Searching for people on the Web is one of the most common query types submitted to Web search engines today. However, when a person name is queried, the returned Webpages often contain documents related to several distinct namesakes who have the queried name. The task of disambiguating and finding the Webpages related to the specific person of interest is left to the user. Many Web People Search (WePS) approaches have been developed recently that attempt to automate this disambiguation process. Nevertheless, the disambiguation quality of these techniques leaves major room for improvement. In this article, we present a new WePS approach. It is based on issuing additional auxiliary queries to the Web to gain additional knowledge about the Webpages that need to be disambiguated. Thus, the approach uses the Web as an external data source by issuing queries to collect co-occurrence statistics. These statistics are used to assess the overlap of the contextual entities extracted from the Webpages. The article also proposes a methodology to make this Web querying technique efficient. Further, the article proposes an approach that is capable of combining various types of disambiguating information, including other common types of similarities, by applying a correlation clustering approach with after-clustering of singleton clusters. These properties allow the framework to get an advantage in terms of result quality over other state-of-the-art WePS techniques.

Modern data processing techniques such as entity resolution, data cleaning, information extraction, and automated tagging often produce results consisting of objects whose attributes may contain uncertainty. This uncertainty is frequently captured in the form of a set of multiple mutually exclusive value choices for each uncertain attribute along with a measure of probability for alternative values. However, the lay end-user, as well as some end-applications, might not be able to interpret the results if outputted in such a form. Thus, the question is how to present such results to the user in practice, for example, to support attribute-value selection and object selection queries the user might be interested in. Specifically, in this article we study the problem of maximizing the quality of these selection queries on top of such a probabilistic representation. The quality is measured using the standard and commonly used set-based quality metrics. We formalize the problem and then develop efficient approaches that provide high-quality answers for these queries. The comprehensive empirical evaluation over three different domains demonstrates the advantage of our approach over existing techniques.

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 Web pages 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 Web pages, one cluster per each distinct person, containing all of the Web pages related to that person. The user then can locate the desired cluster and explore the Web pages it contains.

Searching for people on the Web is one of the most common query types to the web search engines today. However, when a person name is queried, the returned webpages often contain documents related to several distinct namesakes who have the queried name. The task of disambiguating and finding the webpages related to the specific person of interest is left to the user. Many Web People Search (WePS) approaches have been developed recently that attempt to automate this disambiguation process. Nevertheless, the disambiguation quality of these techniques leaves a major room for improvement. This paper presents a new server-side WePS approach. It is based on collecting co-occurrence information from theWeb and thus it uses theWeb as an external data source. A skyline-based classification technique is developed for classifying the collected co-occurrence information in order to make clustering decisions. The clustering technique is specifically designed to (a) handle the dominance that exists in data and (b) to adapt to a given clustering quality measure. These properties allow the framework to get a major advantage in terms of result quality over all the latest WePS techniques we are aware of, including all the 18 methods covered in the recent WePS competition [2].

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.

In this paper we develop a disambiguation algorithm and then study its impact on People Search. The proposed algorithm first uses extraction techniques to automatically extract `significant' entities such as the names of other persons, organizations, and locations on each Web page. In addition, it extracts and parses HTML and Web related data on each Web page, such as hyperlinks and email addresses. The algorithm then views all this information in a unified way: as an entity-relationship graph where entities (e.g., people, organizations, locations, Web pages) are interconnected via relationships (e.g., `Web page-mentions-person', relationships derived from hyperlinks, etc). The algorithm gains its power by being able to analyze several types of information: attributes associated with the entities (e.g., TF/IDF for Web pages) and, most importantly, direct and indirect interconnections that exist among entities in the ER graph. We next outline our approach in Section 2 and then compare it with the state of the art solutions in Section 3.

The empirical investigation of the effectiveness of information retrieval (IR) systems requires a test collection, a set of query topics, and a set of relevance judgments made by human assessors for each query. Previous experiments show that differences in human relevance assessments do not affect the relative performance of retrieval systems. Based on this observation, we propose and evaluate a new approach to replace the human relevance judgments by an automatic method. Ranking of retrieval systems with our methodology correlates positively and significantly with that of human-based evaluations. In the experiments, we assume a Web-like imperfect environment: the indexing information for all documents is available for ranking, but some documents may not be available for retrieval. Such conditions can be due to document deletions or network problems. Our method of simulating imperfect environments can be used for Web search engine assessment and in estimating the effects of network conditions (e.g., network unreliability) on IR system performance.