Foundational Ontologies meet Ontology Matching: A Survey

Ontology matching is a research area aimed at finding ways to make different ontologies interoperable. Solutions to the problem have been proposed from different disciplines, including databases, natural language processing, and machine learning. The role of foundational ontologies for ontology matching is an important one. It is multifaceted and with room for development. This paper presents an overview of the different tasks involved in ontology matching that consider foundational ontologies. We discuss the strengths and weaknesses of existing proposals and highlight the challenges to be addressed in the future

CEDAR: The Dutch Historical Censuses as Linked Open Data

In this document we describe the CEDAR dataset, a five-star Linked Open Data representation of the Dutch historical censuses, conducted in the Netherlands once every 10 years from 1795 to 1971. We produce a linked dataset from a digitized sample of 2,288 tables. The dataset contains more than 6.8 million statistical observations about the demography, labour and housing of the Dutch society in the 18th, 19th and 20th centuries. The dataset is modeled using the RDF Data Cube vocabulary for multidimensional data, uses Open Annotation to express rules of data harmonization, and keeps track of the provenance of every single data point and its transformations using PROV. We link these observations to well known standard classification systems in social history, such as the Historical International Standard Classification of Occupations (HISCO) and the Amsterdamse Code (AC), which in turn link to DBpedia and GeoNames. The two main contributions of the dataset are the improvement of data integration and access for historical research, and the emergence of new historical data hubs, like classifications of historical religions and historical house types, in the Linked Open Data cloud

Testing OWL Axioms against RDF Facts: A Possibilistic Approach

International audienceAutomatic knowledge base enrichment methods rely criti-cally on candidate axiom scoring. The most popular scoring heuristics proposed in the literature are based on statistical inference. We argue that such a probability-based framework is not always completely satis-factory and propose a novel, alternative scoring heuristics expressed in terms of possibility theory, whereby a candidate axiom receives a bipolar score consisting of a degree of possibility and a degree of necessity. We evaluate our proposal by applying it to the problem of testing SubClassOf axioms against the DBpedia RDF dataset

A Linked Data Approach to Know-How

Abstract. The Web is one of the major repositories of human generated know-how, such as step-by-step videos and instructions. This knowledge can be potentially reused in a wide variety of applications, but it cur-rently suffers from a lack of structure and isolation from related knowl-edge. To overcome these challenges we have developed a Linked Data framework which can automate the extraction of know-how from exist-ing Web resources and generate links to related knowledge on the Linked Data Cloud. We have implemented our framework and used it to extract a Linked Data representation of two of the largest know-how reposi-tories on the Web. We demonstrate two possible uses of the resulting dataset of real-world know-how. Firstly, we use this dataset within a Web application to offer an integrated visualization of distributed know-how resources. Lastly, we show the potential of this dataset for inferring common sense knowledge about tasks.

Text Mining for User Query Analysis: A 5-Step Method for Cultural Heritage Institutions

Peer reviewe

Improving Quality Assurance in Multidisciplinary Engineering Environments with Semantic Technologies

In multidisciplinary engineering (MDE) projects, for example, automation systems or manufacturing systems, stakeholders from various disciplines, for example, electrics, mechanics and software, have to collaborate. In industry practice, engineers apply individual and highly specialized tools with strong limitation regarding defect detection in early engineering phases. Experts typically execute reviews with limited tool support which make engineering projects defective and risky. Semantic Web Technologies (SWTs) can help to bridge the gap between heterogeneous sources as foundation for efficient and effective defect detection. Main questions focus on (a) how to bridge gaps between loosely coupled tools and incompatible data models and (b) how SWTs can help to support efficient and effective defect detection in context of engineering process improvement. This chapter describes success-critical requirements for defect detection in MDE and shows how SWTs can provide the foundation for early and efficient defect detection with an adapted review approach. The proposed defect detection framework (DDF) suggests different levels of SWT contributions as a roadmap for engineering process improvement. Two selected industry-related real-life cases show different levels of SWT involvement. Although SWTs have been successfully applied in real-life use cases, SWT applications can be risky if applied without good understanding of success factors and limitations

