Yupeng Fu

Finding Experts Using Social Network Analysis

Searching an organizations document repositories for experts is a frequently occurred problem in intranet information management. A common method for finding experts in an organization is to use social networks -people are not isolated but connected by various kinds of associations. In organizations, people explicitly send email to one another thus social networks are likely to be contained in the patterns of communication. Moreover, in some web pages, the relationship among people is also recorded. In our approach we propose several strategies in discovering the associations among people from emails and web pages. Based on the social networks, we proposed an expertise propagation algorithm: from a ranked list of candidates according to their probability of being expert for a certain topic, we select a small set of the top ones as seed, and then use the social networks among the candidates to discover other potential experts. The experiments on TREC enterprise track show significant performance improvement with the algorithm.Searching an organizations document repositories for experts is a frequently occurred problem in intranet information management. A common method for finding experts in an organization is to use social networks - people are not isolated but connected by various kinds of associations. In organizations, people explicitly send email to one another thus social networks are likely to be contained in the patterns of communication. Moreover, in some web pages, the relationship among people is also recorded. In our approach we propose several strategies in discovering the associations among people from emails and web pages. Based on the social networks, we proposed an expertise propagation algorithm: from a ranked list of candidates according to their probability of being expert for a certain topic, we select a small set of the top ones as seed, and then use the social networks among the candidates to discover other potential experts. The experiments on TREC enterprise track show significant performance improvement with the algorithm.

Automatic search engine performance evaluation with click-through data analysis

Performance evaluation is an important issue in Web search engine researches. Traditional evaluation methods rely on much human efforts and are therefore quite time-consuming. With click-through data analysis, we proposed an automatic search engine performance evaluation method. This method generates navigational type query topics and answers automatically based on search users. querying and clicking behavior. Experimental results based on a commercial Chinese search engines user logs show that the automatically method gets a similar evaluation result with traditional assessor-based ones.

A CDD-based formal model for expert finding

Searching an organizations document repositories for experts is a frequently faced problem in intranet information management. This paper proposes a candidate-centered model which is referred as Candidate Description Document (CDD)-based retrieval model. The expertise evidence about an expert candidate scattered over repositories is mined and aggregated automatically to form a profile called the candidates CDD, which represents his knowledge. We present the model from its foundations through its logical development and argue in favor of this model for expert finding. We devise and compare the different strategies for exploring a variety of expertise evidence. The experiments on TREC enterprise corpora demonstrate that the CDD-based model achieves significant and consistent improvement on performance through comparative studies with non-CDD methods.

Ajax-based report pages as incrementally rendered views

While Ajax-based programming enables faster performance and higher interface quality over pure server-side programming, it is demanding and error prone as each action that partially updates the page requires custom, ad-hoc code. The problem is exacerbated by distributed programming between the browser and server, where the developer uses JavaScript to access the page state and Java/SQL for the database. The FORWARD framework simplifies the development of Ajax pages by treating them as rendered views, where the developer declares a view using an extension of SQL and page units, which map to the view and render the data in the browser. Such a declarative approach leads to significantly less code, as the framework automatically solves performance optimization problems that the developer would otherwise hand-code. Since pages are fueled by views, FORWARD leverages years of database research on incremental view maintenance by creating optimization techniques appropriately extended for the needs of pages (nesting, variability, ordering), thereby achieving performance comparable to hand-coded JavaScript/Java applications.

A PDD-Based searching approach for expert finding in intranet information management

Expert finding is a frequently faced problem in Intranet information management, which aims at locating certain employees in large organizations. A Person Description Document (PDD)-based retrieval model is proposed in this paper for effective expert finding. At first, features and context about an expert are extracted to form a profile which is called the expert’s PDD. A retrieval strategy based on BM2500 algorithm and bi-gram weighting is then used to rank experts which are represented by their PDDs. This model proves effective and the method based on this model achieved the best performance in TREC2005 expert finding task.Comparative studies with traditional non-PDD methods indicate that the proposed model improves the system performance by over 45%.

Forward: data-centric UIS using declarative templates that efficiently wrap third-party JavaScript components

While Ajax programming and the plethora of JavaScript component libraries enable high-quality Uls in web applications, integrating them with page data is laborious and error-prone as a developer has to handcode incremental modifications with trigger-based programming and manual coordination of data dependencies. The FORWARD web framework simplifies the development of Ajax applications through declarative, state-based templates. This declarative, data-centric approach is characterized by the principle of logical/physical independence, which the database community has often deployed successfully. It enables FORWARD to leverage database techniques, such as incremental view maintenance, updatable views, capability-based component wrappers and cost-based optimization to automate efficient live visualizations. We demonstrate an end-to-end system implementation, including a web-based IDE (itself built in FORWARD), academic and commercial applications built in FORWARD and a wide variety of JavaScript components supported by the declarative templates.