Ontology-based Data Access Research Articles

Predicting an Optimal Virtual Data Model for Uniform Access to Large Heterogeneous Data

ABSTRACT The growth of generated data in the industry requires new efficient big data integration approaches for uniform data access by end-users to perform better business operations. Data virtualization systems, including Ontology-Based Data Access (ODBA) query data on-the-fly against the original data sources without any prior data materialization. Existing approaches by design use a fixed model e.g., TABULAR as the only Virtual Data Model — a uniform schema built on-the-fly to load, transform, and join relevant data. While other data models, such as GRAPH or DOCUMENT, are more flexible and, thus, can be more suitable for some common types of queries, such as join or nested queries. Those queries are hard to predict because they depend on many criteria, such as query plan, data model, data size, and operations. To address the problem of selecting the optimal virtual data model for queries on large datasets, we present a new approach that (1) builds on the principal of OBDA to query and join large heterogeneous data in a distributed manner and (2) calls a deep learning method to predict the optimal virtual data model using features extracted from SPARQL queries. OPTIMA — implementation of our approach currently leverages state-of-the-art Big Data technologies, Apache-Spark and Graphx, and implements two virtual data models, GRAPH and TABULAR, and supports out-of-the-box five data sources models: property graph, document-based, e.g., wide-columnar, relational, and tabular, stored in Neo4j, MongoDB, Cassandra, MySQL, and CSV respectively. Extensive experiments show that our approach is returning the optimal virtual model with an accuracy of 0.831, thus, a reduction in query execution time of over 40% for the tabular model selection and over 30% for the graph model selection.

A survey on semantic data management as intersection of ontology-based data access, semantic modeling and data lakes

In recent years, data lakes emerged as a way 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. Such 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 ontology-based 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.

LUBM4OBDA: Benchmarking OBDA Systems with Inference and Meta Knowledge

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.

Integrating data and knowledge to support the selection of service plant species in agroecology

There is a crucial need for tools to help researchers, technicians and farmers designing sustainable agroecosystems based on agroecology Indeed, such agroecosystems are inherently complex and their design requires to integrate various data and unstabilised scientific knowledge. In this paper, we consider the issue of selecting service plant species according to their potential to provide ecosystem services. To tackle that issue, we adopt an approach based both on a formalized representation of domain knowledge, which enables reasoning, and on the exploitation of available data, collected independently of the targeted application. More specifically, we rely on the one hand on recent scientific results in agronomy linking functional traits (i.e., measurable characteristics of plant species) to ecosystem services, and on the other hand on data about functional traits collected by the research community in ecology. The architecture of our system is inspired by the ontology-based data access paradigm, which allows to combine data and knowledge in a principled way. We provide a methodology to acquire scientific knowledge in the form of diagrams linked to data sources, as well as a formalization in a logical rule-based language. Importantly, our rules are independent from specific diagrams and data, to ensure genericity and facilitate the evolution of the system. We detail the construction of a knowledge base devoted to vine grassing, i.e., installing herbaceous service plants in vineyards, and present an evaluation of the system’s results on this use case. We finally discuss the lessons learned and further challenges to be met.

Ontology Based Access Control: A Case Study through Ontology Based Data Access

Ontology Based Data Access (OBDA) is the provision of data access and data integration as a result of the mapping that is established between an ontology and a data source. Thus, storing large amounts of data becomes easier, more powerful queries can be written, and management of complex information systems can be performed quickly and effectively by using Semantic Web technologies. Ontology Based Access Control (OBAC) uses Semantic Web technologies to enable the enforcement of access control mechanism. Therefore, only authorized persons can access data to protect data privacy. In this study, OBDA and OBAC are integrated to improve security while providing data virtualization with a data model-independent access control approach. Therefore, a use case study for the healthcare domain is presented. Hence, a relational database for the hospital domain, a Hospital Ontology for the related hospital database and an access control policy are created. Also, the relevant mappings between the hospital database and the Hospital Ontology are established by using the Ontop framework and finally, various queries are executed by using Ontop SPARQL to evaluate mappings and access rules.

