Ontology matching is the process of finding semantic correspondences between entities from different ontologies. As an effective solution to linking different heterogeneous ontologies, ontology matching has attracted considerable attentions in recent years. In this paper, we propose a novel graph-based approach to ontology matching problem. Different from previous work, we formulate ontology matching as a random walk process on the association graph constructed from the to-be-matched ontologies. In particular, two variants of the conventional random walk process, namely, Affinity-Preserving Random Walk (APRW) and Mapping-Oriented Random Walk (MORW), have been proposed to alleviate the adverse effect of the false-mapping nodes in the association graph and to incorporate the 1-to-1 matching constraints presumed in ontology matching, respectively. Experiments on the Ontology Alignment Evaluation Initiative (OAEI) datasets show that our approach achieves a competitive performance when compared with state-of-the-art systems, even though our approach does not utilize any external resources.

AUC (Area Under ROC Curve) has been an important critrion widely used in diversity learning tasks. To optimize AUC, many learning approaches have been developed, most working with pairwise surrogate losses. Thus, it is important to study the AUC consistency based on minimizing pairwise surrogate losses. In this paper, we introduce the generalized calibration for AUC optimization, and prove that it is a necessary condition for AUC consistency. We then provide a new sufficient condition for AUC consistency, and show its usefulness in studying the consistency of various surrogate losses, as well as the invetion of new consistent losses. Further, we derive regret bounds for exponential and logistic losses, and present regret bounds for more general surrogate losses in realizable setting. Finally, we prove regret bounds that disclose the equivalence between the pairwise exponential loss of AUC and the univariate exponential loss of accuracy.

Among various traditional art forms, brush stroke drawing is one of the widely used styles in modern computer graphic tools such as GIMP, Photoshop and Painter. In this paper, we develop an AI-aided art authoring (A4) system of non-photorealistic rendering that allows users to automatically generate brush stroke paintings in a specific artist's style. Within the reinforcement learning framework of brush stroke generation proposed, our contribution in this paper is to learn artists' drawing styles from video-captured stroke data by inverse reinforcement learning. Through experiments, we demonstrate that our system can successfully learn artists' styles and render pictures with consistent and smooth brush strokes.

Pruning techniques based on partial order reduction and symmetry elimination have recently found increasing attention for optimal planning. Although these techniques appear to be rather different, they base their pruning decisions on similar ideas from a high level perspective. In this paper, we propose safe integrations of partial order reduction and symmetry elimination for cost-optimal classical planning. We show that previously proposed symmetry-based search algorithms can safely be applied with strong stubborn sets. In addition, we derive the notion of symmetrical strong stubborn sets as a more tightly integrated concept. Our experiments show the potential of our approaches.

We propose a new MDS paradigm called reader-aware multi-document summarization (RA-MDS).Specifically, a set of reader comments associated with the news reports are also collected. The generated summaries from the reports for the event should be salient according to not only the reports but also the reader comments. To tackle this RA-MDS problem, we propose a sparse-coding-based method that is able to calculate the salience of the text units by jointly considering news reports and reader comments. Another reader-aware characteristic of our framework is to improve linguistic quality via entity rewriting. The rewriting consideration is jointly assessed together with other summarization requirements under a unified optimization model. To support the generation of compressive summaries via optimization, we explore a finer syntactic unit, namely, noun/verb phrase. In this work, we also generate a data set for conducting RA-MDS. Extensive experiments on this data set and some classical data sets demonstrate the effectiveness of our proposed approach.

Hypernymy identification aims at detecting if isA relationship holds between two words or phrases. Most previous methods are based on lexical patterns or the Distributional Inclusion Hypothesis, and the accuracy of such methods is not ideal. In this paper, we propose a simple yet effective supervision framework to identify hypernymy relations using distributed term representations (a.k.a term embeddings). First, we design a distance-margin neural network to learn term embeddings based on some pre-extracted hypernymy data. Then, we apply such embeddings as term features to identify positive hypernymy pairs through a supervision method. Experimental results demonstrate that our approach outperforms other supervised methods on two popular datasets and the learned term embeddings has better quality than existing term distributed representations with respect to hypernymy identification.

In this paper, we formulate the image classification problem in a multi-task learning framework. We propose a novel method to adaptively share information among tasks (classes). Different from imposing strong assumptions or discovering specific structures, the key insight in our method is to selectively extract and exploit the shared information among classes while capturing respective disparities simultaneously. It is achieved by estimating a composite of two sets of parameters with different regularization. Besides applying it for learning classifiers on pre-computed features, we also integrate the adaptive sharing with deep neural networks, whose discriminative power can be augmented by encoding class relationship. We further develop two strategies for solving the optimization problems in the two scenarios. Empirical results demonstrate that our method can significantly improve the classification performance by transferring knowledge appropriately.

Web page classification has attracted increasing research interest. It is intrinsically a multi-view and semi-supervised application, since web pages usually contain two or more types of data, such a text, hyperlinks and images, and unlabeled pages are generally much more than labeled ones. Web page data is commonly high-dimensional. Thus, how to extract useful features from this kind of data in the multi-view semi-supervised scenario is important for web page classification. To our knowledge, only one method is specially presented for this topic. And with respect to a few semi-supervised multi-view feature extraction methods on other applications, there still exists much room for improvement. In this paper, we firstly design a feature extraction schema called semi-supervised intra-view and inter-view manifold discriminant (SI2MD) learning, which sufficiently utilizes the intra-view and inter-view discriminant information of labeled samples and the local neighborhood structures of unlabeled samples. We then design a semi-supervised uncorrelation constraint for the SI2MD schema to remove the multi-view correlation in the semi-supervised scenario. By combining the SI2MD schema with the constraint, we propose an uncorrelated semi-supervised intra-view and inter-view manifold discriminant (USI2MD) learning approach for web page classification. Experiments on public web page databases validate the proposed approach.

Trivia is any fact about an entity which is interesting due to its unusualness, uniqueness, unexpectedness or weirdness. In this paper, we propose a novel approach for mining entity trivia from their Wikipedia pages. Given an entity, our system extracts relevant sentences from its Wikipedia page and produces a list of sentences ranked based on their interestingness as trivia. At the heart of our system lies an interestingness ranker which learns the notion of interestingness, through a rich set of domain-independent linguistic and entity based features. Our ranking model is trained by leveraging existing user-generated trivia data available on the Web instead of creating new labeled data. We evaluated our system on movies domain and observed that the system performs significantly better than the defined baselines. A thorough qualitative analysis of the results revealed that our rich set of features indeed help in surfacing interesting trivia in the top ranks.

Random feature map is popularly used to scale up kernel methods. However, employing a large number of mapped features to ensure an accurate approximation will still make the training time consuming. In this paper, we aim to improve the training efficiency of shift-invariant kernels by using fewer informative features without sacrificing precision. We propose a novel feature map method by extending Random Kitchen Sinks through fast data-dependent subspace embedding to generate the desired features. More specifically, we describe two algorithms with different tradeoffs on the running speed and accuracy, and prove that O(l) features induced by them are able to perform as accurately as O(l 2 ) features by other feature map methods. In addition, several experiments are conducted on the real-world datasets demonstrating the superiority of our proposed algorithms.