81,495 research outputs found

    Smart Search: A Firefox Add-On to Compute a Web Traffic Ranking

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    Search engines results are typically ordered according to some notion of importance of a web page as well as relevance of the content of a web page to a query. Web page importance is usually calculated based on some graph theoretic properties of the web. Another common technique to measure page importance is to make use of the traffic that goes to a particular web page as measured by a browser toolbar. Currently, there are some traffic ranking tools available like www.alexa.com, www.ranking.com, www.compete.com that give such analytic as to the number of users who visit a web site. Alexa provides the traffic rank for a website based on two factors: The number of users that view a website and the number of pages viewed. The Alexa toolbar is not open-source.The main goal of our project was to create a Smart Search Firefox add-on for the Yioop search engine, an open source search engine developed by my project advisor, Dr. Chris Pollett. This add-on would provide similar analytic data to the Yioop search engine, but in a transparent and open-source way. With the results received from the Smart Search toolbar extension, the Yioop search engine refines the search results as well as provides user centric-search results. Eventually, users would benefit from these better search results

    Knowledge Representation with Ontologies: The Present and Future

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    Recently, we have seen an explosion of interest in ontologies as artifacts to represent human knowledge and as critical components in knowledge management, the semantic Web, business-to-business applications, and several other application areas. Various research communities commonly assume that ontologies are the appropriate modeling structure for representing knowledge. However, little discussion has occurred regarding the actual range of knowledge an ontology can successfully represent

    CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

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    After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

    XRay: Enhancing the Web's Transparency with Differential Correlation

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    Today's Web services - such as Google, Amazon, and Facebook - leverage user data for varied purposes, including personalizing recommendations, targeting advertisements, and adjusting prices. At present, users have little insight into how their data is being used. Hence, they cannot make informed choices about the services they choose. To increase transparency, we developed XRay, the first fine-grained, robust, and scalable personal data tracking system for the Web. XRay predicts which data in an arbitrary Web account (such as emails, searches, or viewed products) is being used to target which outputs (such as ads, recommended products, or prices). XRay's core functions are service agnostic and easy to instantiate for new services, and they can track data within and across services. To make predictions independent of the audited service, XRay relies on the following insight: by comparing outputs from different accounts with similar, but not identical, subsets of data, one can pinpoint targeting through correlation. We show both theoretically, and through experiments on Gmail, Amazon, and YouTube, that XRay achieves high precision and recall by correlating data from a surprisingly small number of extra accounts.Comment: Extended version of a paper presented at the 23rd USENIX Security Symposium (USENIX Security 14

    The Latent Relation Mapping Engine: Algorithm and Experiments

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    Many AI researchers and cognitive scientists have argued that analogy is the core of cognition. The most influential work on computational modeling of analogy-making is Structure Mapping Theory (SMT) and its implementation in the Structure Mapping Engine (SME). A limitation of SME is the requirement for complex hand-coded representations. We introduce the Latent Relation Mapping Engine (LRME), which combines ideas from SME and Latent Relational Analysis (LRA) in order to remove the requirement for hand-coded representations. LRME builds analogical mappings between lists of words, using a large corpus of raw text to automatically discover the semantic relations among the words. We evaluate LRME on a set of twenty analogical mapping problems, ten based on scientific analogies and ten based on common metaphors. LRME achieves human-level performance on the twenty problems. We compare LRME with a variety of alternative approaches and find that they are not able to reach the same level of performance.Comment: related work available at http://purl.org/peter.turney

    City networks in cyberspace and time : using Google hyperlinks to measure global economic and environmental crises

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    Geographers and social scientists have long been interested in ranking and classifying the cities of the world. The cutting edge of this research is characterized by a recognition of the crucial importance of information and, specifically, ICTs to cities’ positions in the current Knowledge Economy. This chapter builds on recent “cyberspace” analyses of the global urban system by arguing for, and demonstrating empirically, the value of Web search engine data as a means of understanding cities as situated within, and constituted by, flows of digital information. To this end, we show how the Google search engine can be used to specify a dynamic, informational classification of North American cities based on both the production and the consumption of Web information about two prominent current issues global in scope: the global financial crisis, and global climate change
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