OpenRaiser/Intern-Atlas huggingface dataset:
Jena: Implementing the semantic web recommendations
Authors: Jeremy J. Carroll, Ian Dickinson, Chris Dollin, Dave Reynolds, Andy Seaborne, Kevin Wilkinson
Venue: Conference WWW 2004
Year: 2004

The new Semantic Web recommendations for RDF, RDFS and OWL have, at their heart, the RDF graph. Jena2, a second-generation RDF toolkit, is similarly centered on the RDF graph. RDFS and OWL reasoning are seen as graph-to-graph transforms, producing graphs of virtual triples. Rich APIs are provided. The Model API includes support for other aspects of the RDF recommendations, such as containers and reification. The Ontology API includes support for RDFS and OWL, including advanced OWL Full support. Jena includes the de facto reference RDF/XML parser, and provides RDF/XML output using the full range of the rich RDF/XML grammar. N3 I/O is supported. RDF graphs can be stored in-memory or in databases. Jena's query language, RDQL, and the Web API are both offered for the next round of standardization.
Search-based query suggestion
Authors: Jiang-Ming Yang, Rui Cai, Feng Jing, Shuo Wang, Lei Zhang, Wei-Ying Ma
Venue: Conference CIKM 2008
Year: 2008

In this paper, we proposed a unified strategy to combine query log and search results for query suggestion. In this way, we leverage both the users' search intentions for popular queries and the power of search engines for unpopular queries. The suggested queries are also ranked according to their relevance and qualities; and each suggestion is described with a rich snippet including a photo and related description.
Clustering short texts using wikipedia
Authors: Somnath Banerjee, Krishnan Ramanathan, Ajay Gupta
Venue: Conference SIGIR 2007
Year: 2007

Subscribers to the popular news or blog feeds (RSS/Atom) often face the problem of information overload as these feed sources usually deliver large number of items periodically. One solution to this problem could be clustering similar items in the feed reader to make the information more manageable for a user. Clustering items at the feed reader end is a challenging task as usually only a small part of the actual article is received through the feed. In this paper, we propose a method of improving the accuracy of clustering short texts by enriching their representation with additional features from Wikipedia. Empirical results indicate that this enriched representation of text items can substantially improve the clustering accuracy when compared to the conventional bag of words representation.
Learning a hierarchy of discriminative space-time neighborhood features for human action recognition
Authors: Adriana Kovashka, Kristen Grauman
Venue: Conference CVPR 2010
Year: 2010

Recent work shows how to use local spatio-temporal features to learn models of realistic human actions from video. However, existing methods typically rely on a predefined spatial binning of the local descriptors to impose spatial information beyond a pure “bag-of-words” model, and thus may fail to capture the most informative space-time relationships. We propose to learn the shapes of space-time feature neighborhoods that are most discriminative for a given action category. Given a set of training videos, our method first extracts local motion and appearance features, quantizes them to a visual vocabulary, and then forms candidate neighborhoods consisting of the words associated with nearby points and their orientation with respect to the central interest point. Rather than dictate a particular scaling of the spatial and temporal dimensions to determine which points are near, we show how to learn the class-specific distance functions that form the most informative configurations. Descriptors for these variable-sized neighborhoods are then recursively mapped to higher-level vocabularies, producing a hierarchy of space-time configurations at successively broader scales. Our approach yields state-of-the-art performance on the UCF Sports and KTH datasets.
Learning human actions via information maximization
Authors: Jingen Liu, Mubarak Shah
Venue: Conference CVPR 2008
Year: 2008

In this paper, we present a novel approach for automatically learning a compact and yet discriminative appearance-based human action model. A video sequence is represented by a bag of spatiotemporal features called video-words by quantizing the extracted 3D interest points (cuboids) from the videos. Our proposed approach is able to automatically discover the optimal number of video-word clusters by utilizing Maximization of Mutual Information(MMI). Unlike the k-means algorithm, which is typically used to cluster spatiotemporal cuboids into video words based on their appearance similarity, MMI clustering further groups the video-words, which are highly correlated to some group of actions. To capture the structural information of the learnt optimal video-word clusters, we explore the correlation of the compact video-word clusters. We use the modified correlgoram, which is not only translation and rotation invariant, but also somewhat scale invariant. We extensively test our proposed approach on two publicly available challenging datasets: the KTH dataset and IXMAS multiview dataset. To the best of our knowledge, we are the first to try the bag of video-words related approach on the multiview dataset. We have obtained very impressive results on both datasets.
Learning semantic visual vocabularies using diffusion distance
Authors: Jingen Liu, Yang Yang, Mubarak Shah
Venue: Conference CVPR 2009
Year: 2009

