Dynamic Data Sources Outpace Static Ontologies

Posted on May 25, 2011 by

John Ford
Senior Director of Sales

A fundamental challenge exists in today’s NLP-based text analytics solutions; the volume of dynamic data is exploding and there are not enough subject matter experts available to map out the relationships between concepts.

Many aspects of advanced text mining capabilities rely on the existence or creation of ontologies, semantic rules, and templates to:

  • Resolve ambiguity to determine the right meaning
  • Determine synonym relationships
  • Identify and categorize entities, actions and events

Creating deep ontologies and semantic rules using traditional text analytic approaches (e.g. CRM, genomics, security intelligence) is a manual process. At times this is the preferred approach, because the relationships need to be mapped for a specific policy  (e.g. practice systems for hospitals or answers to a support question).

The challenge for enterprises today is the majority of their data is unstructured and dynamically changing, making it difficult to surface insights and take action upon it in real-time.  The cost of manually building an ontology and the lack of skilled subject matter experts make it unlikely that the enterprise ever adequately resources this activity.   The rapid growth of dynamic and real-time data feeds across multiple internal and external sources, vital to business intelligence, exacerbates the difficulty of extracting true meaning from unstructured text data.

Atigeo’s xPatterns platform automates the creation and dynamic maintenance of semantic ontologies. xPatterns uses a neural network to model associations between concepts.  It represents “IsAssociatedWith” relationships for domains, derived simply from reading and reviewing large bodies of unstructured text information about the domain that can be streamed to the system in real-time. The technology can be leveraged to determine indirect semantic relationships between queried concepts, and to facilitate understanding of the relevance of a specific document to a specific concept. It can dynamically extract key concepts from any viewed document and surface documents that are more closely related to it.  This allows users to traverse and assimilate bodies of content in ways similar to how the human mind organizes information.  This can allow a user to quickly learn about a new topic and make fact based decisions.

Moreover, it is also possible semantically index a user’s past queries and documents viewed to maintain unstructured semantic profile that can help personalize views, refine relevance or even proactively notify the user when related content gets added to the system.

xPatterns is a cloud based platform that can be easily integrated with existing enterprise applications and collect data from internal and external structured and unstructured sources.   It can be used to augment existing business intelligence and text analytics solutions.  The platform has been used to enhance search and discovery and speed assimilation of complex information.  It can monitor trends in social media, match supplier capabilities to open RFPs, translate physician notes to ICD 10 codes and spot patterns of non-compliance or fraud, waste and abuse.

A picture or a demo is worth more than millions of random words.  Go http://www.atigeo.com/videos/   today so see some of the ways we are connecting related concepts and delivering insights on large dynamic unstructured data sets.

Posted in Big Data, ontologies, semantic web | Leave a comment

Social Media Content Analytics

Posted on April 26, 2011 by

Peyvand Khademi
Director of Product Management

Did you know that more than 20 million people “Like” Starbucks on Facebook? Or that Starbucks increasingly leans on its social media channels for marketing campaigns, outreach efforts and even product development? Ideas pour in to the Coffee Experience conglomerate from its social media fans all the time, including simple practical ones like putting handbag hooks in women’s bathrooms. So ladies, the next time you have a more pleasant, sanitary experience at Starbucks, you’ll know who to thank…that’s right, Facebook.

From the get-go, the main emergent theme of the recent Gartner Customer 360 Summit was Social CRM. It’s not uncommon for companies to now have huge multitudes (in the millions, even) of Friends on Facebook or Followers on Twitter. Those kinds of numbers change the game in terms of the definition, significance, and leverage points of today’s Customer Relationship Management. The boundary between company and customer is becoming more permeable, and the immediacy of access and response is becoming near instantaneous. What stirs the stew even more is that the number of touch points or data channels between company and customer is exploding—as many as 60 according to one company—with the concomitant need to build a unified profile of a customer.

