Zhaoqi Chen

Web People Search via Connection Analysis

Nowadays, searches for Webpages of a person with a given name constitute a notable fraction of queries to web search engines. Such a query would normally return Webpages related to several namesakes, who happened to have the queried name, leaving the burden of disambiguating and collecting pages relevant to a particular person (from among the namesakes) on the user. In this article we develop a Web People Search approach that clusters Webpages based on their association to different people. Our method exploits a variety of semantic information extracted from Web pages, such as named entities and hyperlinks, to disambiguate among namesakes referred to on the Web pages. We demonstrate the effectiveness of our approach by testing the efficacy of the disambiguation algorithms and its impact on person search.

Exploiting context analysis for combining multiple entity resolution systems

Entity Resolution (ER) is an important real world problem that has attracted significant research interest over the past few years. It deals with determining which object descriptions co-refer in a dataset. Due to its practical significance for data mining and data analysis tasks many different ER approaches has been developed to address the ER challenge. This paper proposes a new ER Ensemble framework. The task of ER Ensemble is to combine the results of multiple base-level ER systems into a single solution with the goal of increasing the quality of ER. The framework proposed in this paper leverages the observation that often no single ER method always performs the best, consistently outperforming other ER techniques in terms of quality. Instead, different ER solutions perform better in different contexts. The framework employs two novel combining approaches, which are based on supervised learning. The two approaches learn a mapping of the clustering decisions of the base-level ER systems, together with the local context, into a combined clustering decision. The paper empirically studies the framework by applying it to different domains. The experiments demonstrate that the proposed framework achieves significantly higher disambiguation quality compared to the current state of the art solutions.

Adaptive graphical approach to entity resolution

Entity resolution is a very common Information Quality (IQ) problem with many different applications. In digital libraries, it is related to problems of citation matching and author name disambiguation; in Natural Language Processing, it is related to coreference matching and object identity; in Web application, it is related to Web page disambiguation. The problem of Entity Resolution arises because objects/entities in real world datasets are often referred to by descriptions, which might not be unique identifiers of these entities, leading to ambiguity. The goal is to group all the entity descriptions that refer to the same real world entities. In this paper we present a graphical approach for entity resolution. It complements the traditional methodology with the analysis of the entity-relationship graph constructed for the dataset being analyzed. The paper demonstrates that a technique that measures the degree of interconnectedness between various pairs of nodes in the graph can significantly improve the quality of entity resolution. Furthermore, the paper presents an algorithm for making that technique self-adaptive to the underlying data, thus minimizing the required participation from the domain-analyst and potentially further improving the disambiguation quality.

WEST: Modern Technologies for Web People Search

In this paper we describe WEST (Web Entity Search Technologies) system that we have developed to improve people search over the Internet. Recently the problem of Web People Search (WePS) has attracted significant attention from both the industry and academia. In the classic formulation of WePS problem the user issues a query to a web search engine that consists of a name of a person of interest. For such a query, a traditional search engine such as Yahoo or Google would return webpages that are related to any people who happened to have the queried name. The goal of WePS, instead, is to output a set of clusters of webpages, one cluster per each distinct person, containing all of the webpages related to that person. The user then can locate the desired cluster and explore the webpages it contains.