Tag Archives: semantic enrichment

Semantic Information Extraction: From Data Bits to Knowledge Bytes

Semantic Information Extraction and the New Digital Disorder

Semantic information extraction is a boring name for a fascinating task: pulling out meaningful data from textual sources. With its help, text chunks become data bits, data bits become semantic metadata and semantic metadata become knowledge bytes – data pieces, ready to be leveraged for insights, decisions and actions.

Can Semantics be the Peacemaker between ECM and DAM?

DAM, EMC, Gulliver and Lilliputians

The battle continues as one side suggests an ECM (one system to rule them all) is fine and DAM practitioners point out the lack of optimization of ECM. The role of semantics (content metadata) is to give peace a chance and resemble how humans understand and use the content.

Ontotext technology helps BCA Research become double-award winner

BCA awards
Two awards in two weeks BCA Research (part of Euromoney PLC) has proven once again that they continue to lead the field in investment research by winning two innovation awards: the ‘Best Innovative Technology Solution for Sma

S4 Presented at the NYC Semantic Web Meetup

The NYC Semantic Web Meetup Last week the NYC Semantic Web Meetup took place at the Google Research offices in NYC. This is one of the biggest Semantic Web related meetups, with more than 1,700 members. Last meetup was organized

Text, Data and the Roman Roads: Semantic Enrichment

What do you think is the common thread between the Great Roman Empire and your Great scientific research, journalistic report or financial analysis? In a word, it is interconnectedness. In a sentence, these are the paths tha

4 Things NOW Lets You Do With Content

NOW - New On the Web
News on the Web (NOW) is a free public service, showcasing the opportunities a dynamic semantic publishing platform opens up before media & publishing companies. NOW lets you go beyond conventional publishing and get a real

Text Mining & Graph Databases – Two Technologies that Work Well Together

Text Mining, graph databases and ontologies expand your search
Text Mining and Graph Databases work well together because natural language processing can be used to extract meaning from free flowing text and then stored in graph databases to be used in search, discovery and analysis. Graph

Semantic Publishing – Relevant Recommendations Create a Unique User Experience

Semantic recommendations is part of semantic publishing technology
Semantic Publishing includes a number of techniques including semantic recommendations used to personalize the user experience by delivering contextual content based on natural language processing, search history, user profiles an

Why are graph databases hot? Because they tell a story…

Graph databases store connections
Graph databases like GraphDB™ are popular for a variety of reasons. They make it easy to import data without creating complex schemas. They store relationships extracted from unstructured data. You can combine Linked Open Data w

Back to top