Resource Description Framework Data Model Research Articles

Constructing a knowledge graph for open government data: the case of Nova Scotia disease datasets

The majority of available datasets in open government data are statistical. They are widely published by various governments to be used by the public and data consumers. However, most open government data portals do not provide the five-star Linked Data standard datasets. The published datasets are isolated from one another while conceptually connected. This paper constructs a knowledge graph for the disease-related datasets of a Canadian government data portal, Nova Scotia Open Data. We leveraged the Semantic Web technologies to transform the disease-related datasets into Resource Description Framework (RDF) and enriched them with semantic rules. An RDF data model using the RDF Cube vocabulary was designed in this work to develop a graph that adheres to best practices and standards, allowing for expansion, modification and flexible re-use. The study also discusses the lessons learned during the cross-dimensional knowledge graph construction and integration of open statistical datasets from multiple sources.

Hybrid data model of PACE and quadruple: an efficient data model for cloud computing

Cloud computing is a promising computing paradigm that involves outsourcing of computing resources with the capabilities of on demand provisioning with little or no up-front investment costs. Resource description framework (RDF) is the semantic data model for cloud computing which provides interoperability but is not effective in terms of scalability, formal semantics and query optimisation and reification. One of the key challenges in cloud computing therefore is to enhance RDF data model. This paper introduces a data model which uses hybrid approach of provenance aware context entity (PACE) and quadruples method of reification. The scope of the research is to understand the importance of RDF reification, limitations and its impact on creating an efficient data model. This hybrid RDF data model is deployed and tested for its performance on the AWS public cloud. Experimental results indicate that the proposed hybrid data model enhances accessibility, maintainability, and also accelerates query execution time.

Hybrid data model of PACE and quadruple: an efficient data model for cloud computing

Cloud computing is a promising computing paradigm that involves outsourcing of computing resources with the capabilities of on demand provisioning with little or no up-front investment costs. Resource description framework (RDF) is the semantic data model for cloud computing which provides interoperability but is not effective in terms of scalability, formal semantics and query optimisation and reification. One of the key challenges in cloud computing therefore is to enhance RDF data model. This paper introduces a data model which uses hybrid approach of provenance aware context entity (PACE) and quadruples method of reification. The scope of the research is to understand the importance of RDF reification, limitations and its impact on creating an efficient data model. This hybrid RDF data model is deployed and tested for its performance on the AWS public cloud. Experimental results indicate that the proposed hybrid data model enhances accessibility, maintainability, and also accelerates query execution time.

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.

Retracted: Semantic Information Integration with Linked Data Mashups Approaches

The introduction of semantic web and Linked Data helps facilitate sharing of data on the Internet more easily. Subsequently, the resource description framework (RDF) is the standard in publishing structured data resources on the Internet and is used in interconnecting with other data resources. To remedy the data integration issues of the traditional web mashups, the semantic web technology uses the Linked Data based on RDF data model as the unified data model for combining, aggregating, and transforming data from heterogeneous data resources to build Linked Data mashups. There have been tremendous amounts of efforts of semantic web community to enable Linked Data mashups but there is still lack of a systematic survey on concepts, technologies, applications, and challenges. Therefore, in this paper, we investigate in detail semantic mashups research and application approaches in the information integration. This paper also presents a Linked Data mashup application as an illustration of the proposed approa...

An Analysis of RDF Storage Models and Query Optimization Techniques

The Web provides access to substantial amount of information. Metadata that means data about data enables the discovery of such information. When the metadata is effectively used, it increases the usefulness of the original data/resource and facilitates the resource discovery. Resource Description Framework (RDF) is a basis for handling these metadata and is a graph-based, self-describing data format that represents information about web-based resources. It is necessary to store the data persistently for many Semantic Web applications that were developed on RDF to perform effective queries. Because of the difficulty of storing and querying RDF data, several storage techniques have been proposed for these tasks. In this paper, we present the motivations for using the RDF data model. Several storage techniques are discussed along with the methods for optimizing the queries for RDF datasets. We present the differences between the Relational Database and the XML technology. Additionally, we specify some of the use cases for RDF. Our findings will shed light on the current achievements in RDF research by comparing the different methodologies for storage and optimization proposed so far, thus identifying further research areas.

