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Topic
How to Use Your Enterprise Taxonomies for Auto-Categorization
Description
In this webinar Synaptica will present the Graphite Knowledge Studio tools and illustrate how enterprises can leverage their existing taxonomies to perform auto-categorization, including inline semantic tagging as well as document-level classification.
Graphite Knowledge Studio brings together tools for managing ontology schema, curating taxonomies, ingesting corpora, and performing iterative tagging trials with human-in-the-loop review and rule-refinement. Knowledge Studio is the result of over two year's collaboration between Synaptica and Ontotext to integrate their taxonomy management and text analytics technologies.
Graphite Knowledge Studio is a ground-up ontological and semantic solution. At the foundation level we have created an ontological schema for categorization processes: predicates to describe positive and negative contextual validations, proximity and relevance, and even predicates to capture complex regular expressions. This tagging ontology can be rapidly combined with standard taxonomy ontologies such as SKOS. The solution puts the taxonomist in the driving seat with transparent rules and tools to control how categorization works with their enterprise taxonomies in relation to their enterprise content.
The webinar will draw on examples from two domains: categorization for eCommerce applications, and categorization of job-listings and candidate résumés.
The presentation will be led by Synaptica’s Founder, Dave Clarke, with an explanation of some of the NLP under-the-hood presented by Borislav Ankov, Product Owner of Ontotext Metadata Studio.
Recording Duration
01:02:00
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Hi there, You are invited to a Zoom webinar. When: Mar 1, 2023 05:00 PM London Topic: How to Use Your Enterprise Taxonomies for Auto-Categorization Register in advance for this webinar: https://us02web.zoom.us/webinar/register/WN_RoFe55TySXmGkER--qg7-w After registering, you will receive a confirmation email containing information about joining the webinar.
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