News and Updates on the KRR Group
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Source: Semantic Web world for you
Il y a quelque jours j’ai eu le plaisir, et la chance, de participer à la série de webinaires organisés par l’AIMS. L’objectif que je m’étais fixé pour ma présentation (en Français) intitulée “Clarifier le sens de vos données publiques avec le Web de données” était de démontrer l’avantage de l’utilisation du Web de données […]

Source: Semantic Web world for you
Il y a quelque jours j’ai eu le plaisir, et la chance, de participer à la série de webinaires organisés par l’AIMS. L’objectif que je m’étais fixé pour ma présentation (en Français) intitulée “Clarifier le sens de vos données publiques avec le Web de données” était de démontrer l’avantage de l’utilisation du Web de données […]

Source: Think Links

Below is a post-it note summary made with our students in the Web Science course. This is the capstone class for students doing the Web Science minor here a the VU and the summary highlights the topics they’ve learned about so far in four other courses.

webscience-summary

Filed under: academia Tagged: summary, web science

Source: Think Links

The WordPress.com stats helper monkeys prepared a 2012 annual report for this blog.

Here’s an excerpt:

600 people reached the top of Mt. Everest in 2012. This blog got about 4,900 views in 2012. If every person who reached the top of Mt. Everest viewed this blog, it would have taken 8 years to get that many views.

Click here to see the complete report.

Filed under: Uncategorized

Toekomst Kijken/Looking into the Future

Posted by data2semantics in collaboration | computer science | large scale | semantic web | vu university amsterdam - (Comments Off on Toekomst Kijken/Looking into the Future)

Last Wednesday, Frank van Harmelen appeared on the Dutch science TV program “Labyrint”, where he interviews George Dyson, Luc Steels and François Pachet about their ideas on the future of Computers.

The program can be watched online (in Dutch):

And here’s the discussion session afterwards (in Dutch):

More information at the website of Labyrint.

YASGUI: Web-based SPARQL client with bells ‘n wistles

Posted by data2semantics in collaboration | computer science | large scale | semantic web | vu university amsterdam - (Comments Off on YASGUI: Web-based SPARQL client with bells ‘n wistles)

Source: Data2Semantics

A few months ago Laurens Rietveld was looking for a query interface from which he could easily query any other SPARQL endpoint.

But he couldn’t find any that fit my requirements:

So he decided to make his own!

Give it a try at: http://aers.data2semantics.org/yasgui/

Future work (next year probably):

Comments are appreciated (including feature ideas / bug reports).

Sources are available at https://github.com/LaurensRietveld/yasgui

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Source: Semantic Web world for you
Last week, on the afternoon of November 22, I co-organized a tutorial about Linked Data aimed at researchers from digital humanities. The objective was to give a basic introduction to the core principles and to do that in a very hands-on setting, so that everyone can get a concrete experience with publishing Linked Data. To […]

Source: Semantic Web world for you
Last week, on the afternoon of November 22, I co-organized a tutorial about Linked Data aimed at researchers from digital humanities. The objective was to give a basic introduction to the core principles and to do that in a very hands-on setting, so that everyone can get a concrete experience with publishing Linked Data. To […]

This week we received notification from the EU that the LDBC project has been granted. We think this is great news. The LDBC project (is a STREP and will run until Q2 2015. LDBC stands for Linked Data Benchmark Council, and linked data here of course comprises RDF data management, but also includes the emerging class of graph database systems.

The mission of the LDBC project is to establish a long-term independent association among RDF and Graph database companies that define benchmarks, specify benchmarking practices and publish officially vetted benchmark results. Beyond the project partners, many commercial vendors of RDF and Graph database systems have already expressed their interest in joining this council (once we have founded the legal entity.. it will take a few months still).

The motivation behind the project is to show the strengths (and weaknesses) of RDF and Graph database technologies to the wider IT community pondering the adoption of these technologies, by enabling comparisons between the various products but also with established relational database technologies. Also, by establishing competition on these benchmarks LDBC aims to foment technical progress in the RDF and Graph database systems.

The LDBC project partners include for the RDF database community Ontotext and Openlink; from the graph database side there is Neo Technologies (of neo4j fame) and Sparsity is indirectly involved through academic project partner UPC (Barcelona). Other project partners are University of Innsbruck, FORTH, VU University Amsterdam and Technical University Munich (TUM). The academic partners will help to provide the council with an initial set of benchmarks.

The technical topics of interest for benchmarking are:

  • complex analytical queries for both graph and RDF
  • graph analysis algorithms and traversals
  • large-scale reasoning on RDF data
  • transaction performance
  • systems support for data integration and provenance

The use-case scenarios for these are:

  • social networking (e.g. marketing companies)
  • dynamic publishing (e.g. BBC)
  • telecommunication network analysis
  • bioinformatics data integration (e.g. OpenPhacts)

LDBC interacts with users of Graph and RDF technologies through is Technical User Community (TUC), and the TUC is having its first users workshop in Barcelona next week Nov19+Nov20 (http://www.ldbc.eu:8090/display/TUC/First+TUC+meeting+Nov+2012) on the premises of UPC. The main take-away for users to engage with the TUC is to influence the benchmarking agenda of the LDBC. Talk to us, and RDF vendors might start competing in how to best solve your problems! Even if the Barcelona meeting is too short notice, please drop a note if you want to be involved in the TUC or know people who should.

Finally, please fill in the questionnaire (http://goo.gl/PwGtK) to tell us about your usage (problems) with RDF (or graph) database technologies. We will be looking at the questionnaire results that we have received by Friday November 16 to help set the agenda in the users meeting, so if you want to contribute already this week, that would be highly appreciated.

Thanks for your time, also on behalf of the full LDBC consortium,

Peter Boncz (scientific director LDBC)
Paul Groth
Frank van Harmelen

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Update: Complexity, Learning and Semantics

Posted by data2semantics in collaboration | computer science | large scale | semantic web | vu university amsterdam - (Comments Off on Update: Complexity, Learning and Semantics)

Source: Data2Semantics

Complexity metrics form the backbone of graph analysis. Centrality, betweenness, assortativity and scale freeness are just a handful of selections from a large and quickly growing literature. It seems that every purpose has its own notion of complexity. Can we find a way to tie these disparate notions together?

Algorithmic statistics provide an answer. It posits that any useful property that is induced from data can be used to compress it—to store it more efficiently. If I know that my network is scale free, or that a set of points is distributed normally, that information will allow me to come up with a more efficient representation of the data. If not, the property we have learned is of no use.

This notion allows us to see data compression, learning and complexity analysis as simply three names for the same thing. The less a dataset can be compressed, the more complex it is, the more it can be compressed the more useful our induced information is.

But we can go further than just complexity. Occam’s razor tells us that the simplest explanation is often the best. Algorithmic statistics provides us with a more precise version. If our data is the result of a computational process, and we have found a short description of it, then with high probability the model that allowed that compression is also a description of the process that generated our data. And that is ultimately what semantics is, a description of a generating process. Whether it’s the mental state that led to a linguistic expression, or the provenance trail that turned one form of data into another. When we talk about semantics, we are usually discussing computational processes generating data.

Practically, algorithmic statistics will give us a means to turn any family of network models (from frequent subgraphs to graph grammars) into a family of statistics. If the network model is powerful enough, the statistics should be able to capture any existing property of complex graphs, including scale freeness, assortativity or fractal scaling.

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