Resource Description Framework Storage Research Articles

RDF(S) Store in Object-Relational Databases

The Resource Description Framework (RDF) and RDF Schema (RDFS) recommended by World Wide Web Consortium (W3C) provide a flexible model for semantically representing data on the web. With the widespread acceptance of RDF(S) (RDF and RDFS for short), a large number of RDF(S) is available. Databases play an important role in managing RDF(S). However, there are few studies on using object-relational databases to store RDF(S). In this paper, the authors propose the formal definitions of RDF(S) model and object-relational databases model. Then they introduce the approach for storing RDF(S) in object-relational databases based on the formal definitions. They implement a prototype system to demonstrate the feasibility of the approach and test the performance and semantic retention ability of this prototype system with the benchmark dataset.

Space/time-efficient RDF stores based on circular suffix sorting

The resource description framework (RDF) has gained popularity as a format for the standardized publication and exchange of information in the Web of Data. In this paper, we introduce RDFCSA, a compressed representation of RDF datasets that in addition supports efficient querying. RDFCSA regards the triples of the RDF store as short circular strings and applies suffix sorting on those strings, so that triple-pattern queries reduce to prefix searching on the string set. The RDF store is then represented compactly using a compressed suffix array (CSA), a proved technology in text indexing that efficiently supports prefix searches. Our experiments show that RDFCSA is competitive with state-of-the-art alternatives. It compresses the raw data to 60% of its size, close to the most compact alternatives. While most alternatives perform better in some kinds of triple-patterns than in others, RDFCSA features fast and consistent query times, a few microseconds per result in all cases. This enables efficiently supporting join queries by using either merge- or chaining-join strategies over the triple patterns coupled with some specific optimizations such as variable filling. Our experiments on binary joins show that RDFCSA is faster than the alternatives in most cases.

Distributed subgraph query for RDF graph data based on MapReduce

Nowadays, Resource Description Framework (RDF) query has been widely used in social networks, biomedicine and other fields. With the explosion of RDF data due to the Internet of Things and Semantic Web, people's demand for intelligent computing and intelligent search is increasing, effectively querying RDF has become a major challenge. The current query methods often introduce a large number of join operations, and repeatedly traverse in some subgraphs during the query process, which makes the query efficiency and query performance poor. To address the above problems, this paper proposes a subgraph query algorithm for RDF graph data in a distributed environment. The graph structure is used to decompose the stars of the RDF graph, and the optimal query sequence of the stars is calculated. Fewer intermediate results can be produced based on the query sequence to reduce repeated calculations. Besides, adjacency lists are used to store RDF graphs, which are distributed across multiple tables. Multiple table operations can reduce the scope of subject node traversal, and further improve the query efficiency of RDF subgraph by matching one star per iteration. Experimental results show that our work can improve the query efficiency of RDF subgraphs.

MuSe: a multi-level storage scheme for big RDF data using MapReduce

Resource Description Framework (RDF) model owing to its flexible structure is increasingly being used to represent Linked data. The rise in amount of Linked data and Knowledge graphs has resulted in an increase in the volume of RDF data. RDF is used to model metadata especially for social media domains where the data is linked. With the plethora of RDF data sources available on the Web, scalable RDF data management becomes a tedious task. In this paper, we present MuSe—an efficient distributed RDF storage scheme for storing and querying RDF data with Hadoop MapReduce. In MuSe, the Big RDF data is stored at two levels for answering the common triple patterns in SPARQL queries. MuSe considers the type of frequently occuring triple patterns and optimizes RDF storage to answer such triple patterns in minimum time. It accesses only the tables that are sufficient for answering a triple pattern instead of scanning the whole RDF dataset. The extensive experiments on two synthetic RDF datasets i.e. LUBM and WatDiv, show that MuSe outperforms the compared state-of-the art frameworks in terms of query execution time and scalability.

RDF for temporal data management – a survey

Over the years, a large amount of temporal data needs to be shared and exchanged on the Web. Resource Description Framework (RDF) has been widely accepted and has rapidly gained popularity to al RDF) model as mentioned in represent and share data in many application domains (e.g., the Data of Web, Linked Data, and Knowledge Graph). Accordingly, efficient management of temporal data based on RDF is of increasing importance. Much work has been devoted to the representation, querying, storage, and management of temporal RDF data. Therefore, to investigate and more importantly serve as helping researchers grasp the main ideas and results of temporal RDF data management, and to highlight an ongoing research on them, in this paper, we provide a full up-to-date overview of the current state of the art in temporal RDF data management, including representation, querying, storage, and other directions. In detail, regarding to the representation of temporal RDF models, we first classify the existing temporal RDF models into two main categories according to their focuses (including the new RDF extension models and the original RDF models based on timestamp). We summarize and compare each model from the perspectives of syntax, semantics, and etc. Then, we further summarize the temporal RDF querying and storage techniques in detail. Moreover, to well introduce the main idea of each approach, we use some examples to explain each approach. In addition, the directions for future research and some comparisons and analyses are discussed in our whole survey. This survey will help readers understand and catch some key techniques about the issue and also identify some future research directions.

