Complex correspondences for query patterns rewriting

International audienceThis paper discusses the use of complex alignments in the task of automatic query patterns rewriting. We apply this approach in SWIP, a system that allows for querying RDF data from natural language-based queries, hiding the complexity of SPARQL. SWIP is based on the use of query patterns that characterise families of queries and that are instantiated with respect to the initial user query expressed in natural language. However, these patterns are specific to the vocabulary used to describe the data source to be queried. For rewriting query patterns, we experiment ontology matching approaches in order to find complex correspondences between two ontologies describing data sources. From the alignments and initial query patterns, we rewrite these patterns in order to be able to query the data described using the target ontology. These experiments have been carried out on an ontology on the music domain and DBpedia ontology

A survey of RDB to RDF translation approaches and tools

ISRN I3S/RR 2013-04-FR 24 pagesRelational databases scattered over the web are generally opaque to regular web crawling tools. To address this concern, many RDB-to-RDF approaches have been proposed over the last years. In this paper, we propose a detailed review of seventeen RDB-to-RDF initiatives, considering end-to-end projects that delivered operational tools. The different tools are classified along three major axes: mapping description language, mapping implementation and data retrieval method. We analyse the motivations, commonalities and differences between existing approaches. The expressiveness of existing mapping languages is not always sufficient to produce semantically rich data and make it usable, interoperable and linkable. We therefore briefly present various strategies investigated in the literature to produce additional knowledge. Finally, we show that R2RML, the W3C recommendation for describing RDB to RDF mappings, may not apply to all needs in the wide scope of RDB to RDF translation applications, leaving space for future extensions

Federated Query Processing over Heterogeneous Data Sources in a Semantic Data Lake

Data provides the basis for emerging scientific and interdisciplinary data-centric applications with the potential of improving the quality of life for citizens. Big Data plays an important role in promoting both manufacturing and scientific development through industrial digitization and emerging interdisciplinary research. Open data initiatives have encouraged the publication of Big Data by exploiting the decentralized nature of the Web, allowing for the availability of heterogeneous data generated and maintained by autonomous data providers. Consequently, the growing volume of data consumed by different applications raise the need for effective data integration approaches able to process a large volume of data that is represented in different format, schema and model, which may also include sensitive data, e.g., financial transactions, medical procedures, or personal data. Data Lakes are composed of heterogeneous data sources in their original format, that reduce the overhead of materialized data integration. Query processing over Data Lakes require the semantic description of data collected from heterogeneous data sources. A Data Lake with such semantic annotations is referred to as a Semantic Data Lake. Transforming Big Data into actionable knowledge demands novel and scalable techniques for enabling not only Big Data ingestion and curation to the Semantic Data Lake, but also for efficient large-scale semantic data integration, exploration, and discovery. Federated query processing techniques utilize source descriptions to find relevant data sources and find efficient execution plan that minimize the total execution time and maximize the completeness of answers. Existing federated query processing engines employ a coarse-grained description model where the semantics encoded in data sources are ignored. Such descriptions may lead to the erroneous selection of data sources for a query and unnecessary retrieval of data, affecting thus the performance of query processing engine. In this thesis, we address the problem of federated query processing against heterogeneous data sources in a Semantic Data Lake. First, we tackle the challenge of knowledge representation and propose a novel source description model, RDF Molecule Templates, that describe knowledge available in a Semantic Data Lake. RDF Molecule Templates (RDF-MTs) describes data sources in terms of an abstract description of entities belonging to the same semantic concept. Then, we propose a technique for data source selection and query decomposition, the MULDER approach, and query planning and optimization techniques, Ontario, that exploit the characteristics of heterogeneous data sources described using RDF-MTs and provide a uniform access to heterogeneous data sources. We then address the challenge of enforcing privacy and access control requirements imposed by data providers. We introduce a privacy-aware federated query technique, BOUNCER, able to enforce privacy and access control regulations during query processing over data sources in a Semantic Data Lake. In particular, BOUNCER exploits RDF-MTs based source descriptions in order to express privacy and access control policies as well as their automatic enforcement during source selection, query decomposition, and planning. Furthermore, BOUNCER implements query decomposition and optimization techniques able to identify query plans over data sources that not only contain the relevant entities to answer a query, but also are regulated by policies that allow for accessing these relevant entities. Finally, we tackle the problem of interest based update propagation and co-evolution of data sources. We present a novel approach for interest-based RDF update propagation that consistently maintains a full or partial replication of large datasets and deal with co-evolution

