Engineering optimisations in query rewriting for OBDA

In this paper we study the problem of information disclosure in ontology-based data access (OBDA). Following previous work on Controlled Query Evaluation, we introduce the framework of Policy-Protected OBDA (PPOBDA), which extends OBDA with data protection policies specified over the ontology and enforced through a censor, i.e., a function that alters answers to users’ queries to avoid the disclosure of protected data. We consider PPOBDA systems in which the ontology is expressed in \(\textsc {owl\,2\,ql} \) and the policies are denial constraints, and show that query answering under censors in such a setting can be reduced to standard query answering in OBDA (without data protection policies). The basic idea of our approach is to compile the policies of a PPOBDA system into the mapping of a standard OBDA system. To this aim, we analyze some notions of censor proposed in the literature, show that they are not suited for the above-mentioned compilation, and provide a new definition of censor that enables the effective realization of our idea. We have implemented our technique and evaluated it over the NPD benchmark for OBDA. Our results are very promising and show that controlled query evaluation in OBDA can be realized in the practice by using off-the-shelf OBDA engines.

Data analysis-based decision-making is performed daily by domain experts. As data grows, getting access to relevant data becomes a challenge. In an approach known as Ontology-based data access (OBDA), ontologies are advocated as a suitable formal tool to address complex data access. This technique combines a domain ontology with a data source by using a declarative mapping specification to enable data access using a domain vocabulary. We investigate this approach by studying the theoretical background; conducting a literature review on the implementation of OBDA in production systems; implementing OBDA on a relational dataset using an OBDA tool and; providing results and analysis of query answering. We selected Ontop ( https://ontop-vkg.org ) to illustrate how this technique enhances the data usage of the GitHub community. Ontop is an open-source OBDA tool applied in the domain of relational databases. The implementation consists of the GHTorrent dataset and an extended SemanGit ontology. We perform a set of queries to highlight a subset of the features of this data access approach. The results look positive and can assist various use cases related to GitHub data with a semantic approach. OBDA does provide benefits in practice, such as querying in domain vocabulary and making use of reasoning over the axioms in the ontology. However, the practical impediments we observe are in the “manual” development of a domain ontology and the creation of a mapping specification which requires deep knowledge of a domain and the data. Also, implementing OBDA within the practical context of an information system requires careful consideration for a suitable user interface to facilitate the query construction from ontology vocabulary. Finally, we conclude with a summary of the paper and direction for future research.

A significant portion of information that is nowa-days collected in enterprises and organizations resides in text documents, and thus is inherently unstructured. Turning it into a structured form is the aim of information extraction (IE). Depending on the approach followed, the output of an IE process can fill forms or populate relational tables, or can be presented through an ontology. This last approach is particularly interesting, since ontologies may facilitate the integration with other corporate and external data, and enable data management and governance at an abstract, conceptual level, as in Ontology-based Data Access (OBDA). To this aim, OBDA uses declarative mappings that specify the relation between the ontology and the database to be accessed. In OBDA, however, only mappings towards relational databases have been so far considered, and how to declaratively relate the ontology to unstructured sources is still unexplored. By leveraging the study on document spanners for IE, in this paper we propose a new framework that allows to map text documents to ontologies, in the spirit of the OBDA approach. We then investigate the problem of answering conjunctive queries (CQs) in our framework, and show that, if the ontology is specified in the lightweight Description Logic DL-LiteR, the problem can be solved by reformulating the user query into a new spanner. Interestingly, both the spanners used in the mapping and the one computed by the rewriting algorithm have the same expressiveness, and CQ answering in this case is polynomial in data complexity.

Currently, task support information in aircraft maintenance is mostly provided using paper-based solutions, which are burdensome, slow and prone to error. Aircraft maintenance documentation contains vast amounts of information irrelevant for the task at hand and even for the simplest tasks multiple documents need to be consulted. Next to these issues with the documentation itself, Aircraft Maintenance Technicians (AMTs) have very limited on-site access to support information. These factors lead to 15–20% of hands-on-aircraft time being wasted on acquiring the right information or not using maintenance documentation for task support at all, risking maintenance error. This paper describes the development of a system for a first level of contextualization of maintenance documentation to simplify the retrieval of task support information. Combining a tailor-made ontology with a relational database system for Ontology-Based Data Access (OBDA), maintenance documents relevant to a specific aircraft registration mark can be identified. The system contributes to the research field of knowledge management by using OBDA for selecting relevant maintenance documents stored in a regular file folder. Future work will focus on increasing the level of contextualization, development of a mobile tool for on-site access and prototype verification and validation in an operational environment.