Finite Materialisability of Datalog Programs with Metric Temporal Operators

DatalogMTL is an extension of Datalog with metric temporal operators that has recently found applications in stream reasoning and temporal ontology-based data access. In contrast to plain Datalog, where materialisation (a.k.a. forward chaining) naturally terminates in finitely many steps, reaching a fixpoint in DatalogMTL may require infinitely many rounds of rule applications. As a result, existing reasoning systems resort to other approaches, such as constructing large Büchi automata, whose implementations turn out to be highly inefficient in practice. In this paper, we propose and study finitely materialisable DatalogMTL programs, for which forward chaining reasoning is guaranteed to terminate. We consider a data-dependent notion of finite materialisability of a program, where termination is guaranteed for a given dataset, as well as a data-independent notion, where termination is guaranteed regardless of the dataset. We show that, for bounded programs (a natural DatalogMTL fragment for which reasoning is as hard as in the full language), checking data-dependent finite materialisability is ExpSpace-complete in combined complexity and PSpace-complete in data complexity; furthermore, we propose a practical materialisation-based decision procedure that works in doubly exponential time. We show that checking data-independent finite materialisability for bounded progams is computationally easier, namely ExpTime-complete; moreover, we propose sufficient conditions for data-indenpendent finite materialisability that can be efficiently checked. We provide also the complexity landscape of fact entailment for different classes of finitely materialisable programs; surprisingly, we could identify a large class of finitely materialisable programs, called MTL-acyclic programs, for which fact entailment has exactly the same data and combined complexity as in plain Datalog, which makes this fragment especially well suited for big-scale applications.

First-Order Rewritability and Complexity of Two-Dimensional Temporal Ontology-Mediated Queries

Aiming at ontology-based data access to temporal data, we design two-dimensional temporal ontology and query languages by combining logics from the (extended) DL-Lite family with linear temporal logic LTL over discrete time (Z,<). Our main concern is first-order rewritability of ontology-mediated queries (OMQs) that consist of a 2D ontology and a positive temporal instance query. Our target languages for FO-rewritings are two-sorted FO(<) -- first-order logic with sorts for time instants ordered by the built-in precedence relation < and for the domain of individuals---its extension FO(<, ≡) with the standard congruence predicates t ≡ 0 (mod n), for any fixed n > 1, and FO(RPR) that admits relational primitive recursion. In terms of circuit complexity, FO(<, ≡)- and FO(RPR)-rewritability guarantee answering OMQs in uniform AC0 and NC1, respectively. We proceed in three steps. First, we define a hierarchy of 2D DL-Lite/LTL ontology languages and investigate the FO-rewritability of OMQs with atomic queries by constructing projections onto 1D LTL OMQs and employing recent results on the FO-rewritability of propositional LTL OMQs. As the projections involve deciding consistency of ontologies and data, we also consider the consistency problem for our languages. While the undecidability of consistency for 2D ontology languages with expressive Boolean role inclusions might be expected, we also show that, rather surprisingly, the restriction to Krom and Horn role inclusions leads to decidability (and ExpSpace-completeness), even if one admits full Booleans on concepts. As a final step, we lift some of the rewritability results for atomic OMQs to OMQs with expressive positive temporal instance queries. The lifting results are based on an in-depth study of the canonical models and only concern Horn ontologies.

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.

Performance benchmark on semantic web repositories for spatially explicit knowledge graph applications

Knowledge graph has become a cutting-edge technology for linking and integrating heterogeneous, cross-domain datasets to address critical scientific questions. As big data has become prevalent in today's scientific analysis, semantic data repositories that can store and manage large knowledge graph data have become critical in successfully deploying spatially explicit knowledge graph applications. This paper provides a comprehensive evaluation of the popular semantic data repositories and their computational performance in managing and providing semantic support for spatial queries. There are three types of semantic data repositories: (1) triple store solutions (RDF4j, Fuseki, GraphDB, Virtuoso), (2) property graph databases (Neo4j), and (3) an Ontology-Based Data Access (OBDA) approach (Ontop). Experiments were conducted to compare each repository's efficiency (e.g., query response time) in handling geometric, topological, and spatial-semantic related queries. The results show that Virtuoso achieves the overall best performance in both non-spatial and spatial-semantic queries. The OBDA solution, Ontop, has the second-best query performance in spatial and complex queries and the best storage efficiency, requiring the least data-to-RDF conversion efforts. Other triple store solutions suffer from various issues that cause performance bottlenecks in handling spatial queries, such as inefficient memory management and lack of proper query optimization.

