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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.

To realize the vision of a Semantic Web for Life Sciences, discovering relations between resources is essential. It is very difficult to automatically extract relations from Web pages expressed in natural language formats. On the other hand, because of the explosive growth of information, it is difficult to manually extract the relations. In this paper we present techniques to automatically discover relations between biomedical resources from the Web. For this purpose we retrieve relevant information from Web Search engines and Pubmed database using various lexico-syntactic patterns as queries over SOAP web services. The patterns are initially handcrafted but can be progressively learnt. The extracted relations can be used to construct and augment ontologies and knowledge bases. Experiments are presented for general biomedical relation discovery and domain specific search to show the usefulness of our technique.

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.

Although various ontologies and knowledge sources have been developed in recent years to facilitate biomedical research, it is difficult to assimilate information from multiple knowledge sources. To enable researchers to easily gain understanding of a biomedical concept, a biomedical Semantic Web that seamlessly integrates knowledge from biomedical ontologies, publications and patents would be very helpful. In this paper, current research efforts in representing biomedical knowledge in Semantic Web languages are surveyed. Techniques are presented for information retrieval and knowledge discovery from the Semantic Web that extend traditional keyword search and database querying techniques. Finally, some of the challenges that have to be addressed to make the vision of a biomedical Semantic Web a reality are discussed.

Biomedical text plays a fundamental role in knowledge discovery in life science, in both basic research (in the field of bioinformatics) and in industry sectors devoted to improving medical practice, drug development, and health care (such as medical informatics, clinical genomics, and other sectors). Several groups in the IBM Research Division are collaborating on the development of a prototype system for text analysis, search, and text-mining methods to support problem solving in life science. The system is called "BioTeKS" ("Biological Text Knowledge Services"), and it integrates research technologies from multiple IBM Research labs. BioTeKS is also the first major application of the UIMA (Unstructured Information Management Architecture) initiative also emerging from IBM Research. BioTeKS is intended to analyze biomedical text such as MEDLINETM abstracts, medical records, and patents; text is analyzed by automatically identifying terms or names corresponding to key biomedical entities (e.g., "genes," "proteins," "compounds," or "drugs") and concepts or facts related to them. In this paper, we describe the value of text analysis in biomedical research, the development of the BioTeKS system, and applications which demonstrate its functions.

With the explosive growth of information in the WWW, it is becoming increasingly difficult for the user to find information of interest. Visualisations may be helpful in assisting the users in their information retrieval task. Effective visualisation of the structure of a WWW site is extremely useful for browsing through the site. Visualisation can also be used to augment a WWW search engine when too many or too few results are retrieved. In this paper, we discuss several visualisations we have developed to facilitate information retrieval on the WWW. With VRML becoming the standard for graphics on the Web and efficient VRML browsers becoming available, VRML was used for developing these visualisations. Unique visualisations like focus + context views of WWW nodes and semantic visualisation are presented and examples are given on scenarios where the visualisations are useful.

Search engines are useful because they allow the user to find information of interest from the World-Wide Web. However, most of the popular search engines today are textual; they do not allow the user to find images from the Web. This paper describes a search engine that integrates text and image search. One or more Web sites can be indexed for both textual and image information, allowing the user to search based on keywords or images or both. Another problem with the current search engines is that they show the results as pages of scrolled list; this is not very user-friendly. Therefore our search engine allows the user to visualize the results in various ways. This paper explains the indexing and searching techniques of the search engine and highlights several features of the querying interface to make the retrieval process more efficient. Examples are used to show the usefulness of the technology.

Monitoring and analyzing the performance of mobile applications is challenging when the applications rely on remote services and functioning networks. Root causes of failures and performance anomalies of client–server applications are difficult to pinpoint because of their distributed nature. In this paper, we introduce the Mobile Infrastructure Analytics System (MIAS), which helps efficiently detect and debug application faults in a distributed environment, holistically analyzing application and network activity across client devices, application servers, database servers, etc. MIAS collects hypertext transfer protocol (HTTP) session data and system logs from servers and instrumented mobile applications, automatically correlates HTTP sessions with server activity, detects anomalous behavior using statistical techniques, and extracts a small and relevant set of log entries for manual inspection. We show how faults were detected and root causes pinpointed just by glancing at the evidence, for a real-world bookstore mobile application.

