Tutorial on Stream Reasoning for Linked Data

October 21st/22nd, 2013
Sydney, Australia
Collocated with the 12th International Semantic Web Conference (ISWC 2013)

Prerequisite knowledge
The team of presenters


The tutorial provides a comprehensive view of the Stream Reasoning research area. It consists of two parts. The first one is focused on RDF and SPARQL extensions for stream processing. It will begin with an introduction to RDF Stream processing models, and two concrete approaches that implement these models, namely C-SPARQL Engine and SPARQLStream, including hands on sessions. The second part of the tutorial explores Stream Reasoning approaches: approximate stream reasoning techniques for OWL2-DL, incremental materialization for RDF Streams (IMaRS), and an overview of EP-SPARQL and Sparkwave. Also the second part includes hands-on sessions.


Nowadays, more and more dynamic information is becoming available to decision makers in the form of continuous data streams. These data streams occur in a variety of modern applications, such as network monitoring, traffic engineering, sensor networks, RFID tags, microposts, telecom records, Web logs, click-streams, etc. Processing these continuous flows of information and reasoning taking into account ontological knowledge is certainly one of the key challenges for semantics in the future Internet. While reasoners scale up in the classical, static domain of ontological knowledge, reasoning upon rapidly changing information has received attention only very recently. The combination of reasoning techniques with data streams gives rise to Stream Reasoning, a high impact re- search area that has already stared produced results that are relevant for both the semantic and data processing communities..

This tutorial aims at introducing different existing approaches for reasoning and querying over data streams, and providing the audience with an overview of techniques and tools that can be used for this purpose. The contents of this tutorial can be relevant for ISWC attendees as it focuses in two of the main tasks in semantic data processing, reasoning and querying, in the context of streaming data that is ubiquitous in a large number of applications on the Web.


9.00 – 10.30

Stream Reasoning introduction (30 min) [slides]

The first session gives an overview of the Stream Reasoning research area, covering:

  • Use cases and requirements
  • Challenges
  • How existing systems (DSMS/CEP, Semantic Web) address them
  • Scope of Stream Reasoning research area
  • Existing Systems (quick introduction and high-level comparison)

RDF stream processing models (45 min) [slides]

This session covers:

  • RDF and SPARQL extensions to manage streaming data
  • overview of RDF model extensions (single timestamped RDF, double-timestamped RDF, etc.)
  • quick recap on SPARQL and SPARQL continuous extensions (windows, S2R operators, followed-by operator,…)
  • overview of existing systems w.r.t. models presented above

Naive reasoning on RDF streams (25 min) [slides]

This session covers:

  • the problem
  • full goal drive approaches on each snapshot
  • materialise each snapshot
  • the DReD approach for incremental maintenance of materialisations

11.00 – 12.45

C-SPARQL: A Continuous Extension of SPARQL (20m) [slides]

This session presents an overview of extensions of SPARQL for querying and naively reasoning on highly dynamic data streams using the Continuous-SPARQL (C-SPARQL) Engine. This session covers:

  • Overview of the C-SPARQL language
  • Overview of the C-SPARQL Engine
  • Practical examples of continuous social media analysis using C-SPARQL and the C-SPARQL Engine

SPARQLstream: Ontology-based streaming data access (40m) [slides]

This session presents an overview of extensions of SPARQLStream for querying existing data streams running on different types of Stream Processing Engines. This session covers:

  • Overview of query rewriting and ontology-based access to streams.
  • Semantic processing data streams delegating to stream processing engines and using R2RML mappings.
  • Practical examples of semantic sensor network querying using SPARQLstream

Hands on session (45m)

13:45 – 15.30

Approximate Reasoning and Approximate Stream Reasoning for OWL2-DL (90m) [slides]

This session presents our recent work on faithful approximate reasoning for OWL2-DL, as well as its extensions for ontological stream reasoning in OLW2-DL. This session covers:

