In this paper, we explore a geo-spatial learning-to-rank framework for identifying local experts. Three of the key features of the proposed approach are: (i) a learning-based framework for integrating multiple factors impacting local expertise that leverages the fine-grained GPS coordinates of millions of social media users; (ii) a location-sensitive random walk that propagates crowd knowledge ...

This paper concerns the iterative implementation of a knowledge model in a data mining context. Our approach relies on coupling a Bayesian network design with an association rule discovery technique. First, discovered association rule relevancy isenhanced by exploiting the expert knowledge encoded within a Bayesian network, i.e., avoiding to provide trivial rules w.r.t. known dependencies. More...

For patients suffering from rare diseases it is often hard to find an expert clinician. Existing registries rely on manual registration procedures and cannot easily be kept up to date. A prototype data collection system for discovering experts on rare diseases using MEDLINE has been successfully deployed. Initial manual analyses demonstrate proof of concept and deliver promising results. Examin...

Identification of expert to domain knowledge in any field of interest is essential for consulting in industry, academia and scientific community. The objective of this study is to address the expert-finding task in contemporary communities. We proposed Multifaceted Web Mining Architecture (MfWMA) and implemented a tool with data extracted from Growbag, dblpXML and web authors home page resource...

Expert finding systems employ social networks analysis and natural language processing to identify candidate experts in organization or enterprise datasets based on a user’s profile, her documents, and her interaction with other users. Expert discovery in public social networks such as Facebook faces the challenges of matching users to a wide range of expertise areas, because of the diverse hum...

PILGRM (the platform for interactive learning by genomics results mining) puts advanced supervised analysis techniques applied to enormous gene expression compendia into the hands of bench biologists. This flexible system empowers its users to answer diverse biological questions that are often outside of the scope of common databases in a data-driven manner. This capability allows domain expert...

To support knowledge discovery from data, many pattern mining techniques have been proposed. One of the bottlenecks for their dissemination is the number of computed patterns that appear to be either trivial or uninteresting with respect to available knowledge. Integration of domain knowledge in constraint-based data mining is limited. Relevant patterns still miss because methods partly fail in...

Although expert elicited knowledge and data mining discovered knowledge appear to be completely opposite and competing solutions to the same problems, they are actually complementary concepts. Besides, together they maximize their individual qualities. This chapter highlights how each one profits from the other and illustrates their cooperation in existing systems developed in the medical domai...

This paper is based on the design of a system for collaborative knowledge discovery, in a situation where both some data and a domain expert are available. This system is composed of two elements: a data-mining algorithm (Pasteur) producing association rules organized in graphs, and a module (Filter) for collection, refinement and use of expert’s comments on the algorithm’s output. The two put ...