontology, to which the data sources are mapped. Key features of Ontop are its solid theoretical foundations, a virtual approach to OBDA, which avoids materializing triples and is implemented through the query rewriting technique, extensive optimizations exploiting all elements of the OBDA architecture, its compliance to all relevant W3C recommendations (including SPARQL queries, R2RML mappings, and OWL2QL and RDFS ontologies), and its support for all major relational databases

Virtual Knowledge Graphs: An Overview of Systems and Use Cases

In this paper, we present the virtual knowledge graph (VKG) paradigm for data integration and access, also known in the literature as Ontology-based Data Access. Instead of structuring the integration layer as a collection of relational tables, the VKG paradigm replaces the rigid structure of tables with the flexibility of graphs that are kept virtual and embed domain knowledge. We explain the main notions of this paradigm, its tooling ecosystem and significant use cases in a wide range of applications. Finally, we discuss future research directions