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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]

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]

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 Process of extracting and discovering patterns in large data sets Data mining is the process of extracting and 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 of extracting information (with intelligent methods) from a data set and transforming 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] Background[edit] The manual extraction of patterns from data has occurred for centuries. Process[edit]

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]

Real-Time News Curation - The Complete Guide Part 4: Process, Key Tasks, Workflow I have received a lot of emails from readers asking to illustrate more clearly what the actual typical tasks of a news curator are, and what are the tools that someone would need to use to carry them out. In Part 4 and 5 of this guide I am looking specifically at both the workflow, the tasks involved as well as at the attributes, qualities and skills that a newsmaster, or real-time news curator should have. 1. Sequence your selected news stories to provide the most valuable information reading experience to your readers. There are likely more tasks and elements to the news curator workflow that I have been able to identify right here. Please feel free to suggest in the comment area, what you think should be added to this set of tasks. Photo credits:1.

The Accidental Taxonomist: Taxonomy Trends and Future What are the trends in taxonomies, and where is the field going? The future of taxonomies turned out to be a unifying theme of last week’s annual Taxonomy Boot Camp conference, in Washington, DC, the premier event in taxonomies, from its opening keynote to its closing panel. “From Cataloguer to Designer” was the title of the opening keynote, an excellent presentation by consultant Patrick Lambe of Straits Knowledge. He said that there are new opportunities for taxonomists, especially in the technology space, if they change their mindset and their role from that of cataloguers, who describe the world as it is, to that of designers, who plan things as they could be. New trends involving taxonomies that he described include search-based applications, autoclassification, and knowledge graphs (such as the automatically curated index card of key information on a topic, as appears in some Google search results). New trends and technologies were discussed in individual presentations, too.

Knowledge tags Keyword assigned to information Overview[edit] History[edit] The use of keywords as part of an identification and classification system long predates computers. Paper data storage devices, notably edge-notched cards, that permitted classification and sorting by multiple criteria were already in use prior to the twentieth century, and faceted classification has been used by libraries since the 1930s. Online databases and early websites deployed keyword tags as a way for publishers to help users find content. Examples[edit] Within a blog[edit] Within application software[edit] Assigned to computer files[edit] There are various systems for applying tags to the files in a computer's file system. For an event[edit] In research[edit] Special types[edit] Triple tags[edit] Hashtags[edit] Knowledge tags[edit] Advantages and disadvantages[edit] Complex system dynamics[edit] Spamming[edit] Syntax[edit] See also[edit] Notes[edit] References[edit]

Intute: Encouraging Critical Thinking Online Encouraging Critical Thinking Online is a set of free teaching resources designed to develop students' analytic abilities, using the Web as source material. Two units are currently available, each consisting of a series of exercises for classroom or seminar use. Students are invited to explore the Web and find a number of sites which address the selected topic, and then, in a teacher-led group discussion, to share and discuss their findings. The exercises are designed so that they may be used either consecutively to form a short course, or individually. The resources encourage students to think carefully and critically about the information sources they use. The subject matter of the exercises is of relevance to a range of humanities disciplines (most especially, though by no means limited to, philosophy and religious studies), while the research skills gained will be valuable to all students. Teacher's Guide (Units 1 and 2) Printable version (PDF) Resources for Unit 1 Resources for Unit 2