Extracting Emerging Knowledge from Social Media

Massive data integration technologies have been recently used to produce very large ontologies. However, knowledge in the world continuously evolves, and ontologies are largely incomplete for what concerns low-frequency data, belonging to the so-called long tail. Socially produced content is an excellent source for discovering emerging knowledge: it is huge, and immediately reflects the relevant changes which hide emerging entities. Thus, we propose a method for discovering emerging entities by extracting them from social content. Once instrumented by experts through very simple initialization, the method is capable of finding emerging entities; we use a purely syntactic method as a baseline, and we propose several semantics-based variants. The method uses seeds, i.e. prototypes of emerging entities provided by experts, for generating candidates; then, it associates candidates to feature vectors, built by using terms occurring in their social content, and then ranks the candidates by using their distance from the centroid of seeds, returning the top candidates as result. The method can be continuously or periodically iterated, using the results as new seeds. We validate our method by applying it to a set of diverse domain-specific application scenarios, spanning fashion, literature, and exhibitions.