Mapping Large Scale Research Metadata to Linked Data: A Performance Comparison of HBase, CSV and XML

Vahdati, Sahar; Karim, Farah; Huang, Jyun-Yao; Lange, Christoph

doi:10.1007/978-3-319-24129-6_23

Cited by 12 publications

(12 citation statements)

References 10 publications

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“…The results, detailed in [19], prove that our XML→RDF approach works on large datasets. However, it needs more computation time than the mapping from HBase because it does not employ a parallel processing strategy.…”

Section: Figure 3: Accumulated Processing Time For the Openaire Datasetsupporting

confidence: 54%

“…In the OpenAIRE setting, we compared the performance and maintainability of our XML→RDF approach to mappings from an HBase NoSQL database and from CSV [19] 21 . The results, detailed in [19], prove that our XML→RDF approach works on large datasets.…”

Section: Figure 3: Accumulated Processing Time For the Openaire Datasetmentioning

confidence: 99%

“…The selected records are separately exported to 10 files and the size of each is around 300 MB. In this experiment, 95 construction rules 19 for the five entity types mentioned above are used to generate RDF triples.…”

Section: Practical Evaluation Setupmentioning

confidence: 99%

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Streaming transformation of XML to RDF using XPath-based mappings

Huang

Lange

Auer

2015

Proceedings of the 11th International Conference on Semantic Systems

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The Extensible Markup Language (XML) has become a widely adopted data interchange format. With the rise of Linked Data published using the Resource Description Framework (RDF), a number of tools for transforming XML to RDF have been developed. Specifying XML-RDF mappings for these tools often requires skills in programming languages such as XSLT or XQuery. Moreover, these tools are rarely able to deal with large XML inputs. We introduce an XML to RDF transformation approach, which is based on map- pings comprising RDF triple templates that employ simple XPath expressions. Thanks to the restricted XPath expressions, which can be evaluated against a stream of XML data, our implementation can handle extremely large input XML files. To process the XML input efficiently, we employ XML filtering techniques and a strategy for selecting relevant XML nodes to generate RDF triples from. We show that the time complexity of our mapping algorithm is linear in the size of the XML input and also prove its practical efficiency with an evaluation on large real-world data

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Section: Figure 3: Accumulated Processing Time For the Openaire Datasetsupporting

confidence: 54%

Section: Figure 3: Accumulated Processing Time For the Openaire Datasetmentioning

confidence: 99%

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Streaming transformation of XML to RDF using XPath-based mappings

Huang

Lange

Auer

2015

Proceedings of the 11th International Conference on Semantic Systems

Self Cite

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“…The OpenAIRE metadata can be consumed via OAI-PMH, or, in an even more straightforward way, as linked data (cf. our previous work, Vahdati et al [27]). …”

Section: Discussionmentioning

confidence: 74%

A semi-automatic approach for detecting dataset references in social science texts

Ghavimi

Mayr

Lange

et al. 2017

ISU

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Abstract. Today, full-texts of scientific articles are often stored in different locations than the used datasets. Dataset registries aim at a closer integration by making datasets citable but authors typically refer to datasets using inconsistent abbreviations and heterogeneous metadata (e.g. title, publication year). It is thus hard to reproduce research results, to access datasets for further analysis, and to determine the impact of a dataset. Manually detecting references to datasets in scientific articles is time-consuming and requires expert knowledge in the underlying research domain. We propose and evaluate a semi-automatic three-step approach for finding explicit references to datasets in social sciences articles. We first extract pre-defined special features from dataset titles in the da|ra registry, then detect references to datasets using the extracted features, and finally match the references found with corresponding dataset titles. The approach does not require a corpus of articles (avoiding the cold start problem) and performs well on a test corpus. We achieved an F-measure of 0.84 for detecting references in full-texts and an F-measure of 0.83 for finding correct matches of detected references in the da|ra dataset registry.

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“…Similarly, Vahdati et al [15] present a distributed approach for converting research metadata from HBase, CSV and XML formats to RDF. They use the MapReduce paradigm for processing a large volume of the data in parallel over multiple nodes.…”

Section: Related Workmentioning

confidence: 99%

RDF Link Generation by Exploring Related Links on the Web of Data

Sharma¹,

Marjit²,

Biswas³

2018

IJITCS

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Interlinking RDF resources is a vital aspect of the Semantic Web technology. It is the basis of Linked Data that provides interlinked datasets on the web. One of the principles of Linked Data is interlinking resources from different data sources on the web. Data interlinking is a critical and challenging problem that every Linked Data generation applications face. Various approaches have been evolved for resolving this problem, but, for more massive datasets, it becomes almost indefinite time while linking similar or related resources. Linking RDF resources is like the problem of entity matching, record matching or duplicate resource detection. More or less they attempt to point to the same problem, but the RDF link generation is the task of finding related resources on the web. In this article, we present an approach for generating RDF links using the similarity measure between two RDF resources and by exploring associated relationships of the matched resources. The idea is to find related resources and link them with an RDF resource that is being generated.

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Mapping Large Scale Research Metadata to Linked Data: A Performance Comparison of HBase, CSV and XML

Cited by 12 publications

References 10 publications

Streaming transformation of XML to RDF using XPath-based mappings

Streaming transformation of XML to RDF using XPath-based mappings

A semi-automatic approach for detecting dataset references in social science texts

RDF Link Generation by Exploring Related Links on the Web of Data

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