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Journal articles on the topic 'RDF Data'

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Published: 25 May 2024

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1

Jun, Hee-Gook, and Dong-Hyuk Im. "Semantics-Preserving RDB2RDF Data Transformation Using Hierarchical Direct Mapping." Applied Sciences 10, no. 20 (October 12, 2020): 7070. http://dx.doi.org/10.3390/app10207070.

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Direct mapping is an automatic transformation method used to generate resource description framework (RDF) data from relational data. In the field of direct mapping, semantics preservation is critical to ensure that the mapping method outputs RDF data without information loss or incorrect semantic data generation. However, existing direct-mapping methods have problems that prevent semantics preservation in specific cases. For this reason, a mapping method is developed to perform a semantics-preserving transformation of relational databases (RDB) into RDF data without semantic information loss and to reduce the volume of incorrect RDF data. This research reviews cases that do not generate semantics-preserving results, and the corresponding problems into categories are arranged. This paper defines lemmas that represent the features of RDF data transformation to resolve those problems. Based on the lemmas, this work develops a hierarchical direct-mapping method to strictly abide by the definition of semantics preservation and to prevent semantic information loss, reducing the volume of incorrect RDF data generated. Experiments demonstrate the capability of the proposed method to perform semantics-preserving RDB2RDF data transformation, generating semantically accurate results. This work impacts future studies, which should involve the development of synchronization methods to achieve RDF data consistency when original RDB data are modified.
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Natarajan, Senthilselvan, Subramaniyaswamy Vairavasundaram, Yuvaraja Teekaraman, Ramya Kuppusamy, and Arun Radhakrishnan. "Schema-Based Mapping Approach for Data Transformation to Enrich Semantic Web." Wireless Communications and Mobile Computing 2021 (November 10, 2021): 1–15. http://dx.doi.org/10.1155/2021/8567894.

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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.
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Chystiakova, I. S. "Implementation of mappings between the description logic and the binary relational data model on the RDF level." PROBLEMS IN PROGRAMMING, no. 4 (December 2020): 041–54. http://dx.doi.org/10.15407/pp2020.04.041.

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This paper is dedicated to the data integration problem. In article the task of practical implementation of mappings between description logic and a binary relational data model is discussed. This method was formulated earlier at a theoretical level. A practical technique to test mapping engines using RDF is provided in the current paper. To transform the constructs of the description logic ALC and its main extensions into RDF triplets the OWL 2-to-RDF mappings are used. To convert RDB to RDF graph, the R2R Mapping Language (R2R ML) was chosen. The mappings DL ALC and its main extensions to the RDF triplets are described in the publication. The mapping of the DL axioms into an RDF triplet also is considered in the publication. The main difficulties in describing DL-to-RDF transformations are given in the corresponding section. For each constructor of concepts and roles a corresponding expression in OWL 2 and its mapping into the RDF triplet. A schematic representation of the resulting RDF graph for each mapping is created. The paper also provides an overview of existing methods that relate to the use of RDF when mapping RDB to ontology and vice versa.
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Ri Kim, Ju, Zhanfang Zhao, and Sung Kook Han. "Sparql query processing in relational databases." International Journal of Engineering & Technology 7, no. 3.3 (June 8, 2018): 84. http://dx.doi.org/10.14419/ijet.v7i2.33.13860.

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Background/Objectives: The mapping RDB to RDF has become important to populate Linked Data more efficiently. This paper shows how to implement SPARQL endpoint in RDB using a conceptual level mapping approach.Methods/Statistical analysis: Many diverse approaches and related languages for mapping RDB to RDF have been proposed. The prominent achievements of mapping RDB to RDF are two standard draft Direct Mapping and R2RML proposed by W3C RDB2RDF Working Group. This paper analyzes these conventional mapping approaches and proposes a new approach based on schema mapping. The paper also presents SPARQL query processing in RDB.Findings: There are distinct differences between instance level mapping and conceptual level mapping for RDB2RDF. Data redundancy of instance level mapping causes many inevitable problems during mapping procedure. The conceptual level mapping can provide straightforward and efficient way. The ER model in RDB and RDF model in Linked Data have obvious similarity. The ER model describes entities and relationships, which is the conceptual schema of RDB. RDF model consists of three parts: subject, predicate and object, which is the standard model for data interchange on the Web. The entities in ER model and subjects in RDF model are all the things that can be anything in the real world. Both the relationships in ER model and predicates in RDF model describe the relations between things.Since RDB and RDF share the similar modeling approach at the schema level, it is reasonable that mapping approach should be based on RDB schema. This kind of conceptual level mapping also can provide efficient SPARQL query processing in RDB.Improvements/Applications: The paper realizes SPARQL query processing in RDB, which is based on conceptual level mapping. The query experiments show that it is a concise and efficient way to populate Linked Data.
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Gayo, Jose Emilio Labra, Eric Prud'hommeaux, Iovka Boneva, and Dimitris Kontokostas. "Validating RDF Data." Synthesis Lectures on the Semantic Web: Theory and Technology 7, no. 1 (September 28, 2017): 1–328. http://dx.doi.org/10.2200/s00786ed1v01y201707wbe016.

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Ri Kim, Ju, and Sung Kook Han. "R2RS: schema-based relational databases mapping to linked datasets." International Journal of Engineering & Technology 7, no. 3.3 (June 8, 2018): 119. http://dx.doi.org/10.14419/ijet.v7i2.33.13868.

