OpenRaiser/Intern-Atlas huggingface dataset:
Learning to map between ontologies on the semantic web
Authors: AnHai Doan, Jayant Madhavan, Pedro M. Domingos, Alon Y. Halevy
Venue: Conference WWW 2002
Year: 2002

Ontologies play a prominent role on the Semantic Web. They make possible the widespread publication of machine understandable data, opening myriad opportunities for automated information processing. However, because of the Semantic Web's distributed nature, data on it will inevitably come from many different ontologies. Information processing across ontologies is not possible without knowing the semantic mappings between their elements. Manually finding such mappings is tedious, error-prone, and clearly not possible at the Web scale. Hence, the development of tools to assist in the ontology mapping process is crucial to the success of the Semantic Web.We describe glue, a system that employs machine learning techniques to find such mappings. Given two ontologies, for each concept in one ontology glue finds the most similar concept in the other ontology. We give well-founded probabilistic definitions to several practical similarity measures, and show that glue can work with all of them. This is in contrast to most existing approaches, which deal with a single similarity measure. Another key feature of glue is that it uses multiple learning strategies, each of which exploits a different type of information either in the data instances or in the taxonomic structure of the ontologies. To further improve matching accuracy, we extend glue to incorporate commonsense knowledge and domain constraints into the matching process. For this purpose, we show that relaxation labeling, a well-known constraint optimization technique used in computer vision and other fields, can be adapted to work efficiently in our context. Our approach is thus distinguished in that it works with a variety of well-defined similarity notions and that it efficiently incorporates multiple types of knowledge. We describe a set of experiments on several real-world domains, and show that glue proposes highly accurate semantic mappings.
Combining Clustering and Co-training to Enhance Text Classification Using Unlabelled Data
Authors: Bhavani Raskutti, Herman L. Ferrá, Adam Kowalczyk
Venue: Conference KDD 2002
Year: 2002

In this paper, we present a new co-training strategy that makes use of unlabelled data. It trains two predictors in parallel, with each predictor labelling the unlabelled data for training the other predictor in the next round. Both predictors are support vector machines, one trained using data from the original feature space, the other trained with new features that are derived by clustering both the labelled and unlabelled data. Hence, unlike standard co-training methods, our method does not require a priori the existence of two redundant views either of which can be used for classification, nor is it dependent on the availability of two different supervised learning algorithms that complement each other.We evaluated our method with two classifiers and three text benchmarks: WebKB, Reuters newswire articles and 20 NewsGroups. Our evaluation shows that our co-training technique improves text classification accuracy especially when the number of labelled examples are very few.
Analysis of topic dynamics in web search
Authors: Xuehua Shen, Susan T. Dumais, Eric Horvitz
Venue: Conference WWW 2005
Year: 2005

We report on a study of topic dynamics for pages visited by a sample of people using MSN Search. We examine the predictive accuracies of probabilistic models of topic transitions for individuals and groups of users. We explore temporal dynamics by comparing the accuracy of the models for predicting topic transitions at increasingly distant times in the future. Finally, we discuss directions for applying models of search topic dynamics.
Robustness of adaptive filtering methods in a cross-benchmark evaluation
Authors: Yiming Yang, Shinjae Yoo, Jian Zhang, Bryan Kisiel
Venue: Conference SIGIR 2005
Year: 2005

This paper reports a cross-benchmark evaluation of regularized logistic regression (LR) and incremental Rocchio for adaptive filtering. Using four corpora from the Topic Detection and Tracking (TDT) forum and the Text Retrieval Conferences (TREC) we evaluated these methods with non-stationary topics at various granularity levels, and measured performance with different utility settings. We found that LR performs strongly and robustly in optimizing T11SU (a TREC utility function) while Rocchio is better for optimizing Ctrk (the TDT tracking cost), a high-recall oriented objective function. Using systematic cross-corpus parameter optimization with both methods, we obtained the best results ever reported on TDT5, TREC10 and TREC11. Relevance feedback on a small portion (0.05~0.2%) of the TDT5 test documents yielded significant performance improvements, measuring up to a 54% reduction in Ctrk and a 20.9% increase in T11SU (with b=0.1), compared to the results of the top-performing system in TDT2004 without relevance feedback information.
A re-examination of text categorization methods
Authors: Yiming Yang, Xin Liu
Venue: Conference SIGIR 1999
Year: 1999

