Extracting data records from the web using tag path clustering

Fully automatic methods that extract lists of objects from the Web have been studied extensively. Record extraction, the first step of this object extraction process, identifies a set of Web page segments, each of which represents an individual object (e.g., a product). State-of-the-art methods suffice for simple search, but they often fail to handle more complicated or noisy Web page structures due to a key limitation – their greedy manner of identifying a list of records through pairwise comparison (i.e., similarity match) of consecutive segments. This paper introduces a new method for record extraction that captures a list of objects in a more robust way based on a holistic analysis of a Web page. The method focuses on how a distinct tag path appears repeatedly in the DOM tree of the Web document. Instead of comparing a pair of individual segments, it compares a pair of tag path occurrence patterns (called visual signals) to estimate how likely these two tag paths represent the same list of objects. The paper introduces a similarity measure that captures how closely the visual signals appear and interleave. Clustering of tag paths is then performed based on this similarity measure, and sets of tag paths that form the structure of data records are extracted. Experiments show that this method achieves higher accuracy than previous methods

Generative models for ticket resolution in expert networks

Ticket resolution is a critical, yet challenging, aspect of the delivery of IT services. A large service provider needs to handle, on a daily basis, thousands of tickets that report various types of problems. Many of those tickets bounce among multiple expert groups before being transferred to the group with the right expertise to solve the problem. Finding a methodology that reduces such bouncing and hence shortens ticket resolution time is a long-standing challenge. In this paper, we present a unified generative model, the Optimized Network Model (ONM), that characterizes the lifecycle of a ticket, using both the content and the routing sequence of the ticket. ONM uses maximum likelihood estimation, to represent how the information contained in a ticket is used by human experts to make ticket routing decisions. Based on ONM, we develop a probabilistic algorithm to generate ticket routing recommendations for new tickets in a network of expert groups. Our algorithm calculates all possible routes to potential resolvers and makes globally optimal recommendations, in contrast to existing classification methods that make static and locally optimal recommendations. Experiments show that our method significantly outperforms existing solutions

Efficient browsing of web search results on mobile devices based on block importance model

It is expected that more and more people will search the web when they are on the move. Though conventional search engines can be directly visited from mobile devices with web browsing capabilities, the information is not as conveniently accessible from a handheld device as it is from desktops. Existing information discovery mechanisms for searching the web are not well-suited to mobile devices. In this paper, a block importance model is employed to assign importance values to different segments of a web page, in order to extract and present more condensed search results to mobile users. Based on the block importance model, three presentations for displaying the result pages in different levels of detail have been designed to reduce both the number of user interactions and the overall search time. A set of user study experiments have been carried out to compare the three presentations and a commercial service on typical mobile devices. Experimental results show that our approaches can help users to explore web search results more efficiently. 1

Understanding Task-driven Information Flow in Collaborative Networks

Collaborative networks are a special type of social network formed by members who collectively achieve specific goals, such as fixing software bugs and resolving customers ’ problems. In such networks, information flow among members is driven by the tasks assigned to the network, and by the expertise of its members to complete those tasks. In this work, we analyze real-life collaborative networks to understand their common characteristics and how information is routed in these networks. Our study shows that collaborative networks exhibit significantly different properties compared with other complex networks. Collaborative networks have truncated power-law node degree distributions and other organizational constraints. Furthermore, the number of steps along which information is routed follows a truncated power-law distribution. Based on these observations, we developed a network model that can generate synthetic collaborative networks subject to certain structure constraints. Moreover, we developed a routing model that emulates task-driven information routing conducted by human beings in a collaborative network. Together, these two models can be used to study the efficiency of information routing for different types of collaborative networks – a problem that is important in practice yet difficult to solve without the method proposed in this paper