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Background/Objectives: The vast amounts of high-quality data stored in relational databases (RDB) is the primary resources for Linked Open Data (LOD) datasets. This paper proposes a schema-based mapping approach from RDB to RDF, which provides succinct and efficient mapping.Methods/Statistical analysis: The various approaches, languages and tools for mapping RDB to LOD have been proposed in the recent years. This paper surveys and analyzes classic mapping approach and language such as Direct Mapping and R2RML. The mapping approaches can be categorized by means of their data modeling. After analyzing the conventional RDB-RDF mapping methods, this paper proposes a new mapping method and discusses its typical features and applications.Findings: There are two types of mapping approaches for the translation of RDB to RDF: instance-based and schema-based mapping approaches. The instance-based mapping approaches generate large amounts of RDF graphs by means of mapping rules. These approaches causes data redundancy since the same data is stored in two ways of RDB and RDF. It is very easy to bring the data inconsistence problem when data update operations occur. The schema-based mapping approaches can effectively avoid data redundancy since the mapping can be accomplished in the conceptual schema level.The architecture of SPARQL endpoint based on schema mapping approach consists of five phases:Generation of mapping description based on mapping rules.SPARQL query statements for RDF graph patterns.Translation of SPARQL query into SQL query.Execution of SQL query in RDB.Interpretation of SQL query result into JSON-LD format.Experiments show the schema-based mapping approach is a straightforward, succinct and efficient mapping method for RDB2RDF.Improvements/Applications: This paper proposes a schema-based mapping approach called R2RS, which shows better performance than the conventional mapping methods. In addition, R2RS also provides the efficient implementation of SPARQL endpoint in RDB.
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Soliman, Hatem, Izhar Ahmed Khan, and Yasir Hussain. "Global Sensitivity Analysis for Fuzzy RDF Data." International Journal of Software Engineering and Knowledge Engineering 31, no. 08 (August 2021): 1119–44. http://dx.doi.org/10.1142/s0218194021500352.

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The resource description framework (RDF) was adopted by the World Wide Web (W3C) as an essential semantic web standard and the RDF scheme. It accords the hard semantics in the description and wields the crisp metadata. However, it usually produces vague or ambiguous information. Consequently, fuzzy RDF helps deal with such special data by transforming the crisp values into a fuzzy set. A method for analyzing fuzzy RDF data is proposed in this paper. To this end, first, we decompose the RDF into fuzzy RDF variables. Second, we are designing a model for global sensitivity analysis based on the decomposition of fuzzy RDF. It figures out the ambiguities of fuzzy RDF data. The proposed global sensitivity analysis model provides the importance of fuzzy RDF data by considering the response function’s structure and reselects it to a certain degree. A practical tool for sensitivity analysis of fuzzy RDF data has also been implemented based on the proposed model.
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Fernández, Javier D., Miguel A. Martínez-Prieto, Pablo de la Fuente Redondo, and Claudio Gutiérrez. "Characterising RDF data sets." Journal of Information Science 44, no. 2 (January 9, 2017): 203–29. http://dx.doi.org/10.1177/0165551516677945.

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The publication of semantic web data, commonly represented in Resource Description Framework (RDF), has experienced outstanding growth over the last few years. Data from all fields of knowledge are shared publicly and interconnected in active initiatives such as Linked Open Data. However, despite the increasing availability of applications managing large-scale RDF information such as RDF stores and reasoning tools, little attention has been given to the structural features emerging in real-world RDF data. Our work addresses this issue by proposing specific metrics to characterise RDF data. We specifically focus on revealing the redundancy of each data set, as well as common structural patterns. We evaluate the proposed metrics on several data sets, which cover a wide range of designs and models. Our findings provide a basis for more efficient RDF data structures, indexes and compressors.
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Meng, Xiangfu, Lin Zhu, Qing Li, and Xiaoyan Zhang. "Spatiotemporal RDF Data Query Based on Subgraph Matching." ISPRS International Journal of Geo-Information 10, no. 12 (December 12, 2021): 832. http://dx.doi.org/10.3390/ijgi10120832.

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Resource Description Framework (RDF), as a standard metadata description framework proposed by the World Wide Web Consortium (W3C), is suitable for modeling and querying Web data. With the growing importance of RDF data in Web data management, there is an increasing need for modeling and querying RDF data. Previous approaches mainly focus on querying RDF. However, a large amount of RDF data have spatial and temporal features. Therefore, it is important to study spatiotemporal RDF data query approaches. In this paper, firstly, we formally define spatiotemporal RDF data, and construct a spatiotemporal RDF model st-RDF that is used to represent and manipulate spatiotemporal RDF data. Secondly, we present a spatiotemporal RDF query algorithm stQuery based on subgraph matching. This algorithm can quickly determine whether the query result is empty for queries whose temporal or spatial range exceeds a specific range by adopting a preliminary query filtering mechanism in the query process. Thirdly, we propose a sorting strategy that calculates the matching order of query nodes to speed up the subgraph matching. Finally, we conduct experiments in terms of effect and query efficiency. The experimental results show the performance advantages of our approach.
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Permatasari, Ayu Novira Shinta, and Herlina Jayadianti. "Direct Mapping and Turtle Ontology for Management of Indonesian Movies Knowledge." MATEC Web of Conferences 372 (2022): 04011. http://dx.doi.org/10.1051/matecconf/202237204011.

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Web 2.0 or conventional web has developed into Web 3.0, known as semantic web. Semantic web technology requires ontology as the backbone in understanding a concept of knowledge. In the ontology computing process, the Resource Description Framework (RDF) is used as a framework to define web resources in triple form (subject-predicate-object) so that they can form metadata and describe the information contained on the web. The data used in this study is Indonesian movies data obtained from Kaggle in Comma Separated Values (.csv) format with a total of 242 lines of Indonesian movies data. The data processing is carried out by direct mapping using the help of DB2Triples to generate data from MySQL into RDF in turtle format (.ttl) file. The results shown that direct mapping can be used to map data from RDB to RDF semi-automatically. The data is mapped into the RDF according to the schema on the RDB without input from the user, so the results provided cannot be adjusted to the needs or desires of the user. Furthermore, the RDF generated in the turtle file format has formed classes and individuals automatically, but to be able to be used as a semantic web resource, RDF needs to be processed manually to form data properties and object properties, as well as assigning instance values.
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Ma, Zongmin, Miriam A. M. Capretz, and Li Yan. "Storing massive Resource Description Framework (RDF) data: a survey." Knowledge Engineering Review 31, no. 4 (September 2016): 391–413. http://dx.doi.org/10.1017/s0269888916000217.

