arXiv subjects

Ilkcan Keles

Publications and source records attributed to Ilkcan Keles.

3 recordsLinked to original sources

arXiv2021

Star Pattern Fragments: Accessing Knowledge Graphs through Star Patterns

The Semantic Web offers access to a vast Web of interlinked information accessible via SPARQL endpoints. Such endpoints offer a well-defined interface to retrieve results for complex SPARQL queries. The computational load for processing such SPARQL endpoints offer access to a vast amount of interlinked information. While they offer a well-defined interface for efficiently retrieving results for complex SPARQL queries, complex query loads can easily overload or crash endpoints as all the computational load of answering the queries resides entirely with the server hosting the endpoint. Recently proposed interfaces, such as Triple Pattern Fragments, have therefore shifted some of the query processing load from the server to the client at the expense of increased network traffic in the case of non-selective triple patterns. This paper therefore proposes Star Pattern Fragments (SPF), an RDF interface enabling a better load balancing between server and client by decomposing SPARQL queries into star-shaped subqueries, evaluating them on the server side. Experiments using synthetic data (WatDiv), as well as real data (DBpedia), show that SPF does not only significantly reduce network traffic, it is also up to two orders of magnitude faster than the state-of-the-art interfaces under high query load.

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arXiv2019

Querying Linked Data: An Experimental Evaluation of State-of-the-Art Interfaces

The adoption of Semantic Web technologies, and in particular the Open Data initiative, has contributed to the steady growth of the number of datasets and triples accessible on the Web. Most commonly, queries over RDF data are evaluated over SPARQL endpoints. Recently, however, alternatives such as TPF have been proposed with the goal of shifting query processing load from the server running the SPARQL endpoint towards the client that issued the query. Although these interfaces have been evaluated against standard benchmarks and testbeds that showed their benefits over previous work in general, a fine-granular evaluation of what types of queries exploit the strengths of the different available interfaces has never been done. In this paper, we present the results of our in-depth evaluation of existing RDF interfaces. In addition, we also examine the influence of the backend on the performance of these interfaces. Using representative and diverse query loads based on the query log of a public SPARQL endpoint, we stress test the different interfaces and backends and identify their strengths and weaknesses.

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arXiv2019

Linked Open Data Validity -- A Technical Report from ISWS 2018

Linked Open Data (LOD) is the publicly available RDF data in the Web. Each LOD entity is identfied by a URI and accessible via HTTP. LOD encodes globalscale knowledge potentially available to any human as well as artificial intelligence that may want to benefit from it as background knowledge for supporting their tasks. LOD has emerged as the backbone of applications in diverse fields such as Natural Language Processing, Information Retrieval, Computer Vision, Speech Recognition, and many more. Nevertheless, regardless of the specific tasks that LOD-based tools aim to address, the reuse of such knowledge may be challenging for diverse reasons, e.g. semantic heterogeneity, provenance, and data quality. As aptly stated by Heath et al. Linked Data might be outdated, imprecise, or simply wrong": there arouses a necessity to investigate the problem of linked data validity. This work reports a collaborative effort performed by nine teams of students, guided by an equal number of senior researchers, attending the International Semantic Web Research School (ISWS 2018) towards addressing such investigation from different perspectives coupled with different approaches to tackle the issue.

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