Visualizing Statistical Linked Knowledge for Decision Support

In a global and interconnected economy, decision makers often need to consider information from various domains. A tourism destination manager, for example, has to correlate tourist behavior with financial and environmental indicators to allocate funds for strategic long-term investments. Statistical data underpins a broad range of such cross-domain decision tasks. A variety of statistical datasets are available as Linked Open Data, often incorporated into visual analytics solutions to support decision making. What are the principles, architectures, workflows and implementation design patterns that should be followed for building such visual cross-domain decision support systems. This article introduces a methodology to integrate and visualize cross-domain statistical data sources by applying selected RDF Data Cube (QB) principles. A visual dashboard built according to this methodology is presented and evaluated in the context of two use cases in the tourism and telecommunications domains

Supporting Tools for Automated Generation and Visual Editing of Relational-to-Ontology Mappings

La integració de dades amb formats heterogenis i de diversos dominis mitjançant tecnologies de la web semàntica permet solucionar la seva disparitat estructural i semàntica. L'accés a dades basat en ontologies (OBDA, en anglès) és una solució integral que es basa en l'ús d'ontologies com esquemes mediadors i el mapatge entre les dades i les ontologies per facilitar la consulta de les fonts de dades. No obstant això, una de les principals barreres que pot dificultar més l'adopció de OBDA és la manca d'eines per donar suport a la creació de mapatges entre dades i ontologies. L'objectiu d'aquesta investigació ha estat desenvolupar noves eines que permetin als experts sense coneixements d'ontologies la creació de mapatges entre dades i ontologies. Amb aquesta finalitat, s'han dut a terme dues línies de treball: la generació automàtica de mapatges entre dades relacionals i ontologies i l'edició dels mapatges a través de la seva representació visual. Les eines actualment disponibles per automatitzar la generació de mapatges estan lluny de proporcionar una solució completa, ja que es basen en els esquemes relacionals i amb prou feines tenen en compte els continguts de la font de dades relacional i les característiques de l'ontologia. No obstant això, les dades poden contenir relacions ocultes que poden ajudar a la generació de mapatges. Per superar aquesta limitació, hem desenvolupat AutoMap4OBDA, un sistema que genera automàticament mapatges R2RML a partir de l'anàlisi dels continguts de la font relacional i tenint en compte les característiques de l'ontologia. El sistema fa servir una tècnica d'aprenentatge d'ontologies per inferir jerarquies de classes, selecciona les mètriques de similitud de cadenes en base a les etiquetes de les ontologies i analitza les estructures de grafs per generar els mapatges a partir de l'estructura de l'ontologia. La representació visual per mitjà d'interfícies intuïtives pot ajudar els usuaris sense coneixements tècnics a establir mapatges entre una font relacional i una ontologia. No obstant això, les eines existents per a l'edició visual de mapatges mostren algunes limitacions. En particular, la representació visual de mapatges no contempla les estructures de la font relacional i de l'ontologia de forma conjunta. Per superar aquest inconvenient, hem desenvolupat Map-On, un entorn visual web per a l'edició manual de mapatges. AutoMap4OBDA ha demostrat que supera les prestacions de les solucions existents per a la generació de mapatges. Map-On s'ha aplicat en projectes d'investigació per verificar la seva eficàcia en la gestió de mapatges.La integración de datos con formatos heterogéneos y de diversos dominios mediante tecnologías de la Web Semántica permite solventar su disparidad estructural y semántica. El acceso a datos basado en ontologías (OBDA, en inglés) es una solución integral que se basa en el uso de ontologías como esquemas mediadores y mapeos entre los datos y las ontologías para facilitar la consulta de las fuentes de datos. Sin embargo, una de las principales barreras que puede dificultar más la adopción de OBDA es la falta de herramientas para apoyar la creación de mapeos entre datos y ontologías. El objetivo de esta investigación ha sido desarrollar nuevas herramientas que permitan a expertos sin conocimientos de ontologías la creación de mapeos entre datos y ontologías. Con este fin, se han llevado a cabo dos líneas de trabajo: la generación automática de mapeos entre datos relacionales y ontologías y la edición de los mapeos a través de su representación visual. Las herramientas actualmente disponibles para automatizar la generación de mapeos están lejos de proporcionar una solución completa, ya que se basan en los esquemas relacionales y apenas tienen en cuenta los contenidos de la fuente de datos relacional y las características de la ontología. Sin embargo, los datos pueden contener relaciones ocultas que pueden ayudar a la generación de mapeos. Para superar esta limitación, hemos desarrollado AutoMap4OBDA, un sistema que genera automáticamente mapeos R2RML a partir del análisis de los contenidos de la fuente relacional y teniendo en cuenta las características de la ontología. El sistema emplea una técnica de aprendizaje de ontologías para inferir jerarquías de clases, selecciona las métricas de similitud de cadenas en base a las etiquetas de las ontologías y analiza las estructuras de grafos para generar los mapeos a partir de la estructura de la ontología. La representación visual por medio de interfaces intuitivas puede ayudar a los usuarios sin conocimientos técnicos a establecer mapeos entre una fuente relacional y una ontología. Sin embargo, las herramientas existentes para la edición visual de mapeos muestran algunas limitaciones. En particular, la representación de mapeos no contempla las estructuras de la fuente relacional y de la ontología de forma conjunta. Para superar este inconveniente, hemos desarrollado Map-On, un entorno visual web para la edición manual de mapeos. AutoMap4OBDA ha demostrado que supera las prestaciones de las soluciones existentes para la generación de mapeos. Map-On se ha aplicado en proyectos de investigación para verificar su eficacia en la gestión de mapeos.Integration of data from heterogeneous formats and domains based on Semantic Web technologies enables us to solve their structural and semantic heterogeneity. Ontology-based data access (OBDA) is a comprehensive solution which relies on the use of ontologies as mediator schemas and relational-to-ontology mappings to facilitate data source querying. However, one of the greatest obstacles in the adoption of OBDA is the lack of tools to support the creation of mappings between physically stored data and ontologies. The objective of this research has been to develop new tools that allow non-ontology experts to create relational-to-ontology mappings. For this purpose, two lines of work have been carried out: the automated generation of relational-to-ontology mappings, and visual support for mapping editing. The tools currently available to automate the generation of mappings are far from providing a complete solution, since they rely on relational schemas and barely take into account the contents of the relational data source and features of the ontology. However, the data may contain hidden relationships that can help in the process of mapping generation. To overcome this limitation, we have developed AutoMap4OBDA, a system that automatically generates R2RML mappings from the analysis of the contents of the relational source and takes into account the characteristics of ontology. The system employs an ontology learning technique to infer class hierarchies, selects the string similarity metric based on the labels of ontologies, and analyses the graph structures to generate the mappings from the structure of the ontology. The visual representation through intuitive interfaces can help non-technical users to establish mappings between a relational source and an ontology. However, existing tools for visual editing of mappings show somewhat limitations. In particular, the visual representation of mapping does not embrace the structure of the relational source and the ontology at the same time. To overcome this problem, we have developed Map-On, a visual web environment for the manual editing of mappings. AutoMap4OBDA has been shown to outperform existing solutions in the generation of mappings. Map-On has been applied in research projects to verify its effectiveness in managing mappings

