A Formal Framework for Coupling Document Spanners with Ontologies

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.

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.

Conjunctive Query (CQ) answering is a primary reasoning task over knowledge bases. However, when considering expressive description logics, query answering can be computationally very expensive; reasoners for CQ answering, although heavily optimized, often sacrifice expressive power of the input ontology or completeness of the computed answers in order to achieve tractability and scalability for the problem. In this work, we present a hybrid query answering architecture that combines various services to provide a CQ answering service for OWL. Specifically, it combines scalable CQ answering services for tractable languages with a CQ answering service for a more expressive language approaching the full OWL 2. If the query can be fully answered by one of the tractable services, then that service is used, to ensure maximum performance. Otherwise, the tractable services are used to compute lower and upper bound approximations. The union of the lower bounds and the intersection of the upper bounds are then compared. If the bounds do not coincide, then the “gap” answers are checked using the “full” service. These techniques led to the development of two new systems: (i) RSAComb, an efficient implementation of a new tractable answering service for RSA (role safety acyclic) (ii) ACQuA, a reference implementation of the proposed hybrid architecture combining RSAComb, PAGOdA, and HermiT to provide a CQ answering service for OWL. Our extensive evaluation shows how the additional computational cost introduced by reasoning over a more expressive language like RSA can still provide a significant improvement compared to relying on a fully-fledged reasoner. Additionally, we show how ACQuA can reliably match the performance of PAGOdA, a state-of-the-art CQ answering system that uses a similar approach, and can significantly improve performance when PAGOdA extensively relies on the underlying fully-fledged reasoner.

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.

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.

We give solutions to two fundamental computational problems in ontology-based data access with the W3C standard ontology language OWL 2 QL : the succinctness problem for first-order rewritings of ontology-mediated queries (OMQs) and the complexity problem for OMQ answering. We classify OMQs according to the shape of their conjunctive queries (treewidth, the number of leaves) and the existential depth of their ontologies. For each of these classes, we determine the combined complexity of OMQ answering and whether all OMQs in the class have polynomial-size first-order, positive existential, and nonrecursive datalog rewritings. We obtain the succinctness results using hypergraph programs, a new computational model for Boolean functions, which makes it possible to connect the size of OMQ rewritings and circuit complexity.

We present an scalable algorithm for answering multiple conjunctive queries using views. This is an important problem in query optimization, data integration and ontology-based data access. Since rewriting one conjunctive query using views is an NP-hard problem, we develop an approach where answering n queries takes less than n times the cost of answering one query, by compactly representing and indexing common patterns in the input queries and the views. Our initial experimental results show a promising speed up.

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

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.

Information extraction (IE) is a main approach for constructing database from free text corpus and for automatic collecting information. Frame semantic tagging is suggested to be the base for rule-building in IE. IE based on frame semantic tagging uses a uniform approach to guide the process of IE. Processing at a finer granularity level, the approach has a universal appeal for IE in domains showing strong semantic rules. A system called BA IE (book abstract information extract ion system), which is based on frame semantic, is designed and used to extract information from book abstract. The result shows that the approach is feasible and has practical promise.

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.

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.

Ontology-based data access (OBDA) systems use ontologies to provide views over relational databases. Most of these systems work with ontologies implemented in description logic families of reduced expressiveness, what allows applying efficient query rewriting techniques for query answering. In this paper we describe a set of optimisations that are applicable with one of the most expressive families used in this context (ELHIO). Our resulting system exhibits a behaviour that is comparable to the one shown by systems that handle less expressive logics.

In this paper we present JUDIE (Joint Unsupervised Structure Discovery and Information Extraction), a new method for automatically extracting semi-structured data records in the form of continuous text (e.g., bibliographic citations, postal addresses, classified ads, etc.) and having no explicit delimiters between them. While in state-of-the-art Information Extraction methods the structure of the data records is manually supplied the by user as a training step, JUDIE is capable of detecting the structure of each individual record being extracted without any user assistance. This is accomplished by a novel Structure Discovery algorithm that, given a sequence of labels representing attributes assigned to potential values, groups these labels into individual records by looking for frequent patterns of label repetitions among the given sequence. We also show how to integrate this algorithm in the information extraction process by means of successive refinement steps that alternate information extraction and structure discovery. Through an extensively experimental evaluation with different datasets in distinct domains, we compare JUDIE with state-of-the-art information extraction methods and conclude that, even without any user intervention, it is able to achieve high quality results on the tasks of discovering the structure of the records and extracting information from them.