9484087
Towards automatic incorporation of search engines into a large-scale metasearch engine
A metasearch engine supports unified access to multiple component search engines. To build a very large-scale metasearch engine that can access up to hundreds of thousands of component search engines, one major challenge is to incorporate large numbers of autonomous search engines in a highly effective manner. To solve this problem, we propose automatic search engine discovery, automatic search engine connection, and automatic search engine result extraction techniques. Experiments indicate that these techniques are highly effective and efficient.
16923777
Identifying redundant search engines in a very large scale metasearch engine context
For a given set of search engines, a search engine is redundant if its searchable contents can be found from other search engines in this set. In this paper, we propose a method to identify redundant search engines in a very large-scale metasearch engine context. The general problem is equivalent to an NP hard problem -- the set-covering problem. Due to the large number of search engines that need to be considered and the large sizes of these search engines, approximate solutions must be developed. In this paper, we propose a general methodology to tackle this problem and within the context of this methodology, we propose several new heuristic algorithms for solving the set-covering problem.
957553
A highly scalable and effective method for metasearch
A metasearch engine is a system that supports unified access to multiple local search engines. Database selection is one of the main challenges in building a large-scale metasearch engine. The problem is to efficiently and accurately determine a small number of potentially useful local search engines to invoke for each user query. In order to enable accurate selection, metadata that reflect the contents of each search engine need to be collected and used. This article proposes a highly scalable and accurate database selection method. This method has several novel features. First, the metadata for representing the contents of all search engines are organized into a single integrated representative. Such a representative yields both computational efficiency and storage efficiency. Second, the new selection method is based on a theory for ranking search engines optimally. Experimental results indicate that this new method is very effective. An operational prototype system has been built based on the proposed approach.
3399913
WISE-Integrator: A System for Extracting and Integrating Complex Web Search Interfaces of the Deep Web
We demonstrate WISE-Integrator - an automatic search interface extraction and integration tool. The basic research issues behind this tool will also be explained.
15933707
Automatic extraction of web search interfaces for interface schema integration
This paper provides an overview of a technique for extracting information from the Web search interfaces of e-commerce search engines that is useful for supporting automatic search interface integration. In particular, we discuss how to group elements and labels on a search interface into attributes and how to derive certain meta-information for each attribute.
9755561
A Treemap-Based Result Interface for Search Engine Users
Search engines and metasearch engines on the World Wide Web typically display representative information such as the result document titles and snippets in a one-dimensional ranked list. Alternatively, a few others such as Clusty and Kartoo cluster their results so that users can, to a certain extent, interact with documents, keywords and/or clusters. The number of documents that can be effectively presented in one screen is usually limited to between 10 and 20. In this paper, we propose a method based on treemap visualization that substantially improve the information compactness. In addition, it provides a few unique post-search interaction methods that enable a user to manage a large number of results. An online prototype system is built. Various experiments are designed and done to evaluate the efficiency, effectiveness and usability.
12670883
On Hadamard-Type Output Coding in Multiclass Learning
The error-correcting output coding (ECOC) method reduces the multiclass learning problem into a series of binary classifiers. In this paper, we consider the dense ECOC methods, combining an economical number of base learners. Under the criteria of row separation and column diversity, we suggest the use of Hadamard matrices to design output codes and show them better than other codes of the same size. Comparative experiments based on the support vector machines are made for some real datasets from the UCI machine learning repository.
345054
An Evolutionary Algorithm for Parameters Identification in Parabolic Systems
In this paper we construct a novel evolutionary algorithm. It yields good performance on a collection of parabolic parameter identification problems. The algorithm has a good tolerability for the noise in the observed data. Even when the noise level is up to 10% we can also get such a good result.
7810391
Refinement of Nmr-determined Protein Structures with Database Derived Distance Constraints
The protein structures determined by NMR (Nuclear Magnetic Resonance Spectroscopy) are not as detailed and accurate as those by X-ray crystallography and are often underdetermined due to the inadequate distance data available from NMR experiments. The uses of NMR-determined structures in such important applications as homology modeling and rational drug design have thus been severely limited. Here we show that with the increasing numbers of high quality protein structures being determined, a computational approach to enhancing the accuracy of the NMR-determined structures becomes possible by deriving additional distance constraints from the distributions of the distances in databases of known protein structures. We show through a survey on 462 NMR structures that, in fact, many inter-atomic distances in these structures deviate considerably from their database distributions and based on the refinement results on 10 selected NMR structures that these structures can actually be improved significantly when a selected set of distances are constrained within their high probability ranges in their database distributions.
17480069
Similarity-based Web service matchmaking
With the increasing growth in popularity of Web services, matchmaking of relevant Web services becomes a significant challenge. Commonly, Web service is described by WSDL and published on UDDI registers. UDDI provides limited search facilities allowing only a keyword-based search of businesses, services, and the so called tModels based on names and identifiers. This category-based keyword-browsing method is clearly insufficient. Semantic Web service uses DAML-S instead of WSDL to represent capabilities of Web services. This improvement enables software agents or search engines to automatically find appropriate Web services via ontologies and reasoning algorithm enriched methods. However, the high cost of formally defining to the heavy and complicated services makes this improvement widespread adoption unlikely. To cope with these limitations, we have developed a suite of methods which assesses the similarity of Web services to achieve matchmaking. In particular, we present a conceptual model which classifies properties of Web services into four categories. For each category, a similarity assessment method has been given. In Web service matchmaking process, these similarity assessment methods can be used together or individually. Experiments highlight complementary contributions that our work makes to facilitate Web service matchmaking.
1209241
Spectral Energy Minimization for Semi-supervised Learning
Data mining problems often involve a large amount of unlabeled data and there is often very limited known information on the dataset. In such scenario, semi-supervised learning can often improve classification performance by utilizing unlabeled data for learning. In this paper, we proposed a novel approach to semi-supervised learning as as an optimization of both the classification energy and cluster compactness energy in the unlabeled dataset. The resulting integer programming problem is relaxed by a semi-definite relaxation where efficient solution can be obtained. Furthermore, the spectral graph methods provide improved energy minimization via the incorporation of additional criteria. Results on UCI datasets show promising results.