SPARQL Queries Research Articles

Analysis of Ontology Competency Questions and their Formalisations in SPARQL-OWL

Competency Questions (CQs) are natural language questions outlining and constraining the scope of knowledge represented in an ontology. Despite that CQs are a part of several ontology engineering methodologies, the actual publication of CQs for the available ontologies is very limited and even scarcer is the publication of their respective formalisations in terms of, e.g., SPARQL queries. This paper aims to contribute to addressing the myriad of engineering hurdles to using CQs in ontology development. A prerequisite to this is to understand the relation between CQs and the queries over the ontology. We use a new dataset of 234 competency questions and their SPARQL-OWL queries for several ontologies in dierent domains developed by dierent groups, and analysed the CQs in two principal ways. The first stage focused on a linguistic analysis of the natural language text itself, i.e., a lexico-syntactic analysis without any presuppositions of ontology elements, and a subsequent step of semantic analysis in order to find patterns. This increased diversity of CQ sources resulted in a 4-5-fold increase of hitherto published patterns, to 106 distinct CQ patterns, which have a limited subset of few patterns shared across the CQ sets from the dierent ontologies. Next, we analysed the relation between the found CQ patterns and their respective SPARQL-OWL patterns, which revealed that one CQ pattern may be realised by more than one SPARQL-OWL query pattern, and vice versa. These insights may contribute to establishing common practices, templates, automation, and user tools that will support CQ formulation, formalisation, execution, and general management.

A Robust Optimization Approach of SQL-to-SPARQL Query Rewriting

In order to ensure the interoperability between semantic web and relational databases, several approaches have been developed to ensure SQL-to-SPARQL query transformation direction, but all these approaches have the same weakness. In fact, they convert directly the input SQL query to its equivalent SPARQL one without any pre-processing phase enabling the optimization of this input query filled by users before starting the conversion process. This weakness has motivated us to add a pretreatment phase aiming to optimize the most important SQL statements which seem to have the biggest impact on the effectiveness of the transformed queries. Our main contribution is to enrich these rewriting systems by adding an optimization layer that integrate a set of simplification rules of Left, Right and Full Outer Join in order to avoid, firstly unnecessary operations during the conversion process, and secondly SPARQL queries with a high complexity due to Optional patterns obtained from outer join in this conversion context.

Domain and Schema Independent Semantic Model Verbalization: A Conceptual Overview

Semantic Web-based technologies have become extremely popular and its a success that has spread across many domains, additional to the computer science domain. Nevertheless, the reusability aspects associated with the created and available semantic knowledge models are very low. The main bottleneck associated with this issue is, the difficulty associated in understanding the complex schema of a knowledge model created and barriers associated with querying the knowledge models using SPARQL or SQWRL query formulations. This research emphasizes on proposing a verbalizer which can go beyond existing Controlled Natural Language (CNL) type verbalizers and to verbalizer knowledge stored in a knowledge model file written in either RDF or OWL format, despite its domain and schematics.

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.

A Semantic Ontology for Disaster Trail Management System

Disasters, whether natural or human-made, leave a lasting impact on human lives and require mitigation measures. In the past, millions of human beings lost their lives and properties in disasters. Information and Communication Technology provides many solutions. The issue of so far developed disaster management systems is their inefficiency in semantics that causes failure in producing dynamic inferences. Here comes the role of semantic web technology that helps to retrieve useful information. Semantic web-based intelligent and self-administered framework utilizes XML, RDF, and ontologies for a semantic presentation of data. The ontology establishes fundamental rules for data searching from the unstructured world, i.e., the World Wide Web. Afterward, these rules are utilized for data extraction and reasoning purposes. Many disaster-related ontologies have been studied; however, none conceptualizes the domain comprehensively. Some of the domain ontologies intend for the precise end goal like the disaster plans. Others have been developed for the emergency operation center or the recognition and characterization of the objects in a calamity scene. A few ontologies depend on upper ontologies that are excessively abstract and are exceptionally difficult to grasp by the individuals who are not conversant with theories of the upper ontologies. The present developed semantic web-based disaster trail management ontology almost covers all vital facets of disasters like disaster type, disaster location, disaster time, misfortunes including the causalities and the infrastructure loss, services, service providers, relief items, and so forth. The objectives of this research were to identify the requirements of a disaster ontology, to construct the ontology, and to evaluate the ontology developed for Disaster Trail Management. The ontology was assessed efficaciously via competency questions; externally by the domain experts and internally with the help of SPARQL queries.