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AbstractThe Resource Description Framework (RDF) is a flexible model for representing information about resources on the Web. As a W3C (World Wide Web Consortium) Recommendation, RDF has rapidly gained popularity. With the widespread acceptance of RDF on the Web and in the enterprise, a huge amount of RDF data is being proliferated and becoming available. Efficient and scalable management of RDF data is therefore of increasing importance. RDF data management has attracted attention in the database and Semantic Web communities. Much work has been devoted to proposing different solutions to store RDF data efficiently. This paper focusses on using relational databases and NoSQL (for ‘not only SQL (Structured Query Language)’) databases to store massive RDF data. A full up-to-date overview of the current state of the art in RDF data storage is provided in the paper.
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Singh, Gurdas, and Dr Jaiteg Singh. "Querying RDF Data: Methods and Issues." Indian Journal of Applied Research 3, no. 2 (October 1, 2011): 144–45. http://dx.doi.org/10.15373/2249555x/feb2013/50.

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Zhu, Lin, Nan Li, and Luyi Bai. "Algebraic Operations on Spatiotemporal Data Based on RDF." ISPRS International Journal of Geo-Information 9, no. 2 (January 30, 2020): 80. http://dx.doi.org/10.3390/ijgi9020080.

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In the context of the Semantic Web, the Resource Description Framework (RDF), a language proposed by W3C, has been used for conceptual description, data modeling, and data querying. The algebraic approach has been proven to be an effective way to process queries, and algebraic operations in RDF have been investigated extensively. However, the study of spatiotemporal RDF algebra has just started and still needs further attention. This paper aims to explore an algebraic operational framework to represent the content of spatiotemporal data and support RDF graphs. To accomplish our study, we defined a spatiotemporal data model based on RDF. On this basis, the spatiotemporal semantics and the spatiotemporal algebraic operations were investigated. We defined five types of graph algebras, and, in particular, the filter operation can filter the spatiotemporal graphs using a graph pattern. Besides this, we put forward a spatiotemporal RDF syntax specification to help users browse, query, and reason with spatiotemporal RDF graphs. The syntax specification illustrates the filter rules, which contribute to capturing the spatiotemporal RDF semantics and provide a number of advanced functions for building data queries.
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Cai, Zhao Hui. "Generating Linked Course Data." Advanced Materials Research 718-720 (July 2013): 2359–64. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.2359.

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The uptake of semantic technology depends on the availability of useful tools that enable Web developers to generate linked course data automaticly. RDF triple allows web page to contain machine-readable content that is easier to find and mashable with other content. This paper describes a framework that turns this idea around, using RDF as a template language for the generation of machine-readable triple from human-readable data on Web page. Most existing methords generate RDF triple by combining the template with query results from a relational database. In the Linked Course Data Generating framework, the raw course data is turned into RDF triple, then is turned into linked data, finally is turned into ontology. This paper evaluates the performance of framework.
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Ulutaş Karakol, D., G. Kara, C. Yılmaz, and Ç. Cömert. "SEMANTIC LINKING SPATIAL RDF DATA TO THE WEB DATA SOURCES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4 (September 19, 2018): 639–45. http://dx.doi.org/10.5194/isprs-archives-xlii-4-639-2018.

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<p><strong>Abstract.</strong> Large amounts of spatial data are hold in relational databases. Spatial data in the relational databases must be converted to RDF for semantic web applications. Spatial data is an important key factor for creating spatial RDF data. Linked Data is the most preferred way by users to publish and share data in the relational databases on the Web. In order to define the semantics of the data, links are provided to vocabularies (ontologies or other external web resources) that are common conceptualizations for a domain. Linking data of resource vocabulary with globally published concepts of domain resources combines different data sources and datasets, makes data more understandable, discoverable and usable, improves data interoperability and integration, provides automatic reasoning and prevents data duplication. The need to convert relational data to RDF is coming in sight due to semantic expressiveness of Semantic Web Technologies. One of the important key factors of Semantic Web is ontologies. Ontology means “explicit specification of a conceptualization”. The semantics of spatial data relies on ontologies. Linking of spatial data from relational databases to the web data sources is not an easy task for sharing machine-readable interlinked data on the Web. Tim Berners-Lee, the inventor of the World Wide Web and the advocate of Semantic Web and Linked Data, layed down the Linked Data design principles. Based on these rules, firstly, spatial data in the relational databases must be converted to RDF with the use of supporting tools. Secondly, spatial RDF data must be linked to upper level-domain ontologies and related web data sources. Thirdly, external data sources (ontologies and web data sources) must be determined and spatial RDF data must be linked related data sources. Finally, spatial linked data must be published on the web. The main contribution of this study is to determine requirements for finding RDF links and put forward the deficiencies for creating or publishing linked spatial data. To achieve this objective, this study researches existing approaches, conversion tools and web data sources for relational data conversion to the spatial RDF. In this paper, we have investigated current state of spatial RDF data, standards, open source platforms (particularly D2RQ, Geometry2RDF, TripleGeo, GeoTriples, Ontop, etc.) and the Web Data Sources. Moreover, the process of spatial data conversion to the RDF and how to link it to the web data sources is described. The implementation of linking spatial RDF data to the web data sources is demonstrated with an example use case. Road data has been linked to the one of the related popular web data sources, DBPedia. SILK, a tool for discovering relationships between data items within different Linked Data sources, is used as a link discovery framework. Also, we evaluated other link discovery tools e.g. LIMES, Silk and results are compared to carry out matching/linking task. As a result, linked road data is shared and represented as an information resource on the web and enriched with definitions of related different resources. By this way, road datasets are also linked by the related classes, individuals, spatial relations and properties they cover such as, construction date, road length, coordinates, etc.</p>
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Tomaszuk, Dominik, and David Hyland-Wood. "RDF 1.1: Knowledge Representation and Data Integration Language for the Web." Symmetry 12, no. 1 (January 2, 2020): 84. http://dx.doi.org/10.3390/sym12010084.