  • Approximate reasoning for OWL2-DL
  • Approximate stream reasoning for OWL2-DL
  • hands-on session

16:00 – 17.30

IMaRS: Incremental Materialization for RDF Streams (30m) [slides]

This session presents IMaRS, a variation of DRed for the incremental maintenance of the window materializations. This session covers:

  • Optimization techniques for incrementally maintaining materializations when changes are caused by streaming data
  • Practical examples of continuous social media analysis

Other Stream Reasoning approaches (30 min) [slides]

This session covers:

  • Complex Event Detection and Stream Reasoning in EP-SPARQL
  • Sparkwave: Continuous Schema-Enhanced Pattern Matching over RDF Data Streams

Wrap-up and conclusions (30 min) [slides]

This session covers:

  • Achievements of existing approaches w.r.t. Stream Reasoning Challenges
  • Open problems and a revised Stream Reasoning research agenda
  • Open Q/A

Prerequisite knowledge

Basic knowledge in Semantic Web may allow better following the tutorial and gaining more benefits from it.

The team of presenters


  1. Davide Francesco Barbieri, Daniele Braga, Stefano Ceri, Emanuele Della Valle, Yi Huang, Volker Tresp, Achim Rettinger, Hendrik Wermser: Deductive and Inductive Stream Reasoning for Semantic Social Media Analytics. IEEE Intelligent Systems 25(6): 32-41 (2010)
  2. Davide Francesco Barbieri, Daniele Braga, Stefano Ceri, Emanuele Della Valle, Michael Grossniklaus: C-SPARQL: a Continuous Query Language for RDF Data Streams. Int. J. Semantic Computing 4(1): 3-25 (2010)
  3. Davide Francesco Barbieri, Daniele Braga, Stefano Ceri, Emanuele Della Valle, Michael Grossniklaus: Incremental Reasoning on Streams and Rich Background Knowledge. ESWC (1) 2010: 1-15
  4. Davide Francesco Barbieri, Daniele Braga, Stefano Ceri, Emanuele Della Valle, Michael Grossniklaus: Querying RDF streams with C-SPARQL. SIGMOD Record 39(1): 20-26 (2010)
  5. Emanuele Della Valle, Stefano Ceri, Frank van Harmelen, Dieter Fensel: It’s a Streaming World! Reasoning upon Rapidly Changing Information. IEEE Intelligent Systems 24(6): 83-89 (2009)
  6. Jeff Z. Pan and Edward Thomas. Approximating OWL-DL Ontologies. In Proc. of the 22nd AAAI Conference on Artificial Intelligence (AAAI-07). 1434-1439. 2007.
  7. Yuan Ren, Jeff Z. Pan and Yuting Zhao. Towards Scalable Reasoning on Ontology Streams via Syntactic Approximation. In Proc. of the ISWC2010 Workshop on Ontology Dynamics (IWOD2010). 2010.
  8. Yuan Ren, Jeff Z. Pan, and Yuting Zhao. Soundness Preserving Approximation for TBox Reasoning. In the Proc. of the 25th AAAI Conference Conference (AAAI2010), 2010.
  9. Raphael Volz, Steffen Staab, and Boris Motik. Incrementally maintaining materializations of ontologies stored in logic databases. J. Data Semantics, 2:1–34, 2005.
  10. Jean-Paul Calbimonte, Óscar Corcho, Alasdair J. G. Gray: Enabling Ontology-Based Access to Streaming Data Sources. International Semantic Web Conference (1) 2010: 96-111
  11. Jean-Paul Calbimonte, Hoyoung Jeung, Óscar Corcho, Karl Aberer: Enabling Query Technologies for the Semantic Sensor Web. Int. J. Semantic Web Inf. Syst. 8(1): 43-63 (2012)
  12. Yuan Ren and Jeff Z. Pan. Optimising Ontology Stream Reasoning with Truth Maintenance System. In Proc. of the ACM Conference on Information and Knowledge Management (CIKM 2011). 2011.