Linked Data meets Data Science

As a long-term member of the Linked Data community, which has evolved from W3C’s Semantic Web, the latest developments around Data Science have become more and more attractive to me due to its complementary perspectives on similar challenges. Both disciplines work on questions like these:

  • How to extract meaningful information from large amounts of data?
  • How to connect pieces of information to other pieces in order to generate ‘bigger pictures’ of sometimes complex problems?
  • How to visualize complex information structures in a way that decision-makers benefit from it?

Two complementary approaches
When taking a closer look to the approaches taken by those two ‘schools of advanced data management’ one aspect becomes obvious: Both try to develop models in order to be able to ‘codify and to calculate the data soup’.

While Linked Data technologies are built on top of knowledge models (‘ontologies’), which try to describe first of all data in distributed environments like the web, are Data Science methods mainly based on statistical models. One could say: ‘Causality and Reasoning over Distributed Data’ meets ‘Correlation and Machine Learning on Big Data’.

Graph databases are key to success
In contrast to this supposed contradiction, correlations and complementarities between those two disciplines prevail. Both approaches seek for solutions to overcome the problem with rigid data structures which can hardly adapt to the needs of dynamic knowledge graphs. Whenever relational databases cannot fulfill requirements about performance and simplicity, due to the complexity of database queries, graph databases can be used as an alternative.

Thus, both disciplines make use of these increasingly popular database technologies: While Linked Data can be stored and processed by standards-based RDF stores like VirtuosoMarkLogicGraphDB or Sesame, are the most popular graph databases for Data Scientists mainly based on the property graph model, for example: Titan or Neo4J. Some vendors like Bigdata support even both graph models.

Both graph models are similar and can be mapped to each other, but they try to solve slightly different problems:

  • the property graph model serves better the needs of Graph Data Analysts (e.g. for Social Network Analysis or for real-time recommendations)
  • RDF graph databases are great when distributed information sources should be linked to each other and mashed together (e.g. for Dynamic Semantic Publishing or for context-rich applications).

Connect both approaches and combine methods
I can see at least two options where methods from Data Science will benefit from Linked Data technologies and vice versa:

  • Machine learning algorithms benefit from the linking of various data sets by using ontologies and common vocabularies as well as reasoning, which leads to a broader data basis with (sometimes) higher data quality
  • Linked Data based knowledge graphs benefit from Graph Data Analyses to identify data gaps and potential links (find an example for a semantic knowledge graph about ‘Data Science’ here: http://vocabulary.semantic-web.at/data-science)

Questions on the use of Linked Data in businesses
We want to learn more about the opinion of various stakeholders working in different industry verticals about the status of Linked Data technologies. The main question is: Is Linked Data perceived as mature enough to be used on a large scale in enterprises? The results will contribute to the development of the Linked Data market by reporting how enterprises currently think.

Link: http://j.mp/linked-data-survey

SKOS as a Key Element in Enterprise Linked Data Strategies

The challenges in implementing linked data technologies in enterprises are not limited to technical issues only. Projects like these deal also with organisational hurdles to be crossed, for instance the development of employee skills in the area of knowledge modelling and the implementation of a linked data strategy which foresees a cost-effective and sustainable infrastructure of high-quality and linked knowledge graphs. SKOS is able to play a key role in enterprise linked data strategies due to its relative simplicity in parallel with its ability to be mapped and extended by other controlled vocabularies, ontologies, entity extraction services and linked open data.

Read the full paper >>>

SKOS at the intersection of three disciplines

SKOS is at the intersection of three disciplines and their paradigms:

SKOS is at the intersection of three disciplines and their paradigms

Whilst librarians, taxonomists, and specialists in the fields of text mining and entity extraction have started to embrace SKOS, especially ‘ontologists’ from artificial intelligence community still remain sceptical about the capabilities of SKOS.

With the latest release of PoolParty Thesaurus Server a full-blown ontology management facility has been introduced which can now be used to extend expressivity of SKOS knowledge models. For instance, SKOS concepts can become any other type of resource and by that schemas of additional relations and attributes can be applied to the concept.

Apply ontologies to SKOS thesauri

PoolParty’s philosophy is to support users with Simple Knowledge Organization Systems (SKOS) first, to let them grow instantly by using various mechanisms like ontologies, text corpus analysis or linked data enrichment. All of them can nicely be combined. Users benefit from a step to step approach, not being bothered by an overarching approach from the very initial step. Learn more >>>

 

SEMANTiCS 2014 celebrating its 10th anniversary

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SEMANTiCS conference series will celebrate its 10th anniversary this year in September. SEMANTiCS (formerly known as I-Semantics) has had a long tradition in buidling bridges between practitioners and researchers in the areas of semantic web and linked data technologies. Therefore the SEMANTiCS consortium has developed new strucutures to further elaborate on this special character of this event series:

  • Each conference session will combine talks from industry and research – those two interest groups won’t be seperated from each other anymore
  • DBpedia community will be integrated deeply into SEMANTiCS conference: DBpedia has been the most successful activity in this area so far. A lot of industry-related projects has been built around this dataset. Therefore the 2nd meeting of the DBpedia association will be co-located with SEMANTiCS (taking place on September 3rd, also in Leipzig).
  • Transfer-oriented side events and a centrally located market place will offer great opportunities to discuss about state-of-the-art technologies in the areas of Semantic Web standards based software and products.

I am also happy to announce that PoolParty Semantic Suite is one of the Gold sponsors of this event this year. See you in Leipzig!