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Resource Description Framework (RDF) can seen as a solution in today’s landscape of knowledge representation research. An RDF language has symmetrical features because subjects and objects in triples can be interchangeably used. Moreover, the regularity and symmetry of the RDF language allow knowledge representation that is easily processed by machines, and because its structure is similar to natural languages, it is reasonably readable for people. RDF provides some useful features for generalized knowledge representation. Its distributed nature, due to its identifier grounding in IRIs, naturally scales to the size of the Web. However, its use is often hidden from view and is, therefore, one of the less well-known of the knowledge representation frameworks. Therefore, we summarise RDF v1.0 and v1.1 to broaden its audience within the knowledge representation community. This article reviews current approaches, tools, and applications for mapping from relational databases to RDF and from XML to RDF. We discuss RDF serializations, including formats with support for multiple graphs and we analyze RDF compression proposals. Finally, we present a summarized formal definition of RDF 1.1 that provides additional insights into the modeling of reification, blank nodes, and entailments.
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Mammo, Mulugeta, Mahmudul Hassan, and Srividya K. Bansal. "Distributed SPARQL Querying Over Big RDF Data Using Presto-RDF." Services Transactions on Big Data 2, no. 3 (October 2015): 34–49. http://dx.doi.org/10.29268/stbd.2015.2.3.3.

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Neumann, Thomas, and Gerhard Weikum. "The RDF-3X engine for scalable management of RDF data." VLDB Journal 19, no. 1 (September 1, 2009): 91–113. http://dx.doi.org/10.1007/s00778-009-0165-y.

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Yan, Li, Zheqing Zhang, and Dan Yang. "Temporal RDF(S) Data Storage and Query with HBase." Journal of Computing and Information Technology 27, no. 4 (June 30, 2020): 17–30. http://dx.doi.org/10.20532/cit.2019.1004801.

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Resource Description Framework (RDF) is a metadata model recommended by World Wide Web Consortium (W3C) for describing the Web resources. With the arrival of the era of Big Data, very large amounts of RDF data are continuously being created and need to be stored for management. The traditional centralized RDF storage models cannot meet the need of largescale RDF data storage. Meanwhile, the importance of temporal information management and processing has been acknowledged by academia and industry. In this paper, we propose a storage model to store temporal RDF based on HBase. The proposed storage model applies the built-in time mechanism of HBase. Our experiments on LUBM dataset with temporal information added show that our storage model can store large temporal RDF data and obtain good query efficiency.
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Rivero, Carlos R., Inma Hernández, David Ruiz, and Rafael Cochuelo. "Discovering and Analysing Ontological Models From Big RDF Data." Journal of Database Management 26, no. 2 (April 2015): 48–61. http://dx.doi.org/10.4018/jdm.2015040104.

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We are witnessing an increasing popularity of the Web of Data, which exposes a large variety of web sources that provide their data using RDF. Ontological models are used as the schema to organize this data. These models are usually shared by several communities and, to devise them, there is usually an agreement amongst those communities. As a result, it is common to have more than one ontological model to understand some RDF data; therefore, there might be a gap between the ontological models and the RDF data, which is not negligible in practice. In this article, the authors present a technique to automatically discover ontological models from raw RDF data. It is based on the intensive usage of a set of SPARQL 1.1 structural queries that are generic and independent from the RDF data. The final result of the authors' technique is an ontological model that is derived from the RDF data, and includes types and properties, subtypes, domains and ranges of properties and subproperties. The authors have conducted experiments with millions of triples that prove that their technique is suitable to deal with Big RDF Data. As far as they know, this is the first technique to discover such ontological models in the context of RDF data and the Web of Data.
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Baker, Thomas, Karen Coyle, and Sean Petiya. "Multi-entity models of resource description in the Semantic Web." Library Hi Tech 32, no. 4 (November 11, 2014): 562–82. http://dx.doi.org/10.1108/lht-08-2014-0081.

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Purpose – The 1998 International Federation of Library Associations (IFLA) document “Functional Requirements for Bibliographic Records” (FRBR) has inspired a family of models that view bibliographic resources in terms of multiple entities differentiated with regard to meaning, expression, and physicality. The purpose of this paper is to compare how three FRBR and FRBR-like models have been expressed as Semantic Web vocabularies based on Resource Description Framework (RDF). The paper focusses on IFLA’s own vocabulary for FRBR; RDF vocabularies for Resource Description and Access (RDA), an emergent FRBR-based standard for library cataloging; and BIBFRAME, an emergent FRBR-like, native-RDF standard for bibliographic data. Design/methodology/approach – Simple test records using the RDF vocabularies were analyzed using software that supports inferencing. Findings – In some cases, what the data actually means appears to differ from what the vocabulary developers presumably intended to mean. Data based on the FRBR vocabulary appears particularly difficult to integrate with data based on different models. Practical implications – Some of the RDF vocabularies reviewed in the paper could usefully be simplified, enabling libraries to integrate their data more easily into the wider information ecosystem on the Web. Requirements for data consistency and quality control could be met by emergent standards of the World Wide Web Consortium for validating RDF data according to integrity constraints. Originality/value – There are few such comparisons of the RDF expressions of these models, which are widely assumed to represent the future of library cataloging.
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RAMANUJAM, SUNITHA, ANUBHA GUPTA, LATIFUR KHAN, BHAVANI THURAISINGHAM, and STEVEN SEIDA. "R2D: A FRAMEWORK FOR THE RELATIONAL TRANSFORMATION OF RDF DATA." International Journal of Semantic Computing 03, no. 04 (December 2009): 471–98. http://dx.doi.org/10.1142/s1793351x09000884.

