OpenRaiser/Intern-Atlas huggingface dataset:
Ranking Definitions with Supervised Learning Methods
Authors: Jun Xu, Yunbo Cao, Hang Li, Min Zhao
Venue: Conference WWW 2005
Year: 2005

This paper is concerned with the problem of definition search. Specifically, given a term, we are to retrieve definitional excerpts of the term and rank the extracted excerpts according to their likelihood of being good definitions. This is in contrast to the traditional approaches of either generating a single combined definition or simply outputting all retrieved definitions. Definition ranking is essential for the task. Methods for performing definition ranking are proposed in this paper, which formalize the problem as either classification or ordinal regression. A specification for judging the goodness of a definition is given. We employ SVM as the classification model and Ranking SVM as the ordinal regression model respectively, such that they rank definition candidates according to their likelihood of being good definitions. Features for constructing the SVM and Ranking SVM models are defined. An enterprise search system based on this method has been developed and has been put into practical use. Experimental results indicate that the use of SVM and Ranking SVM can significantly outperform the baseline methods of using heuristic rules or employing the conventional information retrieval method of Okapi. This is true both when the answers are paragraphs and when they are sentences. Experimental results also show that SVM or Ranking SVM models trained in one domain can be adapted to another domain, indicating that generic models for definition ranking can be constructed.
Personalized recommendation on dynamic content using predictive bilinear models
Authors: Wei Chu, Seung-Taek Park
Venue: Conference WWW 2009
Year: 2009

In Web-based services of dynamic content (such as news articles), recommender systems face the difficulty of timely identifying new items of high-quality and providing recommendations for new users. We propose a feature-based machine learning approach to personalized recommendation that is capable of handling the cold-start issue effectively. We maintain profiles of content of interest, in which temporal characteristics of the content, e.g. popularity and freshness, are updated in real-time manner. We also maintain profiles of users including demographic information and a summary of user activities within Yahoo! properties. Based on all features in user and content profiles, we develop predictive bilinear regression models to provide accurate personalized recommendations of new items for both existing and new users. This approach results in an offline model with light computational overhead compared with other recommender systems that require online re-training. The proposed framework is general and flexible for other personalized tasks. The superior performance of our approach is verified on a large-scale data set collected from the Today-Module on Yahoo! Front Page, with comparison against six competitive approaches.
Integrating clustering and multi-document summarization to improve document understanding
Authors: Dingding Wang, Shenghuo Zhu, Tao Li, Yun Chi, Yihong Gong
Venue: Conference CIKM 2008
Year: 2008

Document understanding techniques such as document clustering and multi-document summarization have been receiving much attention in recent years. Current document clustering methods usually represent documents as a term-document matrix and perform clustering algorithms on it. Although these clustering methods can group the documents satisfactorily, it is still hard for people to capture the meanings of the documents since there is no satisfactory interpretation for each document cluster. In this paper, we propose a new language model to simultaneously cluster and summarize the documents. By utilizing the mutual influence of the document clustering and summarization, our method makes (1) a better document clustering method with more meaningful interpretation and (2) a better document summarization method taking the document context information into consideration.
From SPARQL to rules (and back
Authors: Axel Polleres
Venue: Conference WWW 2007
Year: 2007

As the data and ontology layers of the Semantic Web stack have achieved a certain level of maturity in standard recommendations such as RDF and OWL, the current focus lies on two related aspects. On the one hand, the definition of a suitable query language for RDF, SPARQL, is close to recommendation status within the W3C. The establishment of the rules layer on top of the existing stack on the other hand marks the next step to be taken, where languages with their roots in Logic Programming and Deductive Databases are receiving considerable attention. The purpose of this paper is threefold. First, we discuss the formal semantics of SPARQLextending recent results in several ways. Second, weprovide translations from SPARQL to Datalog with negation as failure. Third, we propose some useful and easy to implement extensions of SPARQL, based on this translation. As it turns out, the combination serves for direct implementations of SPARQL on top of existing rules engines as well as a basis for more general rules and query languages on top of RDF.
Mind the data skew: distributed inferencing by speeddating in elastic regions
Authors: Spyros Kotoulas, Eyal Oren, Frank van Harmelen
Venue: Conference WWW 2010
Year: 2010