Temporal RDF(S) Data Storage and Query with HBase

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.

In-memory parallelization of join queries over large ontological hierarchies

The Resource Description Framework (RDF) data model enables the construction of knowledge graphs over various domains, using ontologies in order to encode information about the domain, and simple statements in the form of subject-predicate-object triples for data representation, facilitating the interlinking and exchange of Web data. However, this simplicity comes with the cost of having to execute a large number of joins in order to get the desirable query results, while at the same time large ontological hierarchies complicate the query answering process even more, for systems that provide complete answers with respect to such ontological axioms. In this work we present PARJ, an in-memory RDF store which takes into consideration ontological hierarchies during join processing with very low performance overhead, avoiding expensive preprocessing and materialization of implications, and is also amenable to straightforward parallelization. Specifically, we present a join implementation that allows to achieve any desired degree of parallelism on arbitrary join queries and RDF graphs stored in memory using compact vertical partitioning. We use an adaptive join processing approach, such that we take advantage of complete or even partial ordering of RDF data, which is compactly stored in order to increase spatial locality and keep memory consumption low, coupled with an ID-to-Position vector index used when ordering does not allow for efficient scanning of the input relation. Finally, we experimentally show the efficiency and scalability of our proposal.

Pushing the Scalability of RDF Engines on IoT Edge Devices.

Semantic interoperability for the Internet of Things (IoT) is enabled by standards and technologies from the Semantic Web. As recent research suggests a move towards decentralised IoT architectures, we have investigated the scalability and robustness of RDF (Resource Description Framework)engines that can be embedded throughout the architecture, in particular at edge nodes. RDF processing at the edge facilitates the deployment of semantic integration gateways closer to low-level devices. Our focus is on how to enable scalable and robust RDF engines that can operate on lightweight devices. In this paper, we have first carried out an empirical study of the scalability and behaviour of solutions for RDF data management on standard computing hardware that have been ported to run on lightweight devices at the network edge. The findings of our study shows that these RDF store solutions have several shortcomings on commodity ARM (Advanced RISC Machine) boards that are representative of IoT edge node hardware. Consequently, this has inspired us to introduce a lightweight RDF engine, which comprises an RDF storage and a SPARQL processor for lightweight edge devices, called RDF4Led. RDF4Led follows the RISC-style (Reduce Instruction Set Computer) design philosophy. The design constitutes a flash-aware storage structure, an indexing scheme, an alternative buffer management technique and a low-memory-footprint join algorithm that demonstrates improved scalability and robustness over competing solutions. With a significantly smaller memory footprint, we show that RDF4Led can handle 2 to 5 times more data than popular RDF engines such as Jena TDB (Tuple Database) and RDF4J, while consuming the same amount of memory. In particular, RDF4Led requires 10%–30% memory of its competitors to operate on datasets of up to 50 million triples. On memory-constrained ARM boards, it can perform faster updates and can scale better than Jena TDB and Virtuoso. Furthermore, we demonstrate considerably faster query operations than Jena TDB and RDF4J.

Storing and querying fuzzy RDF(S) in HBase databases

The Resource Description Framework (RDF) and RDF Schema (RDFS) recommended by World Wide Web Consortium (W3C) have been used for meta-data management on the Web. With the rapid development of Web-based applications, the growth of RDF(S) data is increasing dramatically. In the context of open Web, uncertainty naturally arises in RDF and RDFS (RDF(S) in short). To manage large-scale fuzzy RDF(S) data efficiently and effectively, we propose, in this paper, a fuzzy RDF(S) storage schema with fuzzy HBase databases (FHDBs). On the basis, we investigate how to query in FHDBs. We propose a set of query algorithms. We implement a prototype system to demonstrate the feasibility of our approach.

RDF DATABASES – CASE STUDY AND PERFORMANCE EVALUATION

The Resource Description Framework (RDF) data presentation model and the SPARQL query language have been the core of the semantic web technologies since the early 2000’s. In this article, we evaluate three RDF storage technologies. Our motivation is to find a storage solution that can be used to process “big data” RDF sets. Our method is based on measuring query response times with large samples (hundreds of thousands of RDF documents, millions of RDF statements). We find that all the proposed technologies provide much better performance than querying RDF data stored in files. However, with 300 000 documents, even with the fastest technology, an aggregation query still lasts more than 100 seconds in our environment. As a further performance improvement, we test the same data and queries with MongoDB, demonstrate its performance (10 seconds instead of 100) and scalability (up to 1000 000 documents). However, despite its benefits we must note that because of its data presentation and query limitations, MongoDB probably cannot serve as a generic storage for all kinds of RDF documents.

