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.

Read more

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.

Read more

ROCHESTER, NY – June 16th, 2009. Forget about trying to find text by simply using keywords or entities. Today at the Semantic Technology Conference, TextWise announces the release of their Semantic Cloud providing companies the ability to mine content using digital DNA – Semantic Signatures®. Semantic Signatures® represent a deeper meaning from text than either keywords or entities can provide. Semantic Signatures® encode the meaning of text by creating a unique ‘signature’ for every document providing superior retrieval and categorization of information without the ambiguity of keyword or entity services.

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.

Includes Open Source Semantic Web Infrastructure for Structured, Information-Rich Collaboration for the Enterprise

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.
 

With the Web 2.0, ontologies are being used to improve search capabilities and make inferences for improved human or computer reasoning. By relating terms in an ontology, the user doesn’t need to know the exact term actually stored in the document. Data Rationalization is a Managed Meta Data Environment (MME) enabled application which creates/extends an ontology for a domain into the structured data world, based on model objects stored in various models (of varying levels of detail, across model files and modeling tools) and other meta data. Ontology is “the study of the categories of things that exist or may exist in some domain”1. An ontology is comprised of “a collection of taxonomies and thesauri”2 about a domain. Data Models, often unknowingly, express many aspects of ontology, even though they are not stored in OWL or RDF.

Read more

Controlled vocabularies, taxonomies and thesauri have been in use in a wide variety of organizations for decades. With the information explosion fueled by the internet, the importance of these organization structures has become more and more apparent. The problem isn’t where to find vocabularies or how to build them; on the contrary, enterprises typically find that they have several mini-vocabularies, each tuned to a special purpose or business need, just as so-called "folksonomies" have appeared in popular websites. The problem enterprises are facing today is how to manage all these vocabularies in a coherent way and eventually to integrate them so that they can make cross-references from one to another.

Read more

I should explain first, that In addition to my role at Semantic Universe, I oversee the annual educational conference on enterprise data management (EDM), called Enterprise Data World. About 900 enterprise data people will be converging on Tampa for the next event on April 5-9, 2009.

Read more

Executive Summary

SeMuSe is an open and collaborative community based project to work on a Semantic Museum vision, and provides a forum for discussion of the future of applied cultural and natural heritage data management. Members of SeMuSe can greatly benefit from advancements made in the Semantic Technology community. The goal of SeMuSe is to help organizations and practitioners to introduce Semantic Technologies and concepts to cultural and natural heritage data management efforts and to capitalize on the results of more than a decade of Semantic Technology research. Emerging technology standards like RDF, RDFS and OWL and domain specific vocabularies such as museumdat and the CIDOC CRM ontology specification are a marriage made in Semantic Technology heaven, allowing to lead semantic cultural and natural heritage data management to its full potential – SeMuSe.

Read more