KEYWORDS: Databases, Analytical research, Data processing, Gold, Analytics, Crystals, Current controlled current source, Feature extraction, Data modeling, Social networks, Machine learning
Creating entity network graphs is a manual, time consuming process for an intelligence analyst. Beyond the traditional
big data problems of information overload, individuals are often referred to by multiple names and shifting titles as they
advance in their organizations over time which quickly makes simple string or phonetic alignment methods for entities
insufficient. Conversely, automated methods for relationship extraction and entity disambiguation typically produce
questionable results with no way for users to vet results, correct mistakes or influence the algorithm’s future results.
We present an entity disambiguation tool, DRADIS, which aims to bridge the gap between human-centric and machinecentric
methods. DRADIS automatically extracts entities from multi-source datasets and models them as a complex set
of attributes and relationships. Entities are disambiguated across the corpus using a hierarchical model executed in
Spark allowing it to scale to operational sized data. Resolution results are presented to the analyst complete with
sourcing information for each mention and relationship allowing analysts to quickly vet the correctness of results as well
as correct mistakes. Corrected results are used by the system to refine the underlying model allowing analysts to
optimize the general model to better deal with their operational data. Providing analysts with the ability to validate and
correct the model to produce a system they can trust enables them to better focus their time on producing higher quality
analysis products.
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