94,944 research outputs found
All about that - a URI profiling tool for monitoring and preserving linked data
All About That (AAT) is a URI Profiling tool which allows users to monitor and preserve Linked Data in which they are interested. Its design is based upon the principle of adapting ideas from hypermedia link integrity in order to apply them to the Semantic Web. As the Linked Data Web expands it will become increasingly important to maintain links such that the data remains useful and therefore this tool is presented as a step towards providing this maintenance capability
Recovery Guarantees for Quadratic Tensors with Limited Observations
We consider the tensor completion problem of predicting the missing entries
of a tensor. The commonly used CP model has a triple product form, but an
alternate family of quadratic models which are the sum of pairwise products
instead of a triple product have emerged from applications such as
recommendation systems. Non-convex methods are the method of choice for
learning quadratic models, and this work examines their sample complexity and
error guarantee. Our main result is that with the number of samples being only
linear in the dimension, all local minima of the mean squared error objective
are global minima and recover the original tensor accurately. The techniques
lead to simple proofs showing that convex relaxation can recover quadratic
tensors provided with linear number of samples. We substantiate our theoretical
results with experiments on synthetic and real-world data, showing that
quadratic models have better performance than CP models in scenarios where
there are limited amount of observations available
One Year Later: September 11 and the Internet
Presents findings from a survey that looks at how the terror attacks affected Americans' views about access to online information, Internet use, and the Web after September 11. Contains scholarly studies built around analysis of hundreds of Web sites
An Evaluation of Link Neighborhood Lexical Signatures to Rediscover Missing Web Pages
For discovering the new URI of a missing web page, lexical signatures, which
consist of a small number of words chosen to represent the "aboutness" of a
page, have been previously proposed. However, prior methods relied on computing
the lexical signature before the page was lost, or using cached or archived
versions of the page to calculate a lexical signature. We demonstrate a system
of constructing a lexical signature for a page from its link neighborhood, that
is the "backlinks", or pages that link to the missing page. After testing
various methods, we show that one can construct a lexical signature for a
missing web page using only ten backlink pages. Further, we show that only the
first level of backlinks are useful in this effort. The text that the backlinks
use to point to the missing page is used as input for the creation of a
four-word lexical signature. That lexical signature is shown to successfully
find the target URI in over half of the test cases.Comment: 24 pages, 13 figures, 8 tables, technical repor
Link Prediction via Matrix Completion
Inspired by practical importance of social networks, economic networks,
biological networks and so on, studies on large and complex networks have
attracted a surge of attentions in the recent years. Link prediction is a
fundamental issue to understand the mechanisms by which new links are added to
the networks. We introduce the method of robust principal component analysis
(robust PCA) into link prediction, and estimate the missing entries of the
adjacency matrix. On one hand, our algorithm is based on the sparsity and low
rank property of the matrix, on the other hand, it also performs very well when
the network is dense. This is because a relatively dense real network is also
sparse in comparison to the complete graph. According to extensive experiments
on real networks from disparate fields, when the target network is connected
and sufficiently dense, whatever it is weighted or unweighted, our method is
demonstrated to be very effective and with prediction accuracy being
considerably improved comparing with many state-of-the-art algorithms
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