Semantic Web data exhibits very skewed frequency distributions among terms. Efficient large-scale distributed reasoning methods should maintain load-balance in the face of such highly skewed distribution of input data. We show that term-based partitioning, used by most distributed reasoning approaches, has limited scalability due to load-balancing problems.We address this problem with a method for data distribution based on clustering in elastic regions. Instead of as- signing data to fixed peers, data flows semi-randomly in the network. Data items speed-date while being temporarily collocated in the same peer. We introduce a bias in the routing to allow semantically clustered neighborhoods to emerge. Our approach is self-organising, efficient and does not require any central coordination.We have implemented this method on the MaRVIN platform and have performed experiments on large real-world datasets, using a cluster of up to 64 nodes. We compute the RDFS closure over different datasets and show that our clustering algorithm drastically reduces computation time, calculating the RDFS closure of 200 million triples in 7.2 minutes.
Structured objects in owl: representation and reasoning
Authors: Boris Motik, Bernardo Cuenca Grau, Ulrike Sattler
Venue: Conference WWW 2008
Year: 2008

Applications of semantic technologies often require the representation of and reasoning with structured objects - that is, objects composed of parts connected in complex ways. Although OWL is a general and powerful language, its class descriptions and axioms cannot be used to describe arbitrarily connected structures. An OWL representation of structured objects can thus be underconstrained, which reduces the inferences that can be drawn and causes performance problems in reasoning. To address these problems, we extend OWL with description graphs, which allow for the description of structured objects in a simple and precise way. To represent conditional aspects of the domain, we also allow for SWRL-like rules over description graphs. Based on an observation about the nature of structured objects, we ensure decidability of our formalism. We also present a hypertableau-based decision procedure, which we implemented in the HermiT reasoner. To evaluate its performance, we have extracted description graphs from the GALEN and FMA ontologies, classified them successfully, and even detected a modeling error in GALEN.
Efficiently Collecting Relevance Information from Clickthroughs for Web Retrieval System Evaluation
Authors: Jing He, Wayne Xin Zhao, Baihan Shu, Xiaoming Li, Hongfei Yan
Venue: Conference SIGIR 2011
Year: 2011

Various click models have been recently proposed as a principled approach to infer the relevance of documents from the clickthrough data. The inferred document relevance is potentially useful in evaluating the Web retrieval systems. In practice, it generally requires to acquire the accurate evaluation results within minimal users' query submissions. This problem is important for speeding up search engine development and evaluation cycle and acquiring reliable evaluation results on tail queries. In this paper, we propose a reordering framework for efficient evaluation problem in the context of clickthrough based Web retrieval evaluation. The main idea is to move up the documents that contribute more for the evaluation task. In this framework, we propose four intuitions and formulate them as an optimization problem. Both user study and TREC data based experiments validate that the reordering framework results in much fewer query submissions to get accurate evaluation results with only a little harm to the users' utility.
EigenRank: a ranking oriented approach to collaborative filtering
Authors: Nathan Nan Liu, Qiang Yang
Venue: Conference SIGIR 2008
Year: 2008

A recommender system must be able to suggest items that are likely to be preferred by the user. In most systems, the degree of preference is represented by a rating score. Given a database of users' past ratings on a set of items, traditional collaborative filtering algorithms are based on predicting the potential ratings that a user would assign to the unrated items so that they can be ranked by the predicted ratings to produce a list of recommended items. In this paper, we propose a collaborative filtering approach that addresses the item ranking problem directly by modeling user preferences derived from the ratings. We measure the similarity between users based on the correlation between their rankings of the items rather than the rating values and propose new collaborative filtering algorithms for ranking items based on the preferences of similar users. Experimental results on real world movie rating data sets show that the proposed approach outperforms traditional collaborative filtering algorithms significantly on the NDCG measure for evaluating ranked results.
Probabilistic model for discovering topic based communities in social networks
Authors: Mrinmaya Sachan, Danish Contractor, Tanveer A. Faruquie, L. Venkata Subramaniam
Venue: Conference CIKM 2011
Year: 2011

Social graphs have received renewed interest as a research topic with the advent of social networking websites. These online networks provide a rich source of data to study user relationships and interaction patterns on a large scale. In this paper, we propose a generative Bayesian model for extracting latent communities from a social graph. We assume that community memberships depend on topics of interest between users and the link relationships between them in the social graph topology. In addition, we make use of the nature of interaction to gauge user interests. Our model allows communities to be related to multiple topics and each user in the graph can be a member of multiple communities. This gives an insight into user interests and topical distribution in communities. We show the effectiveness of our model using a real world data set and also compare our model with existing community discovery methods.
Human carrying status in visual surveillance
Authors: Dacheng Tao, Xuelong Li 0001, Stephen J. Maybank, Xindong Wu 0001
Venue: Conference CVPR 2006
Year: 2006

