Ontology-based Data Access Systems Research Articles

An Ontology Towards Predicting Terrorism Events

Although there is an increasing amount of information for counter-terrorism operations freely available online, it is a complex process to extract relevant information and to detect useful patterns in the data in order for intelligence functionaries to identify threats and to predict possible terror attacks. Automation is required for intelligent decision-making. To assist with this, in this paper, the researchers propose an ontology-based data access system for counter-terrorism. The system will enable intelligence analysts to perform specialised semantic searches about terrorist events or groups for analysis using an ontology. In this paper, the researchers present the ontology that was created by following an existing methodology for ontology development, and an ontology-based data access system together with all the components used in development (i.e., databases, web-scraper tools, ontology-based data access software, and data sources). Lastly, the ontology is demonstrated by means of use cases with example queries for generating actionable intelligence for operations.

Handling redundant processing in OBDA query execution over relational sources

Redundant processing is a key problem in the translation of initial queries posed over an ontology into SQL queries, through mappings, as it is performed by ontology-based data access systems. Examples of such processing are duplicate answers obtained during query evaluation, which must finally be discarded, or common expressions evaluated multiple times from different parts of the same complex query. Many optimizations that aim to minimize this problem have been proposed and implemented, mostly based on semantic query optimization techniques, by exploiting ontological axioms and constraints defined in the database schema. However, data operations that introduce redundant processing are still generated in many practical settings, and this is a factor that impacts query execution. In this work we propose a cost-based method for query translation, which starts from an initial result and uses information about redundant processing in order to come up with an equivalent, more efficient translation. The method operates in a number of steps, by relying on certain heuristics indicating that we obtain a more efficient query in each step. Through experimental evaluation using the Ontop system for ontology-based data access, we exhibit the benefits of our method.

Modeling and Querying Data in an Ontology-Based Data Access System

The Ontology-Based Data Access (OBDA) systems allows users to access external databases through a conceptual domain view, given in terms of an ontology. This semantic technology addresses such problems as conceptual modeling, query rewriting, an source-to-target mapping. We show how these issues have been defined and implemented in the DAFO system, highlighting those features that distinguish it from the standard OBDA system. We propose an original approach to cope with the trade-off between expressiveness of the ontology-based conceptual modelling and ontology-mediated query rewriting in the DAFO systems. For this purpose, we divide ontological rules into three parts: (a) rewriting rules, (b) rules defining intensional predicates (views), and (c) constraint rules (satisfied in the database). This allows to balance the semantic power of the conceptual modeling and the effciency of query answering in the DAFO system, where queries are based on the faceted query paradigm.

Handling Redundant Processing in OBDA Query Execution Over Relational Sources

Abstract Redundant processing is a key problem in the translation of initial queries posed over an ontology into SQL queries, through mappings, as it is performed by ontology-based data access systems. Examples of such processing are duplicate answers obtained during query evaluation, which must finally be discarded, or common expressions evaluated multiple times from different parts of the same complex query. Many optimizations that aim to minimize this problem have been proposed and implemented, mostly based on semantic query optimization techniques, by exploiting ontological axioms and constraints defined in the database schema. However, data operations that introduce redundant processing are still generated in many practical settings, and this is a factor that impacts query execution. In this work we propose a cost-based method for query translation, which starts from an initial result and uses information about redundant processing in order to come up with an equivalent, more efficient translation. The method operates in a number of steps, by relying on certain heuristics indicating that we obtain a more efficient query in each step. Through experimental evaluation using the Ontop system for ontology-based data access, we exhibit the benefits of our method.

ProGOMap: Automatic Generation of Mappings From Property Graphs to Ontologies

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.

Ontology–based access to temporal data with Ontop: A framework proposal

Abstract 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 it 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), which 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 for temporal OBDA. In this framework, we use extended mapping languages to extract information about temporal events in the 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.

Towards Automating the Creation of OBDA Systems

In the paper, we consider automating the process of constructing and debugging ontology-based data access systems by using the information stored in external ontology resources. In particular, for the constructing stage, we propose to use a tool that suggests concepts from external repositories for those tables from a relational database, which haven’t been matched with concepts from a domain ontology by ontology alignment techniques. For the debugging stage, we show how a tool for navigation in a collection of ontologies can be used to simplify SPARQL query writing by generating lacking parts of query thus enhancing ontology engineer’s productivity. The proposed approaches have been implemented and used in our system, named Reply, which transforms traditional information systems into intelligent systems with a natural language query interface.

