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Entity resolution is an important graph mining problem. Entity resolution is particularly interesting and challenging when there is rich relational structure. In this paper, we study the problem of performing entity resolution in familial networks. In our setting, we are given partial views of a familial network as described from the point of view of(More)
BACKGROUND Complement deficiency predisposes to autoimmune renal disease. Since complement deficient mice are increasingly used to study the immunopathogenesis of renal disease we have determined whether mice deficient in C3 or C4 are susceptible to spontaneous immune-mediated renal injury. METHODS C3-deficient, C4-deficient and complement-sufficient,(More)
As the amount of recorded digital information increases, there is a growing need for flexible recommender systems which can incorporate richly structured data sources to improve recommendations. In this paper, we show how a recently introduced statistical relational learning framework can be used to develop a generic and extensible hybrid recommender(More)
Considering the current transformation of Environmental Information Systems to environmental services accessible over the web, the provision of adaptable environmental services is becoming an emerging challenge. Within this context, solutions that support the adaptation and distributed execution of service chains seem promising. In this paper we present a(More)
This document serves as supplemental material for the paper “User Preferences for Hybrid Explanations”, by Pigi Kouki, James Schaffer, Jay Pujara, John O’Donovan, and Lise Getoor, published on the 11th ACM Conference on Recommender Systems [1]. In this document, we present the details of the survey presented in the paper, and we encourage the reader to read(More)
Hybrid recommender systems combine several different sources of information to generate recommendations. These systems demonstrate improved accuracy compared to single-source recommendation strategies. However, hybrid recommendation strategies are inherently more complex than those that use a single source of information, and thus the process of explaining(More)
Entity resolution in settings with rich relational structure often introduces complex dependencies between coreferences. Exploiting these dependencies is challenging – it requires seamlessly combining statistical, relational, and logical dependencies. One task of particular interest is entity resolution in familial networks. In this setting, multiple(More)
Motivated from the Context Aware Computing, and more particularly from the Data-Driven Process Adaptation approach, we propose the Semantic Context Space (SCS) Engine which aims to facilitate the provision of adaptable business processes. The SCS Engine provides a space which stores semantically annotated data and it is open to other processes, systems, and(More)
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