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Knowledge has generally existed within strict disciplinary boundaries, creating barriers against the free flow of information. The boundaries between disciplines reduces the ability of researchers to fully assess the work that has been accomplished and can lead to redundancy and to situations in which scholars are "reinventing the wheel" when they could instead be advancing knowledge into new frontiers. Is there a solution? There is if we take the time to create a cross-disciplinary understanding of knowledge representation and organization. The solution would require a comprehensive, interdisciplinary effort from scholars in diverse disciplines including communications, sociology, anthropology, information science, biology, computer science and philosophy. The Structure for Encompassing Extensible Knowledge (SEEK) is a model I propose to explore the possibilities for knowledge integration theoretically, technologically and from the perspective of human management.

(aka YALE) data mining, machine learning, knowledge discovery, business intelligence in Java. 400+ operators: data mining incl. Weka,learning,preprocessing,validation,visualization. GUI,API,XML,analysis,knowledge discovery,databases,business intelligenc

The YAGO-NAGA project started in 2006 with the goal of building a conveniently searchable, large-scale, highly accurate knowledge base of common facts in a machine-processible representation. We have already harvested knowledge about millions of entities and facts about their relationships, from Wikipedia and WordNet with careful integration of these two sources. The resulting knowledge base, coined YAGO, has very high precision and is freely available. The facts are represented as RDF triples, and we have developed methods and prototype systems for querying, ranking, and exploring knowledge. Our search engine NAGA provides ranked answers to queries based on statistical models.

Yes, knowledge management is the hottest subject of the day. The question is: what is this activity called knowledge management, and why is it so important to each and every one of us? The following writings, articles, and links offer some emerging perspectives in response to these questions. As you read on, you can determine whether it all makes any sense or not.

The National Center for Biomedical Ontology is a consortium of leading biologists, clinicians, informaticians, and ontologists who develop innovative technology and methods allowing scientists to create, disseminate, and manage biomedical information and knowledge in machine-processable form. Our visionis that all biomedical knowledge and data are disseminated on the Internet using principled ontologies, such that they are semantically interoperable and useful for improving biomedical science and clinical care. Our resources include the Open Biomedical Ontologies (OBO) library, the Open Biomedical Data (OBD) repositories, and tools for accessing and using this information in research. The Center collaborates with biomedical researchers conducting Driving Biological Projects to enable outside research and stimulate technology development in the Center. The Center undertakes outreach and educational activities (Biomedical Informatics Program) to train future researchers to use biomedical ontologies and related tools with the goal of enhancing scientific discovery.

Social Enterprise Innovation Network members help each other get to the next level of impact faster and more effectively thanks to accelerated knowledge discovery, rapid skill development, and new partnership opportunities.

The Bibliographic Knowledge Network (BKN) is a project to develop a suite of tools and services to encourage formation of virtual organizations in scientific communities of various types. BKN is a project started in September 2008 with funding by the NSF Cyber-enabled Discovery and Innovation (CDI) Program. The major participating organizations are the American Institute of Mathematics (AIM), Harvard University, Stanford University and the University of California, Berkeley.

YourVersion continuously discovers new and personally relevant web content based on your interests, and lets you easily bookmark and share your discoveries with friends.

More than 40 years ago the fragmentation of scientific knowledge was a problem actively discussed but without much visible progress toward a solution; perhaps people then had the consummate wisdom to know that no problem is so big that you can't run away from it.

In the process of knowledge discovery, workers examine available information in order to make sense of it. By sensemaking, we mean interacting with and operating on the information with a variety of information processing mechanisms [3,18]. Previously, we introduced a concept that uses the spreadsheet metaphor with cells containing visualizations of complex data. In this paper, we extend and apply a cognitive model called "visual sensemaking " to the Visualization Spreadsheet. We use the task of making sense of a large Web site as a concrete example through out the paper for demonstration. Using a variety of visualization techniques, such as the Disk Tree and Cone Tree, we show that the interactions of the Visualization Spreadsheet help users draw conclusions from the overall relationships of the entire information set.

WikiProteins enables community annotation in a Wiki-based system. Extracts of major data sources have been fused into an editable environment that links out to the original sources. Data from community edits create automatic copies of the original data. Semantic technology captures concepts co-occurring in one sentence and thus potential factual statements. In addition, indirect associations between concepts have been calculated. We call on a 'million minds' to annotate a 'million concepts' and to collect facts from the literature with the reward of collaborative knowledge discovery. The system is available for beta testing at http://www.wikiprofessional.org

Computing all human knowledge from the web

Unstructured Information Management applications are software systems that analyze large volumes of unstructured information in order to discover knowledge that is relevant to an end user. UIMA is a framework and SDK for developing such applications. An example UIM application might ingest plain text and identify entities, such as persons, places, organizations; or relations, such as works-for or located-at. UIMA enables such an application to be decomposed into components, for example "language identification" -> "language specific segmentation" -> "sentence boundary detection" -> "entity detection (person/place names etc.)". Each component must implement interfaces defined by the framework and must provide self-describing metadata via XML descriptor files. The framework manages these components and the data flow between them. Components are written in Java or C++; the data that flows between components is designed for efficient mapping between these languages. UIMA additionally provides capabilities to wrap components as network services, and can scale to very large volumes by replicating processing pipelines over a cluster of networked nodes.

Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large data sets.

Alchemy is a software package providing a series of algorithms for statistical relational learning and probabilistic logic inference, based on the Markov logic representation. Alchemy allows you to easily develop a wide range of AI applications, including: * Collective classification * Link prediction * Entity resolution * Social network modeling * Information extraction

an international team of researchers, headed by Timothy Wright, a professor at New Mexico State University and a researcher at the Smithsonian Institution in Washington DC, collaborated to study the early evolution of the parrots.

there is a different way of thinking about this problem that takes advantage of exciting new developments in molecular biology, evolutionary developmental biology, morphology, neurobiology, and linguistics. In a nutshell, for the first time we have a science that enables us to understand the actual, the possible and the unimaginable, a landscape that will forever change our understanding of what it means to be human, including how we arrived at our current point in evolutionary theory, and where might end up in ten or ten million years.

Our KnowItAll research project is focused on Machine Reading of the World Wide Web.

R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.