This paper reports a controlled study with statistical signicance tests on ve text categorization methods: the Support Vector Machines (SVM), a k-Nearest Neighbor (kNN) classi er, a neural network (NNet) approach, the Linear Leastsquares Fit (LLSF) mapping and a Naive B a yes (NB) classier. We focus on the robustness of these methods in dealing with a skewed category distribution, and their performance as function of the training-set category frequency. Our results show that SVM, kNN and LLSF signi cantly outperform NNet and NB when the number of positive training instances per category are small (less than ten), and that all the methods perform comparably when the categories are su ciently common (over 300 instances).
Explanation-augmented SVM: an approach to incorporating domain knowledge into SVM learning
Authors: Qiang Sun, Gerald DeJong
Venue: Conference ICML 2005
Year: 2005

We introduce a novel approach to incorporating domain knowledge into Support Vector Machines to improve their example efficiency. Domain knowledge is used in an Explanation Based Learning fashion to build justifications or explanations for why the training examples are assigned their given class labels. Explanations bias the large margin classifier through the interaction of training examples and domain knowledge. We develop a new learning algorithm for this Explanation-Augmented SVM (EA-SVM). It naturally extends to imperfect knowledge, a stumbling block to conventional EBL. Experimental results confirm desirable properties predicted by the analysis and demonstrate the approach on three domains.
Analysis and extension of spectral methods for nonlinear dimensionality reduction
Authors: Fei Sha, Lawrence K. Saul
Venue: Conference ICML 2005
Year: 2005

Many unsupervised algorithms for nonlinear dimensionality reduction, such as locally linear embedding (LLE) and Laplacian eigenmaps, are derived from the spectral decompositions of sparse matrices. While these algorithms aim to preserve certain proximity relations on average, their embeddings are not explicitly designed to preserve local features such as distances or angles. In this paper, we show how to construct a low dimensional embedding that maximally preserves angles between nearby data points. The embedding is derived from the bottom eigenvectors of LLE and/or Laplacian eigenmaps by solving an additional (but small) problem in semidefinite programming, whose size is independent of the number of data points. The solution obtained by semidefinite programming also yields an estimate of the data's intrinsic dimensionality. Experimental results on several data sets demonstrate the merits of our approach.
Using additive expert ensembles to cope with concept drift
Authors: Jeremy Z. Kolter, Marcus A. Maloof
Venue: Conference ICML 2005
Year: 2005

We consider online learning where the target concept can change over time. Previous work on expert prediction algorithms has bounded the worst-case performance on any subsequence of the training data relative to the performance of the best expert. However, because these "experts" may be difficult to implement, we take a more general approach and bound performance relative to the actual performance of any online learner on this single subsequence. We present the additive expert ensemble algorithm AddExp, a new, general method for using any online learner for drifting concepts. We adapt techniques for analyzing expert prediction algorithms to prove mistake and loss bounds for a discrete and a continuous version of AddExp. Finally, we present pruning methods and empirical results for data sets with concept drift.
Block-based web search
Authors: Deng Cai, Shipeng Yu, Ji-Rong Wen, Wei-Ying Ma
Venue: Conference SIGIR 2004
Year: 2004

Multiple-topic and varying-length of web pages are two negative factors significantly affecting the performance of web search. In this paper, we explore the use of page segmentation algorithms to partition web pages into blocks and investigate how to take advantage of block-level evidence to improve retrieval performance in the web context. Because of the special characteristics of web pages, different page segmentation method will have different impact on web search performance. We compare four types of methods, including fixed-length page segmentation, DOM-based page segmentation, vision-based page segmentation, and a combined method which integrates both semantic and fixed-length properties. Experiments on block-level query expansion and retrieval are performed. Among the four approaches, the combined method achieves the best performance for web search. Our experimental results also show that such a semantic partitioning of web pages effectively deals with the problem of multiple drifting topics and mixed lengths, and thus has great potential to boost up the performance of current web search engines.
The infocious web search engine: Improving web searching through linguistic analysis
Authors: Alexandros Ntoulas, Gerald Chao, Junghoo Cho
Venue: Conference WWW 2005
Year: 2005

In this paper we present the Infocious Web search engine [23]. Our goal in creating Infocious is to improve the way people find information on the Web by resolving ambiguities present in natural language text. This is achieved by performing linguistic analysis on the content of the Web pages we index, which is a departure from existing Web search engines that return results mainly based on keyword matching. This additional step of linguistic processing gives Infocious two main advantages. First, Infocious gains a deeper understanding of the content of Web pages so it can better match users' queries with indexed documents and therefore can improve relevancy of the returned results. Second, based on its linguistic processing, Infocious can organize and present the results to the user in more intuitive ways. In this paper we present the linguistic processing technologies that we incorporated in Infocious and how they are applied in helping users find information on the Web more efficiently. We discuss the various components in the architecture of Infocious and how each of them benefits from the added linguistic processing. Finally, we experimentally evaluate the performance of a component which leverages linguistic information in order to categorize Web pages.
Improving web search ranking by incorporating user behavior information
Authors: Eugene Agichtein, Eric Brill, Susan T. Dumais
Venue: Conference SIGIR 2006
Year: 2006