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The astronomical growth of the World Wide Web has resulted in data explosion that in turn has given rise to a need for data representation methodologies and standards to present required information in a rapid and automated manner. The Resource Description Framework (RDF) is one such standard proposed by W3C to address the above need. The ubiquitous acceptance of RDF on the Internet has resulted in the emergence of a new data storage paradigm, the RDF Graph Model, which, as with any data storage methodology, requires data modeling and visualization tools to aid with data management. This paper presents R2D (RDF-to-Database), a relational wrapper for RDF Data Stores, which aims to transform, at run-time, semi-structured RDF data into an equivalent domain-specific relational schema, thereby bridging the gap between RDF and RDBMS concepts and making the abundance of relational tools currently in the market available to the RDF Stores. The primary R2D functionalities and mapping constructs, the high-level system architecture, and deployment flowchart are presented along with algorithms and performance graphs for every stage of the transformation process and screenshots of a relational visualization tool using R2D as evidence of the feasibility of the proposed work.
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Faith, Ashleigh, and Michelle Chrzanowski. "Connecting RDA and RDF: Linked Data for a Wide World of Connected Possibilities." Pennsylvania Libraries: Research & Practice 3, no. 2 (November 12, 2015): 122–35. http://dx.doi.org/10.5195/palrap.2015.106.

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Libraries have struggled with connecting a plethora of content and the metadata stored in catalogs to patrons. Adding more value to catalogs, more tools for reference librarians, and enriched patron search, linked data is a means to connect more people with more relevant information. With the recent transition to the Resource Description and Access (RDA) cataloging standard within libraries, linking data in library databases has become a much easier project to tackle, largely because of another standard called Resource Description Framework (RDF). Both focus on resource description and both are components of linked data within the library. Tying them together is the Functional Requirements for Bibliographic Records (FRBR) conceptual framework. Acknowledging that linked data components are most likely new to many librarians, this article seeks to explain what linked data is, how RDA and RDF are connected by FRBR, and how knowledge maps may improve information access.
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Zou, Lei, and M. Tamer Özsu. "Graph-Based RDF Data Management." Data Science and Engineering 2, no. 1 (February 4, 2017): 56–70. http://dx.doi.org/10.1007/s41019-016-0029-6.

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Kaoudi, Zoi, Ioana Manolescu, and Stamatis Zampetakis. "Cloud-Based RDF Data Management." Synthesis Lectures on Data Management 15, no. 1 (February 24, 2020): 1–103. http://dx.doi.org/10.2200/s00986ed1v01y202001dtm062.

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Liu, Wenqiang, Jun Liu, Meng Wang, Qinghua Zheng, Wei Zhang, Lingyun Song, and Siyu Yao. "Faceted fusion of RDF data." Information Fusion 23 (May 2015): 16–24. http://dx.doi.org/10.1016/j.inffus.2014.06.005.

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Leng, Yonglin, Zhikui Chen, and Yueming Hu. "STLIS: A Scalable Two-Level Index Scheme for Big Data in IoT." Mobile Information Systems 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/5341797.

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The rapid development of the Internet of Things causes the dramatic growth of data, which poses an important challenge on the storage and quick retrieval of big data. As an effective representation model, RDF receives the most attention. More and more storage and index schemes have been developed for RDF model. For the large-scale RDF data, most of them suffer from a large number of self-joins, high storage cost, and many intermediate results. In this paper, we propose a scalable two-level index scheme (STLIS) for RDF data. In the first level, we devise a compressed path template tree (CPTT) index based on S-tree to retrieve the candidate sets of full path. In the second level, we create a hierarchical edge index (HEI) and a node-predicate (NP) index to accelerate the match. Extensive experiments are executed on two representative RDF benchmarks and one real RDF dataset in IoT by comparison with three representative index schemes, that is, RDF-3X, Bitmat, and TripleBit. Results demonstrate that our proposed scheme can respond to the complex query in real time and save much storage space compared with RDF-3X and Bitmat.
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Khodadadi, Nima, M. G. El El-Mahgoub, and Rokaia M. Zaki. "Mining Sematic Association Rules from RDF Data." Journal of Artificial Intelligence and Metaheuristics 4, no. 1 (2023): 43–51. http://dx.doi.org/10.54216/jaim.040105.

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Many fields rely heavily on the accurate and consistent portrayal of structured data. In order to effectively express and link information on the Semantic Web, RDF (Resource Description Framework) data is essential. Here, we present a process for extracting semantic association rules from RDF data. For our method, we employ the Apriori algorithm to mine the RDF triples for hidden connections between ideas and relationships. Using metrics such as confidence, support, and lift, we examine how well our model performs. We also give visual representations, like as scatter plots and clustered matrices, to make the correlations easier to understand and analyse. The findings validate our model's potential to unearth significant relationships, which in turn reveal important details about the RDF data's underlying semantics. Our findings are discussed, and suggestions for further study are provided.
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Devi, Runumi, Deepti Mehrotra, and Hajer Baazaoui-Zghal. "RDF Model Generation for Unstructured Dengue Patients' Clinical and Pathological Data." International Journal of Information System Modeling and Design 10, no. 4 (October 2019): 71–89. http://dx.doi.org/10.4018/ijismd.2019100104.

