ArmaTweet: Detecting events by semantic tweet analysis - Tonon, A; Cudré-Mauroux, P; Blarer, A; Lenders, V; Motik, B (Springer, Cham, 2017)
Armasuisse Science and Technology, the R&D; agency for the Swiss Armed Forces, is developing a Social Media Analysis (SMA) system to help detect events such as natural disasters and terrorist activity by analysing Twitter posts. The system currently supports only keyword search, which cannot identify complex events such as ‘politician dying’ or ‘militia terror act’ since the keywords that correctly identify such events are typically unknown. In this paper we present ArmaTweet, an extension of SMA developed in a collaboration between armasuisse and the Universities of Fribourg and Oxford that supports semantic event detection. Our system extracts a structured representation from the tweets’ text using NLP technology, which it then integrates with DBpedia and WordNet in an RDF knowledge graph. Security analysts can thus describe the events of interest precisely and declaratively using SPARQL queries over the graph. Our experiments show that ArmaTweet can detect many complex events that cannot be detected by keywords alone.