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Taxonomies, representing hierarchical data, are a key knowledge source in multiple disciplines. Information processing across taxonomies is not possible unless they are appropriately merged for commonalities and differences. For taxonomy merging, the first task is to identify common concepts between the taxonomies. Then, these common concepts along with their associated concepts in the two taxonomies need to be integrated. Doing this in a conflict-free manner is a challenging task and generally requires human intervention. In this paper, we explore the possibility of asymmetrically merging one taxonomy into another automatically. Given one or more source taxonomies and a destination taxonomy, modeled as directed acyclic graphs, we present intuitive algorithms that merge relevant portions of the source taxonomies into the destination taxonomy. We prove that our algorithms are conflict-free, information lossless, and scalable. We also define precision and recall measures for evaluating enriched taxonomies, such as TA, the result of merging two taxonomies, with TI, the ideal merger. Our experiments indicate the effectiveness of our approach.

With the ever-growing competition in telecommunications markets, operators have to increasingly rely on business intelligence to offer the right incentives to their customers. Existing approaches for telecom business intelligence have almost solely focused on the individual behavior of customers. In this paper, we use the call detail records of a mobile operator to construct call graphs, that is, graphs induced by people calling each other. We determine the structural properties of these graphs and also introduce the Treasure-Hunt model to describe the shape of mobile call graphs. Moreover, we determine how the structure of these call graphs evolve over time. Finally, since short messaging service (SMS) is becoming a preferred mode of communication among many sections of the society, we study the properties of the SMS graph. Our analysis indicates several interesting similarities and differences between the SMS graph and the corresponding call graph. We believe that our analysis techniques can allow telecom operators to better understand the social behavior of their customers and potentially provide major insights for designing effective incentives.

Before undertaking new biomedical research, identifying concepts that have already been patented is essential. A traditional keyword-based search on patent databases may not be sufficient to retrieve all the relevant information, especially for the biomedical domain. This paper presents BioPatentMiner, a system that facilitates information retrieval and knowledge discovery from biomedical patents. The system first identifies biological terms and relations from the patents and then integrates the information from the patents with knowledge from biomedical ontologies to create a semantic Web. Besides keyword search and queries linking the properties specified by one or more RDF triples, the system can discover semantic associations between the Web resources. The system also determines the importance of the resources to rank the results of a search and prevent information overload while determining the semantic associations.