Managing data through the lens of an ontology

Ontology-based data management aims at managing data through the lens of an ontology, that is, a conceptual representation of the domain of interest in the underlying information system. This new paradigm provides several interesting features, many of which have already been proved effective in managing complex information systems. This article introduces the notion of ontology-based data management, illustrating the main ideas underlying the paradigm, and pointing out the importance of knowledge representation and automated reasoning for addressing the technical challenges it introduces

Knowledge Graph Building Blocks: An easy-to-use Framework for developing FAIREr Knowledge Graphs

Knowledge graphs and ontologies provide promising technical solutions for implementing the FAIR Principles for Findable, Accessible, Interoperable, and Reusable data and metadata. However, they also come with their own challenges. Nine such challenges are discussed and associated with the criterion of cognitive interoperability and specific FAIREr principles (FAIR + Explorability raised) that they fail to meet. We introduce an easy-to-use, open source knowledge graph framework that is based on knowledge graph building blocks (KGBBs). KGBBs are small information modules for knowledge-processing, each based on a specific type of semantic unit. By interrelating several KGBBs, one can specify a KGBB-driven FAIREr knowledge graph. Besides implementing semantic units, the KGBB Framework clearly distinguishes and decouples an internal in-memory data model from data storage, data display, and data access/export models. We argue that this decoupling is essential for solving many problems of knowledge management systems. We discuss the architecture of the KGBB Framework as we envision it, comprising (i) an openly accessible KGBB-Repository for different types of KGBBs, (ii) a KGBB-Engine for managing and operating FAIREr knowledge graphs (including automatic provenance tracking, editing changelog, and versioning of semantic units); (iii) a repository for KGBB-Functions; (iv) a low-code KGBB-Editor with which domain experts can create new KGBBs and specify their own FAIREr knowledge graph without having to think about semantic modelling. We conclude with discussing the nine challenges and how the KGBB Framework provides solutions for the issues they raise. While most of what we discuss here is entirely conceptual, we can point to two prototypes that demonstrate the principle feasibility of using semantic units and KGBBs to manage and structure knowledge graphs

A Semantic Problem Solving Environment for Integrative Parasite Research: Identification of Intervention Targets for Trypanosoma cruzi

Effective research in parasite biology requires analyzing experimental lab data in the context of constantly expanding public data resources. Integrating lab data with public resources is particularly difficult for biologists who may not possess significant computational skills to acquire and process heterogeneous data stored at different locations. Therefore, we develop a semantic problem solving environment (SPSE) that allows parasitologists to query their lab data integrated with public resources using ontologies. An ontology specifies a common vocabulary and formal relationships among the terms that describe an organism, and experimental data and processes in this case. SPSE supports capturing and querying provenance information, which is metadata on the experimental processes and data recorded for reproducibility, and includes a visual query-processing tool to formulate complex queries without learning the query language syntax. We demonstrate the significance of SPSE in identifying gene knockout targets for T. cruzi. The overall goal of SPSE is to help researchers discover new or existing knowledge that is implicitly present in the data but not always easily detected. Results demonstrate improved usefulness of SPSE over existing lab systems and approaches, and support for complex query design that is otherwise difficult to achieve without the knowledge of query language syntax

Data Integration Driven Ontology Design, Case Study Smart City

Methods to design of formal ontologies have been in focus of research since the early nineties when their importance and conceivable practical application in engineering sciences had been understood. However, often significant customization of generic methodologies is required when they are applied in tangible scenarios. In this paper, we present a methodology for ontology design developed in the context of data integration. In this scenario, a targeting ontology is applied as a mediator for distinct schemas of individual data sources and, furthermore, as a reference schema for federated data queries. The methodology has been used and evaluated in a case study aiming at integration of buildings' energy and carbon emission related data. We claim that we have made the design process much more efficient and that there is a high potential to reuse the methodology

