Search (51 results, page 2 of 3)

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Date

17.12.2013 11:02:22

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Source

The Semantic Web - ISWC 2010. 9th International Semantic Web Conference, ISWC 2010, Shanghai, China, November 7-11, 2010, Revised Selected Papers, Part I. Eds.: Peter F. Patel-Schneider et al

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Abstract

In recent years, quantum-based methods have promisingly integrated the traditional procedures in information retrieval (IR) and natural language processing (NLP). Inspired by our research on the identification and application of quantum structures in cognition, more specifically our work on the representation of concepts and their combinations, we put forward a 'quantum meaning based' framework for structured query retrieval in text corpora and standardized testing corpora. This scheme for IR rests on considering as basic notions, (i) 'entities of meaning', e.g., concepts and their combinations and (ii) traces of such entities of meaning, which is how documents are considered in this approach. The meaning content of these 'entities of meaning' is reconstructed by solving an 'inverse problem' in the quantum formalism, consisting of reconstructing the full states of the entities of meaning from their collapsed states identified as traces in relevant documents. The advantages with respect to traditional approaches, such as Latent Semantic Analysis (LSA), are discussed by means of concrete examples.

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Abstract

If you're reading the Moz blog, then you probably have a decent understanding of Google and its algorithm changes. However, there is probably a good percentage of the Moz audience that is still confused about the effects that Panda, Penguin, and Hummingbird can have on your site. I did write a post last year about the main differences between Penguin and a Manual Unnautral Links Penalty, and if you haven't read that, it'll give you a good primer. The point of this article is to explain very simply what each of these algorithms are meant to do. It is hopefully a good reference that you can point your clients to if you want to explain an algorithm change and not overwhelm them with technical details about 301s, canonicals, crawl errors, and other confusing SEO terminologies.

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Location

I

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Abstract

The Drawings of The Florentine Painters project created the first online database of Florentine Renaissance drawings by applying Linked Open Data (LOD) techniques to a foundational text of the same name, first published by Bernard Berenson in 1903 (revised and expanded editions, 1938 and 1961). The goal was to make Berenson's catalog information-still an essential information resource today-available in a machine-readable format, allowing researchers to access the source content through open data services. This paper provides a technical overview of the methods and processes applied in the conversion of Berenson's catalog to LOD using the CIDOC-CRM ontology; it also discusses the different phases of the project, focusing on the challenges and issues of data transformation and publishing. The project was funded by the Samuel H. Kress Foundation and organized by Villa I Tatti, The Harvard University Center for Italian Renaissance Studies. Catalog: http://florentinedrawings.itatti.harvard.edu. Data Endpoint: http://data.itatti.harvard.edu.

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Content

Editorial: Surfing versus Drilling for Knowledge in Science: When should you use your computer? When should you use your brain? Blaise Pascal: Les deux infinis - The two infinities / Philippe Hünenberger and Oliver Renn - "Surfing" vs. "drilling" in the modern scientific world / Antonio Loprieno - Of millimeter paper and machine learning / Philippe Hünenberger - From one to many, from breadth to depth - industrializing research / Janne Soetbeer - "Deep drilling" requires "surfing" / Gerd Folkers and Laura Folkers - Surfing vs. drilling in science: A delicate balance / Alzbeta Kubincová - Digital trends in academia - for the sake of critical thinking or comfort? / Leif-Thore Deck - I diagnose, therefore I am a Doctor? Will drilling computer software replace human doctors in the future? / Yi Zheng - Surfing versus drilling in fundamental research / Wilfred van Gunsteren - Using brain vs. brute force in computational studies of biological systems / Arieh Warshel - Laboratory literature boards in the digital age / Jeffrey Bode - Research strategies in computational chemistry / Sereina Riniker - Surfing on the hype waves or drilling deep for knowledge? A perspective from industry / Nadine Schneider and Nikolaus Stiefl - The use and purpose of articles and scientists / Philip Mark Lund - Can you look at papers like artwork? / Oliver Renn - Dynamite fishing in the data swamp / Frank Perabo 34 Streetlights, augmented intelligence, and information discovery / Jeffrey Saffer and Vicki Burnett - "Yes Dave. Happy to do that for you." Why AI, machine learning, and blockchain will lead to deeper "drilling" / Michiel Kolman and Sjors de Heuvel - Trends in scientific document search ( Stefan Geißler - Power tools for text mining / Jane Reed 42 Publishing and patenting: Navigating the differences to ensure search success / Paul Peters