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The automatic extraction of triplets from unstructured patient records and transforming them into resource description framework (RDF) models has remained a huge challenge so far, and would provide significant benefit to potential applications like knowledge discovery, machine interoperability, and ontology design in the health care domain. This article describes an approach that extracts semantics (triplets) from dengue patient case-sheets and clinical reports and transforms them into an RDF model. A Text2Ontology framework is used for extracting relations from text and was found to have limited capability. The TypedDependency parsing-based algorithm is designed for extracting RDF facts from patients' case-sheets and subsequent conversion into RDF models. A mapping-driven semantifying approach is also designed for mapping clinical details extracted from patients' reports to its corresponding triplet components and subsequent RDF model generations. The exhaustiveness of the RDF models generated are measured based on the number of axioms generated with respect to the facts available.
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Zhu, Lin, Xiangfu Meng, and Zehui Mi. "Fuzzy Spatiotemporal Data Modeling and Operations in RDF." Information 13, no. 10 (October 18, 2022): 503. http://dx.doi.org/10.3390/info13100503.

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With the emergence of a large number of fuzzy spatiotemporal data on the Web, how to represent and operate fuzzy spatiotemporal data has become an important research issue. Meanwhile, the Resource Description Framework (RDF) is a standard data and knowledge description language of the Semantic Web and has been applied in many application areas, such as geographic information systems and meteorological systems. In this paper, a model for representing fuzzy spatiotemporal data is proposed and a set of algebraic operations for the model are investigated. First, a representation method of fuzzy spatiotemporal RDF data and a fuzzy spatiotemporal RDF graph model are proposed. In addition, a formal fuzzy spatiotemporal RDF algebra is proposed and a set of algebraic operations for manipulating fuzzy spatiotemporal RDF data are developed. The algebraic operations include: set operation, selection operation, projection operation, join operation, and construction operation. Finally, the existing SPARQL query language is extended and an example that shows how to apply the proposed algebraic operations to capture the queries expressed by the extended SPARQL query language is given.
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Saleh Aloufi, Khalid. "Generating RDF resources from web open data portals." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 3 (December 1, 2019): 1521. http://dx.doi.org/10.11591/ijeecs.v16.i3.pp1521-1529.

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<span>Open data are available from various private and public institutions in different resource formats. There are already great number of open data that are published using open data portals, where datasets and resources are mainly presented in tabular or sheet formats. However, such formats have some barriers with application developments and web standards. One of the web recommenced standards for semantic web application is RDF. There are various research efforts have been focused on presenting open data in RDF formats. However, no framework has transformed tabular open data into RDFs considering the HTML tags and properties of the resources and datasets. Therefore, a methodology is required to generate RDF resources from this type of open data resources. This methodology applies data transformations of open data from a tabular format to RDF files for the Saudi Open Data Portal. The methodology successfully transforms open data resources in sheet format into RDF resources. Recommendations and future work are given to enhance the development of building open data.</span>
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Zhang, Yan Qin, and Jing Bin Wang. "Query Optimization of Distributed RDF Data Based on MapReduce." Applied Mechanics and Materials 441 (December 2013): 970–73. http://dx.doi.org/10.4028/www.scientific.net/amm.441.970.

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As the development of the semantic web, RDF data set has grown rapidly, thus causing the query problem of massive RDF. Using distributed technique to complete the SPARQL (Simple Protocol and RDF Query Language) Query is a new way of solving the large amounts of RDF query problem. At present, most of the RDF query strategies based on Hadoop have to use multiple MapReduce jobs to complete the task, resulting in waste of time. In order to overcome this drawback, MRQJ (using MapReduce to query and join) algorithm is proposed in the paper, which firstly uses a greedy strategy to generate join plan, then only one MapReduce job should be created to get the query results in SPARQL query execution. Finally, a contrast experiment on the LUBM (Lehigh University Benchmark) test data set is conducted, the results of which show that MRQJ method has a great advantage in the case that the query is more complicated.
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Hilal, Median, Christoph G. Schuetz, and Michael Schrefl. "Using superimposed multidimensional schemas and OLAP patterns for RDF data analysis." Open Computer Science 8, no. 1 (July 1, 2018): 18–37. http://dx.doi.org/10.1515/comp-2018-0003.

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Abstract The foundations for traditional data analysis are Online Analytical Processing (OLAP) systems that operate on multidimensional (MD) data. The Resource Description Framework (RDF) serves as the foundation for the publication of a growing amount of semantic web data still largely untapped by companies for data analysis. Most RDF data sources, however, do not correspond to the MD modeling paradigm and, as a consequence, elude traditional OLAP. The complexity of RDF data in terms of structure, semantics, and query languages renders RDF data analysis challenging for a typical analyst not familiar with the underlying data model or the SPARQL query language. Hence, conducting RDF data analysis is not a straightforward task. We propose an approach for the definition of superimposed MD schemas over arbitrary RDF datasets and show how to represent the superimposed MD schemas using well-known semantic web technologies. On top of that, we introduce OLAP patterns for RDF data analysis, which are recurring, domain-independent elements of data analysis. Analysts may compose queries by instantiating a pattern using only the MD concepts and business terms. Upon pattern instantiation, the corresponding SPARQL query over the source data can be automatically generated, sparing analysts from technical details and fostering self-service capabilities.
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Nesrine, Lehireche, Malki Mimoun, Lehireche Ahmed, and Reda Mohamed Hamou. "On Demand ETL of RDB to RDF Mapping for Linked Enterprise Data." International Journal of Strategic Information Technology and Applications 8, no. 3 (July 2017): 91–100. http://dx.doi.org/10.4018/ijsita.2017070106.