In this paper, we propose a novel approach for learning generic visual vocabulary. We use diffusion maps to automatically learn a semantic visual vocabulary from abundant quantized midlevel features. Each midlevel feature is represented by the vector of pointwise mutual information (PMI). In this midlevel feature space, we believe the features produced by similar sources must lie on a certain manifold. To capture the intrinsic geometric relations between features, we measure their dissimilarity using diffusion distance. The underlying idea is to embed the midlevel features into a semantic lower-dimensional space. Our goal is to construct a compact yet discriminative semantic visual vocabulary. Although the conventional approach using k-means is good for vocabulary construction, its performance is sensitive to the size of the visual vocabulary. In addition, the learnt visual words are not semantically meaningful since the clustering criterion is based on appearance similarity only. Our proposed approach can effectively overcome these problems by capturing the semantic and geometric relations of the feature space using diffusion maps. Unlike some of the supervised vocabulary construction approaches, and the unsupervised methods such as pLSA and LDA, diffusion maps can capture the local intrinsic geometric relations between the midlevel feature points on the manifold. We have tested our approach on the KTH action dataset, our own YouTube action dataset and the fifteen scene dataset, and have obtained very promising results.
Understanding captions in biomedical publications
Authors: William W. Cohen, Richard C. Wang, Robert F. Murphy
Venue: Conference KDD 2003
Year: 2003

From the standpoint of the automated extraction of scientific knowledge, an important but little-studied part of scientific publications are the figures and accompanying captions. Captions are dense in information, but also contain many extra-grammatical constructs, making them awkward to process with standard information extraction methods. We propose a scheme for "understanding" captions in biomedical publications by extracting and classifying "image pointers" (references to the accompanying image). We evaluate a number of automated methods for this task, including hand-coded methods, methods based on existing learning techniques, and methods based on novel learning techniques. The best of these methods leads to a usefully accurate tool for caption-understanding, with both recall and precision in excess of 94% on the most important single class in a combined extraction/classification task.
Extraction and search of chemical formulae in text documents on the web
Authors: Bingjun Sun, Qingzhao Tan, Prasenjit Mitra, C. Lee Giles
Venue: Conference WWW 2007
Year: 2007

Often scientists seek to search for articles on the Web related to a particular chemical. When a scientist searches for a chemical formula using a search engine today, she gets articles where the exact keyword string expressing the chemical formula is found. Searching for the exact occurrence of keywords during searching results in two problems for this domain: a) if the author searches for CH4 and the article has H4C, the article is not returned, and b) ambiguous searches like "He" return all documents where Helium is mentioned as well as documents where the pronoun "he" occurs. To remedy these deficiencies, we propose a chemical formula search engine. To build a chemical formula search engine, we must solve the following problems: 1) extract chemical formulae from text documents, 2) index chemical formulae, and 3) designranking functions for the chemical formulae. Furthermore, query models are introduced for formula search, and for each a scoring scheme based on features of partial formulae is proposed tomeasure the relevance of chemical formulae and queries. We evaluate algorithms for identifying chemical formulae in documents using classification methods based on Support Vector Machines(SVM), and a probabilistic model based on conditional random fields (CRF). Different methods for SVM and CRF to tune the trade-off between recall and precision forim balanced data are proposed to improve the overall performance. A feature selection method based on frequency and discrimination isused to remove uninformative and redundant features. Experiments show that our approaches to chemical formula extraction work well, especially after trade-off tuning. The results also demonstrate that feature selection can reduce the index size without changing ranked query results much.
Towards the theoretical foundation of choreography
Authors: Zongyan Qiu, Xiangpeng Zhao, Chao Cai, Hongli Yang
Venue: Conference WWW 2007
Year: 2007

With the growth of interest on the web services, people pay increasinglyattention to the choreography, that is, to describe collaborations ofparticipants in accomplishing a common business goal from a globalviewpoint. In this paper, based on a simple choreography language and arole-oriented process language, we study some fundamental issues relatedto choreography, especially those related to implementation, includingsemantics, projection and natural projection, dominant role in choices anditerations, etc. We propose the concept of dominant role and somenovel languages structures related to it. The study reveals some cluesabout the language, the semantics, the specification and theimplementation of choreography.
Monte-Carlo simulation balancing
Authors: David Silver 0001, Gerald Tesauro
Venue: Conference ICML 2009
Year: 2009

