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128 posts tagged Google
Google Tech Talks May, 23 2008 ABSTRACT Overview: Mark Birbeck has spent a number of years working on flexible user interfaces, both by developing software and working with the W3C on new standards. His latest work involves creating an Ajax framework that uses metadata embedded in HTML documents to drive dynamic user interfaces. The framework makes it easy for authors to build interactive sites, whilst still creating accessible, searchable documents. In this talk Mark will look at how embedded metadata can be used by anyone from scientific researchers to bloggers, through news organisations to photographers, to improve how their pages are understand and interacted with. Speaker: Mark Birbeck Mark Birbeck devised RDFa, a new standard from the W3C that allows metadata to be embedded in HTML and XHTML documents, rather than being stored separately. Web pages enriched in this way provide more accurate information for use in search engines, as well as creating enormous potential for building a new generation of interactive tools for the end-user. Mark is also involved in the XForms Working Group and the XHTML 2 Working Group, has contributed to books on XML and RDF, blogs regularly about XForms, the semantic web, and RIAs, and his company, webBackplane develops a range of open source software for semantic-driven user interfaces. His profile is at webBackplane.com
Google Tech Talks May 25, 2007 ABSTRACT The Semantic Web is a field aiming a the creation, deployment, and interoperation of machine readable data on the Internet. In the talk we present some projects in DERI on Semantic Web technologies - notably Semantic Interlinking of Online Community sites, Social Semantic Collaborative Filtering, and ActiveRDF, a library for Browsing, programming and navigating Semantic Web data. The SIOC (Semantic Interlinking of Online Communities) project [1] is an effort aiming at establishing and deploying a metadata vocabulary for interlinking and connecting distributed conversation on blogs, bulletin boards, and mailing lists. The vocabulary has been implemented…
Google Tech Talks November 11, 2008 ABSTRACT Berners-Lee’s vision of the Semantic Web is hindered by a chicken-and-egg problem, which can be best solved by a bootstrapping method: creating enough structured data to motivate the development of applications. We believe that autonomously `Semantifying Wikipedia’ is the best way to bootstrap. We choose Wikipedia as an initial data source, because it is comprehensive, high-quality, modestly sized, and contains enough manually-derived structure to bootstrap an autonomous, self-supervised process. In this talk I will present our success to date in this endeavor: A novel approach for self-supervised learning of CRF information extractors Automatic construction of a comprehensive ontology via statistical-relational learning Vast improvements in extraction recall through shrinkage over this ontology and retraining The stimulation of a virtuous feedback cycle between communal content creation and information extraction We aim to construct a knowledge base of outstanding size to support inference, automatic question answering, faceted browsing, and potentially to bootstrap the Semantic Web. Speaker: Daniel S. Weld Daniel S. Weld is Thomas J. Cable / WRF Professor of Computer Science and Engineering at the University of Washington. After formative education at Phillips Academy, he received bachelor’s degrees in both Computer Science and Biochemistry at Yale University in 1982. He landed a Ph.D. from the MIT Artificial Intelligence Lab …
Google Tech Talks November 11, 2008 ABSTRACT Berners-Lee’s vision of the Semantic Web is hindered by a chicken-and-egg problem, which can be best solved by a bootstrapping method: creating enough structured data to motivate the development of applications. We believe that autonomously `Semantifying Wikipedia’ is the best way to bootstrap. We choose Wikipedia as an initial data source, because it is comprehensive, high-quality, modestly sized, and contains enough manually-derived structure to bootstrap an autonomous, self-supervised process. In this talk I will present our success to date in this endeavor: A novel approach for self-supervised learning of CRF information extractors Automatic construction of a comprehensive ontology via statistical-relational learning Vast improvements in extraction recall through shrinkage over this ontology and retraining The stimulation of a virtuous feedback cycle between communal content creation and information extraction We aim to construct a knowledge base of outstanding size to support inference, automatic question answering, faceted browsing, and potentially to bootstrap the Semantic Web. Speaker: Daniel S. Weld Daniel S. Weld is Thomas J. Cable / WRF Professor of Computer Science and Engineering at the University of Washington. After formative education at Phillips Academy, he received bachelor’s degrees in both Computer Science and Biochemistry at Yale University in 1982. He landed a Ph.D. from the MIT Artificial Intelligence Lab …
Google Tech Talks March, 25 2008 ABSTRACT This talk is about discovering and modeling previously unspecified, recurring themes in a given set of arbitrary images. Given a set of images, each containing frequent occurrences of objects from multiple categories, the goal is to learn a compact model of the categories as well as their relationships, for the purposes of later recognizing/segmenting any occurrences in new images. Categories are not defined by the user. Also, whether and where instances of any categories appear in a specific image is not known. This problem is challenging, since it involves the following unanswered questions. What is an object category? What image properties should be used and how to combine them to discover category occurrences? What is an efficient multicategory representation? We will examine a methodology, developed during my postdoctoral work at UIUC. Each image is represented by a segmentation tree whose nodes correspond to image regions, segmented at all natural scales present, and edges between tree nodes capture the region embedding. The presence of any categories in the image set is then reflected in the frequent occurrence of similar subtrees within the segmentation trees. Our methodology is designed to: (1) match image trees to find similar subtrees; (2) discover categories by clustering similar subtrees, and use the properties of each cluster to learn the model of the associated category; and (3) learn the grammar of the discovered …
Google Tech Talks June, 26 2008 ABSTRACT The Semantic Web presents the vision of a distributed, dynamically growing knowledge base founded on formal logic. Common users, however, seem to have problems even with the simplest Boolean expression. So how can we help users to query a web of logic that they do not seem to understand? One frequently proposed solution to address this problem is the use of natural language (NL) for knowledge specification and querying. We propose to regard formal query languages and NL as two extremes of a continuum, where semistructured languages lie somewhere in the middle. To evaluate what degree of structuredness casual users prefer, we introduce four query interfaces, each at a different point in the continuum, and evaluate the users’ preference and their query performance in a study with 48 subjects. The results of the study reveal that while the users dislike the constraints of a fully structured formal query language they also seem at a loss with the freedom of a full NLP approach. This suggests that restricted query languages will be preferred by casual users because of their guidance effect, mirroring findings from social science theory on human activity in general. Speaker: Prof. Bernstein Abraham Bernstein is a full Professor at the Department of Information Technology (Institut für Informatik) of the University of Zurich. He conducts research on various aspects of supporting dynamic (intra- and inter-) organizational processes. His …
Demo video of True Knowledge’s (www.trueknowledge.com) ‘Google Enhancer’ firefox plugin (now also available for Internet Explorer). This product uses True Knowledge’s semantic question-answering technology to improve your search results with direct responses to what you are looking for.
“Why We Desperately Need a New (and Better) Google | Socialnews.biz | Semantic RSS/Atom Business News Reader http://t.co/AZ8qVo6”
socialnewsbiz (socialnews.biz)