New Approach based on Model Driven Engineering for Processing Complex SPARQL Queries on Hive

Semantic web technologies are increasingly used in different domains. The core technology of the Semantic Web is the RDF standard. Today with the growth of RDF data it requires systems capable of handling these large volumes of data and responding to very complex requests at the join level, Several RDF data processing systems have been proposed, but are not dedicated to handling complex SPARQL queries. in this paper we present a new approach based on model driven engineering for processing complex SPARQL queries using one of the big data processing tools named Hive. We evaluate our system using three datasets from LUBM Benchmark. The results of this evaluation show the performance, and the scalability of our approach, also give very interesting results when it is compared with existing works.

Temporal Extensions to RDF

The Semantic Web aims at building a foundation of semantic-based data models and languages for not only manipulating data and knowledge, but also in decision making by machines. Naturally, time-varying data and knowledge are required in Semantic Web applications to incorporate time and further reason about it. However, the original specifications of RDF and OWL do not include constructs for handling time-varying data and knowledge. For simplicity, RDF model is confined to binary predicates, hence some form of reification is needed to represent higher-arity predicates. To this date, there are many proposals extending RDF and OWL for handling temporal data and knowledge. They all focus on the valid time. In this paper, we examine each of these proposals and develop a taxonomy to classify them according to the form of reification employed: explicit reification or implicit reification. The implicit reification proposals are further divided into three sub-categories according to semantic constructs they use. Some of these proposals stay compliant to the RDF and OWL standards whereas others add new constructs to RDF model and SPARQL query language. Additionally, we compare these proposed models with respect to characteristics, such as their syntax and semantics, their compliance to RDF and OWL specifications, their need for additional objects, etc. The comparison provides a useful guideline for the researchers and practitioners of the Semantic Web in managing temporal data and knowledge.

Leveraging Linked Open Data to Automatically Answer Arabic Questions

The interchangeably connected Web technologies and the advancements that accompany the semantic web content’s leaps, have raised many challenges in the results’ retrieval process especially for the Arabic Language. This research targets an important, yet insufficiently precedent, area in using Linked Open Data (LOD) for Automatic Question Answering systems in the Arabic Language. The significance of work presented, comes from its ability to overcome many challenges in querying Arabic content. Some of these challenges are: (a) bridging the gap between natural language and linked data by mapping users’ queries to a standard semantic web query language such as SPARQL, (b) facilitating multilingual access to semantic data, and (c) maintaining the quality of data. Another challenging aspect was the lack of related work and publicly available resources for Arabic Question Answering Systems over Linked Data, despite the vastly growing Arabic corpus on the web. This paper presents a novel approach that targets Automatic Arabic Questions’ Answering Systems whilst bypassing many featured challenges in the field. A hybrid approach that evaluates the effectiveness of using LOD to automatically answer Arabic questions is developed. The approach is developed to map users’ questions in Modern Standard Arabic, to a standard query language for LOD (i.e. SPARQL) through: (i) extracting entities from questions and linking them over the web using Named-Entity Recognition and Disambiguation (NER/NED), and (ii) extracting properties among extracted named entities using a dependency parsing approach integrated with Wikidata ontology. To evaluate our proposed system, an Arabic questions dataset was created including: (a) Question body in Arabic language, (b) Question type, (c) SPARQL Query formulation, and (d) Question answer. Evaluation results are promising with a Precision of 84%, a Recall of 81.3%, and an F-Measure of 82.8%.

OPT+: A Monotonic Alternativeto OPTIONAL in SPARQL

Due to the OPTIONAL operator, the core fragment of the SPARQL query language is non-monotonic. That is, some solutions of a query result can be returned to the user only after having consulted all ...