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Content

"Until now, so-called computer vision has largely been limited to recognizing individual objects. The new software, described on Monday by researchers at Google and at Stanford University, teaches itself to identify entire scenes: a group of young men playing Frisbee, for example, or a herd of elephants marching on a grassy plain. The software then writes a caption in English describing the picture. Compared with human observations, the researchers found, the computer-written descriptions are surprisingly accurate. The advances may make it possible to better catalog and search for the billions of images and hours of video available online, which are often poorly described and archived. At the moment, search engines like Google rely largely on written language accompanying an image or video to ascertain what it contains. "I consider the pixel data in images and video to be the dark matter of the Internet," said Fei-Fei Li, director of the Stanford Artificial Intelligence Laboratory, who led the research with Andrej Karpathy, a graduate student. "We are now starting to illuminate it." Dr. Li and Mr. Karpathy published their research as a Stanford University technical report. The Google team published their paper on arXiv.org, an open source site hosted by Cornell University.
In living organisms, webs of neurons in the brain vastly outperform even the best computer-based networks in perception and pattern recognition. But by adopting some of the same architecture, computers are catching up, learning to identify patterns in speech and imagery with increasing accuracy. The advances are apparent to consumers who use Apple's Siri personal assistant, for example, or Google's image search. Both groups of researchers employed similar approaches, weaving together two types of neural networks, one focused on recognizing images and the other on human language. In both cases the researchers trained the software with relatively small sets of digital images that had been annotated with descriptive sentences by humans. After the software programs "learned" to see patterns in the pictures and description, the researchers turned them on previously unseen images. The programs were able to identify objects and actions with roughly double the accuracy of earlier efforts, although still nowhere near human perception capabilities. "I was amazed that even with the small amount of training data that we were able to do so well," said Oriol Vinyals, a Google computer scientist who wrote the paper with Alexander Toshev, Samy Bengio and Dumitru Erhan, members of the Google Brain project. "The field is just starting, and we will see a lot of increases."
Computer vision specialists said that despite the improvements, these software systems had made only limited progress toward the goal of digitally duplicating human vision and, even more elusive, understanding. "I don't know that I would say this is 'understanding' in the sense we want," said John R. Smith, a senior manager at I.B.M.'s T.J. Watson Research Center in Yorktown Heights, N.Y. "I think even the ability to generate language here is very limited." But the Google and Stanford teams said that they expect to see significant increases in accuracy as they improve their software and train these programs with larger sets of annotated images. A research group led by Tamara L. Berg, a computer scientist at the University of North Carolina at Chapel Hill, is training a neural network with one million images annotated by humans. "You're trying to tell the story behind the image," she said. "A natural scene will be very complex, and you want to pick out the most important objects in the image.""

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Date

26.12.2011 13:40:22

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Date

17. 5.2016 19:22:40

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Pages

S.15-22

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Date

22. 2.2017 12:53:49

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Source

The Semantic Web - ISWC 2010. 9th International Semantic Web Conference, ISWC 2010, Shanghai, China, November 7-11, 2010, Revised Selected Papers, Part I. Eds.: Peter F. Patel-Schneider et al

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"Last week, I received an email from Yulia Skora in Ukraine who is working on the mapping between UDC Summary and BBK (Bibliographic Library Classification) Summary. It reminded me of yet another challenging area of work. When responding to Yulia I realised that the issues with mapping, for instance, UDC Summary to Dewey Summaries [pdf] are often made more difficult because we have to deal with classification summaries in both systems and we cannot use a known exactMatch in many situations. In 2008, following advice received from colleagues in the HILT project, two of our colleagues quickly mapped 1000 classes of Dewey Summaries to UDC Master Reference File as a whole. This appeared to be relatively simple. The mapping in this case is simply an answer to a question "and how would you say e.g. Art metal work in UDC?" But when in 2009 we realised that we were going to release 2000 classes of UDC Summary as linked data, we decided to wait until we had our UDC Summary set defined and completed to be able to publish it mapped to the Dewey Summaries. As we arrived at this stage, little did we realise how much more complex the reversed mapping of UDC Summary to Dewey Summaries would turn out to be. Mapping the Dewey Summaries to UDC highlighted situations in which the logic and structure of two systems do not agree. Especially because Dewey tends to enumerate combinations of subject and attributes that do not always logically belong together. For instance, 850 Literatures of Italian, Sardinian, Dalmatian, Romanian, Rhaeto-Romanic languages Italian literature. This class mixes languages from three different subgroups of Romance languages. Italian and Sardinian belong to Italo Romance sub-family; Romanian and Dalmatian are Balkan Romance languages and Rhaeto Romance is the third subgroup that includes Friulian Ladin and Romanch. As UDC literature is based on a strict classification of language families, Dewey class 850 has to be mapped to 3 narrower UDC classes 821.131 Literature of Italo-Romance Languages , 821.132 Literature of Rhaeto-Romance languages and 821.135 Literature of Balkan-Romance Languages, or to a broader class 821.13 Literature of Romance languages. Hence we have to be sure that we have all these classes listed in the UDC Summary to be able to express UDC-DDC many-to-one, specific-to-broader relationships.
Precombined subjects, such as those shown above from Dewey, may be expressed in UDC Summary as examples of combination within various records. To express an exact match UDC class 07 has to contain example of combination 07(7) Journals. The Press - North America. In some cases we have, therefore, added examples to UDC Summary that represent exact match to Dewey Summaries. It is unfortunate that DDC has so many classes on the top level that deal with a selection of countries or languages that are given a preferred status in the scheme, and repeating these preferences in examples of combinations of UDC emulates an unwelcome cultural bias which we have to balance out somehow. This brings us to another challenge.. UDC 913(7) Regional Geography - North America [contains 2 concepts each of which has its URI] is an exact match to Dewey 917 [represented as one concept, 1 URI]. It seems that, because they represent an exact match to Dewey numbers, these UDC examples of combinations may also need a separate URIs so that they can be published as SKOS data. Albeit challenging, mapping proves to be a very useful exercise and I am looking forward to future work here especially in relation to our plans to map UDC Summary to Colon Classification. We are discussing this project with colleagues from DRTC in Bangalore (India)."