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The purpose of the semantic web goes well beyond a simple provision of raw data: it is a matter of linking data together. This data meshing approach, called linked data (LD), refers to a set of best practices for publishing and interlinking data on the web. Due to its principles, a new context appeared called linked enterprise data (LED). The LED is the application of linked data to the information system of the enterprise to answer all the challenge of an IS, in order to have an agile and performing System. Where internal data sources link to external data, with easy access to information in performing time. This article focuses on using the LED to support the challenges of database integration and state-of-the-art for mapping RDB to RDF based on LD. Then, the authors introduce a proposition for on demand extract transform load (ETL) of RDB to RDF mapping using algorithms. Finally, the authors present a conclusion and discussion for their perspectives to implement the solution.
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Ravat, Franck, Jiefu Song, Olivier Teste, and Cassia Trojahn. "Efficient querying of multidimensional RDF data with aggregates: Comparing NoSQL, RDF and relational data stores." International Journal of Information Management 54 (October 2020): 102089. http://dx.doi.org/10.1016/j.ijinfomgt.2020.102089.

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36

Straccia, Umberto, Nuno Lopes, Gergely Lukacsy, and Axel Polleres. "A General Framework for Representing and Reasoning with Annotated Semantic Web Data." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 5, 2010): 1437–42. http://dx.doi.org/10.1609/aaai.v24i1.7499.

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We describe a generic framework for representing and reasoning with annotated Semantic Web data, formalise the annotated language, the corresponding deductive system, and address the query answering problem. We extend previous contributions on RDF annotations by providing a unified reasoning formalism and allowing the seamless combination of different annotation domains. We demonstrate the feasibility of our method by instantiating it on (i) temporal RDF; (ii) fuzzy RDF; (iii) and their combination. A prototype shows that implementing and combining new domains is easy and that RDF stores can easily be extended to our framework.
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37

高, 金. "Research on Data Transformation Method Based on RDB-RDF Schema Mapping." Hans Journal of Data Mining 13, no. 04 (2023): 335–51. http://dx.doi.org/10.12677/hjdm.2023.134033.

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38

Bai, Luyi, Nan Li, Lishuang Liu, and Xuesong Hao. "Querying multi-source heterogeneous fuzzy spatiotemporal data." Journal of Intelligent & Fuzzy Systems 40, no. 5 (April 22, 2021): 9843–54. http://dx.doi.org/10.3233/jifs-202357.

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With the rapid development of the environmental, meteorological and marine data management, fuzzy spatiotemporal data has received considerable attention. Even though some achievements in querying aspect have been made, there are still some unsolved problems. Semantic and structural heterogeneity may exist among different data sources, which will lead to incomplete results. In addition, there are ambiguous query intentions and conditions when the user queries the data. This paper proposes a fuzzy spatiotemporal data semantic model. Based on this model, the RDF local semantic models are converted into a RDF global semantic model after mapping relational data and XML data to RDF local semantic models. The existing methods mainly convert relational data to RDF Schema directly. But our approach converts relational data to XML Schema and then converts it to RDF, which utilizes the semi-structured feature of XML schema to solve the structural heterogeneity between different data sources. The integration process enables us to perform global queries against different data sources. In the proposed query algorithms, the query conditions inputted are converted into exact queries before the results are returned. Finally, this paper has carried out extensive experiments, calculated the recall, precision and F-Score of the experimental results, and compared with other state-of-the-art query methods. It shows the importance of the data integration method and the effectiveness of the query method proposed in this paper.
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Elzein, Nahla Mohammed, Mazlina Abdul Majid, Ibrahim Abaker Targio Hashem, Ashraf Osman Ibrahim, Anas W. Abulfaraj, and Faisal Binzagr. "JQPro:Join Query Processing in a Distributed System for Big RDF Data Using the Hash-Merge Join Technique." Mathematics 11, no. 5 (March 6, 2023): 1275. http://dx.doi.org/10.3390/math11051275.

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In the last decade, the volume of semantic data has increased exponentially, with the number of Resource Description Framework (RDF) datasets exceeding trillions of triples in RDF repositories. Hence, the size of RDF datasets continues to grow. However, with the increasing number of RDF triples, complex multiple RDF queries are becoming a significant demand. Sometimes, such complex queries produce many common sub-expressions in a single query or over multiple queries running as a batch. In addition, it is also difficult to minimize the number of RDF queries and processing time for a large amount of related data in a typical distributed environment encounter. To address this complication, we introduce a join query processing model for big RDF data, called JQPro. By adopting a MapReduce framework in JQPro, we developed three new algorithms, which are hash-join, sort-merge, and enhanced MapReduce-join for join query processing of RDF data. Based on an experiment conducted, the result showed that the JQPro model outperformed the two popular algorithms, gStore and RDF-3X, with respect to the average execution time. Furthermore, the JQPro model was also tested against RDF-3X, RDFox, and PARJs using the LUBM benchmark. The result showed that the JQPro model had better performance in comparison with the other models. In conclusion, the findings showed that JQPro achieved improved performance with 87.77% in terms of execution time. Hence, in comparison with the selected models, JQPro performs better.
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Sima, Ana Claudia, Christophe Dessimoz, Kurt Stockinger, Monique Zahn-Zabal, and Tarcisio Mendes de Farias. "A hands-on introduction to querying evolutionary relationships across multiple data sources using SPARQL." F1000Research 8 (October 29, 2019): 1822. http://dx.doi.org/10.12688/f1000research.21027.1.

