Abstract-In bibliographies like DBLP and Citeseer, there are three kinds of entity-name problems that need to be solved. First, multiple entities share one name, which is called the name sharing problem. Second, one entity has different names, which is called the name variant problem. Third, multiple entities share multiple names, which is called the name mixing problem. We aim to solve these problems based on one model in this paper. We call this task complete entity resolution. Different from previous work, our work use global information based on data with two types of information, words and author names. We propose a generative latent topic model that involves both author names and words -the LDA-dual model, by extending the LDA (Latent Dirichlet Allocation) model. We also propose a method to obtain model parameters that is global information. Based on obtained model parameters, we propose two algorithms to solve the three problems mentioned above. Experimental results demonstrate the effectiveness and great potential of the proposed model and algorithms.

Abstract-In many telecom and web applications, there is a need to identify whether data objects in the same source or different sources represent the same entity in the real-world. This problem arises for subscribers in multiple services, customers in supply chain management, and users in social networks when there lacks a unique identifier across multiple data sources to represent a real-world entity. Entity resolution is to identify and discover objects in the data sets that refer to the same entity in the real world.We investigate the entity resolution problem for large data sets where efficient and scalable solutions are needed. We propose a novel unsupervised blocking algorithm, namely SPectrAl Neighborhood (SPAN), which constructs a fast bipartition tree for the records based on spectral clustering such that real entities can be identified accurately by neighborhood records in the tree. There are two major novel aspects in our approach: 1) We develop a fast algorithm that performs spectral clustering without computing pairwise similarities explicitly, which dramatically improves the scalability of the standard spectral clustering algorithm; 2) We utilize a stopping criterion specified by Newman-Girvan modularity in the bipartition process. Our experimental results with both synthetic and real-world data demonstrate that SPAN is robust and outperforms other blocking algorithms in terms of accuracy while it is efficient and scalable to deal with large data sets.

Result merging is a key component in a metasearch engine. Once the results from various search engines are collected, the metasearch system merges them into a single ranked list. The effectiveness of a metasearch engine is closely related to the result merging algorithm it employs. In this paper, we investigate a variety of resulting merging algorithms based on a wide range of available information about the retrieved results, from their local ranks, their titles and snippets, to the full documents of these results. The effectiveness of these algorithms is then compared experimentally based on 50 queries from the TREC Web track and 10 most popular general-purpose search engines. Our experiments yield two important results. First, simple result merging strategies can outperform Google. Second, merging based on the titles and snippets of retrieved results can outperform that based on the full documents.

Abstract. Information in a deep Web source can be accessed through queries submitted on its query interface. Many Web applications need to interact with the query interfaces of deep Web sources such as deep Web crawling and comparison-shopping. Analyzing the querying capability of a query interface is critical in supporting such interactions automatically and effectively. In this paper, we propose a querying capability model based on the concept of atomic query which is a valid query with a minimal attribute set. We also provide an approach to construct the querying capability model automatically by identifying atomic queries for any given query interface. Our experimental results show that the accuracy of our algorithm is good. [2,7,11,12]. The focus of this paper is on the automatic analysis of the querying capability of complex query interfaces, i.e., we want to find out what kinds of queries are valid (acceptable) by any given interface. A typical complex query interface consists of a series of attributes, each of which has one or more control elements like textbox, selection list, radio button and checkbox. A query is a combination of pairs, each consisting of an attribute and its assigned value. Those combinations that are accepted by the query interface syntax are valid queries; others are invalid queries. Determining whether a query is valid is an important aspect of querying capability Keywords

In applications of Web data integration, we frequently need to identify whether data objects in different data