To make adequate decisions, businesses have to combine their databases – their own view of the world – with non-proprietary data. However, combining diverse data from multiple sources is a complex task.
Matching concepts and entities across disparate data sources and recognizing their mentions in texts requires disambiguation of their meaning – something that comes easy to people, but computers often fail to do right. Because an average graduate has awareness about wide set of entities and concepts and computers do not.
Ontotext developed technology to build Big Knowledge Graphs and apply cognitive analytics to them to provide entity awareness – semantic fingerprints derived from rich entity descriptions.
Ontotext’s Company Graph provides entity awareness about all locations on Earth and the most popular companies and people. I will use Ontotext GraphDB to demonstrate analytics on this knowledge graph of 2 billion triples. The Company Graph combines several open data sources, mapped to the FIBO ontology, and interlinks their entities to 1 million news articles.
The demonstration includes: importance ranking of nodes, based on graph centrality; popularity ranking, based on news mentions of the company and its subsidiaries; retrieval of similar nodes in a knowledge graph and determining distinguishing features of entity.