Compressed vertical partitioning for efficient RDF management

The Web of Data has been gaining momentum in recent years. This leads to increasingly publish more and more semi-structured datasets following, in many cases, the RDF (Resource Description Framework) data model based on atomic triple units of subject, predicate, and object. Although it is a very simple model, specific compression methods become necessary because datasets are increasingly larger and various scalability issues arise around their organization and storage. This requirement is even more restrictive in RDF stores because efficient SPARQL solution on the compressed RDF datasets is also required. This article introduces a novel RDF indexing technique that supports efficient SPARQL solution in compressed space. Our technique, called $$\hbox {k}^2$$k2-triples, uses the predicate to vertically partition the dataset into disjoint subsets of pairs (subject, object), one per predicate. These subsets are represented as binary matrices of subjects $$\times $$× objects in which 1-bits mean that the corresponding triple exists in the dataset. This model results in very sparse matrices, which are efficiently compressed using $$\hbox {k}^2$$k2-trees. We enhance this model with two compact indexes listing the predicates related to each different subject and object in the dataset, in order to address the specific weaknesses of vertically partitioned representations. The resulting technique not only achieves by far the most compressed representations, but also achieves the best overall performance for RDF retrieval in our experimental setup. Our approach uses up to 10 times less space than a state-of-the-art baseline and outperforms its time performance by several orders of magnitude on the most basic query patterns. In addition, we optimize traditional join algorithms on $$\hbox {k}^2$$k2-triples and define a novel one leveraging its specific features. Our experimental results show that our technique also overcomes traditional vertical partitioning for join solution, reporting the best numbers for joins in which the non-joined nodes are provided, and being competitive in most of the cases.

TogoTable: cross-database annotation system using the Resource Description Framework (RDF) data model

TogoTable (http://togotable.dbcls.jp/) is a web tool that adds user-specified annotations to a table that a user uploads. Annotations are drawn from several biological databases that use the Resource Description Framework (RDF) data model. TogoTable uses database identifiers (IDs) in the table as a query key for searching. RDF data, which form a network called Linked Open Data (LOD), can be searched from SPARQL endpoints using a SPARQL query language. Because TogoTable uses RDF, it can integrate annotations from not only the reference database to which the IDs originally belong, but also externally linked databases via the LOD network. For example, annotations in the Protein Data Bank can be retrieved using GeneID through links provided by the UniProt RDF. Because RDF has been standardized by the World Wide Web Consortium, any database with annotations based on the RDF data model can be easily incorporated into this tool. We believe that TogoTable is a valuable Web tool, particularly for experimental biologists who need to process huge amounts of data such as high-throughput experimental output.

Retracted: Semantic Information Integration with Linked Data Mashups Approaches

The introduction of semantic web and Linked Data helps facilitate sharing of data on the Internet more easily. Subsequently, the resource description framework (RDF) is the standard in publishing structured data resources on the Internet and is used in interconnecting with other data resources. To remedy the data integration issues of the traditional web mashups, the semantic web technology uses the Linked Data based on RDF data model as the unified data model for combining, aggregating, and transforming data from heterogeneous data resources to build Linked Data mashups. There have been tremendous amounts of efforts of semantic web community to enable Linked Data mashups but there is still lack of a systematic survey on concepts, technologies, applications, and challenges. Therefore, in this paper, we investigate in detail semantic mashups research and application approaches in the information integration. This paper also presents a Linked Data mashup application as an illustration of the proposed approaches.

