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 >>>

 

Linked Data – The End of the Document?

The ‘document’ has been the most prominent metaphor to present information as well as being the predominant information carrier for ages. With the rise of the Semantic Web, information has been broken down to tiny pieces, which can be put in various contexts dynamically.

Solving-the-Semantic-Puzzle

This principle can be applied to tackle some of the most important challenges faced by publishers nowadays: the most efficient reuse of media assets and personalisation of information services.

In a workshop, I will moderate at this year’s Publishers’ Forum (Berlin, May 5-6), you will find out, why semantic web principles & linked data technologies are the key for ‘Dynamic Semantic Publishing’. Attendees will learn from best practices and get an overview over state-of-the-art technologies.

I would be happy to meet you in Berlin!

Linked Data 2014: My expectations for the New Year

2014 is only a couple of days old. I have some expectations and visions for the new year with regards to linked data and its next evolution steps.

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  • Smart data will receive a lot of attention: big data is the wave on which this certain topic surfs.
  • Trust and provenance of data has been discussed for a while and has been mentioned frequently to be an important step for linked data to be accepted especially by enterprises. W3C’s PROV ontology was just a first step towards this direction. More specifications and implementations will follow this year.
  • Automatic quality-checks for several types of linked data will become a matter of course (similar to test automation in software testing). One example is qSKOS which is provided as a web service for all people interested in controlled vocabularies like taxonomies or thesauri.
  • The LOD cloud as we know it won’t be updated anymore: the periodical updates of the LOD cloud won’t happen anymore in 2014. The image would be much too big. Instead, several domains will generate their own LOD clouds, each of them with a couple of central hubs in the middle (see also: The LOD cloud is dead, long live the trusted LOD cloud). Those sub-hubs connected will represent the overall LOD cloud in the future. DBpedia will remain in the centre.
  • Traditional database vendors will embrace RDF and SPARQL: MarkLogic Semantics and IBM’s DB2-RDF is just the beginning. It will be hard for them to deliver scalability and performance as good as ‘traditional’ RDF database providers like OpenLink Software or Ontotext can do.
  • Linked Data “Killer applications” will be established: Automatic linking of structured and unstructured information based on RDF could become a killer application for Linked Data technologies. Take a look at two example applications in the areas of medicine and clean energy which make use of this principle: true semantic search will become possible (the two demos wont’t work properly behind the firewall due to some software libraries used by it).
  • The year of semantic web standards: The Open Government Data movement will finally arrive at the point where standards based technologies like linked data become the obvious solution to the more or less chaotic collections of open data which have been accumulated in recent years.
  • Enterprise Linked Data: More and more integrations of linked data technologies like Semantic SP into enterprise platforms like SharePoint will be available as products on the software market.
  • SEMANTICS 2014 will take place in September in Germany and will be a great event. More to come soon.
  • ISWC 2014 will take place in October at beautiful Lake Garda (Italy) and will be a great event, too.
  • I am looking forward to meeting some of you once again, and also to meet some new linked data aficionados!!