In the last 30 years Refuse Derived Fuel (RDF) has grown in popularity due to its perception as a readily available, renewable and sustainable fuel for power stations. This increased use of RDF has ...been closely followed by an escalation of industrial fire and explosion-related incidents associated with this fuel, showing the new hazards and inherent dangers brought by it. The re-evaluation of specific fire and explosion protective measures is required. For RDF to have a continued role as an energy source in a volatile and difficult energy market, it must be perceived as: sustainable; safe; easy; cheap; and reliable. If financial losses due to business interruption occur frequently, then confidence in this area will dissolve, and while safety is paramount to prevent injury and/or death, reputational damage must also be considered to secure energy supply and maintain market confidence in RDF. This paper presents a review of previous investigations and scientific studies, which, combined with the authors’ own RDF fire and explosion investigatory experience, allows for a logical hypothesis to be made in relation to relative practices in storage and fuel route fire safety management.
RDF compression and querying are consolidated topics in the Web of Data, with a plethora of solutions to efficiently store and query static datasets. However, as RDF data changes along time, it ...becomes necessary to keep different versions of RDF datasets, in what is called an RDF archive. For large RDF datasets, naive techniques to store these versions lead to significant scalability problems. In this paper, we present v-RDF-SI, one of the first RDF archiving solutions that aim at joining both compression and fast querying. In v-RDF-SI, we extend existing RDF representations based on compact data structures to provide efficient support of version-based queries in compressed space. We present two implementations of v-RDF-SI, named v-RDFCSA and v-HDT, based, respectively, on RDFCSA (an RDF self-index) and HDT (a W3C-supported compressed RDF representation). We experimentally evaluate v-RDF-SI over a public benchmark named BEAR, showing that v-RDF-SI drastically reduces space requirements, being up to 40 times smaller than the baselines provided by BEAR, and 4 times smaller than alternatives based on compact data structures, while yielding significantly faster query times in most cases. On average, the fastest variants of v-RDF-SI outperform the alternatives by almost an order of magnitude.
We propose techniques for processing SPARQL queries over a large RDF graph in a distributed environment. We adopt a “partial evaluation and assembly” framework. Answering a SPARQL query
Q
is ...equivalent to finding subgraph matches of the query graph
Q
over RDF graph
G
. Based on properties of subgraph matching over a distributed graph, we introduce
local partial match
as partial answers in each fragment of RDF graph
G
. For assembly, we propose two methods: centralized and distributed assembly. We analyze our algorithms from both theoretically and experimentally. Extensive experiments over both real and benchmark RDF repositories of billions of triples confirm that our method is superior to the state-of-the-art methods in both the system’s performance and scalability.
The relational database (RDB) to resource description framework (RDF) transformation is a major semantic information extraction method because most web data are managed by RDBs. Existing automatic ...RDB-to-RDF transformation methods generate RDF data without losing the semantics of original relational data. However, two major problems have been observed during the mapping of multi-column key constraints: repetitive data generation and semantic information loss. In this article, we propose an improved RDB-to-RDF transformation method that ensures mapping without the aforementioned problems. Optimised rules are defined to generate an accurate semantic data structure for a multi-column key constraint and to reduce repetitive constraint data. Experimental results show that the proposed method achieves better accuracy in transforming multi-column key constraints and generates compact semantic results without repetitive data.
Data integration is the dominant use case for RDF Knowledge Graphs. However, Web resources come in formats with weak semantics (for example, CSV and JSON), or formats specific to a given application ...(for example, BibTex, HTML, and Markdown). To solve this problem, Knowledge Graph Construction (KGC) is gaining momentum due to its focus on supporting users in transforming data into RDF. However, using existing KGC frameworks result in complex data processing pipelines, which mix structural and semantic mappings, whose development and maintenance constitute a significant bottleneck for KG engineers. Such frameworks force users to rely on different tools, sometimes based on heterogeneous languages, for inspecting sources, designing mappings, and generating triples, thus making the process unnecessarily complicated. We argue that it is possible and desirable to equip KG engineers with the ability of interacting with Web data formats by relying on their expertise in RDF and the well-established SPARQL query language 2. In this article, we study a unified method for data access to heterogeneous data sources with Facade-X, a meta-model implemented in a new data integration system called SPARQL Anything. We demonstrate that our approach is theoretically sound, since it allows a single meta-model, based on RDF, to represent data from (a) any file format expressible in BNF syntax, as well as (b) any relational database. We compare our method to state-of-the-art approaches in terms of usability (cognitive complexity of the mappings) and general performance. Finally, we discuss the benefits and challenges of this novel approach by engaging with the reference user community.
Due to significant inter-band correlation resulting from use of hundreds of contiguous spectral bands, band selection (BS) is one of most widely used methods to reduce data dimensionality for band ...redundancy removal. A challenge for BS is how to design an effective criterion which can select bands with preserving crucial spectral information, while also avoiding selecting highly correlated bands. Information theory turns out to be one of best means to address such issue in terms of information redundancy, specifically, the rate distortion function (RDF) of Shannon's 3 rd noisy source coding (or joint source and channel coding) theorem, which has been widely used in image compression/coding. This paper presents a novel unsupervised RDF-based band subset selection (RDFBSS) for hyperspectral image classification (HSIC). To accomplish this goal, a new concept of the area under an RDF curve, A RDF similar to the area under a receiver operating characteristic (ROC), A z defined in hyperspectral target detection is coined and defined as a criterion for BSS. Since BSS generally requires an exhaustive search for an optimal band subset, two iterative algorithms similar to sequential (SQ) N-FINDR and successive (SC) N-FINDR for finding endmembers, called sequential (SQ) RDFBSS and successive (SC) RDFBSS, can be derived and coupled with Ardf as a criterion to find optimal band subsets. The experimental results demonstrate that RDFBSS is indeed a very effective BS method to find best possible band subsets and also performs better than most recent BS methods.
The Resource Description Framework (RDF) is a flexible model for representing information about resources on the Web. As a 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. In real‐world applications, there are many practical situations where the descriptions of objects are accompanied by a degree of uncertainty. In an open Web environment, it is common case that the data from different sources may often contain inconsistent or imprecise information. In this paper, a fuzzy RDF data model is proposed. We present the syntax and semantics of fuzzy RDF data model. In particular, we investigate in the paper how to formally map fuzzy RDF data to relational databases.
This article introduces an algorithm to automatically translate a user-specified keyword-based query K to a SPARQL query Q so that the answers Q returns are also answers for K. The algorithm does not ...rely on an RDF schema, but it synthesizes SPARQL queries by exploring the similarity between the property domains and ranges, and the class instance sets observed in the RDF dataset. It estimates set similarity based on set synopses, which can be efficiently pre-computed in a single pass over the RDF dataset. The article includes two sets of experiments with an implementation of the algorithm. The first set of experiments shows that the implementation outperforms a baseline RDF keyword search tool that explores the RDF schema, while the second set of experiments indicate that the implementation performs better than the state-of-the-art TSA+BM25 and TSA+VDP keyword search systems over RDF datasets based on the “virtual documents” approach.
•An algorithm to automatically translate keyword-based queries to SPARQL queries is introduced.•The algorithm does not rely on RDF schemas.•The algorithm outperforms a baseline schema-based RDF keyword search tool.•The algorithm outperforms two “virtual documents” RDF keyword search tools.•A measure named Graph Relevance Ratio (GRR) is proposed.
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