TripleID-Q: RDF Query Processing Framework Using GPU

Resource Description Framework (RDF) data represents information linkage around the Internet. It uses Inter- nationalized Resources Identifier (IRI) which can be referred to external information. Typically, an RDF data is serialized as a large text file which contains millions of relationships. In this work, we propose a framework based on TripleID-Q, for query processing of large RDF data in a GPU. The key elements of the framework are 1) a compact representation suitable for a Graphics Processing Unit (GPU) and 2) its simple representation conversion method which optimizes the preprocessing overhead. Together with the framework, we propose parallel algorithms which utilize thousands of GPU threads to look for specific data for a given query as well as to perform basic query operations such as union, join, and filter. The TripleID representation is smaller than the original representation 3-4 times. Querying from TripleID using a GPU is up to 108 times faster than using the traditional RDF tool. The speedup can be more than 1,000 times over the traditional RDF store when processing a complex query with union and join of many subqueries.

A survey of RDF management technologies and benchmark datasets

With the fast development of semantic web and some other areas, the amount of resource description framework (RDF) data has increased significantly. How to efficiently manage these masses of RDF data has become a challenging task, and has attracted many scholars to research. This paper introduces the state-of-the-art of the RDF storage and query technologies according to some classification criteria. In addition, several prevailing benchmark datasets are introduced and compared. Finally, research challenges and opportunities in future are discussed.

E-Assessment Data Compatibility Resolution Methodology with Bidirectional Data Transformation

Electronic Assessment (E-Assessment) also known as computer aided assessment for the purposes involving diagnostic, formative or summative examining using data analysis. Digital assessments come commonly from social, academic, and adaptive learning in machine readable forms to deliver the machine scoring function. To achieve real-time and smart e-assessment, data modeling needs dramatic improvements at the level of representation which will improve examinees to gain prompt response instantly after attempting exams. Whereas, computer based inference to gain intelligence in assessing results through computations is becoming a useful feature in todays’ testing systems. Induction of rule base linked data is desired to be reformed from the old tradition data model found either in spread sheet or relational database used for data storage. These data forms are essential to be converted into semantical annotated form to support Artificial Intelligence. This can be done with the use of Semantic Web data model Resource Description Framework (RDF) built-up using hierarchal and linked data representation. Updating assessment source data later for results is one of the hardest problem of all viabilities in traditional and semantically augmented systems when combined for evaluating. This study purposes a methodology of bidirectional data transformation back and forth from Relational Database (RDB) and RDF. A case study representing qualitative analysis of transforming student’s results information into RDF store reforming data as ready to be analyzed. At the end of this study outcomes show how data updating becomes feasible by following proposed data transformation procedure.

Characterising RDF data sets

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.

Storing massive Resource Description Framework (RDF) data: a survey

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.

A study about integrating video contents with web services based on the RDF

The purpose of this study is to examine how to combine broadcasting video clips with other contents offered by a third party. This paper focused on the field of education, particularly the study of 'animals', and verified methods of collaboration between videos and other contents such as Wikipedia. When it comes to considering collaboration, it is well-known that metadata are absolutely necessary, and this paper constructed a resource description framework (RDF) store to describe video clips from various viewpoints which cover themes to explain animals, motions about animals, translation from Japanese animal's name to other languages and so on. In order to construct RDF stores and integrate videos with other web services effectively, some natural language processing and dictionary were used and the ontology was generated. By using RDF store, we developed demonstration applications that combine videos with other services.

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.

Semantic Web repositories for genomics data using the eXframe platform

BackgroundWith the advent of inexpensive assay technologies, there has been an unprecedented growth in genomics data as well as the number of databases in which it is stored. In these databases, sample annotation using ontologies and controlled vocabularies is becoming more common. However, the annotation is rarely available as Linked Data, in a machine-readable format, or for standardized queries using SPARQL. This makes large-scale reuse, or integration with other knowledge bases very difficult.MethodsTo address this challenge, we have developed the second generation of our eXframe platform, a reusable framework for creating online repositories of genomics experiments. This second generation model now publishes Semantic Web data. To accomplish this, we created an experiment model that covers provenance, citations, external links, assays, biomaterials used in the experiment, and the data collected during the process. The elements of our model are mapped to classes and properties from various established biomedical ontologies. Resource Description Framework (RDF) data is automatically produced using these mappings and indexed in an RDF store with a built-in Sparql Protocol and RDF Query Language (SPARQL) endpoint.ConclusionsUsing the open-source eXframe software, institutions and laboratories can create Semantic Web repositories of their experiments, integrate it with heterogeneous resources and make it interoperable with the vast Semantic Web of biomedical knowledge.

R3F: RDF triple filtering method for efficient SPARQL query processing

With the rapid growth in the amount of graph-structured Resource Description Framework (RDF) data, SPARQL query processing has received significant attention. The most important part of SPARQL query processing is its method of subgraph pattern matching. For this, most RDF stores use relation-based approaches, which can produce a vast number of redundant intermediate results during query evaluation. In order to address this problem, we propose an RDF Triple Filtering (R3F) method that exploits the graph-structural information of RDF data. We design a path-based index called the RDF Path index (RP-index) to efficiently provide filter data for the triple filtering. We also propose a relational operator called the RDF Filter (RFLT) that can conduct the triple filtering with little overhead compared to the original query processing. Through comprehensive experiments on large-scale RDF datasets, we demonstrate that R3F can effectively and efficiently reduce the number of redundant intermediate results and improve the query performance.