Semantic Internet (former known as Semantic Web, see also RDF) has the possibility to record different semantic trends occurring at different sources with a certain frequency inside the in principle accessible to the public internet.
Day after day, month after month, semantic contexts are published on the Internet. area23 semantic web filters all semantic statements that occur with a certain frequency from different sources. Furthermore, not all trends and semantically significant events are more relevant for most semantic miners.
With area23 semantic web you can filter by region, topic categories, relevance from different sources and subsequent complexity.
A filter for a region can be set similiar to Google Trends, e.g. for United States or for Germany, etc.
Basic main categories are:
- politics (Brexit, Sinn Féin, Thüringen, ...)
- sports (soccer, american football events, ...)
- entertainement (music, cinema, tv, ...)
- technology
- business (stock markets, trading, bonds, central bank news, different economic indictors)
- health
- lifestyle (eating, drinking, other events)
- housing (appartments, flat, hotel, camping / caravan sites, vacation rentals, accommodations, e.g.: Airbnb, Wimdu)
- infrastructure (traffic reports, flights & airports occupancy, train connections, ships & ferries connections)
- weather (including unexpected temperatures / weather effects, like ice, heavy rain, storm, dry periods plus enviroment disasters, like hurricanes, floodings, earthquakes, volcanic eruptions)
- and many more
Once you created your filter enviroment, you can start collecting & recording semantic events.
After some time, collected semantic events will appear, e.g.: 'coronavirus'
In that example, 'coronavirus' the most common and reliable semantic logical statements are shown (extracted from different internet sites / ressources), e.g.: number of infections, behavior to stay healty, flights canceled to / from China, stock market risk for China in the next year.
Every statement extracted from the data pool that directly makes a statement or an assumption regarding matters other than the corona virus is now checked with other data sources as to whether the statement actually has a formal fuzzy truth content. So in that (our) example, the flight connections from and to China will be verified immediately as a result. Chinese economic data and the change behavior of futures in Hang Seng, which changed in the period since the outbreak of the coronavirus, will be checked too.
Warning, formally epistemologically an extracted statement is not necessarily true, even if 15 different articles from different countries in different languages in the web claim: "Corona virus has negative effects on the current Chinese fiscal year 2020." and if the outlook for futures in Hang Seng and the economic data have deteriorated in the same period.
to be continued...
Links about semantic web and similar topics:
https://www.semantic-mediawiki.org/wiki/Semantic_MediaWiki
https://www.opensemanticdata.org/
http://jena.apache.org/
https://www.nngroup.com/articles/user-need-statements/
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