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Big Data Technology, Semantics and Analytics
Trends, success and applications for big data including the use of semantic technology
Curated by Tony Agresta
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Not All Graph Databases Are Created Equally - An Interview with Atanas Kiryakov - Ontotext

Not All Graph Databases Are Created Equally - An Interview with Atanas Kiryakov - Ontotext | Big Data Technology, Semantics and Analytics | Scoop.it
Graph databases help enterprise organizations transform the management of unstructured data and big data.
Tony Agresta's insight:

Atanas Kiryakov is a 15 year veteran of semantic technology and graph databases.   He will be interviewed on September 30th at 11 AM EDT.   I would suggest you sign up for this webinar which will focus on the following:


  • Significant use cases for semantic technology - How are they transforming business applications today?
  • The importance of graph databases - What makes them unique?
  • Creating text mining pipelines - How are they used in conjunction with graph databases?
  • The Semantic Platform - What other tools make up a complete semantic platform and how are they used?


You can review the webinar using the link above and sign up.  Details about the webinar itself will be e-mailed to you around the middle of September.


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LMI Named a Winner in Destination Innovation Competition - Semanticweb.com

LMI Named a Winner in Destination Innovation Competition - Semanticweb.com | Big Data Technology, Semantics and Analytics | Scoop.it
Tony Agresta's insight:

More news about Open Policy was just published on SemanticWeb.com.    With Ontotext inside..."LMI has developed a tool—OpenPolicy™—to provide agencies with the ability to capture the knowledge of their experts and use it to intuitively search their massive storehouse of policy at hyper speeds. Traditional search engines produce document-level results. There’s no simple way to search document contents and pinpoint appropriate paragraphs. OpenPolicy solves this problem. The search tool, running on a semantic-web database platform, LMI SME-developed ontologies, and web-based computing power, can currently host tens of thousands of pages of electronic documents. Using domain-specific vocabularies (ontologies), the tool also suggests possible search terms and phrases to help users refine their search and obtain better results.”

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Text Mining & Graph Databases - Two Technologies that Work Well Together - Ontotext

Text Mining & Graph Databases - Two Technologies that Work Well Together - Ontotext | Big Data Technology, Semantics and Analytics | Scoop.it
Graph databases, also known as triplestores, have a very powerful capability – they can store hundreds of billions of semantic facts (triples) from any subject imaginable.  The number of free semantic facts on the market today from sources such as DbPedia, GeoNames and others is high and continues to grow every day.   Some estimates have this total between 150 and 200 billion right now.   As a result, Linked Open Data can be a good source of information with which to load your graph databases. Linked Open Data is one source of data. When does it become really powerful?  When you create your own semantic triples from your own data and use them in conjunction with linked open data to enrich your database.  This process, commonly referred to as text mining,  extracts the salient facts from free flowing text and typically stores the results in some database.  With this done, you can analyze your enriched data, visualize it, aggregate it and report on it.  In a recent project Ontotext undertook on behalf of FIBO (Finanical Information Business Ontology), we enhanced the FIBO ontologies with Linked Open Data allowing us to query company names and stock prices at the same time to show the lowest trading prices for all public stocks in North America in the last 50 years.   To do this, we needed to combine semantic data sources,  something that’s easy to do with the Ontotext Semantic Platform. We have found that the optimal way to apply text mining is in conjunction with a graph database.  Many of our customers use our Text Mining to do just that. Some vendors only sell graph databases and leave it up to you to figure out how to mine the text.  Other vendors only sell the text mining part and leave it up to…
Tony Agresta's insight:

Here's a summary of how text mining works with graph databases.  It describes the major steps in the text mining process and ends with how entities, articles and relationships are indexed inside the graph database.  The blend of these two major classes of technology allow all of your unstructured data to be discoverable.  Search results are informed by much more than just the metadata associated with the document or e-mail.  They are informed by the meaning inside the document, the text itself which contains important insights about people, places, organizations, events and their relationship to other things. 

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Semantics: The Next Big Issue in Big Data

Semantics: The Next Big Issue in Big Data | Big Data Technology, Semantics and Analytics | Scoop.it
State Street s David Saul argues big data is better when it s smart data.
Tony Agresta's insight:

Banking, like many industries, faces challenges in the area of data consolidation.  Addressing this challenge can require the use of semantic technology to accomplish the following:

 

  • A common taxonomy across banking divisions allowing everyone to speak the same language
  • Applications that integrate data including structured data with unstructured data and semantic facts about trading instruments, transactions that pose risk and derivatives
  • Ways to search all of the data instantly and represent results using different types of analysis, data visualization or through relevance rankings that highlight risk to the bank.

 

"What's needed is a robust data governance structure that puts underlying meaning to the information.  You can have the technology and have the standards, but within your organization, if you don't know who owns the data, who's responsible for the data, then you don't have good control."

 

Some organizations have built data governance taxonomies to identify the important pieces of data that need to be surfaced in rich semantic applications focused on risk or CRM, for example.  Taxonomies and ontologies understand how data is classified and relationships between the types of data.  In turn, they can be used to create facts about the data which can be stored in modern databases (enterprise NoSQL) and used to drive smart applications. 

 

Lee Fulmer, a London-based managing director of cash management for JPMorgan Chase says the creation of [data governance] standards is paramount for fueling adoption, because even if global banks can work out internal data issues, they still have differing regulatory regimes across borders that will require that the data be adapted.

 

"The big paradigm shift that we need, that would allow us to leverage technology to improve how we do our regulatory agenda in our banking system.  If we can come up with a set of standards where we do the same sanction reporting, same format, same data transcription, same data transmission services, to the Canadians, to the Americans, to the British, to the Japanese, it would reduce a huge amount of costs in all of our banks."

 

Semantic technology is becoming an essential way to govern data, create a common language, build rich applications and, in turn, reduce risk, meet regulatory requirements and reduce costs. 

 




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