6299405
Web Based Pattern Mining and Matching Approach to Question Answering
We describe herein a Web based pattern mining and matching approach to question answering. For each type of questions, a lot of textual patterns can be learned from the Web automatically, using the TREC QA track data as training examples. These textual patterns are assessed by the concepts of support and confidence, which are borrowed from the data mining community. Given a new unseen question, these textual patterns can be utilized to extract and rank the plausible answers on the Web. The performance of this approach has been evaluated also by the TREC QA track data.
18058823
A novel Web usage mining approach for search engines
Abstract Web usage mining can be very useful to search engines. This paper proposes a novel effective approach to exploit the relationships among users, queries and resources based on the search engine's log. How this method can be applied is illustrated by a Chinese image search engine.
8536303
Web Based Question Answering with Aggregation Strategy
The Web is apparently an ideal source of answers to a large variety of questions, due to the tremendous amount of information available online. This paper describes a question answering system LAMP, which can instantly find answers from the Web. LAMP is publicly accessible on the Web, and its performance is comparable to the state-of-the-art question answering systems. One novel characteristic of this system is that its answer selection method is based on aggregation, which is shown to be more effective than the common methods based on individual or redundancy.
2001196
Web taxonomy integration using support vector machines
We address the problem of integrating objects from a source taxonomy into a master taxonomy. This problem is not only currently pervasive on the web, but also important to the emerging semantic web. A straightforward approach to automating this process would be to train a classifier for each category in the master taxonomy, and then classify objects from the source taxonomy into these categories. In this paper we attempt to use a powerful classification method, Support Vector Machine (SVM), to attack this problem. Our key insight is that the availability of the source taxonomy data could be helpful to build better classifiers in this scenario, therefore it would be beneficial to do transductive learning rather than inductive learning, i.e., learning to optimize classification performance on a particular set of test examples. Noticing that the categorizations of the master and source taxonomies often have some semantic overlap, we propose a method, Cluster Shrinkage (CS), to further enhance the classification by exploiting such implicit knowledge. Our experiments with real-world web data show substantial improvements in the performance of taxonomy integration.
42579325
A data model and algebra for the Web
The recent emergence of XML as a standard for representation and exchange of data on the Web has drawn significant attention. But existing semistructured data models, such as OEM, cannot support XML naturally and fully. A new data model named XOM is proposed in this paper as the common data model for the Web data source. Based on rooted connected directed graphs both cyclic and acyclic, whose nodes are labeled and edges are ranked, XOM can describe the aggregation relationship as well as the general reference relationship (linkage) between XML elements. Through ranked closure operation, both tree-oriented and graph-oriented models can be unified in XOM. As the formal foundation for query processing and optimization for Web data, a new algebra called XOM algebra, which includes six operations, i.e., object union, difference, select, project, paste and cut, is also proposed. In comparison with relational algebra, XOM algebra is more flexible and powerful.
30422301
Web Taxonomy Integration Using Spectral Graph Transducer
We address the problem of integrating objects from a source taxonomy into a master taxonomy. This problem is not only currently pervasive on the web, but also important to the emerging semantic web. A straightforward approach to automating this process would be to train a classifier for each category in the master taxonomy, and then classify objects from the source taxonomy into these categories. Our key insight is that the availability of the source taxonomy data could be helpful to build better classifiers in this scenario, therefore it would be beneficial to do transductive learning rather than inductive learning, i.e., learning to optimize classification performance on a particular set of test examples. In this paper, we attempt to use a powerful transductive learning algorithm, Spectral Graph Transducer (SGT), to attack this problem. Noticing that the categorizations of the master and source taxonomies often have some semantic overlap, we propose to further enhance SGT classifiers by incorporating the affinity information present in the taxonomy data. Our experiments with real-world web data show substantial improvements in the performance of taxonomy integration.
