On Read/WriteWeb there was a post about 10 semantic Apps to watch. It seems that the terminology of semantics is used for all sort of kinds. Our understanding is that semantic applications should use some form of ontology in order to describe the meaning of data/text and this includes named entities and relations between the named entities. In my opinion not all of the presented candidates fulfil this requirement. Nevertheless it will be interesting to see if the semantic search engines will leave the beta status and be able to answer questions to all sort of domains. Another point to watch is if the other Apps add more semantic meaning to their solution. At least Ontos with his news portal already uses the semantic meaning and offers first semantic services and therefore we are happy not to be alone in the take off of semantic Apps.

Today we are all flooded with news that we receive by email or RSS feeds. We all have the same problem of filtering the important ones from the less important ones and to identify what is linked together. The internet community has introduced several methods on how to aggregate news. Which of the methods is the best is difficult to say. One method that doesn’t seem to be mentioned is the possibility of automatic aggregation using semantic technologies, especially the functions of NLP. Automatic aggregation is subscribing to different sources and then analyzing the content with natural language processing (NLP) technologies. Doing it this way the system can extract objects of interest (e.g. person, organization, location etc) and the relation between the objects. After the information extraction process the system has to merge the same objects like Hillary Clinton is the same like she or Hillary or Clinton assuming the NLP can distinguish inside the text the meaning and then merge the objects together. This approach is driven and controlled by a clear knowledge model (ontology) which allows growing a semantic network of information. Over the time the more object – relation – object information we have we can start to rank information on his semantic meaning which provides a much better view. This is different to approaches introduced by Mixx or Digg where people create votes and therefore influence the ranking of the news. Another part that seems to be important for a successful start is the up to date information as seen when Tailrank started.
We’ll keep working on our approach and watch the other methods of aggregation and ranking. Would be interesting to hear about experiences from others when trying to understand how information is linked together. Can it be solved by filters or by votes or by using bookmarks?

Welcome to the Ontos blog. We will use it to inform about new trends and features concerning our technologies especially in the area of NLP (Natural Language Processing), aggregating and merging of news and information sources. We will share ideas and concepts on how to use information and enrich them in a semantic way - for example in our new semantic portal.