A person’s gait changes when he or she is carrying an object such as a bag, suitcase or rucksack. As a result, human identification and tracking are made more difficult because the averaged gait image is too simple to represent the carrying status. Therefore, in this paper we first introduce a set of Gabor based human gait appearance models, because Gabor functions are similar to the receptive field profiles in the mammalian cortical simple cells. The very high dimensionality of the feature space makes training difficult. In order to solve this problem we propose a general tensor discriminant analysis (GTDA), which seamlessly incorporates the object (Gabor based human gait appearance model) structure information as a natural constraint. GTDA differs from the previous tensor based discriminant analysis methods in that the training converges. Existing methods fail to converge in the training stage. This makes them unsuitable for practical tasks. Experiments are carried out on the USF baseline data set to recognize a human’s ID from the gait silhouette. The proposed Gabor gait incorporated with GTDA is demonstrated to significantly outperform the existing appearance-based methods.
Spectrally optimal factorization of incomplete matrices
Authors: Pedro M. Q. Aguiar, João M. F. Xavier, Marko Stosic
Venue: Conference CVPR 2008
Year: 2008

From the recovery of structure from motion to the separation of style and content, many problems in computer vision have been successfully approached by using bilinear models. The reason for the success of these models is that a globally optimal decomposition is easily obtained from the singular value decomposition (SVD) of the observation matrix. However, in practice, the observation matrix is often incomplete, the SVD can not be used, and only suboptimal solutions are available. The majority of these solutions are based on iterative local refinements of a given cost function, and lack any guarantee of convergence to the global optimum. In this paper, we propose a globally optimal solution, for particular patterns of missing entries. To achieve this goal, we re-formulate the problem as the minimization of the spectral norm of the matrix of residuals, i.e., we seek the completion of the observation matrix such that the largest singular value of its difference to a low rank matrix is the smallest possible. The class of patterns of missing entries we deal with is known as the Young diagram, which includes, as particular cases, many relevant situations, such as the missing of an entire submatrix. We describe experiments that illustrate how our globally optimal solution has impact in practice.
Learning with dynamic group sparsity
Authors: Junzhou Huang, Xiaolei Huang 0001, Dimitris N. Metaxas
Venue: Conference ICCV 2009
Year: 2009

This paper investigates a new learning formulation called dynamic group sparsity. It is a natural extension of the standard sparsity concept in compressive sensing, and is motivated by the observation that in some practical sparse data the nonzero coefficients are often not random but tend to be clustered. Intuitively, better results can be achieved in these cases by reasonably utilizing both clustering and sparsity priors. Motivated by this idea, we have developed a new greedy sparse recovery algorithm, which prunes data residues in the iterative process according to both sparsity and group clustering priors rather than only sparsity as in previous methods. The proposed algorithm can recover stably sparse data with clustering trends using far fewer measurements and computations than current state-of-the-art algorithms with provable guarantees. Moreover, our algorithm can adaptively learn the dynamic group structure and the sparsity number if they are not available in the practical applications. We have applied the algorithm to sparse recovery and background subtraction in videos. Numerous experiments with improved performance over previous methods further validate our theoretical proofs and the effectiveness of the proposed algorithm.
Names and faces in the news
Authors: Tamara L. Berg, Alexander C. Berg, Jaety Edwards, Michael Maire, Ryan White, Yee Whye Teh, Erik G. Learned-Miller, David A. Forsyth
Venue: Conference CVPR 2004
Year: 2004

We show quite good face clustering is possible for a dataset of inaccurately and ambiguously labelled face images. Our dataset is 44,773 face images, obtained by applying a face finder to approximately half a million captioned news images. This dataset is more realistic than usual face recognition datasets, because it contains faces captured "in the wild" in a variety of configurations with respect to the camera, taking a variety of expressions, and under illumination of widely varying color. Each face image is associated with a set of names, automatically extracted from the associated caption. Many, but not all such sets contain the correct name. We cluster face images in appropriate discriminant coordinates. We use a clustering procedure to break ambiguities in labelling and identify incorrectly labelled faces. A merging procedure then identifies variants of names that refer to the same individual. The resulting representation can be used to label faces in news images or to organize news pictures by individuals present. An alternative view of our procedure is as a process that cleans up noisy supervised data. We demonstrate how to use entropy measures to evaluate such procedures.
Mining social images with distance metric learning for automated image tagging
Authors: Pengcheng Wu, Steven C. H. Hoi, Peilin Zhao, Ying He
Venue: Conference WSDM 2011
Year: 2011

