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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]

http://en.wikipedia.org/wiki/Knowledge_extraction

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.

Data mining Data mining is an interdisciplinary subfield of computer science.[1][2][3] It is the computational process of discovering patterns in large data sets ("big data") involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.[1] The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.[1] Aside from the raw analysis step, it 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] Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.[4] Etymology[edit] Background[edit] The manual extraction of patterns from data has occurred for centuries. Research and evolution[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. 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.

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. Grammar induction There is now a rich literature on learning different types of grammar and automata, under various different learning models and using various different methodologies. Grammar Classes[edit] Grammatical inference has often been very focused on the problem of learning finite state machines of various types (see the article Induction of regular languages for details on these approaches), since there have been efficient algorithms for this problem since the 1980s. More recently these approaches have been extended to the problem of inference of context-free grammars and richer formalisms, such as multiple context-free grammars and parallel multiple context-free grammars.

edtechpost - PLE Diagrams A Collection of PLE diagramsNOTE: You can log in with the guest account (edtechpost_guest, same password) to add your own PLE image to the wiki or email them to me at edtechpost@gmail.com. Index Tool-Oriented Use/Action Oriented People Oriented Knowledge Management Software 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.

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