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]
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.
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?
IBM - Knowledge Discovery and Data Mining Knowledge Discovery and Data Mining (KDD) is an interdisciplinary area focusing upon methodologies for extracting useful knowledge from data. The ongoing rapid growth of online data due to the Internet and the widespread use of databases have created an immense need for KDD methodologies. The challenge of extracting knowledge from data draws upon research in statistics, databases, pattern recognition, machine learning, data visualization, optimization, and high-performance computing, to deliver advanced business intelligence and web discovery solutions. IBM Research has been at the forefront of this exciting new area from the very beginning. With the explosive growth of online data and IBM’s expansion of offerings in services and consulting, data-based solutions are increasingly crucial.
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. The NDIIPP network of partners have collected a diverse array of digital content, including social science data-sets; geospatial information; Web sites and blogs; e-journals; audiovisual materials; and digital government records ( 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. Specific goals for the Recollection project are to: Future Work
Systems Engineering Systems engineering techniques are used in complex projects: spacecraft design, computer chip design, robotics, software integration, and bridge building. Systems engineering uses a host of tools that include modeling and simulation, requirements analysis and scheduling to manage complexity. Systems engineering is an interdisciplinary field of engineering that focuses on how to design and manage complex engineering systems over their life cycles. The systems engineering process is a discovery process that is quite unlike a manufacturing process. History[edit] The term systems engineering can be traced back to Bell Telephone Laboratories in the 1940s.[1] The need to identify and manipulate the properties of a system as a whole, which in complex engineering projects may greatly differ from the sum of the parts' properties, motivated various industries to apply the discipline.[2] Concept[edit] Systems engineering signifies only an approach and, more recently, a discipline in engineering.
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. Identify NicheIdentify your specific topic-theme. The more specific, the better. The broader your coverage the less relevant it will be to your readers, unless you are already a very popular individual that people trust on a number od different topics. 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.
Inference Engine An Inference Engine is a tool from Artificial Intelligence. The first inference engines were components of expert systems. The typical expert system consisted of a knowledge base and an inference engine. Architecture[edit] The logic that an inference engine uses is typically represented as IF-THEN rules. A simple example of Modus Ponens often used in introductory logic books is "If you are human then you are mortal". Rule1: Human(x) => Mortal(x) A trivial example of how this rule would be used in an inference engine is as follows. This innovation of integrating the inference engine with a user interface led to the second early advancement of expert systems: explanation capabilities. An inference engine cycles through three sequential steps: match rules, select rules, and execute rules. In the first step, match rules, the inference engine finds all of the rules that are triggered by the current contents of the knowledge base. Implementations[edit] See also[edit] References[edit]