Metadata and entity classification

A deeper understanding of an enterprise’s metadata and its business usage can be achieved by uncovering knowledge from unstructured information assets.

The intense manual nature of this exercise leaves little to no time for validation and finalization. Without this analysis, the meaning of critical business terms, where they are used as well as their ownership remains unclear.

Beyond text analytics: The Parabole way

There are myriad software components in the marketplace providing solutions that in whole or in part are based on text analysis. This paper discusses techniques used to process voluminous information in the form of text, images, PDFs, or any document utilizing Text Analysis and Semantic or Sentiment Analysis (“SSA”). We intend to demonstrate how SSA is rapidly eclipsing Text Analysis and how this technology can reduce not solely the time required to process information but increase, greatly, the quality and usefulness of the output. This advance has significant, even game-changing, ramifications across the enterprise.

Contextual-Regulation-Analysis

An ontology is a set of concepts and categories in a subject area or domain that possesses the properties and relations between them. If we were to create a set of concepts as defined for, let’s say, a small grocery store, we would include concepts including: vendor name, item, category, customer name, POS sale data, etc. We would also introduce further detailed concepts including; apples, lettuce, ground beef, paper plates, cereal, bread, etc., by their respective vendor and brand names. Based on the grocer’s purchases, the connections with his vendors, his customers preferences, the relationships between sales and days of the week, connections between items purchased by individual customers, and other data, we could begin to build an ontology for our grocer. It becomes quite apparent, even at this small scale, that manually building truly comprehensive ontologies is a herculean task.

Why context is key in knowledge extraction?

With the advent and widespread acceptance of Knowledge Graphs amongst large enterprises, expert level knowledge has become critical for the successful rollout of any AI powered application. This requirement has made ontology development pivotal for all learning-based solutions that, necessarily, must capture and leverage the knowledge possessed by Subject Matter Experts (SME’s). If the true scope of this sea-change is to be understood, currently implemented manual processes of ontology development can never successfully contend with a massive and ever-expanding data universe.

NLP: Brute force or intelligent design?

Natural-language processing (NLP) is a field of computer science and artificial intelligence being developed and used in the analysis of interactions between computers and humans, in particular the development of methods to program computers to successfully process large volumes of natural language data. Once considered fantastical, NLP in its various forms is now utilized in many facets of business, entertainment and social media and is being used increasingly, albeit with varying efficiency, within machine learning systems. Machines may be trained using a variety of complex and unstructured sources assembled under myriad contexts.



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