In this project, such knowledge is extracted and distilled using the Reveal Relation Extractor (RelExt): it processes input texts in order to identify entities of interest to analysts together with the relationships existing among them.
The RelExt system implements Machine Learning approaches for text processing, based on neural methods such as Support Vector Machine and/or Deep Learning. Since the type of entities and relations of interest in the target domain may change across the different domains, a team of analysts identified mentions to entities and relationships of interest within the client’s documents.
The labelling of less than one hundred texts allow to deploy a system able to “read” a document collection of several thousand documents.
This material was then used to automatically derive the neural models useful to automate the semantic processing of documents internalized by the system and to define benchmarks useful for the quantitative measurement of the semantic quality of the processors. The labeling of fewer than one hundred texts allows to deployment of a system able to “read” a document collection of several thousand documents. As a consequence, several hundreds of thousands of mentions to entities and relations were automatically extracted and used to populate a Database and a Semantic Search Engine. These can be finally queried through standard query languages, such as SQL or SPARQL. This enables the straightforward implementation of powerful navigation and analysis software, such as graphical dashboards, useful to navigate in this huge amount of knowledge.