Instance matching, which aims at discovering the correspondences of instances between knowledge bases, is a fundamental issue for the ontological data sharing and integration in Semantic Web. Although considerable instance matching approaches have already been proposed, how to ensure both high accuracy and efficiency is still a big challenge when dealing with large-scale knowledge bases. This paper proposes an iterative framework, RiMOM-IM (RiMOM-Instance Matching). The key idea behind this framework is to fully utilize the distinctive and available matching information to improve the efficiency and control the error propagation. We participated in the 2013 and 2014 competition of Ontology Alignment Evaluation Initiative (OAEI), and our system was ranked the first. Furthermore, the experiments on previous OAEI datasets also show that our system performs the best.
As cellular users change mobile phone frequently, mobile phone recommendation system is of great importance for mobile operator to achieve business benefit. There are essential challenges for researchers to design such system. Among them, a critical one is how to obtain and model user's interest of mobile phone. So far, recommendation approaches based on phone's hardware features or personalized web behavior could not achieve satisfactory results. In this paper, we propose phone interest for mobile phone recommendation. Phone interest is a latent level concept which is extracted from a group of users' web log data, who have the same mobile phone. We propose a novel probabilistic model named "Phone Interest Model" only based on mobile web log data. All the log data are from cellular operators server, not from mobile phone's application. The model proves its effectiveness on large scale of station cellular data from real cellular operator. In experiments, we validated the model against 1.3 billion of mobile Web logs for 4 million distinct users in Beijing metropolitan areas, and show that the model achieves a good performance in the phone recommendation, also outperforms the baseline methods and offers significantly high fidelity.
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