ONTOLOGY-BASED SYSTEM DEVELOPMENT FOR MEDICAL DATABASE ACCESS

Medical research is a complex multi-disciplinary task involving specialists from different fields and professions, not only medical professionals. Medical databases are structured by information technology experts, but the contents must be tailored to the medical field. When the medical staff defines the information they use, terminology from their particular field of expertise is employed. This leads to misunderstandings between the maintainers and developers of information technology solutions, and the users of those solutions. When the time comes that a user, who is a medical professional, requires very specific data from the database, the chance of obtaining the data incorrectly is very high. By defining specific concepts and relationships between the data, in an explicit shared specification, some of the above problems can be avoided. The developed ontology-based data access system, described in this paper, provides a tool to store, manage and use definitions of common terminology and their mappings to the database. It is also capable of reasoning about the relationships between terms and indicates inconsistencies of term definitions, if any are present. By defining these interconnected terms in the ontology and by working through the system, all experts and software tools, who use the data, are able to use and reuse these terms to obtain data in a reliable and predefined way. This paper discusses the development and implementation of the ontology-based data access system, the ontology describing the medical data and the data mapping system, linking data from the database to concepts and virtual ontology individuals.

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.

Semantic access to streaming and static data at Siemens

We present a description and analysis of the data access challenge in Siemens Energy. We advocate Ontology Based Data Access (OBDA) as a suitable Semantic Web driven technology to address the challenge. We derive requirements for applying OBDA in Siemens, review existing OBDA systems and discuss their limitations with respect to the Siemens requirements. We then introduce the Optique platform as a suitable OBDA solution for Siemens. The platform is based on a number of novel techniques and components including a deployment module, BootOX for ontology and mapping bootstrapping, a query language STARQL that allows for a uniform querying of both streaming and static data, a highly optimised backend, ExaStream, for processing such data, and a query formulation interface, OptiqueVQS, that allows to formulate STARQL queries without prior knowledge of its formal syntax. Finally, we describe our installation and evaluation of the platform in Siemens.

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.

Semantic Access to Streaming and Static Data at Siemens

We present a description and analysis of the data access challenge in Siemens Energy. We advocate Ontology Based Data Access (OBDA) as a suitable Semantic Web driven technology to address the challenge. We derive requirements for applying OBDA in Siemens, review existing OBDA systems and discuss their limitations with respect to the Siemens requirements. We then introduce the Optique platform as a suitable OBDA solution for Siemens. The platform is based on a number of novel techniques and components including a deployment module, BootOX for ontology and mapping bootstrapping, a query language STARQL that allows for a uniform querying of both streaming and static data, a highly optimised backend, ExaStream, for processing such data, and a query formulation interface, OptiqueVQS, that allows to formulate STARQL queries without prior knowledge of its formal syntax. Finally, we describe our installation and evaluation of the platform in Siemens.

A Way How to Impart Data Science Skills to Computer Science Students Exemplified by OBDA-Systems Development

Nowadays to explore and examine data from a variety of angles to tackle Big Data problems, to devise data-driven solutions to the most pressing challenges, it is necessary to build multidisciplinary students’ skills set for innovative methods not only for master’s in Data Science programs but in traditional Computer Science programs also. In the paper, we describe how teaching methods and tools, which are used to train students to develop Ontology-Based Data Access systems with natural language interface to relational databases helps Master’s Degree students in Computer Science to collaborate with Data Scientists in real-world interdisciplinary projects and to prepare them for a data science career. We use ontology engineering in a combination with Natural Language Processing methods based on lexico-syntactic patterns, in particular, to extract needful data from structured, semi-structured and unstructured datasets in a uniform way to analyze real-world Russian social networks related to new building area.

