Did you know that more U.S. patents were granted last year than for any previous year in the past forty-odd years, with the exception of 2006? So reports Dr. Mark J. Perry, professor of economics and finance in the School of Management at the Flint campus of the University of Michigan, here. That added a heap of patents – near to some 200,000 – to an already full plate that organizations often have to dig into, whether to identify patent infringers, to sell off some IP, to improve their existing portfolios with additional IP from other sources, or even to explore whether one of their own innovations is worth patenting.

Just as those patent grants surged in 2009, TextWise introduced as part of its semantic similarity matching API a service to help businesses expedite U.S. patent searching. That semantic search capability now is included as part of Innography’s IP business intelligence system for managing, protecting and getting ROI out of patents.

The semantic service Innography has partnered up with aims at helping the general population of business users – not just high-priced expert patent searchers – get “more like this” matches to generate good results without having to run iterative searches, Textwise president Connie Kenneally explains.

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SemTechBiz is Less Than 2 Weeks Away

The Semantic Tech & Business Conference (SemTechBiz) is coming to San Francisco on June 3-7! Join us for case studies, innovative panels, tutorials, and keynotes that will provide you with practical advice, hands-on guidance, and breakthrough approaches to solving business problems with semantic technology. Passes go up $200 at the door. Sign up now and save !

MODERATOR:
Wen Ruan, TextWise

PANELISTS:
Ronald M. Kaplan, Powerset division of bing
Christian F. Hempelmann, RiverGlass, Inc.
Riza Berkan, hakia

Semantic Search technology in the Semantic Web community is often understood as retrieval of knowledge from tagged data such as RDF sources, which require substantial formatting and markup to realize. Understanding unstructured query and document text and conducting searches according to their meaning is another approach, exemplified by linguistically rooted semantic matching, ontological knowledge-based semantic interpretation, and statistically based semantic similarity search.

This panel will look at different ways to tackle semantic search as a problem of text understanding. Powerset division of bing’s natural-language processing engine does deep syntactical analysis to determine the meaning of a query or a sentence. Hakia relies on a language-independent ontology model and an ontology-based English lexicon to translate text into a representation of its meaning. RiverGlass has developed an ontological semantic approach to search and text analytics, emphasizing the in-context, linguistic meaning of textual content in order to return truly relevant results in response to information requests. TextWise’s Semantic Signature matching looks for similarities between a query and text at the topic level.

Semantic Search Beyond RDF from Semantic Universe on Vimeo.

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.