With the popularity of various social media applications, massive social images associated with high quality tags have been made available in many social media web sites nowadays. Mining social images on the web has become an emerging important research topic in web search and data mining. In this paper, we propose a machine learning framework for mining social images and investigate its application to automated image tagging. To effectively discover knowledge from social images that are often associated with multimodal contents (including visual images and textual tags), we propose a novel Unified Distance Metric Learning (UDML) scheme, which not only exploits both visual and textual contents of social images, but also effectively unifies both inductive and transductive metric learning techniques in a systematic learning framework. We further develop an efficient stochastic gradient descent algorithm for solving the UDML optimization task and prove the convergence of the algorithm. By applying the proposed technique to the automated image tagging task in our experiments, we demonstrate that our technique is empirically effective and promising for mining social images towards some real applications.
Boosted deformable model for human body alignment
Authors: Xiaoming Liu 0002, Ting Yu 0003, Thomas Sebastian, Peter H. Tu
Venue: Conference CVPR 2008
Year: 2008

This paper studies image alignment, the problem of learning a shape and appearance model from labeled data and efficiently fitting the model to a non-rigid object with large variations. Given a set of images with manually labeled landmarks, our model representation consists of a shape component represented by a point distribution model and an appearance component represented by a collection of local features, trained discriminatively as a two-class classifier using boosting. Images with ground truth landmarks are the positive training samples while those with perturbed landmarks are considered as negatives. Enabled by piece-wise affine warping, corresponding local feature positions across all training samples form a hypothesis space for boosting. Image alignment is performed by maximizing the boosted classifier score, which is our distance measure, through iteratively mapping the feature positions to the image, and computing the gradient direction of the score with respect to the shape parameter. We apply this approach to human body alignment from surveillance-type images. We conduct experiments on the MIT pedestrian database where the body size is approximately 110 times 46 pixels, and demonstrate our real-time alignment capability.
Learning ordinal discriminative features for age estimation
Authors: Changsheng Li, Qingshan Liu 0001, Jing Liu 0001, Hanqing Lu
Venue: Conference CVPR 2012
Year: 2012

In this paper, we present a new method for facial age estimation based on ordinal discriminative feature learning. Considering the temporally ordinal and continuous characteristic of aging process, the proposed method not only aims at preserving the local manifold structure of facial images, but also it wants to keep the ordinal information among aging faces. Moreover, we try to remove redundant information from both the locality information and ordinal information as much as possible by minimizing nonlinear correlation and rank correlation. Finally, we formulate these two issues into a unified optimization problem of feature selection and present an efficient solution. The experiments are conducted on the public available Images of Groups dataset and the FG-NET dataset, and the experimental results demonstrate the power of the proposed method against the state-of-the-art methods.
Optimizing relevance and revenue in ad search: a query substitution approach
Authors: Filip Radlinski, Andrei Z. Broder, Peter Ciccolo, Evgeniy Gabrilovich, Vanja Josifovski, Lance Riedel
Venue: Conference SIGIR 2008
Year: 2008

The primary business model behind Web search is based on textual advertising, where contextually relevant ads are displayed alongside search results. We address the problem of selecting these ads so that they are both relevant to the queries and profitable to the search engine, showing that optimizing ad relevance and revenue is not equivalent. Selecting the best ads that satisfy these constraints also naturally incurs high computational costs, and time constraints can lead to reduced relevance and profitability. We propose a novel two-stage approach, which conducts most of the analysis ahead of time. An offine preprocessing phase leverages additional knowledge that is impractical to use in real time, and rewrites frequent queries in a way that subsequently facilitates fast and accurate online matching. Empirical evaluation shows that our method optimized for relevance matches a state-of-the-art method while improving expected revenue. When optimizing for revenue, we see even more substantial improvements in expected revenue.
Learning to detect phishing emails
Authors: Ian Fette, Norman M. Sadeh, Anthony Tomasic
Venue: Conference WWW 2007
Year: 2007

Each month, more attacks are launched with the aim of making web users believe that they are communicating with a trusted entity for the purpose of stealing account information, logon credentials, and identity information in general. This attack method, commonly known as "phishing," is most commonly initiated by sending out emails with links to spoofed websites that harvest information. We present a method for detecting these attacks, which in its most general form is an application of machine learning on a feature set designed to highlight user-targeted deception in electronic communication. This method is applicable, with slight modification, to detection of phishing websites, or the emails used to direct victims to these sites. We evaluate this method on a set of approximately 860 such phishing emails, and 6950 non-phishing emails, and correctly identify over 96% of the phishing emails while only mis-classifying on the order of 0.1% of the legitimate emails. We conclude with thoughts on the future for such techniques to specifically identify deception, specifically with respect to the evolutionary nature of the attacks and information available.