AlchemyAPI has created the first text mining service to extract the authors of articles online. According to the article, “In an attempt to navigate what the New York Times calls the ‘Age of Big Data,’ companies often turn to social media engagement platforms to analyze thousands of blog posts and articles. Until now, these platforms have only been able to automatically provide results by media source, not by author. However, AlchemyAPI recently developed the first text mining service able to isolate the author of a blog or news post — at a rate of one thousand posts per second.” Read more

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 !

Wikimeta, the semantic tagging and annotation architecture for incorporating semantic knowledge within documents, websites, content management systems, blogs and applications, this month is incorporating itself as a company called Wikimeta Technologies.  Wikimeta, which has a heritage linked with the NLGbAse project, last year was provided as its own web service.

Dr. Eric Charton, Ph.D, MSc at École Polytechnique de Montréal, is project leader and author of the Wikimeta code. The NLGbAse project was conducted by Charton at the University of Avignon as part of his Ph.D. Thesis.  The Semantic Web Blog recently hosted an email discussion with him to learn more about the Wikimeta architecture and its evolution.

 

The Semantic Web Blog: Tell us about the NLGBase project and Wikimeta’s relationship to it.

Charton: NLGbAse is an ontology extracted from Wikipedia. It is used in Wikimeta as a resource for semantic disambiguation. For each Wikipedia document (aka Semantic Concept), NLGbAse provides various ways of word-writing (for example, “General Motors” can be written “GM Company”, “GM”, “General Motors Corp” and so on), used for detection.

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Selventa and Linguamatics have announced a strategic partnership to provide a “complete pipeline of scientific knowledge extraction to their life science partners.” According to the article, “The alliance will bring together established analytical capabilities of both companies to efficiently extract complex life science knowledge in a computable, structured, biological expression language (BEL) format that can be used to interpret large-scale experimental data in the context of published literature.” Read more

Since Nstein was acquired by OpenText a little over a year ago, work has been underway to build the former’s semantic technology for text mining and analytics and search into the latter’s enterprise content management platform. So far, that’s resulted in adding Semantic Navigation, the on-premise or cloud web site search and content discovery solution, to OpenText’s Web content management (WCM) products, such as OpenText Web Experience Management and Web Site Management.

This covers aspects such as content tagging and semantic faceting at the content and document levels. This year and the following should see further integration of Nstein technologies into the OpenText solutions set, as well as some new offerings emerging to support other use cases.

As an example, the company is working on a listening platform application, drawing on work Nstein had done for the Canadian government’s public health agency that used its Text Mining Engine to identify potential threats to human health by scouring multiple sources — including news aggregators like Factiva – that were parsed for about 1,000 or so concepts such as “mysterious ailments” and “outbreak.” It’s building up a framework for ingesting different data sources to support this, says Charles-Olivier Simard, product manager for semantic technologies at OpenText.

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A recent article highlights Syllabs, “a web service built to apply semantic analysis to website text in a few extremely useful ways. The Syllabs API can detect the language of text, detect which ‘named entities’ a block of text mentions (people, businesses) and find related keywords to any keyword. When you hear about the semantic web, this is the sort of API driving that future.” Read more

Collective Intellect is helping businesses learn more about the wants and needs of their customers by utilizing the piles of data that are already available to companies. Collective Intellect makes use of sentiment analysis in their innovative text mining software. The software “scrutinizes posts on Twitter, Facebook, MySpace, blogs, and message boards to help businesses learn what’s being said about their products. The company’s software uses a suite of algorithms to scan text and work out what a person is referring to and with what emotional tone.”

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Come tomorrow, you may be looking for a chance to do some relaxing reading after the Thanksgiving football and food fest and the Black Friday shopping chaos.

To that end, The Semantic Web Blog thought it would offer some guidance on a few analyst reports and surveys that you might enjoy spending some time with – and find useful for your business efforts, especially as they relate to how you can help drive value by better engaging with the exploding world of social media  – for your weekend reading:

 

•  Gleansight: Social Media Monitoring: A new analyst firm, Gleanster LLC, has produced a new report on what companies are learning about the emerging area of mining social media content as they attempt to better understand the voice of the customer to improve marketing and consumer satisfaction and reduce research and support costs, among other things. That includes, of course, using text mining apps to identify topics and using semantic technologies to classify comments by positive or negative sentiment. The report focuses on the challenges — the technological ones, of course, such as finding the valuable nuggets in the volume of data.

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The evolution of the semantic web brings new possibilities to handle the information overload. This paper focuses on the motivation to integrate two applications and the usage of semantic content for information managers. Analysts and executives need to understand today the value of semantic technologies in relation to business intelligence and decision making support. There are a number of mature semantic technology categories including automatic annotation, information extraction techniques, text mining and semantic navigation and search within the content.

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Entity Extraction is the process of automatically extracting document metadata from unstructured text documents.  Extracting key entities such as person names, locations, dates, specialized terms and product terminology from free-form text can empower organizations to not only improve keyword search but also open the door to semantic search, faceted search and document repurposing.  This article defines the field of entity extraction, shows some of the technical challenges involved, and shows how RDF can be used to store document annotations. It then shows how new tools such as Apache UIMA are poised to make entity extraction much more cost effective to an organization.

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