KT provides semantic VOD search service for IPTV and various smart mobile devices using OWLIM as semantic repository. We found that BigOWLIM is suitable for our service, as it has shown good performance for its price compared with other products in the actual simulation tests. Best of all, OWLIM could be easily tested, installed and used thanks to open library, detailed guidelines, and lots of samples etc. By and large, our experience is that OWLIM is a very powerful and reliable high-performance semantic repository.

In 2013, Euromoney Institutional Investor PLC, the international online information and events group and its subsidiary, BCA Research, set out to create a new publishing and information platform which would include the latest authoring, storing, and display technologies including semantic search and a triple store repository. Following a successful proof of concept using the OWLIM (now GraphDB) semantic repository and KIM text extraction (which is using Gate), Euromoney commissioned Ontotext to create a semantic database for both their macroeconomic investment research reports and their more market oriented content, like merger and acquisition deals news, IPOs, Equity prospects, Bonds issuance, etc.

The training is overall very helpful to get me familiar with GraphDB quickly. The training can help me understand important semantic web and ontology concepts and knowledge, and understand how to utilize and implement the knowledge in GraphDB. The two pre-webinar training introduces semantic web knowledge and then gave me the chance to tackle 12 exercises to get familiar with the learned knowledge. The pre-training helps build a solid foundation for using GraphDB and the webinar. The webinar is very good to introduce OntoRefine for batch generating triples to be inserted into GraphDB, and query the data to get insight. Then an exercise is asking the participants to carry out a similar task. The webinar is overall helpful to get me started with real world tasks. I think that a justification of why to use OntoRefine would be good. It would be helpful to cover other real world tasks for inserting and updating data. Are there other tools for doing these tasks? I would also like to know how to maintain the data consistency and integrity, how to find and deal with bad or inconsistent data, and how to measure the quality of data etc. An introduction to connectors would also be helpful. Overall, many thanks for your training!

The training demonstrates a clear path between flat data sources and a useful knowledge graph.

Designing Semantic Technology PoC with GraphDB Training” was conducted on Dec 13, 2016. It was a detailed session covering various features of GraphDB. It also included few hands-on exercises on SPARQL queries. Session was detailed and had enough time to try out exercises during the session. We also got practical exposure of RDFizing the data and importing it to GraphDB repository using OntoRefine tool, this tool is used for converting tabular data into RDF and has been integrated with GraphDB 8. The pre-training materials were very useful in understanding the basic concepts of the ontology world. I would recommend this session to anyone who is looking for practical exposure to Semantics, ontologies, RDFizing the data and building of Data sets.

We had undergone an excellent training on introduction to semantic technology, and this gave a deep insight to RDF, ontologies and GraphDB’s behaviour and semantic technology. The training materials, online training and POC’s were excellent. Thanks to Ontotext for giving such a training.