Semantic Retrieval Based on SPARQL and Fuzzy Ontology for Electronic Commerce

Information retrieval is the important work for Electronic Commerce. Ontology-based semantic retrieval is a hotspot of current research. In order to achieve fuzzy semantic retrieval, this paper proposes an approach using Resource Description Framework (RDF) and fuzzy ontology. First, we apply RDF/RDFS data model to represent e-commerce information on the Semantic Web. Then, introducing new data type referred as fuzzy linguistic variables to RDF data model, the semantic query expansion in SPARQL query language is constructed by order relation, equivalence relation and inclusion relation between fuzzy concepts defined in linguistic variable ontologies. Examples show that this research facilitates the semantic retrieval through fuzzy concepts for Electronic Commerce on the Semantic Web.

Foundations of Semantic Web databases

The Semantic Web is based on the idea of a common and minimal language to enable large quantities of existing data to be analyzed and processed. This triggers the need to develop the database foundations of this basic language, which is the Resource Description Framework ( RDF). This paper addresses this challenge by: 1) developing an abstract model and query language suitable to formalize and prove properties about the RDF data and query language; 2) studying the RDF data model, minimal and maximal representations, as well as normal forms; 3) studying systematically the complexity of entailment in the model, and proving complexity bounds for the main problems; 4) studying the notions of query answering and containment arising in the RDF data model; and 5) proving complexity bounds for query answering and query containment.

Can Bibliographic Data be Put Directly onto the Semantic Web?

This paper is a think piece about the possible future of bibliographic control; it provides a brief introduction to the Semantic Web and defines related terms, and it discusses granularity and structure issues and the lack of standards for the efficient display and indexing of bibliographic data. It is also a report on a work in progress—an experiment in building a Resource Description Framework (RDF) model of more FRBRized cataloging rules than those about to be introduced to the library community (Resource Description and Access) and in creating an RDF data model for the rules. I am now in the process of trying to model my cataloging rules in the form of an RDF model, which can also be inspected at http://myee.bol.ucla.edu/. In the process of doing this, I have discovered a number of areas in which I am not sure that RDF is sophisticated enough yet to deal with our data. This article is an attempt to identify some of those areas and explore whether or not the problems I have encountered are soluble—in other words, whether or not our data might be able to live on the Semantic Web. In this paper, I am focusing on raising the questions about the suitability of RDF to our data that have come up in the course of my work.

Fuzzy Semantic Retrieval for Traffic Information Based on Fuzzy Ontology and RDF on the Semantic Web

Normal 0 7.8 磅 0 2 false false false MicrosoftInternetExplorer4 Information retrieval is the essential task for Traffic Information Service System in Intelligent Transportation Systems (ITS). There a lot of fuzzy traffic information derived from human factor. To achieve fuzzy semantic retrieval, this paper proposes an approach using Resource Description Framework (RDF) and fuzzy ontology. First, we apply RDF data model to represent traffic information on the Semantic Web. Then we present fuzzy linguistic variable ontology models and its formal representation with RDF. Introducing new data type referred as fuzzy linguistic variables to RDF data model, the semantic query expansions in SeRQL query language are constructed by order relation, equivalence relation, inclusion relation and complement relation between fuzzy concepts defined in linguistic variable ontologies. Examples show that the extended query can return all results which satisfy research requirement at semantic level without upgrading current main search algorithm, and this research facilitates the semantic retrieval of traffic information through fuzzy concepts for ITS on the Semantic Web.

N3Logic: A logical framework for the World Wide Web

AbstractThe Semantic Web drives toward the use of the Web for interacting with logically interconnected data. Through knowledge models such as Resource Description Framework (RDF), the Semantic Web provides a unifying representation of richly structured data. Adding logic to the Web implies the use of rules to make inferences, choose courses of action, and answer questions. This logic must be powerful enough to describe complex properties of objects but not so powerful that agents can be tricked by being asked to consider a paradox. The Web has several characteristics that can lead to problems when existing logics are used, in particular, the inconsistencies that inevitably arise due to the openness of the Web, where anyone can assert anything. N3Logic is a logic that allows rules to be expressed in a Web environment. It extends RDF with syntax for nested graphs and quantified variables and with predicates for implication and accessing resources on the Web, and functions including cryptographic, string, math. The main goal of N3Logic is to be a minimal extension to the RDF data model such that the same language can be used for logic and data. In this paper, we describe N3Logic and illustrate through examples why it is an appropriate logic for the Web.

