Investigating Ontology-Based Data Access with GitHub

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.

In the last years, enterprises increase their effort to collect large amounts of data from many heterogeneous data sources and store it in modern architectures like data lakes. However, this approach faces different drawbacks for finding and understanding data sources. Ontology-Based Data Access (OBDA) originating from the Semantic Web enables a homogeneous access to the data sources by using a mapping, called semantic model, between a data source and a target ontology. However, OBDA requires a detailed ontology, which is usually created by ontology engineers and domain experts resulting along with high effort for designing and maintaining. To overcome these limitations, we develop an approach consisting of a knowledge graph, which features an internal growing ontology and linked data-source specific semantic models. The ontology continuously evolves on-demand based on newly added data sources along with their corresponding semantic models, which are created by domain experts. To ensure the knowledge graph's stability, we develop an intuitive user-oriented assistant and combine it with a semi-supervised evolving strategy that assists the user with the help of external knowledge bases. We evaluate accuracy and usability of our approach by conducting a user study with a heterogeneous group of participants that define semantic models upon pre-defined data sets. The results show that semantic models become more objective and consistent with our provided user assistant and thus lead to a knowledge graph with higher interconnectivity and stability.

Property Graph databases (PGs) are emerging as efficient graph stores with flexible schemata. This raises the need to have a unified view over heterogenous data produced from these stores. Ontology based Data Access (OBDA) has become the most dominant approach to integrate heterogeneous data sources by providing a unified conceptual view (ontology) over them. The corner stone of any OBDA system is to define mappings between the data source and the target (domain) ontology. However, manual mapping generation is time consuming and requires great efforts. This paper proposes ProGOMap ( Pro perty G raph to O ntology M apper) system that automatically generates mappings from property graphs to a domain ontology. ProGOMap starts by generating a putative ontology with direct axioms from PG. A novel ontology learning algorithm is proposed to enrich the putative ontology with subclass axioms inferred from PG. The putative ontology is then aligned to an existing domain ontology using string similarity metrics. Another algorithm is proposed to align object properties between the two ontologies considering different modelling criteria. Finally, mappings are generated from alignment results. Experiments were done on eight data sets with different scenarios to evaluate the effectiveness of the generated mappings. The experimental results achieved mapping accuracy up to 97% and 81% when addressing PG-to-ontology terminological and structural heterogeneities, respectively. Ontology learning by inferring subclass axioms from a property graph helps to address the heterogeneity between the PG and ontology models.

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.

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.

Data sourcing or integration is inevitable in current business scenario. Major issues of data sourcing from heterogeneous data sources are lack of semantic richness and deprived querying. To overcome these issues, an Ontology Based Data federation using Object Relational Database (OBDF-ORDB) architecture has been proposed and implemented. This OBDF-ORDB architecture consists of semantic layer and transformation & query layer. In semantic layer the ontology used to create local and global schema to enrich the semantics. In transformation & querying layer, Object Relational Database (ORDB) is used for storing the local ontology, global ontology to improve storage, maintenance and retrieval. The transformation rule engine proposed for the architecture converts and stores the local ontology and global ontology from flat OWL file to ORDB. The user queries are passed to ORDB for result extraction. To analyze the performance of the OBDF-ORDB architecture E-shopping application is selected. Experimental results shows that the proposed OBDF-ORDB architecture is relatively better than the traditional data access and ontology based data access in recall and response time. It is observed that the recall mechanism in OBDF-ORDB architecture has been improved by 25% compared to traditional data federation and response time is reduced by 15% compared to ontology based data federation.

An important application of semantic technologies in industry has been the formalisation of information models using OWL 2 ontologies and the use of RDF for storing and exchanging application data. Moreover, legacy data can be virtualised as RDF using ontologies following the ontology-based data access (OBDA) approach. In all these applications, it is important to provide domain experts with query formulation tools for expressing their information needs in terms of queries over ontologies. In this work, we present such a tool, OptiqueVQS, which is designed based on our experience with OBDA applications in Statoil and Siemens and on best HCI practices for interdisciplinary engineering environments. OptiqueVQS implements a number of unique techniques distinguishing it from analogous query formulation systems. In particular, it exploits ontology projection techniques to enable graph-based navigation over an ontology during query construction. Secondly, while OptiqueVQS is primarily ontology driven, it exploits sampled data to enhance selection of data values for some data attributes. Finally, OptiqueVQS is built on well-grounded requirements, design rationale, and quality attributes. We evaluated OptiqueVQS with both domain experts and casual users and qualitatively compared our system against prominent visual systems for ontology-driven query formulation and exploration of semantic data. OptiqueVQS is available online and can be downloaded together with an example OBDA scenario.

Engineering ontology-based access to real-world data sources

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.

Providing both end-users and applications with a uniform way to query legacy databases through a high-level ontology that models both the business logic and the underlying data sources is the main concern in Ontology-based Data Access (OBDA). Our goal in this research is providing tools for performing OBDA with relational and non-relational data sources. Within the OBDA framework, in this work, we present a prototype tool that can access an H2 database, allowing the user to explicitly express mappings, and populating an ontology that can be saved for later querying. We report on the current functionality of our tool, which includes creating, loading, saving a global ontology populated with a database or a CSV file. For the latter, we devised a language for specifying the underlying schema of the CSV file. We argue that this language is better suited than current alternatives such as JSON. Also, the system allows the user to visually express mappings from the database to the ontology and the ability to create databases for testing the behavior of the system in the presence of increasing workloads. Our tests indicate that the system can handle a moderate workload of tables of tens of thousands of records but fails to handle tables of millions of records.

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.

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.

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.

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.

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.