We show that incorporating user behavior data can significantly improve ordering of top results in real web search setting. We examine alternatives for incorporating feedback into the ranking process and explore the contributions of user feedback compared to other common web search features. We report results of a large scale evaluation over 3,000 queries and 12 million user interactions with a popular web search engine. We show that incorporating implicit feedback can augment other features, improving the accuracy of a competitive web search ranking algorithms by as much as 31% relative to the original performance.
Optimizing search engines using clickthrough data
Authors: Thorsten Joachims
Venue: Conference KDD 2002
Year: 2002

This paper presents an approach to automatically optimizing the retrieval quality of search engines using clickthrough data. Intuitively, a good information retrieval system should present relevant documents high in the ranking, with less relevant documents following below. While previous approaches to learning retrieval functions from examples exist, they typically require training data generated from relevance judgments by experts. This makes them difficult and expensive to apply. The goal of this paper is to develop a method that utilizes clickthrough data for training, namely the query-log of the search engine in connection with the log of links the users clicked on in the presented ranking. Such clickthrough data is available in abundance and can be recorded at very low cost. Taking a Support Vector Machine (SVM) approach, this paper presents a method for learning retrieval functions. From a theoretical perspective, this method is shown to be well-founded in a risk minimization framework. Furthermore, it is shown to be feasible even for large sets of queries and features. The theoretical results are verified in a controlled experiment. It shows that the method can effectively adapt the retrieval function of a meta-search engine to a particular group of users, outperforming Google in terms of retrieval quality after only a couple of hundred training examples.
Detecting Changes in Aerial Views of Man-Made Structures
Authors: Andres Huertas, Ramakant Nevatia
Venue: Conference ICCV 1998
Year: 1998

Many applications require detecting structural changes in a scene over a period of time. Comparing intensity values of successive images is not effective as such changes don't necessarily reflect actual changes at a site but might be caused by changes in the view point, illumination and seasons. We take the approach of comparing a 3-D model of the site, prepared from previous images, with new images to infer significant changes. This task is difficult as the images and the models have very different levels of abstract representations. Our approach consists of several steps: registering a site model to a new image, model validation to confirm the presence of model objects in the image; structural change detection seeks to resolve matching problems and indicate possibly changed structures; and finally updating models to reflect the changes. Our system is able to detect missing (or mis-modeled) buildings, changes in model dimensions, and new buildings under some conditions.
Random subspaces and subsampling for 2-d face recognition
Authors: Nitesh V. Chawla, Kevin W. Bowyer
Venue: Conference CVPR 2005
Year: 2005

Random subspaces are a popular ensemble construction technique that improves the accuracy of weak classifiers. It has been shown, in different domains, that random subspaces combined with weak classifiers such as decision trees and nearest neighbor classifiers can provide an improvement in accuracy. In this paper, we apply the random subspace methodology to the 2-D face recognition task. The main goal of the paper is to see if the random subspace methodology can do as well, if not better, than the single classifier constructed on the tuned face space. We also propose the use of a validation set for tuning the face space, to avoid bias in the accuracy estimation. In addition, we also compare the random subspace methodology to an ensemble of subsamples of image data. This work shows that a random subspaces ensemble can outperform a well-tuned single classifier for a typical 2-D face recognition problem. The random subspaces approach has the added advantage of requiring less careful tweaking.
Bagging in computer vision
Authors: Bruce A. Draper, Kyungim Baek
Venue: Conference CVPR 1998
Year: 1998

Previous research has shown that aggregated predictors improve the performance of non-parametric function approximation techniques. This paper presents the results of applying aggregated predictors to a computer vision problem, and shows that the method of bagging significantly improves performance. In fact, the results are better than those previously reported on other domains. This paper explains this performance in terms of the variance and bias.
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
Authors: Kushal Dave, Steve Lawrence, David M. Pennock
Venue: Conference WWW 2003
Year: 2003

The web contains a wealth of product reviews, but sifting through them is a daunting task. Ideally, an opinion mining tool would process a set of search results for a given item, generating a list of product attributes (quality, features, etc.) and aggregating opinions about each of them (poor, mixed, good). We begin by identifying the unique properties of this problem and develop a method for automatically distinguishing between positive and negative reviews. Our classifier draws on information retrieval techniques for feature extraction and scoring, and the results for various metrics and heuristics vary depending on the testing situation. The best methods work as well as or better than traditional machine learning. When operating on individual sentences collected from web searches, performance is limited due to noise and ambiguity. But in the context of a complete web-based tool and aided by a simple method for grouping sentences into attributes, the results are qualitatively quite useful.
A kernel view of the dimensionality reduction of manifolds
Authors: Jihun Ham, Daniel D. Lee, Sebastian Mika, Bernhard Schölkopf
Venue: Conference ICML 2004
Year: 2004

