I Wed: An Integrated Multigraph Cut-Based Approach for Detecting Events from a Website

Abstract

AbstractThe web is a sensor of the real world. Often, content of web pages correspond to real world objects or events whereas the web usage data reflect users’ opinions and actions to the corresponding events. Moreover, the evolution patterns of the web usage data may reflect the evolution of the corresponding events over time. In this paper, we present two variants of i Wed(Integrated Web Event Detector) algorithm to extract events from website data by integrating author-centric data and visitor-centric data. We model the website related data as a multigraph, where each vertex represents a web page and each edge represents the relationship between the connected web pages in terms of structure, semantic, and/or usage pattern. Then, the problem of event detection is to extract strongly connected subgraphs from the multigraph to represent real world events. We solve this problem by adopting the normalized graph cut algorithm. Experiments show that the usage patterns play an important role in i Wed algorithms and can produce high quality results.KeywordsEvent DetectionSemantic SimilarityNovelty DetectionHigh Quality ResultContent GraphThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.