205667500
FFT-based algorithms for the string matching with mismatches problem
The string matching with mismatches problem requires finding the Hamming distance between a pattern P of length m and every length m substring of text T with length n. Fischer and Paterson's FFT-based algorithm solves the problem without error in O(@snlogm), where @s is the size of the alphabet @S [SIAM-AMS Proc. 7 (1973) 113-125]. However, this in the worst case reduces to O(nmlogm). Atallah, Chyzak and Dumas used the idea of randomly mapping the letters of the alphabet to complex roots of unity to estimate the score vector in time O(nlogm) [Algorithmica 29 (2001) 468-486]. We show that the algorithm's score variance can be substantially lowered by using a bijective mapping, and specifically to zero in the case of binary and ternary alphabets. This result is extended via alphabet remappings to deterministically solve the string matching with mismatches problem with a constant factor of 2 improvement over Fischer-Paterson's method.
34860384
Computer Simulation Study on Central Pattern Generator: from Biology to Engineering
Central pattern generator (CPG) is a neuronal circuit in the nervous system that can generate oscillatory patterns for the rhythmic movements. Its simplified format, neural oscillator, is wildly adopted in engineering application. This paper explores the CPG from an integral view that combines biology and engineering together. Biological CPG and simplified CPG are both studied. Computer simulation reveals the mechanism of CPG. Some properties, such as effect of tonic input and sensory feedback, stable oscillation, robustness, entrainment etc., are further studied. The promising results provide foundation for the potential engineering application in future.
452018
Online event-driven subsequence matching over financial data streams
Subsequence similarity matching in time series databases is an important research area for many applications. This paper presents a new approximate approach for automatic online subsequence similarity matching over massive data streams. With a simultaneous on-line segmentation and pruning algorithm over the incoming stream, the resulting piecewise linear representation of the data stream features high sensitivity and accuracy. The similarity definition is based on a permutation followed by a metric distance function, which provides the similarity search with flexibility, sensitivity and scalability. Also, the metric-based indexing methods can be applied for speed-up. To reduce the system burden, the event-driven similarity search is performed only when there is a potential event. The query sequence is the most recent subsequence of piecewise data representation of the incoming stream which is automatically generated by the system. The retrieved results can be analyzed in different ways according to the requirements of specific applications. This paper discusses an application for future data movement prediction based on statistical information. Experiments on real stock data are performed. The correctness of trend predictions is used to evaluate the performance of subsequence similarity matching.
10487566
Progressive computation of the min-dist optimal-location query
This paper proposes and solves the min-dist optimal-location query in spatial databases. Given a set S of sites, a set O of weighted objects, and a spatial region Q, the min-dist optimal-location query returns a location in Q which, if a new site is built there, minimizes the average distance from each object to its closest site. This query can help a franchise (e.g. McDonald's) decide where to put a new store in order to maximize the benefit to its customers. To solve this problem is challenging, for there are theoretically infinite number of locations in Q, all of which could be candidates. This paper first provides a theorem that limits the number of candidate locations without losing the power to find exact answers. Then it provides a progressive algorithm that quickly suggests a location, tells the maximum error it may have, and keeps refining the result. When the algorithm finishes, the exact answer can be found. The intermediate result of early runs can be used to prune the search space for later runs. Crucial to the pruning technique are novel lower-bound estimators. The proposed algorithm, the effect of several optimizations, and the progressiveness are experimentally evaluated.
13991122
Multi-scale classification of 3-D objects
We describe an approach to the classification of 3-D objects using a multi-scale representation. This approach starts with a smoothing algorithm for representing objects at different scales. Smoothing is applied in curvature space directly, thus avoiding the usual shrinkage problems and allowing for efficient implementations. A 3-D similarity measure that integrates the representations of the objects at multiple scales is introduced. Given a library of models, objects that are similar based on this multi-scale measure are grouped together into classes. The objects that are in the same class are combined into a single prototype object. Finally, the prototypes are used for hierarchical recognition by first comparing the scene representation to the prototypes and then matching it only to the objects in the most likely class rather than to the entire library of models. Beyond its application to object recognition, this approach provides an attractive implementation of the intuitive notions of scale and approximate similarity for 3-D shapes.