In this paper we introduce the first algorithms for efficiently learning a simulation policy for Monte-Carlo search. Our main idea is to optimise the balance of a simulation policy, so that an accurate spread of simulation outcomes is maintained, rather than optimising the direct strength of the simulation policy. We develop two algorithms for balancing a simulation policy by gradient descent. The first algorithm optimises the balance of complete simulations, using a policy gradient algorithm; whereas the second algorithm optimises the balance over every two steps of simulation. We compare our algorithms to reinforcement learning and supervised learning algorithms for maximising the strength of the simulation policy. We test each algorithm in the domain of 5 x 5 and 6 x 6 Computer Go, using a softmax policy that is parameterised by weights for a hundred simple patterns. When used in a simple Monte-Carlo search, the policies learnt by simulation balancing achieved significantly better performance, with half the mean squared error of a uniform random policy, and similar overall performance to a sophisticated Go engine.
A non-convex relaxation approach to sparse dictionary learning
Authors: Jianping Shi, Xiang Ren, Guang Dai, Jingdong Wang 0001, Zhihua Zhang
Venue: Conference CVPR 2011
Year: 2011

Dictionary learning is a challenging theme in computer vision. The basic goal is to learn a sparse representation from an overcomplete basis set. Most existing approaches employ a convex relaxation scheme to tackle this challenge due to the strong ability of convexity in computation and theoretical analysis. In this paper we propose a non-convex online approach for dictionary learning. To achieve the sparseness, our approach treats a so-called minimax concave (MC) penalty as a nonconvex relaxation of the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> penalty. This treatment expects to obtain a more robust and sparse representation than existing convex approaches. In addition, we employ an online algorithm to adaptively learn the dictionary, which makes the non-convex formulation computationally feasible. Experimental results on the sparseness comparison and the applications in image denoising and image inpainting demonstrate that our approach is more effective and flexible.
Searching patterns for relation extraction over the web: rediscovering the pattern-relation duality
Authors: Yuan Fang, Kevin Chen-Chuan Chang
Venue: Conference WSDM 2011
Year: 2011

While tuple extraction for a given relation has been an active research area, its dual problem of pattern search-- to find and rank patterns in a principled way-- has not been studied explicitly. In this paper, we propose and address the problem of pattern search, in addition to tuple extraction. As our objectives, we stress reusability for pattern search and scalability of tuple extraction, such that our approach can be applied to very large corpora like the Web. As the key foundation, we propose a conceptual model PRDualRank to capture the notion of precision and recall for both tuples and patterns in a principled way, leading to the "rediscovery" of the Pattern-Relation Duality-- the formal quantification of the reinforcement between patterns and tuples with the metrics of precision and recall. We also develop a concrete framework for PRDualRank, guided by the principles of a perfect sampling process over a complete corpus. Finally, we evaluated our framework over the real Web. Experiments show that on all three target relations our principled approach greatly outperforms the previous state-of-the-art system in both effectiveness and efficiency. In particular, we improved optimal F-score by up to 64%.
Online multiclass learning by interclass hypothesis sharing
Authors: Michael Fink 0002, Shai Shalev-Shwartz, Yoram Singer, Shimon Ullman
Venue: Conference ICML 2006
Year: 2006

We describe a general framework for online multiclass learning based on the notion of hypothesis sharing. In our framework sets of classes are associated with hypotheses. Thus, all classes within a given set share the same hypothesis. This framework includes as special cases commonly used constructions for multiclass categorization such as allocating a unique hypothesis for each class and allocating a single common hypothesis for all classes. We generalize the multiclass Perceptron to our framework and derive a unifying mistake bound analysis. Our construction naturally extends to settings where the number of classes is not known in advance but rather is revealed along the online learning process. We demonstrate the merits of our approach by comparing it to previous methods on both synthetic and natural datasets. 1.
Towards scalable representations of object categories: Learning a hierarchy of parts
Authors: Sanja Fidler, Ales Leonardis
Venue: Conference CVPR 2007
Year: 2007

This paper proposes a novel approach to constructing a hierarchical representation of visual input that aims to enable recognition and detection of a large number of object categories. Inspired by the principles of efficient indexing (bottom-up), robust matching (top-down), and ideas of compositionality, our approach learns a hierarchy of spatially flexible compositions, i.e. parts, in an unsupervised, statistics-driven manner. Starting with simple, frequent features, we learn the statistically most significant compositions (parts composed of parts), which consequently define the next layer. Parts are learned sequentially, layer after layer, optimally adjusting to the visual data. Lower layers are learned in a category-independent way to obtain complex, yet sharable visual building blocks, which is a crucial step towards a scalable representation. Higher layers of the hierarchy, on the other hand, are constructed by using specific categories, achieving a category representation with a small number of highly generalizable parts that gained their structural flexibility through composition within the hierarchy. Built in this way, new categories can be efficiently and continuously added to the system by adding a small number of parts only in the higher layers. The approach is demonstrated on a large collection of images and a variety of object categories. Detection results confirm the effectiveness and robustness of the learned parts.
Gelling, and melting, large graphs by edge manipulation
Authors: Hanghang Tong, B. Aditya Prakash, Tina Eliassi-Rad, Michalis Faloutsos, Christos Faloutsos
Venue: Conference CIKM 2012
Year: 2012