TECHNOLOGY OF APPLICATION OF ONTOLOGY DESIGN PATTERNS FOR ACCELERATION OF QUERIES EXECUTION IN ONTOLOGY BASED DATA ACCESS SYSTEMS

One of the known approaches to the construction of knowledge-based systems is the Ontology-based data access (OBDA) approach, which involves combining an ontology that represents a domain conceptual system and a relational database used to store data that corresponds to the concepts defined in the ontology. In this case, the ontology is used as the basis for the formation of user queries, and the database for obtaining the results of their execution. The article proposes a technology for improving the performance of queries to such systems by preliminarily database preparation, taking into account that the used ontology have been developed with using content ontology design patterns (CDP). These patterns are small ontology fragments that describes generalized situations of the domain (for example, participation in an event, the execution of some role, the parts of an object, etc.). The novelty of this technology determines the possibility of its application for a whole class of ontologies based on ontological patterns. The general principle of ontology-based data access system is considered. The description of the proposed technology, as well as its implementation for a specific ontology, and an experimental evaluation of the obtained results are described.

Beyond OWL 2 QL in OBDA: Rewritings and Approximations

Ontology-based data access (OBDA) is a novel paradigm facilitating access to relational data, realized by linking data sources to an ontology by means of declarative mappings. DL-Lite_R, which is the logic underpinning the W3C ontology language OWL 2 QL and the current language of choice for OBDA, has been designed with the goal of delegating query answering to the underlying database engine, and thus is restricted in expressive power. E.g., it does not allow one to express disjunctive information, and any form of recursion on the data. The aim of this paper is to overcome these limitations of DL-Lite_R, and extend OBDA to more expressive ontology languages, while still leveraging the underlying relational technology for query answering. We achieve this by relying on two well-known mechanisms, namely conservative rewriting and approximation, but significantly extend their practical impact by bringing into the picture the mapping, an essential component of OBDA. Specifically, we develop techniques to rewrite OBDA specifications with an expressive ontology to "equivalent" ones with a DL-Lite_R ontology, if possible, and to approximate them otherwise. We do so by exploiting the high expressive power of the mapping layer to capture part of the domain semantics of rich ontology languages. We have implemented our techniques in the prototype system OntoProx, making use of the state-of-the-art OBDA system Ontop and the query answering system Clipper, and we have shown their feasibility and effectiveness with experiments on synthetic and real-world data.

Query Abduction for ELH Ontologies

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.

Optique: Zooming in on Big Data

Optique overcomes problems in current ontology-based data access systems pertaining to installation overhead, usability, scalability, and scope by integrating a user-oriented query interface, semi-automated managing methods, new query rewriting techniques, and temporal and streaming data processing in one platform.

Ontology-based data access systems

Studies aimed at ensuring semantic access to databases have a long history and originated at early stages of database technology development. Unfortunately, they have not led yet to the creation of widely accepted industrial technologies. In the last decade, the activity of the W3C consortium in the field of Semantic Web and development of standards of the ontology description languages induced a new activity wave in developing tools for systems of semantic access to databases and a new class of database systems, the so-called ontology-based data access (OBDA) systems. In such systems, ontology is used as a conceptual schema of the subject domain and as a basis of the user interface for SQL database systems. Approaches proposed in recent years do not ensure "final" solution of the problem. Nevertheless, ontology description languages were created that make it possible to achieve an acceptable compromise between their expressiveness, which remains sufficient for many applications, and computational complexity of reasoning on ontologies and processing queries to data stored in large databases. Prerequisites have been created for appearance of industrial technologies for development of systems of the above-specified class. In the paper, a survey of recent basic results and developments in this field is presented.

Detecting and Reparing Broken Mappings for Ontology Based Data Access System

Detecting and Reparing Broken Mappings for Ontology Based Data Access System

The MASTRO system for ontology-based data access

In this paper we present MASTRO, a Java tool for ontology-based data access (OBDA) developed at Sapienza Universita di Roma and at the Free University of Bozen-Bolzano. MASTRO manages OBDA systems in which the ontology is specified in DL-Lite A,id, a logic of the DL-Lite family of tractable Description Logics specifically tailored to ontology-based data access, and is connected to external JDBC enabled data management systems through semantic mappings that associate SQL queries over the external data to the elements of the ontology. Advanced forms of integrity constraints, which turned out to be very useful in practical applications, are also enabled over the ontologies. Optimized algorithms for answering expressive queries are provided, as well as features for intensional reasoning and consistency checking. MASTRO provides a proprietary API, an OWLAPI compatible interface, and a plugin for the Protege 4 ontology editor. It has been successfully used in several projects carried out in collaboration with important organizations, on which we briefly comment in this paper.