“People sparking together” is how Patricia Lopez, marketing manager at Adele Robots, the robotics startup behind the platform, describes the system. Contributors create code or design that gets wrapped in the FIONA API so that it can be converted into a Spark – which is an application that can become part of the avatar, whether that be its voice, language or a function (NLP, text-to-speech, computer vision, or 3D design, for instance). The company will host a Sparkstore where developers can sell, or freely share, their Sparks with the world, and those interested in using avatars can then combine different Sparks together in the Sparklink environment. Sparkrender is a capability it’s developed for users to post their avatars – which run on Adele Robots’ servers in the cloud – on their websites or mobile apps.

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Image Courtesy; Flickr/Sean MacEntee

When it comes to social CRM, it’s a world of semantics, and text and sentiment analytics.

Recently Gartner released its Magic Quadrant report on the space, and a reading of it makes it pretty clear that the category, which the research group defines as “a business strategy that generates opportunities for sales, marketing and customer service, while also benefiting online communities,” demands such intelligence.

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“The important thing for us was to show we had capacity,” explains Lee Sinclair, product director of the Australian-based company, which also has developed CelebTweety Social to connect users to information about their favorite celebrities, and Chat Search for Skype for mining conversation histories on that platform, based on its SSI technology. The basic concept behind Semantic Social Intelligence is something that the company internally calls the “.Me” to filter the world for the individual user.

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Coming up now is a complete integration of the NetBase technology into SAP’s Social On Demand customer relationship management (CRM) console. “Having access to social data is becoming critical to every part of the organization,” says NetBase chief marketing officer Lisa Joy Rosner. So, “social media [becomes] just one more data point” for which the enterprise must account.

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While sentiment analysis continues to generate a lot of press, it is not clear how much real value organizations are deriving from it.  One reason for that is that the standard approach to sentiment has been mostly statistical and/or long lists of sentiment terms.  However, if you add in other, advanced text analytics capabilities such as auto-categorization using advanced operators, you can not only develop more sophisticated sentiment analysis, you can also develop a whole new class of applications that either enhance and/or go beyond simple sentiment analysis.

These advanced operators include such commands as DEST_6 (count two words as a positive indicator only if they are with 6 words of each other) or SENT (only count words in the same sentence).

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The company today launches its Tautona natural language processing platform into the CRM space. The platform for searching structured and unstructured data in such systems is described as using deep linguistic analysis and reasoning technology to extract meaning from language to understand stored information. And via the web-based system that can run on their desktops, tablets or smart phones, users can query these databases in natural language in order to fulfill complex search requests. They can do it across languages, too, which can be important to organizations with global salesforces.

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Aditya Kalyanpur, research staff member for IBM Research will lead a session entitled “Building Watson: An Overview of the DeepQA Project for the Jeopardy! Challenge,” a discussion of the the DeepQA technology and describe what it was like to build a Watson, the computer system that won on Jeopardy!.

“The rise of the Interest Graph: How Semantic Technology Will Lead What’s Next for the Social Web.” Dave S Copps, CEO of PureDiscovery Corporation, will discuss the reasons why transactional searches will be replaced by social filtering. The real power and potential of semantic technologies will be unleashed as semantic vendors integrate the richness of the data being generated by the social graph (Twitter, Facebook, etc.) to create networks that share more than just a relationship.

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Fellow Consulting AG and NetBreeze GmbH integrate Web Monitoring in the CRM
PR-USA.net (press release)
The Fellow Consulting AG, CRM solutions provider with headquarters in Munich, in cooperation with NetBreeze, technology pioneer for semantic web analytics …

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PR-inside.com (press release)

Fellow Consulting AG and NetBreeze GmbH integrate Web Monitoring in the CRM
PR-inside.com (press release)
Semantic technologies help to make the huge amount of unstructured information usable. Especially in the CRM environment, the comprehensive knowledge of …

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Tony Shaw: Hi Olly, so to get us started, could you give me a high-level overview of what Atigeo does?

Olly Downs: Absolutely. Atigeo’s platform, xPatterns, enables enterprises to derive insights from large, disparate sources of unstructured data. In doing so, we’ve taken two approaches toward how our platform is productized. The first approach is around aspects of our core technology, which allows us to build simple ontologies for domains of unstructured data and then act upon the understanding of the data – in response to queries or profiles of entities, for example. The second aspect of our product enables enterprises to provide customers the ability to access and manage their profile or persona.

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Destination CRM

Attensity Buys Biz360 for Social Media Monitoring
Destination CRM
At Attensity’s core is semantic technology that intersects with business processes, such as customer service and customer feedback. By bringing social media …

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Introduction to the AXIS-CRM and to its implementation

1. The AXIS-Conceptual Reference Model

1.1 Generalities

A new modular and tailorable approach for the semantic modeling of static and dynamic knowledge has been elaborated under the name “AXIS Conceptual Reference Model” (AXIS-CRM). AXIS organizes that modeling as networks of Autonomous Semantic Objects (ASO). In turn, each ASO is expressed as a network of Elementary Semantic Entities (ESE). The ASO wraps the instances and their models to becomes ‘autonomous’. At Elementary Semantic Entity level (simply called ‘Entities’) the modeling uses four leveled constructors: Term; Document; Relation; Profile. The knowledge models and their instances are represented by a collection of Documents (among with the OWL files expressing the models) bundled by a Configuration Management Document (based on RDF). These collections are semantic Entities that can represent any topical subject or object. These Entities are linked through typed Relations. The dynamic aspects (events) and the imports / exports are also managed by dedicated Entities.