Efficient Distributed SPARQL Queries on Apache Spark

RDF is a widely-accepted framework for describing metadata in the web due to its simplicity and universal graph-like data model. Owing to the abundance of RDF data, existing query techniques are rendered unsuitable. To this direction, we adopt the processing power of Apache Spark to load and query a large dataset much more quickly than classical approaches. In this paper, we have designed experiments to evaluate the performance of several queries ranging from single attribute selection to selection, filtering and sorting multiple attributes in the dataset. We further experimented with the performance of queries using distributed SPARQL query on Apache Spark GraphX and studied different stages involved in this pipeline. The execution of distributed SPARQL query on Apache Spark GraphX helped us study its performance and gave insights into which stages of the pipeline can be improved. The query pipeline comprised of Graph loading, Basic Graph Pattern and Result calculating. Our goal is to minimize the time during graph loading stage in order to improve overall performance and cut the costs of data loading.

Integration of Web APIs and Linked Data Using SPARQL Micro-Services—Application to Biodiversity Use Cases

In recent years, Web APIs have become a de facto standard for exchanging machinereadable data on the Web. Despite this success, however, they often fail in making resource descriptions interoperable due to the fact that they rely on proprietary vocabularies that lack formal semantics. The Linked Data principles similarly seek the massive publication of data on the Web, yet with the specific goal of ensuring semantic interoperability. Given their complementary goals, it is commonly admitted that cross-fertilization could stem from the automatic combination of Linked Data and Web APIs. Towards this goal, in this paper we leverage the micro-service architectural principles to define a SPARQL Micro-Service architecture, aimed at querying Web APIs using SPARQL. A SPARQL micro-service is a lightweight SPARQL endpoint that provides access to a small, resource-centric, virtual graph. In this context, we argue that full SPARQL Query expressiveness can be supported efficiently without jeopardizing servers availability. Furthermore, we demonstrate how this architecture can be used to dynamically assign dereferenceable URIs to Web API resources that do not have URIs beforehand, thus literally “bringing” Web APIs into the Web of Data. We believe that the emergence of an ecosystem of SPARQL micro-services published by independent providers would enable Linked Data-based applications to easily glean pieces of data from a wealth of distributed, scalable, and reliable services. We describe a working prototype implementation and we finally illustrate the use of SPARQL micro-services in the context of two real-life use cases related to the biodiversity domain, developed in collaboration with the French National Museum of Natural History.

Building self-clustering RDF databases using Tunable-LSH

The Resource Description Framework (RDF) is a W3C standard for representing graph-structured data, and SPARQL is the standard query language for RDF. Recent advances in information extraction, linked data management and the Semantic Web have led to a rapid increase in both the volume and the variety of RDF data that are publicly available. As businesses start to capitalize on RDF data, RDF data management systems are being exposed to workloads that are far more diverse and dynamic than what they were designed to handle. Consequently, there is a growing need for developing workload-adaptive and self-tuning RDF data management systems. To realize this objective, we introduce a fast and efficient method for dynamically clustering records in an RDF data management system. Specifically, we assume nothing about the workload upfront, but as SPARQL queries are executed, we keep track of records that are co-accessed by the queries in the workload and physically cluster them. To decide dynamically and in constant-time where a record needs to be placed in the storage system, we develop a new locality-sensitive hashing (LSH) scheme, Tunable-LSH. Using Tunable-LSH, records that are co-accessed across similar sets of queries can be hashed to the same or nearby physical pages in the storage system. What sets Tunable-LSH apart from existing LSH schemes is that it can auto-tune to achieve the aforementioned clustering objective with high accuracy even when the workloads change. Experimental evaluation of Tunable-LSH in an RDF data management system as well as in a standalone hashtable shows end-to-end performance gains over existing solutions.

Design and Analysis of a Query Processor for Brick

Brick is a recently proposed metadata schema and ontology for describing building components and the relationships between them. It represents buildings as directed labeled graphs using the RDF data model. Using the SPARQL query language, building-agnostic applications query a Brick graph to discover the set of resources and relationships they require to operate. Latency-sensitive applications, such as user interfaces, demand response, and model-predictive control, require fast queries—conventionally less than 100ms. We benchmark a set of popular open source and commercial SPARQL databases against three real Brick models using seven application queries and find that none of them meet this performance target. This lack of performance can be attributed to design decisions that optimize for queries over large graphs consisting of billions of triples but give poor spatial locality and join performance on the small dense graphs typical of Brick. We present the design and evaluation of HodDB, a RDF/SPARQL database for Brick built over a node-based index structure. HodDB performs Brick queries 3--700× faster than leading SPARQL databases and consistently meets the 100ms threshold, enabling the portability of important latency-sensitive building applications. This article is an extension of a previously published work [16].