Case representation and retrieval for complex product design based on case-based reasoning

Reusing design knowledge of products is a useful way to solve the efficiency issue of complex product design. The design knowledge is tacit, empirical, and unstructured and there exists insufficient case matching and inefficient design reuse in complex products design process. Aiming at these problems, this paper presents an improved case-based reasoning methodology combining ontology with two-stage retrieval. Firstly, a knowledge domain ontology model of complex product design is constructed, and the technology of ontology-based data access is introduced to automatically generate a case knowledge base with semantic information. Then, a new two-stage case retrieval method integrated semantic query with similarity calculation is proposed. The case subset is selected by query statements. It has the characteristic of isomorphism with design problem. The retrieval mechanism is applied to compress the traversal space, reduce the redundancy of semantic similarity calculation, improve the retrieval efficiency, and fulfill the target of case reuse. Finally, a variant design of the chiller unit as an example is executed to illustrate the use of the proposed method, and experiments are organized to evaluate its performance. The result shows that the proposed approach has an average precision of 92% and high stability, outperforming existing methods.

Ontology‐based data access control model supported with grid computing for improving security in healthcare data

AbstractOntology is broadly applied in various fields for creating trust‐based data access control models. In the case of the medical field, utilizing ontology is highly recommended for securing the medical report and patient information. Here, ontology is integrated with the grid computing system for achieving better authentication and security in the healthcare system. Various ontology based data access control models are designed by different authors for achieving enhanced user authorization and security. Security is still found to be a major issue in the existing techniques. To overcome this issue, the proposed work developed a medical ontology with three tier secure access control. The proposed method is processed in three layers. In the first layer, job scheduling for user is achieved. Job scheduling is carried out by scheduler using a deadline priority based scheduling algorithm. Users are scheduled with a different job based on their priority level such as low, medium, and high. In the second layer, role based policy is developed for finding and eliminating the accessing of unauthorized users. The third layer is used for securing medical database. Medical database is secured from various through encrypting the data using XOR cryptography algorithm. The proposed architecture is validated using some of the metrics such as turnaround time, tardiness, actual time delay, and throughput. The turnaround time, tardiness, and actual‐time delay attains for the proposed architecture are 64.60, 4.90, and 1.52 seconds, respectively. Then, the throughput value reaches for 25 bits length of encryption is 0.88 Mbps. The simulation analysis reveals that the proposed architecture reduces the waiting time of users and improves medical data security.

Handling qualitative preferences in SPARQL over virtual ontology-based data access

With the increase of data volume in heterogeneous datasets that are being published following Open Data initiatives, new operators are necessary to help users to find the subset of data that best satisfies their preference criteria. Quantitative approaches such as top-k queries may not be the most appropriate approaches as they require the user to assign weights that may not be known beforehand to a scoring function. Unlike the quantitative approach, under the qualitative approach, which includes the well-known skyline, preference criteria are more intuitive in certain cases and can be expressed more naturally. In this paper, we address the problem of evaluating SPARQL qualitative preference queries over an Ontology-Based Data Access (OBDA) approach, which provides uniform access over multiple and heterogeneous data sources. Our main contribution is Morph-Skyline++, a framework for processing SPARQL qualitative preferences by directly querying relational databases. Our framework implements a technique that translates SPARQL qualitative preference queries directly into queries that can be evaluated by a relational database management system. We evaluate our approach over different scenarios, reporting the effects of data distribution, data size, and query complexity on the performance of our proposed technique in comparison with state-of-the-art techniques. Obtained results suggest that the execution time can be reduced by up to two orders of magnitude in comparison to current techniques scaling up to larger datasets while identifying precisely the result set.