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Better text mining makes it possible to connect information in a variety of sources. The technology can connect information in CRM databases with consumer e-mails and help desk reports to provide a more complete view of the customer. Text mining can also be used in national security applications to better identify terrorists and security threats; it can assist in marketing to mine reviews for feedback on products such as movies, books and music. It can help in scientific research by providing a way to better connect scientific articles.

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The TextWise Semantic Cloud provides a service for enterprises to retrieve real-time, highly relevant matches within their own private and secure dynamic content collections.

Using Semantic Signatures® a whole new class of search and matching applications is possible using documents as exemplars for finding very similar documents. TextWise has created one such application – Similarity Search and Matching (SSM). SSM enables fast and accurate information discovery for unstructured text.

Semantic Signatures® are customizable for any domain or content type, from blogs and online advertising to patent or pharmaceutical verticals, and new languages. Signatures quickly adjust to chromosomal variances among domains, languages, and collections. Signatures facilitate application development for a wide range of applications from social CRM, to difficult verticals such as patent lead generation and medical/pharma SSM.

“Semantic Signatures® are a big leap forward for jumpstarting wider adoption of semantic applications. The Signatures serve as a bridge between keyword search and entity extraction to the promise of scalable RDF-based inference systems that won’t deliver for a few years” said Connie Kenneally, TextWise CEO. “Now, companies can start making semantics a reality by utilizing automated concept-encoded text, a document’s digital DNA, to facilitate relevant retrieval of information with large volumes of unstructured text.”

SemanticHacker API for Testing and Developing:

A free version of SSM is available on the TextWise API site www.semantichacker.com.

Developers can organize and match their content against our public content collections of news, blog articles, Wikipedia articles and images, Flickr images and YouTube videos.

Web publishers can experience the power of Semantic Signatures® immediately with our Similarity Search widget or WordPress plug-in also available on our API.

Enterprises can place their own content in our semantic cloud to facilitate SSM against their own content.

To quickly experience the power of SSM in action, try our hands-on demo or view the video at www.textwise.com.

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SAN FRANCISCO, CA – June 15, 2009 – zAgile, Inc., the commercial open source leader in information collaboration, today announced the general availability of Wikidsmart v1.1 Professional. Wikidsmart’s groundbreaking semantic wiki features for Atlassian’s Confluence enterprise wiki, and soon for MindTouch, enable users to:

• enter content in a consistent and contextually organized fashion;
• generate pages of inferred wiki content; automate wiki page and link maintenance; and
• easily find precise information with context-sensitive navigation and search.

Information collaboration is the deep integration of information across teams, tools, and applications, with a common semantic web based infrastructure, enabling comprehensive organization-wide collaboration. The zAgile infrastructure provides a foundation for information integration with other applications in the enterprise.

Although wikis are extremely popular for collaboration, users struggle with organizing, maintaining, and finding content, as well as entering data in a consistent way that captures meaningful information. And users waste time maintaining links manually which often cause wrong or outdated information. These inefficiencies and inconsistencies create bottlenecks for users to collaborate quickly and easily. Wikidsmart removes these bottlenecks by enabling users to semantically capture content.

Content may be entered in a consistent fashion via semantically annotated forms. Annotation captures properties, behaviors, and relationships of content. Information may be retrieved by contextually navigating through topics or simple context-sensitive searches. Inferred content may be generated automatically with simple embedded queries.

Before Wikidsmart, no way existed to deeply integrate information across systems and expose the information within the wiki. Plug-ins attempt to integrate data silos but do not provide a comprehensive semantic foundation. And other semantic wikis are limited to content within the wiki itself.

zAgile’s infrastructure, driven by ontologies that describe the artifacts, relationships, and behaviors for a domain, enables rich integration with other systems based on open semantic web standards. Extensible ontologies for the software engineering domain are included, and zAgile provides professional services for ontology development for other domains including CRM and ERP. Other ontologies from third party providers may be used as well.

Connectors are currently available for Jira, Subversion, and Perforce, and others will be added by zAgile and its open source community.

Product Technical Specifications

• Support for ontologies based on OWL-DL
• Ontology import/export tools to implement domain-specific or customized ontologies
• Built-in reasoner
• Customizable wiki forms built from macro-based templates for capturing content semantics. Templates derive from the underlying ontologies and support attribute data type and cardinality.
• The templates support mixing of both semantic and unstructured content on a page.
• Templates can be nested and support the instantiation of multiple concepts per page
• Smart Search leverages the ontologies to search and retrieve concept instances and their attributes. Semantic navigation in the result set allows for contextual traversal across the information network
• Auto-generated properties page for each instance displays values associated with object and data properties
• Customizable concept-specific templates for default rendering of instances based on their defined concept
• Embedded query support via macros on any page, using SPARQL syntax
• zCALM infrastructure separates the semantic store from the wiki to support federated semantic repositories
• XML/RPC-based API for access to semantic repository from any application, allowing wiki semantic content to be accessible via external applications

Availability and Pricing
Wikidsmart Professional is available for evaluation at www.zagile.com. A hosted “sandbox” allows users to test-drive a sample Wikidsmart v1.1 for Confluence via the web without installing products (and integration with JIRA included). Pricing starts at $4,000 for 25 users.
 

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