Komprise announced Komprise Smart Data Workflows, a systematic process to discover relevant file and object data across cloud, edge and on-premises datacenters and feed data in native format to AI and machine learning (ML) tools and data lakes.
Komprise has expanded Deep Analytics Actions to include copy and confine operations based on Deep Analytics queries, added the ability to execute external functions such as running natural language processing functions via API and expanded global tagging and search to support these workflows. Komprise Smart Data Workflows allow you to define and execute a process with as many of these steps needed in any sequence, including external functions at the edge, datacenter or cloud. Komprise Global File Index and Smart Data Workflows together reduce the time it takes to find, enrich and move the right unstructured data. Komprise Smart Data Workflows are relevant across many sectors. Here’s an example from the pharmaceutical industry.
Finch Computing, developers of real-time natural language processing solution Finch for Text, announced that it has added relationship extraction and co-references to the product. Relationship extraction gives users an ability to decipher relationships between entities, and co-reference enables words like “her” or “him” or “the leader” appearing in text to be resolved to a specific, named entity.
Finch for Text can now find important relationships between entities such as: Acquired-by, Co-Investor-with, Competitor-with, Customer-of, Director-of, Educated-at, Employer-of, Founder-of, Invested-in, Organization-Location, Owner-of, Partner-of, Person-Location, Relative-of, and Subsidiary-of. For companies and people in particular, understanding these connections helps users perform faster, richer and deeper analysis.
Entity co-reference refers to the ability to resolve otherwise obscure references to an entity – like her, him, the company, the product – to a disambiguated entity. The value of this capability is that it helps users understand all mentions of an entity even if that mention isn’t by name. It improves salience scores because the product can better gauge how much an article is about a given entity. It also improves sentiment scores with more mentions to analyze, and the same is true for relationship extraction – there are more relationships discovered because there are more mentions linked to an entity.