Uses of HOLMES
Information extraction is the task consisting in transforming unstructured information contained in texts into structured information to be used by other applications. Nowadays, there are a number of information extraction applications, for instance:
- In the financial domain, applications that extract from news facts that are relevant for analysts: people’s specific actions, company mergers and acquisitions, specific events able to influence economic trends.
- In the security domain: detection of dangerous events, detection of weak signals, identification of security breaches, and detection of patent violation.
- In the scientific domain: detection of the usages of specific technologies, technology watch, automatic identification of experimental patterns from scientific texts.
- In the personal domain: email analysis, automatic identification of relevant events, such meetings and assigned tasks.
In general, all applications including some kind of intelligent semantic search make use of information extraction, for instance, in order to identify entities that are pertinent to a given domain, or to characterize the relationships between different entities, event dates, etc.
Recently, information extraction has found two big fields of application, namely the paradigms of (Open) Linked Data and Big Data. In the former, it is mainly used to automatically link resources and find relations that have not been manually coded. In the latter, information extraction provides central cues for analyzing texts (typically coming from social networks) that, because of their quantity, would be difficult to analyze by human operators.