We interpret several well-known algorithms for dimensionality reduction of manifolds as kernel methods. Isomap, graph Laplacian eigenmap, and locally linear embedding (LLE) all utilize local neighborhood information to construct a global embedding of the manifold. We show how all three algorithms can be described as kernel PCA on specially constructed Gram matrices, and illustrate the similarities and differences between the algorithms with representative examples.
Learning with non-positive kernels
Authors: Cheng Soon Ong, Xavier Mary, Stéphane Canu, Alexander J. Smola
Venue: Conference ICML 2004
Year: 2004

In this paper we show that many kernel methods can be adapted to deal with indefinite kernels, that is, kernels which are not positive semidefinite. They do not satisfy Mercer's condition and they induce associated functional spaces called Reproducing Kernel Kreĭn Spaces (RKKS), a generalization of Reproducing Kernel Hilbert Spaces (RKHS).Machine learning in RKKS shares many "nice" properties of learning in RKHS, such as orthogonality and projection. However, since the kernels are indefinite, we can no longer minimize the loss, instead we stabilize it. We show a general representer theorem for constrained stabilization and prove generalization bounds by computing the Rademacher averages of the kernel class. We list several examples of indefinite kernels and investigate regularization methods to solve spline interpolation. Some preliminary experiments with indefinite kernels for spline smoothing are reported for truncated spectral factorization, Landweber-Fridman iterations, and MR-II.
Agglomerative clustering of a search engine query log
Authors: Doug Beeferman, Adam L. Berger
Venue: Conference KDD 2000
Year: 2000

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Query clustering using content words and user feedback
Authors: Ji-Rong Wen, HongJiang Zhang, Jian-Yun Nie
Venue: Conference SIGIR 2001
Year: 2001

Query clustering is crucial for automatically discovering frequently asked queries (FAQs) or most popular topics on a question-answering search engine. Due to the short length of queries, the traditional approaches based on keywords are not suitable for query clustering. This paper describes our attempt to cluster similar queries according to their contents as well as the document click information in the user logs.
Learning hierarchical models of scenes, objects, and parts
Authors: Erik B. Sudderth, Antonio Torralba 0001, William T. Freeman, Alan S. Willsky
Venue: Conference ICCV 2005
Year: 2005

We describe a hierarchical probabilistic model for the detection and recognition of objects in cluttered, natural scenes. The model is based on a set of parts which describe the expected appearance and position, in an object centered coordinate frame, of features detected by a low-level interest operator. Each object category then has its own distribution over these parts, which are shared between objects. We learn the parameters of this model via a Gibbs sampler which uses the graphical model's structure to analytically average over many parameters. Applied to a database of images of isolated objects, the sharing of parts among objects improves detection accuracy when few training examples are available. We also extend this hierarchical framework to scenes containing multiple objects
Topic evolution and social interactions: how authors effect research
Authors: Ding Zhou, Xiang Ji, Hongyuan Zha, C. Lee Giles
Venue: Conference CIKM 2006
Year: 2006

We propose a method for discovering the dependency relationships between the topics of documents shared in social networks using the latent social interactions, attempting to answer the question: given a seemingly new topic, from where does this topic evolve? In particular, we seek to discover the pair-wise probabilistic dependency in topics of documents which associate social actors from a latent social network, where these documents are being shared. By viewing the evolution of topics as a Markov chain, we estimate a Markov transition matrix of topics by leveraging social interactions and topic semantics. Metastable states in a Markov chain are applied to the clustering of topics. Applied to the CiteSeer dataset, a collection of documents in academia, we show the trends of research topics, how research topics are related and which are stable. We also show how certain social actors, authors, impact these topics and propose new ways for evaluating author impact.
Automatic web news extraction using tree edit distance
Authors: Davi de Castro Reis, Paulo Braz Golgher, Altigran Soares da Silva, Alberto H. F. Laender
Venue: Conference WWW 2004
Year: 2004

The Web poses itself as the largest data repository ever available in the history of humankind. Major efforts have been made in order to provide efficient access to relevant information within this huge repository of data. Although several techniques have been developed to the problem of Web data extraction, their use is still not spread, mostly because of the need for high human intervention and the low quality of the extraction results.In this paper, we present a domain-oriented approach to Web data extraction and discuss its application to automatically extracting news from Web sites. Our approach is based on a highly efficient tree structure analysis that produces very effective results. We have tested our approach with several important Brazilian on-line news sites and achieved very precise results, correctly extracting 87.71% of the news in a set of 4088 pages distributed among 35 different sites.