Het aantal digitale documenten in bedrijfsomgevingen neemt dermate toe dat men het beoordelen ervan niet kan overlaten aan experts. Met behulp van slimme software kan men de relevantie van elektronische documenten voorspellen. Een van de nieuwste technieken is predictive coding die de voorspelling doet aan de hand van een model.
E-discovery projects typically start with an assessment of the collected electronic data in order to estimate the risk to prosecute or defend a legal case. This is not a review task but is appropriately called early case assessment, which is better known as exploratory search in the information retrieval community. This paper first describes text mining methodologies that can be used for enhancing exploratory search. Based on these ideas we present a semantic search dashboard that includes entities that are relevant to investigators such as who knew who, what, where and when. We describe how this dashboard can be powered by results from our ongoing research in the “Semantic Search for E-Discovery” project on topic detection and clustering, semantic enrichment of user profiles, email recipient recommendation, expert finding and identity extraction from digital forensic evidence.
MULTIFILE
