Ontotext Metadata Studio Makes Life Simple For Enterprises Working With Complex Text Analysis

Orchestrate content tagging and evaluate text analysis services to get the best results and maintain metadata quality

Ontotext is happy to introduce Ontotext Metadata Studio – the latest addition to the company’s portfolio, built on top of GraphDB and Ontotext Platform.

Ontotext Metadata Studio enables organizations to get more out of their content by unlocking new business models or achieving significant cost optimizations by putting their own Subject Matter Experts (SME) at the heart of any text analysis task.

A typical problem organizations have is the ability to automate the process of transforming their content into structured knowledge in a predictable and manageable way, while dealing with the changes in the reference dataset and the model drift that comes along with it.

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With the help of Ontotext Metadata Studio, these organizations can use their business analysts to define Semantic Objects as specific views, abstracting developers away from the complexity and peculiarities of the knowledge graph. This allows them to reference the pre-existing domain knowledge modeled in their ontologies and annotate relevant documents following the established Annotation Guidelines for the specific use case.

Ontotext Metadata Studio can be integrated with many text analysis services via GraphDB’s Text Mining Plugin, e.g., spaCy, IBM Watson, Amazon Comprehend, Google NLP, Ontotext Tag (powering the Ontotext NOW demonstrator), etc. This enables the evaluation of a service or the suitability of a combination of services for the currently explored use case against the ground truth data produced by the annotators. This can significantly shorten the Time to market (TTM) for new product development, which studies highlight as a significant factor for a product’s success.

In many domains, such as Legal and Life Science, for example, the time and availability of a domain expert is highly valuable. Ontotext Metadata Studio helps utilize this time in the most efficient way. If the reference dataset lives in GraphDB, users don’t have to worry that it would ever become out of sync with the annotated data. Instead, they can use the time they’d usually spend for rework for more value-focused endeavors.

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When working with rich content, it’s always valuable to have a birds eye view of the metadata associated with it, so this is where different reporting dashboards come into play.

A certain text analysis service returns a ton of common nouns, while another one struggles with recognizing metonymy in context? With the Ontotext Metadata Studio, all of this knowledge and much more is now just a couple of clicks away!

“If you can’t measure it, you can’t improve it. Ontotext Metadata Studio and its report dashboards helps measuring the outcome of the investment into various natural language technologies. The product and its common annotation model becomes a central point to evaluate the organizational natural language strategy and improve the synergy between SMEs and text engineers.” – Vassil Momtchev, CTO of Ontotext

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