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The dblp Knowledge Graph and SPARQL Endpoint

Authors: Marcel R. Ackermann, Hannah Bast, Benedikt Maria Beckermann, Johannes Kalmbach, Patrick Neises, and Stefan Ollinger

Published in: TGDK, Volume 2, Issue 2 (2024): Special Issue on Resources for Graph Data and Knowledge. Transactions on Graph Data and Knowledge, Volume 2, Issue 2

Abstract

For more than 30 years, the dblp computer science bibliography has provided quality-checked and curated bibliographic metadata on major computer science journals, proceedings, and monographs. Its semantic content has been published as RDF or similar graph data by third parties in the past, but most of these resources have now disappeared from the web or are no longer actively synchronized with the latest dblp data. In this article, we introduce the dblp Knowledge Graph (dblp KG), the first semantic representation of the dblp data that is designed and maintained by the dblp team. The dataset is augmented by citation data from the OpenCitations corpus. Open and FAIR access to the data is provided via daily updated RDF dumps, persistently archived monthly releases, a new public SPARQL endpoint with a powerful user interface, and a linked open data API. We also make it easy to self-host a replica of our SPARQL endpoint. We provide an introduction on how to work with the dblp KG and the added citation data using our SPARQL endpoint, with several example queries. Finally, we present the results of a small performance evaluation.

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Marcel R. Ackermann, Hannah Bast, Benedikt Maria Beckermann, Johannes Kalmbach, Patrick Neises, and Stefan Ollinger. The dblp Knowledge Graph and SPARQL Endpoint. In Special Issue on Resources for Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 2, Issue 2, pp. 3:1-3:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{ackermann_et_al:TGDK.2.2.3,
  author =	{Ackermann, Marcel R. and Bast, Hannah and Beckermann, Benedikt Maria and Kalmbach, Johannes and Neises, Patrick and Ollinger, Stefan},
  title =	{{The dblp Knowledge Graph and SPARQL Endpoint}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{3:1--3:23},
  ISSN =	{2942-7517},
  year =	{2024},
  volume =	{2},
  number =	{2},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.2.2.3},
  URN =		{urn:nbn:de:0030-drops-225870},
  doi =		{10.4230/TGDK.2.2.3},
  annote =	{Keywords: dblp, Scholarly Knowledge Graph, Resource, RDF, SPARQL}
}

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DOI: 10.4230/LIPIcs.STACS.2011.308

Analysis of Agglomerative Clustering

Authors: Marcel R. Ackermann, Johannes Bloemer, Daniel Kuntze, and Christian Sohler

Published in: LIPIcs, Volume 9, 28th International Symposium on Theoretical Aspects of Computer Science (STACS 2011)

Abstract

The diameter k-clustering problem is the problem of partitioning a finite subset of R^d into k subsets called clusters such that the maximum diameter of the clusters is minimized. One early clustering algorithm that computes a hierarchy of approximate solutions to this problem for all values of k is the agglomerative clustering algorithm with the complete linkage strategy. For decades this algorithm has been widely used by practitioners. However, it is not well studied theoretically. In this paper we analyze the agglomerative complete linkage clustering algorithm. Assuming that the dimension dis a constant, we show that for any k the solution computed by this algorithm is an O(log k)-approximation to the diameter k-clustering problem. Moreover, our analysis does not only hold for the Euclidean distance but for any metric that is based on a norm.

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Marcel R. Ackermann, Johannes Bloemer, Daniel Kuntze, and Christian Sohler. Analysis of Agglomerative Clustering. In 28th International Symposium on Theoretical Aspects of Computer Science (STACS 2011). Leibniz International Proceedings in Informatics (LIPIcs), Volume 9, pp. 308-319, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2011)

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@InProceedings{ackermann_et_al:LIPIcs.STACS.2011.308,
  author =	{Ackermann, Marcel R. and Bloemer, Johannes and Kuntze, Daniel and Sohler, Christian},
  title =	{{Analysis of Agglomerative Clustering}},
  booktitle =	{28th International Symposium on Theoretical Aspects of Computer Science (STACS 2011)},
  pages =	{308--319},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-25-5},
  ISSN =	{1868-8969},
  year =	{2011},
  volume =	{9},
  editor =	{Schwentick, Thomas and D\"{u}rr, Christoph},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.STACS.2011.308},
  URN =		{urn:nbn:de:0030-drops-29942},
  doi =		{10.4230/LIPIcs.STACS.2011.308},
  annote =	{Keywords: agglomerative clustering, hierarchical clustering, complete linkage, approximation guarantees}
}

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Documents authored by Ackermann, Marcel R.

The dblp Knowledge Graph and SPARQL Endpoint

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Analysis of Agglomerative Clustering

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