Ontology-based data access focuses on enabling query evaluation over heterogeneous relational databases according to the model represented by an ontology. The relationships between the ontology and the data sources are commonly defined with declarative mappings, which are used by systems to perform SPARQL-to-SQL query translation or to generate RDF dumps from the relational databases. Besides the potential homogenization of data because of using an ontology, some additional advantages of this paradigm are that it may allow applying reasoning thanks to the ontology, as well as querying for meta knowledge, which describes statements with information such as provenance or certainty. In this paper, (i) we adapt a widely used RDF graph store benchmark, namely LUBM, for ontology-based data access, (ii) extend the benchmark for the evaluation of queries that exploit meta knowledge, and (iii) apply it for performance evaluation of state-of-the-art declarative mapping systems. Our proposal, the LUBM4OBDA Benchmark, considers inference capabilities that are not covered by previous ontology-based data access benchmarks, and it is the first one for the evaluation of meta knowledge and the RDF-star data model. The experimental evaluation shows that current virtualization systems cannot handle some advanced inference tasks, and that optimizations are needed to scale RDF-star materialization.

Recently, ontology-based data access is more and more employed in the context of data integration. That is, to the usual data layer of an information system we superimpose a conceptual layer to be exposed to the client. In this paper, we discuss a way to query XML data over conceptual layer using Description Logics. Given a XML data file, we first map the XML DTD to a corresponding relational schema preserving the original semantics of the XML DTD, and at the meantime, we store the XML data into the relational database. After that, we create an ontology described in Description Logics with respect to the relational schema. When a Description Logic query is posted to the ontology TBox, we map it to SQL query with respect to the relational database. In this way, we can query XML data using Description Logics. The main contribution of this thesis can be seen as the following two parts: 1. We proposed a mapping from XML DTD to relational schema with constraints preserving the original XML DTD semantics. 2. We described a framework of querying XML data using Description Logics, more precisely, the DL-LiteA logic.

No SQL stores are emerging as an efficient alternative to relational database management systems in the context of big data. Many actors in this domain consider that to gain a wider adoption, several extensions have to be integrated. Some of them focus on the ways of proposing more schema, supporting adapted declarative query languages and providing integrity constraints in order to control data consistency and enhance data quality. We consider that these issues can be dealt with in the context of Ontology Based Data Access (OBDA). OBDA is a new data management paradigm that exploits the semantic knowledge represented in ontologies when querying data stored in a database. We provide a proof of concept of OBDA's ability to tackle these three issues in a social application related to the medical domain.

This paper is devoted to new aspects of Natural Language Interface to Relational Database (NLIDB) integration into third-party corporate environments related to control data access. Because there is no schema information in the input NL query and the different relational database management system (RDBMS) requires different meta-data types and rules to control data access, developers meet a problem addressed to automatic data access control in the case of NL interface implementation to relational databases. In the paper, we suggest a comprehensive approach which takes into account permissions throughout the pipeline of transforming NL query into SQL query with an intermediate SPARQL representation. Our integration solutions based on well-known Ontology Based Data Access (OBDA) approach, which gives us the opportunity to adapt the proposed solutions to the specifics of access control facilities of various RDBMS. Suggested approach has been implemented within intelligent service, named Reply.

With the current upward trend in semantically annotated data, ontology-based data access (OBDA) was formulated to tackle the problem of data integration and query answering, where an ontology is formalized as a description logic TBox. In order to meet usability requirements set by users, efforts have been made to equip OBDA system with explanation facilities. One important explanation tool for DL ontologies, referred to as query abduction, can be formalised as abductive reasoning. In particular, given an ontology and an observation (i.e., a query with an answer), an explanation to the observation is a set of facts that together with the ontology can entail the observation. In this paper, we develop a sound and complete algorithm of query abduction for general conjunctive queries in ELH ontologies. This is achieved through ontology approximation and query rewriting. We implemented a prototypical system using the highly optimized Prolog engine XSB. We evaluated our algorithm over university benchmark ontology and our experimental results show that the system is capable of handling query abduction problems for ontology that has approximately 10 millions ABox assertions.

Predictive analysis gradually gains importance in industry. For instance, service engineers at Siemens diagnostic centres unveil hidden knowledge in huge amounts of historical sensor data and use this knowledge to improve the predictive systems analysing live data. Currently, the analysis is usually done using data-dependent rules that are specific to individual sensors and equipment. This dependence poses significant challenges in rule authoring, reuse, and maintenance by engineers. One solution to this problem is to employ ontology-based data access (OBDA) that provides a conceptual view of data via an ontology. However, classical OBDA systems do not support access to temporal data and reasoning over it. To address this issue, we propose a framework of temporal OBDA. In this framework, we use extended mapping languages to extract information about temporal events in RDF format, classical ontology and rule languages to reflect static information, as well as a temporal rule language to describe events. We also propose a SPARQL-based query language for retrieving temporal information and, finally, an architecture of system implementation extending the state-of-the-art OBDA platform Ontop.