Semantic Data Management in Data Lakes

In recent years, data lakes emerged as away to manage large amounts of heterogeneous data for modern data analytics. One way to prevent data lakes from turning into inoperable data swamps is semantic data management. Some approaches propose the linkage of metadata to knowledge graphs based on the Linked Data principles to provide more meaning and semantics to the data in the lake. Such a semantic layer may be utilized not only for data management but also to tackle the problem of data integration from heterogeneous sources, in order to make data access more expressive and interoperable. In this survey, we review recent approaches with a specific focus on the application within data lake systems and scalability to Big Data. We classify the approaches into (i) basic semantic data management, (ii) semantic modeling approaches for enriching metadata in data lakes, and (iii) methods for ontologybased data access. In each category, we cover the main techniques and their background, and compare latest research. Finally, we point out challenges for future work in this research area, which needs a closer integration of Big Data and Semantic Web technologies

Strategies for Managing Linked Enterprise Data

Data, information and knowledge become key assets of our 21st century economy. As a result, data and knowledge management become key tasks with regard to sustainable development and business success. Often, knowledge is not explicitly represented residing in the minds of people or scattered among a variety of data sources. Knowledge is inherently associated with semantics that conveys its meaning to a human or machine agent. The Linked Data concept facilitates the semantic integration of heterogeneous data sources. However, we still lack an effective knowledge integration strategy applicable to enterprise scenarios, which balances between large amounts of data stored in legacy information systems and data lakes as well as tailored domain specific ontologies that formally describe real-world concepts. In this thesis we investigate strategies for managing linked enterprise data analyzing how actionable knowledge can be derived from enterprise data leveraging knowledge graphs. Actionable knowledge provides valuable insights, supports decision makers with clear interpretable arguments, and keeps its inference processes explainable. The benefits of employing actionable knowledge and its coherent management strategy span from a holistic semantic representation layer of enterprise data, i.e., representing numerous data sources as one, consistent, and integrated knowledge source, to unified interaction mechanisms with other systems that are able to effectively and efficiently leverage such an actionable knowledge. Several challenges have to be addressed on different conceptual levels pursuing this goal, i.e., means for representing knowledge, semantic data integration of raw data sources and subsequent knowledge extraction, communication interfaces, and implementation. In order to tackle those challenges we present the concept of Enterprise Knowledge Graphs (EKGs), describe their characteristics and advantages compared to existing approaches. We study each challenge with regard to using EKGs and demonstrate their efficiency. In particular, EKGs are able to reduce the semantic data integration effort when processing large-scale heterogeneous datasets. Then, having built a consistent logical integration layer with heterogeneity behind the scenes, EKGs unify query processing and enable effective communication interfaces for other enterprise systems. The achieved results allow us to conclude that strategies for managing linked enterprise data based on EKGs exhibit reasonable performance, comply with enterprise requirements, and ensure integrated data and knowledge management throughout its life cycle

an approach for semantic integration of heterogeneous data sources

Integrating data from multiple heterogeneous data sources entails dealing with data distributed among heterogeneous information sources, which can be structured, semi-structured or unstructured, and providing the user with a unified view of these data. Thus, in general, gathering information is challenging, and one of the main reasons is that data sources are designed to support specific applications. Very often their structure is unknown to the large part of users. Moreover, the stored data is often redundant, mixed with information only needed to support enterprise processes, and incomplete with respect to the business domain. Collecting, integrating, reconciling and efficiently extracting information from heterogeneous and autonomous data sources is regarded as a major challenge. In this paper, we present an approach for the semantic integration of heterogeneous data sources, DIF (Data Integration Framework), and a software prototype to support all aspects of a complex data integration process. The proposed approach is an ontology-based generalization of both Global-as-View and Local-as-View approaches. In particular, to overcome problems due to semantic heterogeneity and to support interoperability with external systems, ontologies are used as a conceptual schema to represent both data sources to be integrated and the global view

