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Knowledge retrieval

Knowledge retrieval
Knowledge Retrieval seeks to return information in a structured form, consistent with human cognitive processes as opposed to simple lists of data items. It draws on a range of fields including epistemology (theory of knowledge), cognitive psychology, cognitive neuroscience, logic and inference, machine learning and knowledge discovery, linguistics, and information technology. Overview[edit] In the field of retrieval systems, established approaches include: Data Retrieval Systems (DRS), such as database management systems, are well suitable for the storage and retrieval of structured data.Information Retrieval Systems (IRS), such as web search engines, are very effective in finding the relevant documents or web pages. Both approaches require a user to read and analyze often long lists of data sets or documents in order to extract meaning. The goal of knowledge retrieval systems is to reduce the burden of those processes by improved search and representation. References[edit]

15 Effective Tools for Visual Knowledge Management Since I started my quest a few years ago searching for the ultimate knowledge management tool, I’ve discovered a number of interesting applications that help people efficiently organize information. There certainly is no shortage of solutions for this problem domain. Many tools exist that offer the ability to discover, save, organize, search, and retrieve information. However, I’ve noticed a trend in recent years, and some newer applications are focusing more on the visual representation and relationship of knowledge. I believe this is in part due to the wider adoption of mind mapping (and concept mapping), and leveraging concepts and advances in the semantic web community. Most traditional personal knowledge management (PKM) or personal information management (PIM) applications offer the same basic set of features: These are essential features, however don’t offer too much to the more visually-inclined knowledge junkies. 15. 14. 13. 12. eyePlorer 11. Pages: 1 2 3

Knowledge extraction Knowledge extraction is the creation of knowledge from structured (relational databases, XML) and unstructured (text, documents, images) sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing. Although it is methodically similar to information extraction (NLP) and ETL (data warehouse), the main criteria is that the extraction result goes beyond the creation of structured information or the transformation into a relational schema. It requires either the reuse of existing formal knowledge (reusing identifiers or ontologies) or the generation of a schema based on the source data. Overview[edit] After the standardization of knowledge representation languages such as RDF and OWL, much research has been conducted in the area, especially regarding transforming relational databases into RDF, identity resolution, knowledge discovery and ontology learning. Examples[edit] XML[edit]

Data warehouse Data Warehouse Overview In computing, a data warehouse (DW, DWH), or an enterprise data warehouse (EDW), is a database used for reporting and data analysis. Integrating data from one or more disparate sources creates a central repository of data, a data warehouse (DW). Data warehouses store current and historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons. The data stored in the warehouse is uploaded from the operational systems (such as marketing, sales, etc., shown in the figure to the right). A data warehouse constructed from integrated data source systems does not require ETL, staging databases, or operational data store databases. A data mart is a small data warehouse focused on a specific area of interest. This definition of the data warehouse focuses on data storage. Benefits of a data warehouse[edit] A data warehouse maintains a copy of information from the source transaction systems. History[edit]

Knowledge Management using Mind Maps Click to download this Mind Map document. In this article, we’ll what a look at knowledge management. Actually more than just knowledge management – we’ll examine how knowledge is created, what it is, and how you can use it. In these days of information overload, the compact way of representing ideas that is embodied in Mind Mapping is essential. From Data to Information We are fed with a huge amount of data all the time, and we are pretty good at sorting through the incoming data and applying our understanding of the relationships between the different pieces of data and its meaning to us, so we can turn the data into information. delete, distort, and filter the data to fit our understanding of the world. From Information to Knowledge This massively reduces the amount of material we need to deal with, and also helps us process new data as it comes along, but still we are overwhelmed with the amount of information. From Knowledge to Wisdom and Understanding Overcoming Boredom

What’s the law around aggregating news online? A Harvard Law report on the risks and the best practices [So much of the web is built around aggregation — gathering together interesting and useful things from around the Internet and presenting them in new ways to an audience. It’s the foundation of blogging and social media. But it’s also the subject of much legal debate, particularly among the news organizations whose material is often what’s being gathered and presented. Kimberley Isbell of our friends the Citizen Media Law Project has assembled a terrific white paper on the current state of the law surrounding aggregation — what courts have approved, what they haven’t, and where the (many) grey areas still remain. This should be required reading for anyone interested in where aggregation and linking are headed. You can get the full version of the paper (with footnotes) here; I’ve added some links for context. During the past decade, the Internet has become an important news source for most Americans. What is a news aggregator? Can they do that? AFP v. Associated Press v. So is it legal?

Data mining Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.[1] Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use.[1][2][3][4] Data mining is the analysis step of the "knowledge discovery in databases" process or KDD.[5] Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.[1] Etymology[edit] In the 1960s, statisticians and economists used terms like data fishing or data dredging to refer to what they considered the bad practice of analyzing data without an a-priori hypothesis.

Knowledge Management Software | Document Management Software | Enterprise Content Management | vdocs | Document Scanning | Medical Records Scanning | On-Site Scanning | Wide Format Knowledge Management Knowledge Management (KM) comprises a range of practices used in an organisation to identify, create, represent, distribute and enable adoption of insights and experiences. Such insights and experiences comprise knowledge, either embodied in individuals or embedded in organisational processes or practice. An established discipline since 1991, KM includes courses taught in the fields of business administration, information systems, management, and library and information sciences. More recently, other fields have started contributing to KM research; these include information and media, computer science, public health, and public policy. Many large companies and non-profit organisations have resources dedicated to internal KM efforts, often as a part of their 'Business Strategy', 'Information Technology', or 'Human Resource Management' departments. Strategies Knowledge Management Knowledge may be accessed at three stages: before, during, or after KM-related activities.

Museums and the Web 2010: Papers: Miller, E. and D. Wood, Recollection: Building Communities for Distributed Curation and Data Sharing Background The National Digital Information Infrastructure and Preservation Program at the Library of Congress is an initiative to develop a national strategy to collect, archive and preserve the burgeoning amounts of digital content for current and future generations. It is based on an understanding that digital stewardship on a national scale depends on active cooperation between communities. These diverse collections are held in the dispersed repositories and archival systems of over 130 partner institutions where each organization collects, manages, and stores at-risk digital content according to what is most suitable for the industry or domain that it serves. NDIIPP partners understand through experience that aggregating and sharing diverse collections is very challenging. Early in 2009, a pilot project recognizing the specific characteristics of this community was initiated by the Library of Congress and Zepheira. Specific goals for the Recollection project are to: How It Works

Information retrieval Information retrieval is the activity of obtaining information resources relevant to an information need from a collection of information resources. Searches can be based on metadata or on full-text (or other content-based) indexing. Automated information retrieval systems are used to reduce what has been called "information overload". Many universities and public libraries use IR systems to provide access to books, journals and other documents. Web search engines are the most visible IR applications. Overview[edit] An information retrieval process begins when a user enters a query into the system. An object is an entity that is represented by information in a database. Most IR systems compute a numeric score on how well each object in the database matches the query, and rank the objects according to this value. History[edit] Model types[edit] For effectively retrieving relevant documents by IR strategies, the documents are typically transformed into a suitable representation. Recall[edit]

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