Structured peer-to-peer-based publication and sharing of ontologies to automatically process SPARQL query on a semantic sensor network

In recent years, semantic sensor networks are proposed, which apply ontologies to provide query capabilities and data access and allow users to express their needs at a conceptual level. In such sensor networks, a large amount of web ontologies are separately created by sensors to represent their own knowledge. These ontologies are distributed in different sensors and provide knowledge for semantic queries. It has become a very pressing issue to locate the desired ontologies for a given semantic query. To address this issue, we propose an approach based on structured peer-to-peer protocol to publish shareable ontologies on different sensors and automatically discover the ontologies useful for a given SPARQL query. Therefore, if a SPARQL query is given, our approach can locate ontologies desired and further send the query to them to find out solutions for the query. In addition, if solutions can be found out from a web ontology published, our approach makes sure to discover the ontology and get the solutions from it for the query. We conduct three experiments to evaluate the approach, the results of which demonstrate that our approach is effective and efficient.

RDF Keyword Search by Query Computation

Keyword searches based on the keywords-to-SPARQL translation is attracting more attention because of a growing number of excellent SPARQL search engines. Current approaches for keyword search based on the keywords-to-SPARQL translation suffer from returning incomplete answers or wrong answers due to a lack of underlying schema information. To overcome these difficulties, in this article, we propose a new keyword search paradigm by translating keyword queries into SPARQL queries for exploring RDF data. An inter-entity relationship summary with complete schema information is distilled from the RDF data graph for composing SPARQL queries. To avoid potentially wasteful summary graph expansion, we develop a new search prioritization scheme by combining the degree of a vertex with the distance from the original keyword element. Starting from the ordered priority list that is built in advance, we apply the forward path index to faster find the top-k subgraphs, which are relevant to the conjunction of the entering keywords. The experimental results show that our approach is efficient and scalable.

Distributed Join Query Processing for Big RDF Data

The expansion of the services of the Semantic Web and the evolution of cloud computing technologies have significantly enhanced the capability of preserving and publishing information in standard open web formats, such that data can be both human-readable and machine-processable. This situation meets the challenge in the current big data era to effectively store, retrieve, and analyze resource description framework (RDF) data in swarms. Moreover, efficient data storage and retrieval that can scale to large amounts of possibly schema-less data have proven to be quite difficult to achieve, specifically, RDF data storage with complex and large graph patterns for representing semantic data, and SPARQL query languages. In this paper, we provide comprehensive discussion about the proposed algorithms of Join.Query processing of RDF data by considering MapReduce Framework in a distributed environment. Moreover, we introduced a framework for RDF query processing and the benchmark that is used for the performance evaluation. Finally, we offer an evaluation discussion on distributed join query processing for big RDF data.

Expressive Languages for Querying the Semantic Web

The problem of querying RDF data is a central issue for the development of the Semantic Web. The query language SPARQL has become the standard language for querying RDF since its W3C standardization in 2008. However, the 2008 version of this language missed some important functionalities: reasoning capabilities to deal with RDFS and OWL vocabularies, navigational capabilities to exploit the graph structure of RDF data, and a general form of recursion much needed to express some natural queries. To overcome these limitations, a new version of SPARQL, called SPARQL 1.1, was released in 2013, which includes entailment regimes for RDFS and OWL vocabularies, and a mechanism to express navigation patterns through regular expressions. Unfortunately, there are a number of useful navigation patterns that cannot be expressed in SPARQL 1.1, and the language lacks a general mechanism to express recursive queries. To the best of our knowledge, no efficient RDF query language that combines the above functionalities is known. It is the aim of this work to fill this gap. To this end, we focus on a core fragment of the OWL 2 QL profile of OWL 2 and show that every SPARQL query enriched with the above features can be naturally translated into a query expressed in a language that is based on an extension of Datalog, which allows for value invention and stratified negation. However, the query evaluation problem for this language is highly intractable, which is not surprising since it is expressive enough to encode some inherently hard queries. We identify a natural fragment of it, and we show it to be tractable and powerful enough to define SPARQL queries enhanced with the desired functionalities.

A Rule-Based Transducer for Querying Incompletely Aligned Datasets

A growing number of Linked Open Data sources (from diverse provenances and about different domains) that can be freely browsed and searched to find and extract useful information have been made available. However, access to them is difficult for different reasons. This study addresses access issues concerning heterogeneity. It is common for datasets to describe the same or overlapping domains while using different vocabularies. Our study presents a transducer that transforms a SPARQL query suitably expressed in terms of the vocabularies used in a source dataset into another SPARQL query suitably expressed for a target dataset involving different vocabularies. The transformation is based on existing alignments between terms in different datasets. Whenever the transducer is unable to produce a semantically equivalent query because of the scarcity of term alignments, the transducer produces a semantic approximation of the query to avoid returning the empty answer to the user. Transformation across datasets is achieved through the management of a wide range of transformation rules. The feasibility of our proposal has been validated with a prototype implementation that processes queries that appear in well-known benchmarks and SPARQL endpoint logs. Results of the experiments show that the system is quite effective in achieving adequate transformations.

Efficiently Processing and Storing Library Linked Data using Apache Spark and Parquet

Resource Description Framework (RDF) is a commonly used data model in the Semantic Web environment. Libraries and various other communities have been using the RDF data model to store valuable data after it is extracted from traditional storage systems. However, because of the large volume of the data, processing and storing it is becoming a nightmare for traditional data-management tools. This challenge demands a scalable and distributed system that can manage data in parallel. In this article, a distributed solution is proposed for efficiently processing and storing the large volume of library linked data stored in traditional storage systems. Apache Spark is used for parallel processing of large data sets and a column-oriented schema is proposed for storing RDF data. The storage system is built on top of Hadoop Distributed File Systems (HDFS) and uses the Apache Parquet format to store data in a compressed form. The experimental evaluation showed that storage requirements were reduced significantly as compared to Jena TDB, Sesame, RDF/XML, and N-Triples file formats. SPARQL queries are processed using Spark SQL to query the compressed data. The experimental evaluation showed a good query response time, which significantly reduces as the number of worker nodes increases.

Дослідження методів і технологій інтеграції онтологічної моделі з реляційними даними

A study of methods and technological solutions on Data Mapping concerning the integration of an ontological and relational data models has been carried out.The main objective of the investigation is to accelerate and reduce the cost of constructing some ontological models for systems that process distributed data in a heterogeneous environment.At present, the theoretical basis for the integration of various data models (Data Mapping) is a di-rection that has been defined as Ontology-based data integration. The theoretical and practical develop-ment of this area is the Ontology-Based Data Access (OBDA) approach, which integrates ontological models presented in the form of RDF graphs with relational data.The methodology of Data Mapping application for distributed data processing systems has been developed. As an example the process of data models consolidation for Ukrainian State Budget monitor-ing system is given. The database of Ukrainian State Budget monitoring system consists of many rela-tional tables, which contain reports of the State Treasury on the implementation of revenue and expendi-ture parts of both state and municipal budgets, as well as the regional section. In addition, the database contains data on lending and arrears of budget institutions.The ontology model connects to data sources through a declarative specification provided in terms of display, including classes and their properties.The given application converts a SPARQL queries into a SQL query to the relational database. The generated SQL query may be execute by the Oracle 11g database driver, which returns the result as a data snapshot. Then, to improve the performance of SPARQL queries, it should be used the semantic query optimization method.Indicative application of the methodology on the example of the construction of an ontological model of the system of monitoring the state budget of Ukraine in Protege 5. It has been demonstrated the results of the execution of the SPARQL-queries to the relational data of the budget process under the Oracle 11g Database. It has been shown the directions semantic optimization for SPARQL-queries, which allow improving obtained data quality.The proposed methodology allows: to integrate data presented by different models — ontological and relational knowledge acquisition; to overcome constraints when databases based on outdated data models are being merged with modern ontological-oriented systems; to eliminate data redundancy in designing knowledge-based systems.