Enabling Complex Semantic Queries to Bioinformatics Databases through Intuitive Search Over Data

Data integration promises to be one of the main catalysts in enabling new insights to be drawn from the wealth of biological data already available publicly. However, the heterogene- ity of the existing data sources still poses significant challenges for achieving interoperability among biological databases. Furthermore, merely solving the technical challenges of data in- tegration, for example through the use of common data representation formats, leaves open the larger problem. Namely, the steep learning curve required for understanding the data models of each public source, as well as the technical language through which the sources can be queried and joined. As a consequence, most of the available biological data remain practically unexplored today. In this thesis, we address these problems jointly, by first introducing an ontology-based data integration solution in order to mitigate the data source heterogeneity problem. We illustrate through the concrete example of Bgee, a gene expression data source, how relational databases can be exposed as virtual Resource Description Framework (RDF) graphs, through relational-to-RDF mappings. This has the important advantage that the original data source can remain unmodified, while still becoming interoperable with external RDF sources. We complement our methods with applied case studies designed to guide domain experts in formulating expressive federated queries targeting the integrated data across the domains of evolutionary relationships and gene expression. More precisely, we introduce two com- parative analyses, first within the same domain (using orthology data from multiple, inter- operable, data sources) and second across domains, in order to study the relation between expression change and evolution rate following a duplication event. Finally, in order to bridge the semantic gap between users and data, we design and im- plement Bio-SODA, a question answering system over domain knowledge graphs, that does not require training data for translating user questions to SPARQL. Bio-SODA uses a novel ranking approach that combines syntactic and semantic similarity, while also incorporating node centrality metrics to rank candidate matches for a given user question. Our results in testing Bio-SODA across several real-world databases that span multiple domains (both within and outside bioinformatics) show that it can answer complex, multi-fact queries, be- yond the current state-of-the-art in the more well-studied open-domain question answering. -- L’intégration des données promet d’être l’un des principaux catalyseurs permettant d’extraire des nouveaux aperçus de la richesse des données biologiques déjà disponibles publiquement. Cependant, l’hétérogénéité des sources de données existantes pose encore des défis importants pour parvenir à l’interopérabilité des bases de données biologiques. De plus, en surmontant seulement les défis techniques de l’intégration des données, par exemple grâce à l’utilisation de formats standard de représentation de données, on laisse ouvert un problème encore plus grand. À savoir, la courbe d’apprentissage abrupte nécessaire pour comprendre la modéli- sation des données choisie par chaque source publique, ainsi que le langage technique par lequel les sources peuvent être interrogés et jointes. Par conséquent, la plupart des données biologiques publiquement disponibles restent pratiquement inexplorés aujourd’hui. Dans cette thèse, nous abordons l’ensemble des deux problèmes, en introduisant d’abord une solution d’intégration de données basée sur ontologies, afin d’atténuer le problème d’hété- rogénéité des sources de données. Nous montrons, à travers l’exemple de Bgee, une base de données d’expression de gènes, une approche permettant les bases de données relationnelles d’être publiés sous forme de graphes RDF (Resource Description Framework) virtuels, via des correspondances relationnel-vers-RDF (« relational-to-RDF mappings »). Cela présente l’important avantage que la source de données d’origine peut rester inchangé, tout en de- venant interopérable avec les sources RDF externes. Nous complétons nos méthodes avec des études de cas appliquées, conçues pour guider les experts du domaine dans la formulation de requêtes fédérées expressives, ciblant les don- nées intégrées dans les domaines des relations évolutionnaires et de l’expression des gènes. Plus précisément, nous introduisons deux analyses comparatives, d’abord dans le même do- maine (en utilisant des données d’orthologie provenant de plusieurs sources de données in- teropérables) et ensuite à travers des domaines interconnectés, afin d’étudier la relation entre le changement d’expression et le taux d’évolution suite à une duplication de gène. Enfin, afin de mitiger le décalage sémantique entre les utilisateurs et les données, nous concevons et implémentons Bio-SODA, un système de réponse aux questions sur des graphes de connaissances domaine-spécifique, qui ne nécessite pas de données de formation pour traduire les questions des utilisateurs vers SPARQL. Bio-SODA utilise une nouvelle ap- proche de classement qui combine la similarité syntactique et sémantique, tout en incorporant des métriques de centralité des nœuds, pour classer les possibles candidats en réponse à une question utilisateur donnée. Nos résultats suite aux tests effectués en utilisant Bio-SODA sur plusieurs bases de données à travers plusieurs domaines (tantôt liés à la bioinformatique qu’extérieurs) montrent que Bio-SODA réussit à répondre à des questions complexes, en- gendrant multiples entités, au-delà de l’état actuel de la technique en matière de systèmes de réponses aux questions sur les données structures, en particulier graphes de connaissances