As with other internet data, a great majority of the data exchange in social CRM is in the form of unstructured text—be it Facebook updates, tweets, or customer support logs (emails or transcriptions of customer support audio interactions). What was evident at the 360 Summit was that just about every company presentation—whether from vendors or consumers of technology solutions—touched on analytics as a main component of their social CRM systems. Companies need to have an understanding (in real-time) of their customer’s complaints/requests/wishes based on an ocean of unstructured and structured data and a multiplicity of channels. As one analyst put it, “it’s a good time to be in the business of data analytics.”

Today’s analytics solutions are both much improved and have much room for improvement, and by all accounts, no single vendor solution or solution path has emerged as a clear market winner or technology choice. The conference chair, Gartner CRM analyst Gene Alvarez, addressed this in his opening “Road to 2015” remarks. He pointed out that the key market drivers are: Social, Mobile, Explosion of Data and Explosion of Channels; emphasizing that today’s CRM leaders have access to lots of data but no way to put it all together.

There is no doubt that the analytics and semantic markets are ripe with opportunity. And revenue! The annual online ad market alone is estimated at $140 billion, and single-digit upticks in click rates are enormously value-laden. What’s exciting is that Atigeo’s Intelligence Platform, xPatterns, built on a foundation of knowledge auto-discovery (unencumbered by the need for hierarchical taxonomies and OWL ontologies) is well-poistioned to bring together all the needed elements of an encompassing solution: Concepts, Content, Context, and People.

That’s not to trivialize the complexity or magnitude of the problem: driving data to analysis to action is a multifaceted problem, especially for large enterprises, and technology solutions are only part of the mix in positioning a company for a successful Customer 360-type engagement. But what’s undeniable is that the Voice of the Customer is increasingly loud (if not always clear–and clarity is precisely the point here). And companies are and will continue to invest heavily in setting up their social CRM strategy to be responsive to that voice. It all adds up to enormous opportunity for Atigeo.

Posted in Big Data, ontologies, Science, semantic web | Tagged , , | Leave a comment

I want my ME TV!

Posted on April 20, 2011 by

David Boardman
VP of Product Strategy

It was the 1980s.  Every teenager was saying it or singing it: “I want my MTV.” Cable TV broke the broadcast model and opened the door for niche content providers such as MTV, ESPN, and more.  Today, the internet is breaking the broadcast and cable model by providing even more choice in the form of Netflix, YouTube, iTunes, and more.  If you listen closely you can almost hear the teens of today singing “I want my ME TV.”

I recently attended a Telco 2.0 Executive Brainstorm and Developer Forum in Palo Alto, CA. It was clear from the discussions that the market for Smart TV is ripe for disruptive technologies enabling a new era of relevance and personalization – the pillars of “ME TV.” Key players are falling into line around a common three screen vision, where content is connected, personalized, and available across devices. There are three experiences of this new media environment that emerged from our discussions. First is the traditional experience of watching content on the TV, American Idol or Rattle and Hum for example. The second is a social experience, such as tweeting about the show, posting Facebook updates, or interacting with a customized app for the show. The social aspect could enable viewers to interact with each other via video and have a shared media experience. The third experience involves consumption of related metadata or content, such as relevant wiki articles, ecommerce, or news stories. Metadata content can also include maps or travel information related to a particular scene in a show or movie, which provides the opportunity for deeper interaction, context, and connection.

Of the three experiences (consuming traditional TV content, social connection, and interaction with metadata) the latter two require a relevance or recommendation engine. One of the challenges with existing relevance and recommendation engines is that current capabilities only present similar TV shows or movies to viewers based on, “people who liked this also liked that.” To date, solutions have not been generic enough to be able to arrange and create relevant social experiences and metadata experiences based on the TV content being consumed across devices.

For me, a big “aha” moment that surfaced from the executive brainstorming session at Telco 2.0 is that the market is beginning to mature and our xPatterns solution can play a pivotal role in the smart TV ecosystem. xPatterns can drive relevance based on TV content and what’s changing in the social or metadata landscape and how each of these elements impacts the relevance of the rest.  xPatterns can put the ME in the “I want my ME TV” anthem of the next generation.

Posted in customer profile, personalization, privacy | Tagged , , | Leave a comment