Le evidenze materiali che raccontano la storia dell’Istituto di Geografia dell’Università Sapienza di Roma sono costituite da materiale eterogeneo: carte geografiche, lastre fotografiche, strumenti, globi, plastici, volumi, lettere, manoscritti. Riuscire a interpretare e tradurre le informazioni derivanti da tali testimonianze è un’operazione complessa, ma fondamentale al fine di produrre nuove basi di conoscenza. A questo scopo le informazioni possono essere integrate in modo strutturato ricorrendo al paradigma dell’Ontology-Based Data Access (OBDA), un sistema di gestione integrata delle informazioni che permette agli utenti di interrogare un database relazionale al fine di ottenere informazioni utili per ricerche e studi, quindi generare nuova conoscenza. Il paradigma dell’OBDA, l’organizzazione delle strutture tassonomiche e delle rappresentazioni concettuali hanno indirizzato le scelte in fase di selezione delle informazioni, archiviazione dei dati e modalità di organizzazione degli stessi. Il livello zero di questo processo è rappresentato dal catalogo il quale costituisce l’unità minima di strutturazione dei dati ed è uno strumento fondamentale per descrivere le risorse le quali, se non ordinate, non potrebbero essere utilizzate e capitalizzate. Il livello superiore prevede l’inserimento delle schede di catalogo all’interno di una struttura relazionale, ovvero un sistema che attribuisce senso e significato all’opera. Il database relazionale costituisce il punto di partenza per la successiva costruzione di strutture ontologiche complesse.

In OBDA an ontology defines a high level global vocabulary for user queries, and such vocabulary is mapped to (typically relational) databases. Extending this paradigm with rules, e.g., expressed in SWRL or RIF, boosts the expressivity of the model and the reasoning ability to take into account features such as recursion and n-ary predicates. We consider evaluation of SPARQL queries under rules with linear recursion, which in principle is carried out by a 2-phase translation to SQL: (1) The SPARQL query, together with the RIF/SWRL rules, and the mappings is translated to a Datalog program, possibly with linear recursion; (2) The Datalog program is converted to SQL by using recursive common table expressions. Since a naive implementation of this translation generates inefficient SQL code, we propose several optimisations to make the approach scalable. We implement and evaluate the techniques presented here in the Ontop system. To the best of our knowledge, this results in the first system supporting all of the following W3C standards: the OWL 2 QL ontology language, R2RML mappings, SWRL rules with linear recursion, and SPARQL queries. The preliminary but encouraging experimental results on the NPD benchmark show that our approach is scalable, provided optimisations are applied.

The growth of available information sources in the industry requires new efficient methods for data access by end-users whose ability to analyse data is at the core of making business decisions. Data analysis suffer from end-users' ability to efficiently clean and integrate and query data spread across a multitude of different data sources. Current centralised approaches, where an IT expert translates the requirements of domain experts into Extract-Transform-Load (ETL) processes to integrate the data and to apply predefined analytical reporting tools, are too heavy-weight and inflexible. To support interactive and efficient data exploration directly by domain experts, without IT expert involvement, a more flexible and modular information systems architecture is needed. The Optique platform reduces the time and cost of data access dramatically by automating the process of translating an information requirement into the retrieval of the relevant data. The key to this automated translation is Ontology-Based Data Access (OBDA), where the central idea is to use an ontology, a semantically rich conceptual model of the problem domain, and mappings, that describe the relationship between the ontology and the data sources, to allow users to formulate queries and receive answers in a domain-centric and intelligible form, without any knowledge of the implementational details of the data sources. We present the Optique platform as a next generation OBDA system featuring user-friendly query formulation interface(s), automated query translation over multiple sources using novel ontology and mapping management components, and distributed query optimisation and execution exploiting cloud resources for scale-out. At Statoil Exploration, the Optique platform integrates multiple large and complex data sources from different vendors, and allows G&G experts to execute and present detailed information needs in the subsurface domain directly into existing expert client tools and also into web portals for easy information distribution of central assets across the enterprise.

The problems of Data Integration/Exchange (DE) and Ontology Based Data Access (OBDA) have been extensively studied across different communities. The underlying problem is common: using a number of differently structured data-sources mapped to a mediating schema/ontology/knowledge-graph, answer a query posed on the latter. In DE, forward-chaining algorithms, collectively known as the chase, transform source data to a new materialised instance that satisfies the ontology and can be directly queried. In OBDA, backward-chaining algorithms rewrite the query over the source schema, taking the ontology into account, in order to execute the rewriting directly on the sources. These two reasoning approaches have seen an individual rise in algorithms, practical implementations, and benchmarks. However, there has not been a principled methodology to compare solutions across both areas. In this paper we provide an original methodology and a benchmark infrastructure - a set of test scenarios, generator and translator tools, and an experimental infrastructure - to allow the translation and execution of a DE/OBDA scenario across areas and among different chase and query-rewriting systems. In the process, we also present a syntactic restriction of linear Tuple Generating Dependencies that precisely captures DL-Lite R , a correspondence previously uninvestigated. We perform cross-approach experiments under a wide range of assumptions, such as the use of different source-to-target mapping languages, shedding light to the interplay between forward-and backward-chaining. Our preliminary results show that, indeed, chase can compete and might overcome query rewriting even in the face of large data especially for complex mapping languages.

Answering regular path queries in expressive Description Logics via alternating tree-automata