Efficient Management for Geospatial and Temporal Data using Ontology-based Data Access Techniques

Το μοντέλο δεδομένων RDF και η γλώσσα επερωτήσεων SPARQL είναι ευρέως διαδεδομένα για την χρήση τους με σκοπό την ενοποίηση πληροφορίας που προέρχεται από διαφορετικές πηγές. Ο αυξανόμενος αριθμός των γεωχωρικών συνόλων δεδομένων που είναι πλέον διαθέσιμα σαν γεωχωρικά διασυνδεδεμένα δεδομένα οδήγησε στην εμφάνιση επεκτάσεων του μοντέλου δεδομένων RDF και της γλώσσας επερωτήσεων SPARQL. Δύο από τις σημαντικότερες επεκτάσεις αυτές είναι η γλώσσα GeoSPARQL, η οποία έγινε OGC πρότυπο, και το πλαίσιο του μοντέλου δεδομένων stRDF και της γλώσσας επερωτήσεων stSPARQL. Και οι δύο προσεγγίσεις μπορούν να χρησιμοποιηθούν για την αναπαράσταση και επερώτηση διασυνδεδεμένων γεωχωρικών δεδομένων, ενώ το μοντέλο stRDF και η γλώσσα stSPARQL παρέχουν επίσης επιπλέον λειτουργικότητα για την αναπαράσταση και επερώτηση χρονικών δεδομένων. Παρότι ο αριθμός των δεδομένων που είναι διαθέσιμα σαν γεωχωρικά ή και χρονικά διασυνδεδεμένα δεδομένα αυξάνεται, η μετατροπή των γεωχωρικών δεδομένων σε RDF και η αποθήκευσή τους σε αποθετήρια RDF δεν είναι πάντα η βέλτιστη λύση, ειδικά όταν τα δεδομένα βρίσκονται εξαρχής σε σχεσιακές βάσεις οι οποίες μπορεί να έχουν αρκετά μεγάλο μέγεθος ή και να ενημερώνονται πολύ συχνά. Στα πλαίσια αυτής της διδακτορικής διατριβής, προτείνουμε μια λύση βασισμένη στην ανάκτηση πληροφορίας με χρήση οντολογιών και αντιστοιχίσεων για την επερώτηση δεδομένων πάνω από γεωχωρικές σχεσιακές βάσεις δεδομένων. Επεκτείνουμε τεχνικές επανεγγραφής GeoSPARQL ερωτημάτων σε SQL ώστε η αποτίμηση των επερωτήσεων να γίνεται εξολοκλήρου στο γεωχωρικό σύστημα διαχείρισης βάσεων δεδομένων. Επίσης, εισαγάγουμε επιπλέον λειτουργικότητα στη χρονική συνιστώσα του μοντέλου δεδομένων stRDF και της γλώσσας επερωτήσεων stSPARQL, προκειμένου να διευκολυνθεί η υποστήριξη χρονικών τελεστών σε συστήματα OBDA. Στη συνέχεια, επεκτείνουμε τις παραπάνω μεθόδους με την υποστήριξη διαφορετικών πηγών δεδομένων πέρα από σχεσιακές βάσεις και παρουσιάζουμε μια OBDA προσέγγιση που επιτρέπει τη δημιουργία εικονικών RDF γράφων πάνω από δεδομένα που βρίσκονται διαθέσιμα στο διαδίκτυο σε διάφορες μορφές (πχ. HTML πίνακες, web διεπαφές), με χρήση οντολογιών και αντιστοιχίσεων. Συγκρίναμε την απόδοση του συστήματός μας με ένα σχετικό σύστημα και τα αποτελέσματα έδειξαν ότι πέραν του ότι το σύστημά μας παρέχει μεγαλύτερη λειτουργικότητα (πχ. υποστηρίζει περισσότερα είδη πηγών δεδομένων, περιλαμβάνει απλούστερες διαδικασίες και εξασφαλίζει καλύτερη απόδοση. Τέλος, παρουσιάζουμε την εφαρμογή των μεθόδων και συστημάτων που περιγράφονται στη διατριβή σε πραγματικά σενάρια χρήσης.The data model RDF and query language SPARQL have been widely used for the integration of data coming from different souces. Due to the increasing number of geospatial datasets that are being available as linked open data, a lot of effort