background preloader

Knowledge extraction

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] Related:  Curate Content ResearchMachine Learning

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. Most traditional personal knowledge management (PKM) or personal information management (PIM) applications offer the same basic set of features: * Storage of notes and documents * Search functionality and keyword/tagging capability * Outline view in a traditional hierarchy, or user-defined views * Task management, calendar, and contact management (mainly PIM, not KM) These are essential features, however don’t offer too much to the more visually-inclined knowledge junkies.

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?

Knowledge Management as Educational Science Knowledge Management as Educational Science Our brains naturally function systematically, and if we can learn to teach and learn to this biological strength we can become far more effective. Image provided by Walter Smith. Can we create a science of knowledge management that teachers can use to influence learning? Lowest Common Denominator of Knowledge The human brain is made up of networks of hundreds of billions of interneurons. Our brains are made up of millions of interneuron chain combinations that reflect the actions and interactions of our bodies with the environment. In simple terms, we are what we think we are, and we are all different. Managing Knowledge for Systems Thinking Systems of brain neurons function as complex versions of single brain neurons. Our brains function systematically. Educating to Think Systematically Can we create education systems that enable us to be inherently successful? The articles in this series outline a model for educating for success. Tags: Business

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 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. Process[edit]

Knowledge Management Tools 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. You can summarize a huge amount of information in a very compact space. 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.

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

Related: