Academic literature on the topic 'SPARQL query transformation'

Context and motivation: Multiple-viewed requirements modeling method describes the system to-be from different perspectives. Some requirements models are then specified in various UML diagrams. Question/problem: Managing those models can be tedious and error-prone, since a lot of CASE tools provide poor support for reasoning and consistency checking. Principal ideas/results: Ontology is a formal notation for describing concepts and their relations in a domain. Since software requirements are a kind of knowledge, we propose to adopt a knowledge engineering approach for managing the consistency of requirements models. In this paper, an ontology for three most commonly used UML diagrams is developed in Web Ontology Language (OWL). The transformation of UML class, sequence and state diagrams to OWL knowledge base is presented. Owing to the underlying logical reasoning capability of OWL, a semantic query language, SPARQL (SPARQL Protocol and RDF Query Language), is used to query the knowledge base for consistency checking. Contribution: This paper introduces a semantic web-based knowledge engineering approach to represent and manage software requirements knowledge in OWL. By experimenting with a concrete software system, we demonstrate the feasibility and applicability of this knowledge approach.

Generating SPARQL queries from natural language questions is challenging in Knowledge Base Question Answering (KBQA) systems. The current state-of-the-art models heavily rely on fine-tuning pretrained models such as T5. However, these methods still encounter critical issues such as triple-flip errors (e.g., (subject, relation, object) is predicted as (object, relation, subject)). To address this limitation, we introduce TSET (Triplet Structure Enhanced T5), a model with a novel pretraining stage positioned between the initial T5 pretraining and the fine-tuning for the Text-to-SPARQL task. In this intermediary stage, we introduce a new objective called Triplet Structure Correction (TSC) to train the model on a SPARQL corpus derived from Wikidata. This objective aims to deepen the model’s understanding of the order of triplets. After this specialized pretraining, the model undergoes fine-tuning for SPARQL query generation, augmenting its query-generation capabilities. We also propose a method named “semantic transformation” to fortify the model’s grasp of SPARQL syntax and semantics without compromising the pre-trained weights of T5. Experimental results demonstrate that our proposed TSET outperforms existing methods on three well-established KBQA datasets: LC-QuAD 2.0, QALD-9 plus, and QALD-10, establishing a new state-of-the-art performance (95.0% F1 and 93.1% QM on LC-QuAD 2.0, 75.85% F1 and 61.76% QM on QALD-9 plus, 51.37% F1 and 40.05% QM on QALD-10).

"The purpose of the present paper is to research a rule-based approach for transforming X3D (eXtensible 3D) models to RDF (Resource Description Framework). The transformation is performed by using the RDF Mapping Language (RML). Its advantages are summarized, which are mainly due to the fact that the rules created build a knowledge base. By applying SPARQL (SPARQL Protocol and RDF Query Language) queries to it, the possibility of explore in order to validate and improve the defined RML rules themselves, is pointed out. An approach for reversing from the RDF triples to the original X3D in a unique way is considered, and the types of SPARQL queries needed for its implementation are systematized. Rules are formulated for all elements defined in the X3D schema, their attributes and properties are described. Their accessibility is ensured. The conversion of X3D models to RDF is confirmed to be consistent with guidelines and best practices for creating accessible, understandable, and reusable ontologies on the Web. The systematized SPARQL query types for reversing from RDF triples to the original X3D are checked for specific elements and sample data, and the obtained results establish their correctness. The prerequisites and limitations of the represented approach are explained. The proposed approach allows building a comprehensive knowledge base that includes the RML rules, the transformed X3D models and the domain-specific ontology and its use to analyzing data and semantic reasoning. The electronic libraries that include 3D content could take advantage from the benefits and possible future applications of the solutions discussed in this study."

Abstract The paper studies the performance of various languages used to work with ontological models. The languages are used in the methodology for modifying and verifying the 3D construction ontology presented in this paper. The methodology consists of three stages: building an ontological model of the assembly system and an ontological model of the desired 3D structure, modifying the original 3D structure model, and verifying the result in order to decide on further actions. The paper considers two groups of languages: query languages and modification languages. SQWRL and SPARQL are analyzed as query languages, and OWL DL and SWRL are used as modification languages. The comparison is based on the speed of performing such basic Boolean operations as conjunction and disjunction. To achieve the greatest objectivity a study is carried out on models of different dimensions. All measurements are made in the Protégé system since this system supports all 4 languages, the dependence of the runtime results on the runtime environment is minimized. The evaluation results are presented as a graph of the dependence of the rate of change in the process of performing an operation on the number of elements in the ontological model. The conclusion shows that the languages OWL DL (reasoner Pellet) and SWRL as ontology transformation languages and SPARQL as a query language are most suitable for working with the assembly ontology of 3D structures.

In the paper we discuss how to support the process of creating tools which transform natural language (NL) queries into SPARQL queries (hereinafter referred to as a transformation tool). In the introduction, we describe the relevance of the task of understanding natural language queries by information systems, as well as the advantages of using ontologies as a means of representing knowledge for solving this problem. This ontology-based data access approach can be also used in systems which provide natural language interface to databases. Based on the analysis of problems related to the integration and testing of existing transformation tools, as well as to support the creation and testing own transformation modules, the concept of a software platform that simplifies these tasks is proposed. The platform architecture satisfies the requirements for ease of connecting third party transformation tools, reusing individual modules, as well as integrating the resulting transformation tools into other systems, including testing systems. The building blocks of the created transformation systems are the individual transformation modules packaged in Docker containers. Program access to each module is carried out using gRPC. Modules loaded into the platform can be built into the transformation pipeline automatically or manually using the built-in third party SciVi data flow diagram editor. Compatibility of individual modules is controlled by automatic analysis of application programming interfaces. The resulting pipeline is combined according to specified data flow into a single multi-container application that can be integrated into other systems, as well as tested on extendable test suites. The expected and actual results of the query transformation are available for viewing in graphical form in the visualization tool developed earlier.

Modern web wants the data to be in Resource Description Framework (RDF) format, a machine-readable form that is easy to share and reuse data without human intervention. However, most of the information is still available in relational form. The existing conventional methods transform the data from RDB to RDF using instance-level mapping, which has not yielded the expected results because of poor mapping. Hence, in this paper, a novel schema-based RDB-RDF mapping method (relational database to Resource Description Framework) is proposed, which is an improvised version for transforming the relational database into the Resource Description Framework. It provides both data materialization and on-demand mapping. RDB-RDF reduces the data retrieval time for nonprimary key search by using schema-level mapping. The resultant mapped RDF graph presents the relational database in a conceptual schema and maintains the instance triples as data graph. This mechanism is known as data materialization, which suits well for the static dataset. To get the data in a dynamic environment, query translation (on-demand mapping) is best instead of whole data conversion. The proposed approach directly converts the SPARQL query into SQL query using the mapping descriptions available in the proposed system. The mapping description is the key component of this proposed system which is responsible for quick data retrieval and query translation. Join expression introduced in the proposed RDB-RDF mapping method efficiently handles all complex operations with primary and foreign keys. Experimental evaluation is done on the graphics designer database. It is observed from the result that the proposed schema-based RDB-RDF mapping method accomplishes more comprehensible mapping than conventional methods by dissolving structural and operational differences.

Abstract For the scenario of limited hardware resources and restricted software environment in edge computing architecture, the method of building and applying knowledge graph in edge analytics environment is proposed. The main process includes: building knowledge graph in the cloud, storing knowledge base with RDF format at the edge through customization and transformation, and performing query and analytics at the edge with SPARQL graph search language. The method is simulated in a communication anti-jamming test environment, and the results show that the relevant technical solutions can better meet the requirements of construction of knowledge graph and data analysis in a restricted edge analytics environment.

<span lang="EN-US">Extensible markup language (XML) is well-known as the standard for data exchange over the Internet. It is flexible and has high expressibility to express the relationship between the data stored. Yet, the structural complexity and the semantic relationships are not well expressed. On the other hand, ontology models the structural, semantic and domain knowledge effectively. By combining ontology with visualization effect, one will be able to have a closer view based on respective user requirements. In this paper, we propose several mapping rules for the transformation of XML into ontology representation. Subsequently, we show how the ontology is constructed based on the proposed rules using the sample domain ontology in University of Wisconsin-Milwaukee (UWM) and mondial datasets. <br /> We also look at the schemas, query workload, and evaluation, to derive the extended knowledge from the existing ontology. The correctness of the ontology representation has been proven effective through supporting various types of complex queries in simple protocol and resource description framework query language (SPARQL) language.</span>

De nombreux travaux historiques s'intéressent à la vie et à l'œuvre d'Henri Poincaré (1854-1912), notamment par l'étude et la publication du corpus de sa correspondance, qui se compose d'environ 2000 lettres et qui comprend des échanges relevant du cadre académique, privé ou scientifique. Depuis plusieurs années, des travaux numériques se sont développés pour stocker, publier et exploiter les données de ce corpus par la mise en œuvre de standards et de technologies du Web sémantique, en particulier RDF RDFS et SPARQL. Lors de l'interrogation d'un graphe RDF plusieurs situations peuvent mener à une volonté de formuler des interrogations flexibles. Ce terme caractérise des méthodes de recherche allant au-delà des systèmes de recherche classiques, qui se cantonnent aux interrogations exactes et qui ne permettent pas ou peu d'exprimer des préférences utilisateurs. La contribution principale de ce travail de recherche s'intéresse à la formalisation, à l'étude et aux applications d'un mécanisme d'interrogation flexible s'appuyant sur l'utilisation de règles de transformation de requêtes SPARQL. Ce système permet, à partir d'une requête initiale, d'un graphe RDF et d'un ensemble de règles, de générer des requêtes SPARQL afin d'offrir des résultats alternatifs à ceux initialement retournés suite à l'interrogation d'un corpus. Les règles de transformation peuvent être génériques, et donc facilement transposables à d'autres graphes, ou être dépendantes d'un domaine d'application. Plusieurs outils s'appuyant sur ce mécanisme ont été développés pour assister l'exploitation numérique du corpus de la correspondance d'Henri Poincaré. Un outil d'aide à l'édition manuelle de données RDF a été implémenté pour assister cette tâche parfois longue et fastidieuse et comportant un risque d'erreurs significatif. Celui-ci s'appuie sur les connaissances du domaine et sur l'utilisation du raisonnement à partir de cas pour fournir une liste ordonnée de suggestions lors de l'édition d'un triplet RDF. Le système d'interrogation flexible défini a également été intégré à un outil de navigation, qui propose une interface pour l'exploration visuelle de graphes RDF, et qui exploite les similarités entre les ressources d'un graphe pour générer des filtres de recherche. Ces outils exploitent les connaissances associées au corpus de la correspondance qui sont intégrées à diverses règles de transformation. Au travers de l'utilisation de ce mécanisme, ces travaux s'interrogent également sur l'évolution des pratiques de recherche en histoire, et tendent à illustrer comment un tel système d'interrogation flexible peut contribuer à la méthode heuristique. Les méthodes et les outils proposés peuvent être appliqués pour d'autres corpus, en particulier dans le cadre de projets d'humanités numériques
Numerous historical works are devoted to the life and works of Henri Poincaré (1854-1912), in particular through the study and the publication of his correspondence, which consists of about 2000 letters and includes academic, private and scientific exchanges. For several years, digital projects have been carried out to store, publish and exploit corpus data by implementing standards and technologies of the Semantic Web, including RDF, RDFS and SPARQL. When browsing an RDF graph, several situations may lead to a desire of flexible querying. This term describes search methods that go beyond conventional search systems, which are restricted to exact queries and allow limited or no expression of user preferences. The main contribution of this research work is the formalization, study and applications of a flexible query mechanism based on the use of SPARQL query transformation rules. This system allows, from an initial query, an RDF graph and a set of rules, to generate SPARQL queries which can provide alternative results to those initially returned. Some rules are generic, and therefore easily transposable to other graphs, and other rules are domain-dependent. Several tools based on this mechanism have been developed to assist the digital exploitation of the Henri Poincaré correspondence. A system has been implemented to assist the manual editing of RDF data, a task which can sometimes be tedious. This system relies on domain knowledge and the use of case-based reasoning to provide an ordered list of suggestions when editing an RDF triple. The proposed flexible querying system has also been integrated into a navigation tool, which provides an interface for visual exploration of RDF graphs, and which exploits similarities between resources in a graph to generate search filters. These tools exploit knowledge associated with the correspondence corpus which is represented through various transformation rules. Through the use of this mechanism, this work also considers the evolution of research practices in history, and tends to show how such a flexible querying system can contribute to the heuristic method. The methods and tools proposed can be applied to other corpora, in particular in the context of digital humanities projects

The Semantic Web research community understood since its beginning how crucial it is to equip practitioners with methods to transform non-RDF resources into RDF. Proposals focus on either engineering content transformations or accessing non-RDF resources with SPARQL. Existing solutions require users to learn specific mapping languages (e.g. RML), to know how to query and manipulate a variety of source formats (e.g. XPATH, JSON-Path), or to combine multiple languages (e.g. SPARQL Generate). In this paper, we explore an alternative solution and contribute a general-purpose meta-model for converting non-RDF resources into RDF: Facade-X. Our approach can be implemented by overriding the SERVICE operator and does not require to extend the SPARQL syntax. We compare our approach with the state of art methods RML and SPARQL Generate and show how our solution has lower learning demands and cognitive complexity, and it is cheaper to implement and maintain, while having comparable extensibility and efficiency.

Many industries prefer worldwide business operations due to the economic advantage of globalization on product design and development. These industries increasingly operate globalized multi-tier supply chains and deliver products and services all over the world. This global approach produces huge amounts of heterogeneous data residing at various business operations, and the integration of these data plays an important role. Integrating data from multiple heterogeneous sources need to deal with different data models, database schema, and query languages. This chapter presents a semantic web technology-based data integration framework that uses relational databases and XML data with the help of ontology. To model different source schemas, this chapter proposes a method based on the resource description framework (RDF) graph patterns and query rewriting techniques. The semantic translation between the source schema and RDF ontology is described using query and transformational language SPARQL.