A tetrachotomy of ontology-mediated queries with a covering axiom

Our concern is the problem of efficiently determining the data complexity of answering queries mediated by description logic ontologies and constructing their optimal rewritings to standard database queries. Originated in ontology-based data access and datalog optimisation, this problem is known to be computationally very complex in general, with no explicit syntactic characterisations available. In this article, aiming to understand the fundamental roots of this difficulty, we strip the problem to the bare bones and focus on Boolean conjunctive queries mediated by a simple covering axiom stating that one class is covered by the union of two other classes. We show that, on the one hand, these rudimentary ontology-mediated queries, called disjunctive sirups (or d-sirups), capture many features and difficulties of the general case. For example, answering d-sirups is Π2p-complete for combined complexity and can be in ▪ or L-, NL-, P-, or coNP-complete for data complexity (with the problem of recognising FO-rewritability of d-sirups being 2ExpTime-hard); some d-sirups only have exponential-size resolution proofs, some only double-exponential-size positive existential FO-rewritings and single-exponential-size nonrecursive datalog rewritings. On the other hand, we prove a few partial sufficient and necessary conditions of FO- and (symmetric/linear-) datalog rewritability of d-sirups. Our main technical result is a complete and transparent syntactic ▪/NL/P/coNP tetrachotomy of d-sirups with disjoint covering classes and a path-shaped Boolean conjunctive query. To obtain this tetrachotomy, we develop new techniques for establishing P- and coNP-hardness of answering non-Horn ontology-mediated queries as well as showing that they can be answered in NL.

ONTOLOGY-BASED DATA ACCESS TO HETEROGENEOUS DATA SOURCES: STATE OF THE ART APPROACHES AND APPLICATIONS

ONTOLOGY-BASED DATA ACCESS TO HETEROGENEOUS DATA SOURCES: STATE OF THE ART APPROACHES AND APPLICATIONS

ForBackBench

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.

Semantically Query All (Squerall): A Scalable Framework to Analyze Data from Heterogeneous Sources at Different Levels of Granularity

In terms of data formats, codecs, and storage capacity, the previous two decades have seen a phenomenal progress. Rather than needing to adjust to one's application demands to the restricted storage solutions available in the past, there is now a wide range of options to pick from in order to best satisfy an application's needs. As a result, there is massive volume of data available in many forms and formats that, when linked and searched together, may yield significant knowledge and insights. We offer data warehouse as a solution and Big data as a solution in this study in order to handle heterogeneous data. However, both of these techniques have drawbacks when it comes to handling diverse data. Afterwards we propose another framework (Squerall), which relies on the ideas of Ontology-Based Data Access (OBDA) to allow querying of diverse heterogeneous sources using a single query language, SPARQL. In Squerall original data is queried on the fly, with no prior data materialization or modification. Squerall, in particular, enables the distributed aggregate and combining of massive data sets. It comes with five data sources out of the box, and it may be programmatically expanded to include other sources and query engines. The framework includes user interfaces for creating required inputs and for assisting non-SPARQL specialists through the process of writing SPARQL queries. It declares data mappings and transformations using RML and FnO, and employs Spark and Presto as query engines. The initiative underscored the importance of developing in this framework, technologies, and processes that enable for decentralized Big Data administration. Furthermore, demonstrating the feasibility and usefulness of OBDA on top of the growing NoSQL movement has a beneficial impact on Semantic Web principles acceptance. This demonstrates Squerall's importance and contribution to the organization.

Loop restricted existential rules and first-order rewritability for query answering

Abstract In ontology-based data access (OBDA), the classical database is enhanced with an ontology in the form of logical assertions generating new intensional knowledge. A powerful form of such logical assertions is the tuple-generating dependencies (TGDs), also called existential rules, where Horn rules are extended by allowing existential quantifiers to appear in the rule heads. In this paper, we introduce a new language called loop restricted (LR) TGDs (existential rules), which are TGDs with certain restrictions on the loops embedded in the underlying rule set. We study the complexity of this new language. We show that the conjunctive query answering (CQA) under the LR TGDs is decidable. In particular, we prove that this language satisfies the so-called bounded derivation-depth property (BDDP), which implies that the CQA is first-order rewritable, and its data complexity is in Ac$^{0}$. We also prove that the combined complexity of the CQA is 2-ExpTime complete, while the language membership is Pspace complete. Then we extend the LR TGDs language to the generalized loop restricted (GLR) TGDs language and prove that this class of TGDs still remains to be first-order rewritable and properly contains most of other first-order rewritable TGDs classes discovered in the literature so far.

