All this is possible due to the ElasticSearch tool running in the background. We have indexed the complete DBPedia database from August 2015. The aggregations are configured to deal with string fields, number and date ranges. ElasticSearch provides instant aggregations (counts, statistics) of the data. Full text queries are available all the time. It is even possible to input phrase searches, multi-field searches, fuzzy operators, filed boosting. The text data are analyzed (stemmed, tokenized) and the analyzers are available for many languages. In our demo, we show that the Semantic Web content can be effectively browsed using advanced indexing tools.
Complete DBPedia knowledge at one glance.
Visual overview of the data count distribution.
Searching and filtering the data by seeing the visualized results.
Full-text search with a rich query language.
The ability to select most restrictive property (small bubble) or the property with highest count of results (big bubble).
We are a team of skilled research and professionals that stay on the top of currently extremely attractive fields:
Natural Language Processing via Machine Learning.
Semi-structured or semantic web data search and analysis (we are skilled in the ElasticSearch framework).
Modern web application development (Angular.JS / Node.JS stack).
Dr. Miloslav Konopík, firstname.lastname@example.org
Web pages: http://nlp.kiv.zcu.cz/