focuses in the development of geospatial (and temporal, accordingly) extensions of the framework of RDF and SPARQL. Two highlights of these efforts are the query language GeoSPARQL, that is an OGC standard, and the framework of stRDF and stSPARQL. Both frameworks can be used for the representation and querying of linked geospatial data, and stSPARQL also includes a temporal dimension. Although a lot of geospatial (and some temporal) RDF stores started to emerge, converting geospatial data into RDF and then storing it into an RDF stores is not always best practice, especially when the data exists in a relational database that is fairly large and/or it gets updated frequently. In this thesis, we propose an Ontology-based Data Access (OBDA) approach for accessing geospatial data stored in geospatial relational databases, using the OGC standard GeoSPARQL and R2RML or OBDA mappings. We introduce extensions to an existing SPARQL-to-SQL translation method to support GeoSPARQL features. We describe the implementation of our approach in the system Ontop-spatial, an extension of the OBDA system Ontop for creating virtual geospatial RDF graphs on top of geospatial relational databases. Ontop-spatial is the first geospatial OBDA system and outperforms state-of-the-art geospatial RDF stores. We also show how to answer queries with temproal operators in the OBDA framework, by utilizing the framework stRDF and the query language stSPARQL which we extend with some new features. Next, we extend the data sources supported by Ontop-spatial going beyond relational database management systems, and we present our OBDA solutions for creating virtual RDF graphs on top of various web data sources (e.g., HTML tables, Web APIs) using ontologies and mappings. We compared the performance of our approach with a related implementation and evaluation results showed that not only does Ontop-spatial support more functionalities (e.g., more data sources, more simple workflow), but it also achieves better performance. Last, we describe how the work described in this thesis is applied in real-world application scenarios

A dataflow platform for applications based on Linked Data

Modern software applications increasingly benefit from accessing the multifarious and heterogeneous Web of Data, thanks to the use of web APIs and Linked Data principles. In previous work, the authors proposed a platform to develop applications consuming Linked Data in a declarative and modular way. This paper describes in detail the functional language the platform gives access to, which is based on SPARQL (the standard query language for Linked Data) and on the dataflow paradigm. The language features interactive and meta-programming capabilities so that complex modules/applications can be developed. By adopting a declarative style, it favours the development of modules that can be reused in various specific execution context