Posts Tagged ‘keyword extraction’

Studio Ousia Envisions A World Of Semantic Augmented Reality

Image courtesy: Flickr/by Filter Forge

Image courtesy: Flickr/by Filter Forge

Ikuya Yamada, co-founder and CTO of Studio Ousia, the company behind Linkify – the technology to automatically extract certain keywords and add intelligent hyperlinks to them to accelerate mobile search – recently sat down with The Semantic Web Blog to discuss the company’s work, including its vision of Semantic AR (augmented reality).

The Semantic Web Blog: You spoke at last year’s SEEDS Conference on the subject of linking things and information and the vision of Semantic AR, which includes the idea of delivering additional information to users before they even launch a search for it. Explain your technology’s relation to that vision of finding and delivering the information users need while they are consuming content – even just looking at a word.

Yamada: The main focus of our technology is extracting accurately only a small amount of interesting keywords from text [around people, places, or things]. …We also develop a content matching system that matches those keywords with other content on the web – like a singer [keyword] with a song or a location [keyword] with a map. By combining keyword extraction and the content matching engine, we can augment text using information on the web.

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Scope Launches New Version of ConSCIse

Scope eKnowledge Center has announced ” the launch of Version 1.3 of its unique ConSCIse solution for generating abstracts and keywords for scholarly literature such as books, book chapters, journal articles, patents, conference proceedings, clinical summaries and other documents. ConSCIse automatically extracts keywords from the document based on sophisticated and appropriate statistical and linguistic rules. Based on the extracted keywords, ConSCIse then extracts sentences from the document to form the abstract. This proprietary technology has been developed based on an analysis of the common traits of effective abstracts of unstructured documents from Scope’s extensive experience.” Read more

SemanticWeb.com “Innovation Spotlight” Interview with Elliot Turner, CEO of AlchemyAPI.

If you would like your company to be considered for an interview please email editor[ at ]semanticweb[ dot ]com.

In this segment of our “Innovation Spotlight” we spoke with Elliot Turner (@eturner303), the founder and CEO of AlchemyAPI.com. AlchemyAPI’s cloud-based platform processes around 2.5 billion requests per month. Elliot describes how their API helps companies with sentiment analysis, entity extraction, linked data, text mining, and keyword extraction.

Sean: Hi Elliot, thanks for joining us, how did AlchemyAPI get started?

Elliot: AlchemyAPI was founded in 2005 and in the past seven years has become one of the most widely used semantic analysis APIs, processing billions of transactions monthly for customers across dozens of countries.

I am the Founder and CEO and a serial entrepreneur who comes from the information security space.  My previous company built and sold high-speed network security appliances. After it was acquired, I started AlchemyAPI to focus on the problem of understanding natural human language and written communications.

Sean: Can you describe how your API works? What does it allow your customers to accomplish?

Elliot: Customers submit content via a cloud-based API, and AlchemyAPI analyzes that information in real-time, transforming opaque blobs of text into structured data that can be used to drive a number of business functions. The service is capable of processing thousands of customer transactions every second, enabling our customers to perform large-scale text analysis and content analytics without significant capital investment.

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AKSW Announces Federated Knowledge Extraction

AKSW is back in the news after announcing “the first version of the Federated knOwledge eXtraction (FOX) framework. FOX integrates and merges the results of frameworks for Named Entity Recognition, Keyword/Keyphrase Extraction and Relation Extraction by using machine learning techniques. By these means, FOX can generate RDF out of natural language with improved accuracy. FOX has been shown to be up to 15% more accurate than other frameworks, including commercial software.” Read more