Switcher

  • <img class="language-icon" typeof="foaf:Image" src="http://www.averbis.de/sites/all/modules/languageicons/flags/en.png" width="16" height="12" alt="English" title="English" />
  • <img class="language-icon" typeof="foaf:Image" src="http://www.averbis.de/sites/all/modules/languageicons/flags/de.png" width="16" height="12" alt="Deutsch" title="Deutsch" />

Averbis Search Platform

The Averbis Search Platform (ASP) is a novel and yet field-proven technology for searching complex information inventories. The solution offers an extensive handling of linguistic phenomena through the integration of core technology MSITM. Even phrases, synonyms or single components of assembled words are recognized, lay- and expert languages are standardized.

Comfortable search thanks to Search Assistant

The intelligent semantic analysis of all acquired data guarantees a quick, intuitive and exact search. The search results can be shown in all languages relevant to the user. The software makes the search particularly comfortable. It receives queries entered via the free text field, searches the data inventory according to the required information and shows the results in a user-friendly manner.
Users often wish to quickly and simply narrow down a large amount of search results, not knowing however which key words they should use. In these cases, navigation searches help (faceted search). With the help of defined categories from classifications of various fields of expertise, users can browse an otherwise unmanageable amount of information. The solution is based on the latest JEE technology. Owing to open interface architecture, it can be easily integrated into existing information systems and other applications.

Scalability & stability based on an established search server

All linguistic and semantic analysis components are fully integrated into Solr, the Enterprise Search Server of the Apache Foundation. Due to a scalable mechanism for replication and load balancing the application guarantees the highest performance even for large data sets.