Enhancing virtual ontology based access over tabular data with Morph-CSV

Ontology-Based Data Access (OBDA) has traditionally focused on providing a unified view of heterogeneous datasets (e.g., relational databases, CSV and JSON files), either by materializing integrated data into RDF or by performing on-the-fly querying via SPARQL query translation. In the specific case of tabular datasets represented as several CSV or Excel files, query translation approaches have been applied by considering each source as a single table that can be loaded into a relational database management system (RDBMS). Nevertheless, constraints over these tables are not represented (e.g., referential integrity among sources, datatypes, or data integrity); thus, neither consistency among attributes nor indexes over tables are enforced. As a consequence, efficiency of the SPARQL-to-SQL translation process may be affected, as well as the completeness of the answers produced during the evaluation of the generated SQL query. Our work is focused on applying implicit constraints on the OBDA query translation process over tabular data. We propose Morph-CSV, a framework for querying tabular data that exploits information from typical OBDA inputs (e.g., mappings, queries) to enforce constraints that can be used together with any SPARQL-to-SQL OBDA engine. Morph-CSV relies on both a constraint component and a set of constraint operators. For a given set of constraints, the operators are applied to each type of constraint with the aim of enhancing query completeness and performance. We evaluate Morph-CSV in several domains: e-commerce with the BSBM benchmark; transportation with the GTFS-Madrid benchmark; and biology with a use case extracted from the Bio2RDF project. We compare and report the performance of two SPARQL-to-SQL OBDA engines, without and with the incorporation of Morph-CSV. The observed results suggest that Morph-CSV is able to speed up the total query execution time by up to two orders of magnitude, while it is able to produce all the query answers.

Ontology-Based Data Access with Dynamic TBoxes in DL-Lite

In this paper we introduce the notion of mapping-based knowledge base (MKB) to formalize the situation where both the extensional and the intensional level of the ontology are determined by suitable mappings to a set of (relational) data sources. This allows for making the intensional level of the ontology as dynamic as traditionally the extensional level is. To do so, we resort to the meta-modeling capabilities of higher-order Description Logics, which allow us to see concepts and roles as individuals, and vice versa. The challenge in this setting is to design tractable query answering algorithms. Besides the definition of MKBs, our main result is that answering instance queries posed to MKBs expressed in Hi(DL-LiteR) can be done efficiently. In particular, we define a query rewriting technique that produces first-order (SQL) queries to be posed to the data sources.

Benchmarking Ontology-Based Query Rewriting Systems

Query rewriting is a prominent reasoning technique in ontology-based data access applications. A wide variety of query rewriting algorithms have been proposed in recent years and implemented in highly optimised reasoning systems. Query rewriting systems are complex software programs; even if based on provably correct algorithms, sophisticated optimisations make the systems more complex and errors become more likely to happen. In this paper, we present an algorithm that, given an ontology as input, synthetically generates ``relevant'' test queries. Intuitively, each of these queries can be used to verify whether the system correctly performs a certain set of ``inferences'', each of which can be traced back to axioms in the input ontology. Furthermore, we present techniques that allow us to determine whether a system is unsound and/or incomplete for a given test query and ontology. Our evaluation shows that most publicly available query rewriting systems are unsound and/or incomplete, even on commonly used benchmark ontologies; more importantly, our techniques revealed the precise causes of their correctness issues and the systems were then corrected based on our feedback. Finally, since our evaluation is based on a larger set of test queries than existing benchmarks, which are based on hand-crafted queries, it also provides a better understanding of the scalability behaviour of each system.