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The increasing use of Semantic Web technologies in the life sciences, in particular the use of the Resource Description Framework (RDF) and the RDF query language SPARQL, opens the path for novel integrative analyses, combining information from multiple sources. However, analyzing evolutionary data in RDF is not trivial, due to the steep learning curve required to understand both the data models adopted by different RDF data sources, as well as the SPARQL query language. In this article, we provide a hands-on introduction to querying evolutionary data across multiple sources that publish orthology information in RDF, namely: The Orthologous MAtrix (OMA), the European Bioinformatics Institute (EBI) RDF platform, the Database of Orthologous Groups (OrthoDB) and the Microbial Genome Database (MBGD). We present four protocols in increasing order of complexity. In these protocols, we demonstrate through SPARQL queries how to retrieve pairwise orthologs, homologous groups, and hierarchical orthologous groups. Finally, we show how orthology information in different sources can be compared, through the use of federated SPARQL queries.
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41

Vaisman, Alejandro, and Kevin Chentout. "Mapping Spatiotemporal Data to RDF: A SPARQL Endpoint for Brussels." ISPRS International Journal of Geo-Information 8, no. 8 (August 10, 2019): 353. http://dx.doi.org/10.3390/ijgi8080353.

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This paper describes how a platform for publishing and querying linked open data for the Brussels Capital region in Belgium is built. Data are provided as relational tables or XML documents and are mapped into the RDF data model using R2RML, a standard language that allows defining customized mappings from relational databases to RDF datasets. In this work, data are spatiotemporal in nature; therefore, R2RML must be adapted to allow producing spatiotemporal Linked Open Data.Data generated in this way are used to populate a SPARQL endpoint, where queries are submitted and the result can be displayed on a map. This endpoint is implemented using Strabon, a spatiotemporal RDF triple store built by extending the RDF store Sesame. The first part of the paper describes how R2RML is adapted to allow producing spatial RDF data and to support XML data sources. These techniques are then used to map data about cultural events and public transport in Brussels into RDF. Spatial data are stored in the form of stRDF triples, the format required by Strabon. In addition, the endpoint is enriched with external data obtained from the Linked Open Data Cloud, from sites like DBpedia, Geonames, and LinkedGeoData, to provide context for analysis. The second part of the paper shows, through a comprehensive set of the spatial extension to SPARQL (stSPARQL) queries, how the endpoint can be exploited.
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Alaoui, Khadija, and Mohamed Bahaj. "Categorization of RDF Data Management Systems." Advances in Science, Technology and Engineering Systems Journal 6, no. 2 (March 2021): 221–33. http://dx.doi.org/10.25046/aj060225.

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43

Angles, Renzo, and Roberto Garcia. "Transforming RDF Data into Property Graphs." IEEE Latin America Transactions 18, no. 01 (January 2020): 130–37. http://dx.doi.org/10.1109/tla.2020.9049470.

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Vega-Gorgojo, Guillermo, Laura Slaughter, Bjorn Marius Von Zernichow, Nikolay Nikolov, and Dumitru Roman. "Linked Data Exploration With RDF Surveyor." IEEE Access 7 (2019): 172199–213. http://dx.doi.org/10.1109/access.2019.2956345.

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Eddamiri, Siham, El Moukhtar Zemmouri, and Asmaa Benghabrit. "An improved RDF data Clustering Algorithm." Procedia Computer Science 148 (2019): 208–17. http://dx.doi.org/10.1016/j.procs.2019.01.038.

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Darari, Fariz, Werner Nutt, Giuseppe Pirrò, and Simon Razniewski. "Completeness Management for RDF Data Sources." ACM Transactions on the Web 12, no. 3 (July 18, 2018): 1–53. http://dx.doi.org/10.1145/3196248.

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Abidi, Amna, Sayda Elmi, Mohamed Anis Bach Tobji, Allel HadjAli, and Boutheina Ben Yaghlane. "Skyline queries over possibilistic RDF data." International Journal of Approximate Reasoning 93 (February 2018): 277–89. http://dx.doi.org/10.1016/j.ijar.2017.11.005.

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48

A., V., and Amruta A. "Semantic Web Mining using RDF Data." International Journal of Computer Applications 133, no. 10 (January 15, 2016): 14–19. http://dx.doi.org/10.5120/ijca2016908022.

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49

Urbani, Jacopo, Jason Maassen, Niels Drost, Frank Seinstra, and Henri Bal. "Scalable RDF data compression with MapReduce." Concurrency and Computation: Practice and Experience 25, no. 1 (April 23, 2012): 24–39. http://dx.doi.org/10.1002/cpe.2840.

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50

Sima, Ana Claudia, Christophe Dessimoz, Kurt Stockinger, Monique Zahn-Zabal, and Tarcisio Mendes de Farias. "A hands-on introduction to querying evolutionary relationships across multiple data sources using SPARQL." F1000Research 8 (July 22, 2020): 1822. http://dx.doi.org/10.12688/f1000research.21027.2.

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The increasing use of Semantic Web technologies in the life sciences, in particular the use of the Resource Description Framework (RDF) and the RDF query language SPARQL, opens the path for novel integrative analyses, combining information from multiple data sources. However, analyzing evolutionary data in RDF is not trivial, due to the steep learning curve required to understand both the data models adopted by different RDF data sources, as well as the equivalent SPARQL constructs required to benefit from this data – in particular, recursive property paths. In this article, we provide a hands-on introduction to querying evolutionary data across several data sources that publish orthology information in RDF, namely: The Orthologous MAtrix (OMA), the European Bioinformatics Institute (EBI) RDF platform, the Database of Orthologous Groups (OrthoDB) and the Microbial Genome Database (MBGD). We present four protocols in increasing order of complexity. In these protocols, we demonstrate through SPARQL queries how to retrieve pairwise orthologs, homologous groups, and hierarchical orthologous groups. Finally, we show how orthology information in different data sources can be compared, through the use of federated SPARQL queries.
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