Controlling the dissemination of an entity (e.g., meme, virus, etc) on a large graph is an interesting problem in many disciplines. Examples include epidemiology, computer security, marketing, etc. So far, previous studies have mostly focused on removing or inoculating nodes to achieve the desired outcome.
Emerging topic detection using dictionary learning
Authors: Shiva Prasad Kasiviswanathan, Prem Melville, Arindam Banerjee, Vikas Sindhwani
Venue: Conference CIKM 2011
Year: 2011

Streaming user-generated content in the form of blogs, microblogs, forums, and multimedia sharing sites, provides a rich source of data from which invaluable information and insights maybe gleaned. Given the vast volume of such social media data being continually generated, one of the challenges is to automatically tease apart the emerging topics of discussion from the constant background chatter. Such emerging topics can be identified by the appearance of multiple posts on a unique subject matter, which is distinct from previous online discourse. We address the problem of identifying emerging topics through the use of dictionary learning. We propose a two stage approach respectively based on detection and clustering of novel user-generated content. We derive a scalable approach by using the alternating directions method to solve the resulting optimization problems. Empirical results show that our proposed approach is more effective than several baselines in detecting emerging topics in traditional news story and newsgroup data. We also demonstrate the practical application to social media analysis, based on a study on streaming data from Twitter.
You are what you say: privacy risks of public mentions
Authors: Dan Frankowski, Dan Cosley, Shilad Sen, Loren G. Terveen, John Riedl
Venue: Conference SIGIR 2006
Year: 2006

In today's data-rich networked world, people express many aspects of their lives online. It is common to segregate different aspects in different places: you might write opinionated rants about movies in your blog under a pseudonym while participating in a forum or web site for scholarly discussion of medical ethics under your real name. However, it may be possible to link these separate identities, because the movies, journal articles, or authors you mention are from a sparse relation space whose properties (e.g., many items related to by only a few users) allow re-identification. This re-identification violates people's intentions to separate aspects of their life and can have negative consequences; it also may allow other privacy violations, such as obtaining a stronger identifier like name and address.This paper examines this general problem in a specific setting: re-identification of users from a public web movie forum in a private movie ratings dataset. We present three major results. First, we develop algorithms that can re-identify a large proportion of public users in a sparse relation space. Second, we evaluate whether private dataset owners can protect user privacy by hiding data; we show that this requires extensive and undesirable changes to the dataset, making it impractical. Third, we evaluate two methods for users in a public forum to protect their own privacy, suppression and misdirection. Suppression doesn't work here either. However, we show that a simple misdirection strategy works well: mention a few popular items that you haven't rated.
Annosearch: Image auto-annotation by search
Authors: Xin-Jing Wang, Lei Zhang 0001, Feng Jing, Wei-Ying Ma
Venue: Conference CVPR 2006
Year: 2006

Although it has been studied for several years by computer vision and machine learning communities, image annotation is still far from practical. In this paper, we present AnnoSearch, a novel way to annotate images using search and data mining technologies. Leveraging the Web-scale images, we solve this problem in two-steps: 1) searching for semantically and visually similar images on the Web, 2) and mining annotations from them. Firstly, at least one accurate keyword is required to enable text-based search for a set of semantically similar images. Then content-based search is performed on this set to retrieve visually similar images. At last, annotations are mined from the descriptions (titles, URLs and surrounding texts) of these images. It worth highlighting that to ensure the efficiency, high dimensional visual features are mapped to hash codes which significantly speed up the content-based search process. Our proposed approach enables annotating with unlimited vocabulary, which is impossible for all existing approaches. Experimental results on real web images show the effectiveness and efficiency of the proposed algorithm.
Towards cross-category knowledge propagation for learning visual concepts
Authors: Guo-Jun Qi, Charu C. Aggarwal, Yong Rui, Qi Tian 0001, Shiyu Chang, Thomas S. Huang
Venue: Conference CVPR 2011
Year: 2011

In recent years, knowledge transfer algorithms have become one of most the active research areas in learning visual concepts. Most of the existing learning algorithms focuses on leveraging the knowledge transfer process which is specific to a given category. However, in many cases, such a process may not be very effective when a particular target category has very few samples. In such cases, it is interesting to examine, whether it is feasible to use cross-category knowledge for improving the learning process by exploring the knowledge in correlated categories. Such a task can be quite challenging due to variations in semantic similarities and differences between categories, which could either help or hinder the cross-category learning process. In order to address this challenge, we develop a cross-category label propagation algorithm, which can directly propagate the inter-category knowledge at instance level between the source and the target categories. Furthermore, this algorithm can automatically detect conditions under which the transfer process can be detrimental to the learning process. This provides us a way to know when the transfer of cross-category knowledge is both useful and desirable. We present experimental results on real image and video data sets in order to demonstrate the effectiveness of our approach.