Ontology Based Data Access in Statoil

Ontology Based Data Access (OBDA) is a prominent approach to query databases which uses an ontology to expose data in a conceptually clear manner by abstracting away from the technical schema-level details of the underlying data. The ontology is ‘connected’ to the data via mappings that allow to automatically translate queries posed over the ontology into data-level queries that can be executed by the underlying database management system. Despite a lot of attention from the research community, there are still few instances of real world industrial use of OBDA systems. In this work we present data access challenges in the data-intensive petroleum company Statoil and our experience in addressing these challenges with OBDA technology. In particular, we have developed a deployment module to create ontologies and mappings from relational databases in a semi-automatic fashion; a query processing module to perform and optimise the process of translating ontological queries into data queries and their execution over either a single DB of federated DBs; and a query formulation module to support query construction for engineers with a limited IT background. Our modules have been integrated in one OBDA system, deployed at Statoil, integrated with Statoil’s infrastructure, and evaluated with Statoil’s engineers and data

e-Social Science and Evidence-Based Policy Assessment : Challenges and Solutions

Peer reviewedPreprin

Ontology-based data integration in EPNet: Production and distribution of food during the Roman Empire

Semantic technologies are rapidly changing the historical research. Over the last decades, an immense amount of new quantifiable data have been accumulated, and made available in interchangeable formats, in social sciences and humanities, opening up new possibilities for solving old questions and posing new ones. This paper introduces a framework that eases the access of scholars to historical and cultural data about food production and commercial trade system during the Roman Empire, distributed across different data sources. The proposed approach relies on the Ontology-Based Data Access (OBDA) paradigm, where the different datasets are virtually integrated by a conceptual layer (an ontology) that provides to the user a clear point of access and a unified and unambiguous conceptual view

GeoDI: Geoscientific Data Integration

This report summarises the findings of the GeoDI project. Large volumes of geoscientific (i.e., geological and geophysical) datasets have been gathered by the Marine Institute and its partners over the past number of years, A key challenge now exists to derive maximum value from these very costly and valuable products by integrating these geoscientific datasets together, and with other resources such as biological, chemical, and environmental data. The project aimed to address this challenge by examining the critical issues involved in the integration of Irish marine geoscientific datasets, and by assessing tools and services for enhanced management, discovery, access, and analyses of geoscientific data.Funder: Marine Institut

Towards a service-oriented e-infrastructure for multidisciplinary environmental research

Research e-infrastructures are considered to have generic and thematic parts. The generic part provids high-speed networks, grid (large-scale distributed computing) and database systems (digital repositories and data transfer systems) applicable to all research commnities irrespective of discipline. Thematic parts are specific deployments of e-infrastructures to support diverse virtual research communities. The needs of a virtual community of multidisciplinary envronmental researchers are yet to be investigated. We envisage and argue for an e-infrastructure that will enable environmental researchers to develop environmental models and software entirely out of existing components through loose coupling of diverse digital resources based on the service-oriented achitecture. We discuss four specific aspects for consideration for a future e-infrastructure: 1) provision of digital resources (data, models & tools) as web services, 2) dealing with stateless and non-transactional nature of web services using workflow management systems, 3) enabling web servce discovery, composition and orchestration through semantic registries, and 4) creating synergy with existing grid infrastructures

Documenting Data Integration Using Knowledge Graphs

With the increasing volume of data on the Web and the proliferation of published knowledge graphs, there is a growing need for improved data management and information extraction. However, heterogeneity issues across the data sources, i.e., various formats and systems, negatively impact efficient access, manage, reuse, and analyze the data. A data integration system (DIS) provides uniform access to heterogeneous data sources and their relationships; it offers a unified and comprehensive view of the data. DISs resort to mapping rules, expressed in declarative languages like RML, to align data from various sources to classes and properties defined in an ontology. This work defines a knowledge graph where data integration systems are represented as factual statements. The aim of this work is to provide the basis for integrated analysis of data collected from heterogeneous data silos. The proposed knowledge graph is also specified as a data integration system, that integrates all data integration systems. The proposed solution includes a unified schema, which defines and explains the relationships between all elements in the data integration system DIS=⟨G, S, M, F⟩. The results suggest that factual statements from the proposed knowledge graph, improve the understanding of the features that characterize knowledge graphs declaratively defined like data integration systems