Product Life-Cycle Metadata Modeling and Its Application with RDF

The whole product life cycle consists of three phases: beginning of life (BOL), middle of life (MOL), and end of life (EOL). Although large amounts of product life-cycle data are generated over the whole product life cycle, data flows are rather vague after BOL. Over the last decade, however, emerging Internet, wireless mobile telecommunications, and product identification technologies have created the potential of making the whole product life cycle visible. As a result, the scope of data to be managed has expanded over the whole product life cycle. Hence, it becomes important to describe product life-cycle metadata in a systematic manner. Although much attention has been paid to data modeling over several objects such as products and processes, modeling methodology for product life-cycle metadata is not well developed. To cope with this limitation, we develop a modeling method for product life-cycle metadata by using the resource description framework (RDF). We define an RDF data model and its schema for describing and managing product life-cycle metadata. In addition, we describe how the proposed RDF model can be usefully applied to track, trace, and infer product life-cycle data with an RDF query language.

AlzPharm: integration of neurodegeneration data using RDF

BackgroundNeuroscientists often need to access a wide range of data sets distributed over the Internet. These data sets, however, are typically neither integrated nor interoperable, resulting in a barrier to answering complex neuroscience research questions. Domain ontologies can enable the querying heterogeneous data sets, but they are not sufficient for neuroscience since the data of interest commonly span multiple research domains. To this end, e-Neuroscience seeks to provide an integrated platform for neuroscientists to discover new knowledge through seamless integration of the very diverse types of neuroscience data. Here we present a Semantic Web approach to building this e-Neuroscience framework by using the Resource Description Framework (RDF) and its vocabulary description language, RDF Schema (RDFS), as a standard data model to facilitate both representation and integration of the data.ResultsWe have constructed a pilot ontology for BrainPharm (a subset of SenseLab) using RDFS and then converted a subset of the BrainPharm data into RDF according to the ontological structure. We have also integrated the converted BrainPharm data with existing RDF hypothesis and publication data from a pilot version of SWAN (Semantic Web Applications in Neuromedicine). Our implementation uses the RDF Data Model in Oracle Database 10g release 2 for data integration, query, and inference, while our Web interface allows users to query the data and retrieve the results in a convenient fashion.ConclusionAccessing and integrating biomedical data which cuts across multiple disciplines will be increasingly indispensable and beneficial to neuroscience researchers. The Semantic Web approach we undertook has demonstrated a promising way to semantically integrate data sets created independently. It also shows how advanced queries and inferences can be performed over the integrated data, which are hard to achieve using traditional data integration approaches. Our pilot results suggest that our Semantic Web approach is suitable for realizing e-Neuroscience and generic enough to be applied in other biomedical fields.

TRANSFORMATION FROM SEMANTIC DATA MODEL TO RDF

There have been several efforts to use relational model and database to store and manipulate Resource Description Framework (RDF). They have one general disadvantage, i.e. one is forced to map the model of semantics of RDF into relational model, which will end up in constraints and additional properties, such as, validating each assertion against the RDF schema which also stored as a triplets table. In this paper, we introduce Semantic Data Model as a proposed data model language to store and manipulate Resource Description Framework. This study also tries to prescribe the procedure on transforming a semantic data model into a RDF data model. Keyworsd: Semantic Data Model, Resource Description Framework.

Automatic RDF metadata generation for resource discovery

Automatic metadata generation may provide a solution to the problem of inconsistent, unreliable metadata describing resources on the Web. The Resource Description Framework (RDF) provides a domain-neutral foundation on which extensible element sets can be defined and expressed in a standard notation. This paper describes how an automatic classifier, that classifies HTML documents according to Dewey Decimal Classification, can be used to extract context sensitive metadata which is then represented using RDF. The process of automatic classification is described and an appropriate metadata element set is identified comprising those elements that can be extracted during classification. An RDF data model and an RDF schema are defined representing the element set and the classifier is configured to output the elements in RDF syntax according to the defined schema.