Génération automatique d'alignements complexes d'ontologies

Le web de données liées (LOD) est composé de nombreux entrepôts de données. Ces données sont décrites par différents vocabulaires (ou ontologies). Chaque ontologie a une terminologie et une modélisation propre ce qui les rend hétérogènes. Pour lier et rendre les données du web de données liées interopérables, les alignements d'ontologies établissent des correspondances entre les entités desdites ontologies. Il existe de nombreux systèmes d'alignement qui génèrent des correspondances simples, i.e., ils lient une entité à une autre entité. Toutefois, pour surmonter l'hétérogénéité des ontologies, des correspondances plus expressives sont parfois nécessaires. Trouver ce genre de correspondances est un travail fastidieux qu'il convient d'automatiser. Dans le cadre de cette thèse, une approche d'alignement complexe basée sur des besoins utilisateurs et des instances communes est proposée. Le domaine des alignements complexes est relativement récent et peu de travaux adressent la problématique de leur évaluation. Pour pallier ce manque, un système d'évaluation automatique basé sur de la comparaison d'instances est proposé. Ce système est complété par un jeu de données artificiel sur le domaine des conférences.The Linked Open Data (LOD) cloud is composed of data repositories. The data in the repositories are described by vocabularies also called ontologies. Each ontology has its own terminology and model. This leads to heterogeneity between them. To make the ontologies and the data they describe interoperable, ontology alignments establish correspondences, or links between their entities. There are many ontology matching systems which generate simple alignments, i.e., they link an entity to another. However, to overcome the ontology heterogeneity, more expressive correspondences are sometimes needed. Finding this kind of correspondence is a fastidious task that can be automated. In this thesis, an automatic complex matching approach based on a user's knowledge needs and common instances is proposed. The complex alignment field is still growing and little work address the evaluation of such alignments. To palliate this lack, we propose an automatic complex alignment evaluation system. This system is based on instances. A famous alignment evaluation dataset has been extended for this evaluation