Overview diagrams are one of the best tools for orientation and navigation in hypermedia systems. However, constructing effective overview diagrams is a challenging task. This paper describes the Navigational View Builder, a tool which allows the user to interactively create useful visualizations of the information space. It uses four strategies to form effective views. These are binding, clustering, filtering and hierarchization. These strategies use a combination of structural and content analysis of the underlying space for forming the visualizations. This paper discusses these strategies and shows how they can be applied for forming visualizations for the World-Wide Web.

Glyphmaker allows nonexpert users to customize their own graphical representations using a simple glyph editor and a point-and-click binding mechanism. In particular, users can create and then alter bindings to visual representations, bring in new data or glyphs with associated bindings, change ranges for bound data, and do these operations interactively. They can also focus on data down to any level of detail, including individual elements, and then isolate or highlight the focused region. These features empower users, letting them employ their specialized domain knowledge to create customized visual representations for further exploration and analysis. For ease of design and use, we built Glyphmaker on top of Iris Explorer, the Silicon Graphics Inc. (SGI) dataflow visualization system. The current version of Glyphmaker has been successfully tested on a materials system simulation. We are planning a series of tests and evaluations by scientists and engineers using real data. >

Proceedings of the 11th international conference on Extending database technology: Advances in database technology, Social Network Analysis has emerged as a key paradigm in modern sociology, technology, and information sciences. The paradigm stems from the view that the attributes of an individual in a network are less important than their ties (relationships) with other individuals in the network. Exploring the nature and strength of these ties can help understand the structure and dynamics of social networks and explain real-world phenomena, ranging from organizational efficiency to the spread of information and disease. In this paper, we examine the communication patterns of millions of mobile phone users, allowing us to study the underlying social network in a large-scale communication network. Our primary goal is to address the role of social ties in the formation and growth of groups, or communities, in a mobile network. In particular, we study the 'evolution of churners in an operator's network spanning over a period of four months. Our analysis explores the propensity of a subscriber to churn out of a service provider's network depending on the number of ties (friends) that have already churned. Based on our findings, we propose a spreading activation-based technique that predicts potential churners by examining the current set of churners and their underlying social network. The efficiency of the prediction is expressed as a lift curve, which indicates the fraction of all churners that can be caught when a certain fraction of subscribers were contacted.

Proceedings of the 15th ACM international conference on Information and knowledge management, With ever growing competition in telecommunications markets, operators have to increasingly rely on business intelligence to offer the right incentives to their customers. Toward this end, existing approaches have almost solely focussed on the individual behaviour of customers. Call graphs, that is, graphs induced by people calling each other, can allow telecom operators to better understand the interaction behaviour of their customers, and potentially provide major insights for designing effective incentives. In this paper, we use the Call Detail Records of a mobile operator from four geographically disparate regions to construct call graphs, and analyse their structural properties. Our findings provide business insights and help devise strategies for Mobile Telecom operators. Another goal of this paper is to identify the shape of such graphs. In order to do so, we extend the well-known reachability analysis approach with some of our own techniques to reveal the shape of such massive graphs. Based on our analysis, we introduce the Treasure-Hunt model to describe the shape of mobile call graphs. The proposed techniques are general enough for analysing any large graph. Finally, how well the proposed model captures the shape of other mobile call graphs needs to be the subject of future studies.

Community identif ication has been a major research area in social network analysis. One popular type of community is one in which every member of the community knows every other member, which can be viewed as a clique in a graph representing the social network. In this paper, we present a novel highly scalable method for finding large maximal cliques that is validated with experimental results on several real-life social networks. In addition, while the importance of finding tightly knit communities has been widely accepted, the influence of community on the behavior of the individuals belonging to those communities is relatively unexplored. We also attempt to answer various questions in the context of cliques as communities in telecom social networks: how individuals in communities behave, what influence a community has on the behavior of an individual, and whether communities have a characteristic behavior of their own. We also examine whether the behavior of individuals who belong to communities differs from those who do not. We believe that the findings of such a study will reassert the importance of finding communities in telecom social networks and will help telecom operators improve group targeting and customer relationship management.

Information Visualization can make hypermedia systems more usable by presenting a site map to assist in navigation. In this paper we explain some of the difficulties in developing effective visualizations of hypermedia information spaces and highlight some of the key systems and techniques that have been developed to overcome the difficulties. Finally, we discuss some of the major challenges for the next generation of information visualization researchers.