SociO. Help:Curators. While an article's Curator always has the final say and responsibility for an article's contents, Scholarpedia permits both Curator and Community approval of articles. "Curator" approval occurs when the curator explicitly registers approval for a particular revision. "Community" approval occurs at least one week after submission, when the revision has been neither rejected nor approved by the article's Curator, but has received approval votes from at least two of the article's Contributors, and has received no votes for revision rejection.
Any revision on which there is disagreement among Contributors as to whether it should be approved is left pending. Article Contributors must agree, more often than not, with the evaluation of the article's Curator in order to be able to continue to participate in votes on whether to approve or reject a revision, and thus community of article contributors should soon begin to emulate the judgment of the article's Curator. Scholarpedia:Curator. Each article has a single Curator (or simply "curator") who is ultimately responsible for the article's contents.
Each article's curator is a world-recognized authority on the topic covered by the article . When an article is first published, its established expert becomes the article's curator. After publication, the curator can be changed based on the decision of the contributors to each article, in which case the article's curator need not have been among the article's original authors. Scholarpedia relies its curators to ensure the integrity and quality of its articles by requiring that each article be publicly approved (or anonymously rejected) by a Scholarpedia curator.
Permissions All Scholarpedia Curators are able to: Sponsor a proposed article, thereby vouching for the credibility and authority of the article's authorship, and Approve a Scholarpedia article for publication. The decisions of each curator regarding revisions to the articles they curate are final. Rationale (stub) Ple. How to Build a Content Marketing Machine. Content Marketing is hot. White hot. SEO and digital marketing thought leaders are declaring that Content Marketing is the next big thing.
Even Rand is touting its importance. The strategy of Content Marketing makes sense: instead of pushing messages about your product at prospects, pull prospects towards you by publishing content about your prospects’ interests. Search rank, traffic, leads and all sort of goodness flow from this approach. So the conversation is no longer about if or why an organization should practice Content Marketing. But the still unanswered question is “How?” So if you’re wondering “How?” The Machine First, let’s take a look at the machine, all of its pistons, cogs, smokestacks and miscellaneous parts.
Now we’ll go over the machine, part by part. Goals & Plan What is the goal, the end output for your Content Marketing Machine? Your plan then becomes to create a content-powered path that takes your prospect from where they are today to the end goal. Team Ideas Production. Curator's ǝpoɔ. The Future Of Content: Content Is The Future. Editor’s note: Contributor Ashkan Karbasfrooshan is the founder and CEO of WatchMojo, he hosts a show on business and has published books on success. Follow him @ashkan. “I thought the analysis of content vs other video companies very convincing.
But I’m curious: the content game hasn’t worked out so well for AOL and Yahoo. Audiences are fickle. Are you predicting a rosier future?” Infrastructure, Platforms & Content Today, the Web’s infrastructure is built, and we’re filling the pipes with content — mainly free, ad-supported content. It might seem like the real opportunities are in user-generated content and aggregation, but anyone who’s worked in those fields recognize their limitations: Simply put, marketers want to advertise alongside professional content. Content is marketing; Marketing as content Content – video in particular – may be promotional or commercial, in either case it’s a means to an end. Content isn’t only increasingly free, it’s also short. The Economics of Content. NeWeb.
Note This. Curative Collective. Memento. NeWeb Teams. PEARL_terna_TREE_ves. More Visually Appealing Bookmarking. Internet topology. Pearltrees Alternatives. Reverse image search engine. Folksonomies - Cooperative Classification and Communication Through Shared Metadata. The Creation of Metadata: Professionals, Content Creators, Users Metadata is often characterized as “data about data.” Metadata is information, often highly structured, about documents, books, articles, photographs, or other items that is designed to support specific functions. These functions are usually to facilitate some organization and access of information. Administrative, structural, and descriptive metadata are three broad categories of metadata (Taylor, 2004). This paper focus primarily on descriptive metadata which identifies and functions to organize information based on its intellectual content.
Traditionally metadata is created by dedicated professionals. While professionally created metadata are often considered of high quality, it is costly in terms of time and effort to produce. User created metadata is a third approach, and this paper focuses on grassroots community classification of digital assets. Tagging Content in Del.icio.us and Flickr “a social bookmarks manager. Tags & Folksonomies - What are they, and why should you care? Tags, or folksonomies are actually a lot simpler than much of the acedemic debate surrounding them. Put simply, they are a user defined method for organizing data.
Im going to try to explain what they are, why they are important to marketers and web devs and suggest some ways you might use them. Follow the title link above for the full post. First, Some Examples of Tags in Action There are only a few good, working examples of tagging in operation right now. They include: del.icio.us - a social bookmarking systemFlickr - a photo publishing / sharing siteTechnorati Tags - a recent feature added to the popular blog search engineMetaFilter Tags - another recently added feature to the original group blog.TagSurf - an experimental forum based on tags rather than the standard way of organizing topics del.icio.us and flickr were the first systems to use tagging as far as im aware, at least to become popular because of it.
So How does it Work? So What Makes Tags Important? Some Further Reading Quote: Folksonomy :: vanderwal.net. This page is a static permanent web document. It has been written to provide a place to cite the coinage of folksonomy. This is response the request from many in the academic community to document the circumstances and date of the creation of the term folksonomy. The definition at creation is also part of this document. This document pulls together bits of conversations and ideas I wrote regarding folksonomy on listserves, e-mail, in my blogs and in blog comments on other's sites in 2004.
Background I have been a fan of ad hoc labeling and tagging systems since at least the late 1980s after watching a co-worker work his magic with Lotus Magellan (he would add his own ad hoc keywords or tags to the documents on his hard drive, paying particular attention to add these tags to documents others created so to add his context). In 2003 del.icio.us was started by Joshua Schacter and it included identity in its social bookmarking. Creation of Folksonomy Term Definition of Folksonomy. Folksonomy. An empirical analysis of the complex dynamics of tagging systems, published in 2007, has shown that consensus around stable distributions and shared vocabularies does emerge, even in the absence of a central controlled vocabulary.
For content to be searchable, it should be categorized and grouped. While this was believed to require commonly agreed on sets of content describing tags (much like keywords of a journal article), recent research has found that, in large folksonomies, common structures also emerge on the level of categorizations. Accordingly, it is possible to devise mathematical models of collaborative tagging that allow for translating from personal tag vocabularies (personomies) to the vocabulary shared by most users. Origin Folksonomy is a type of collaborative tagging system in which the classification of data is done by users. Folksonomies consist of three basic entities: users, tags, and resource.
There are two different groups of folksonomies. What database does Google use. Beyond Bookmarks: Schemes for Organizing the Web. How to Burst the "Filter Bubble" that Protects Us from Opposing Views. The term “filter bubble” entered the public domain back in 2011when the internet activist Eli Pariser coined it to refer to the way recommendation engines shield people from certain aspects of the real world. Pariser used the example of two people who googled the term “BP”. One received links to investment news about BP while the other received links to the Deepwater Horizon oil spill, presumably as a result of some recommendation algorithm. This is an insidious problem. Much social research shows that people prefer to receive information that they agree with instead of information that challenges their beliefs.
This problem is compounded when social networks recommend content based on what users already like and on what people similar to them also like. This is the filter bubble—being surrounded only by people you like and content that you agree with. And the danger is that it can polarise populations creating potentially harmful divisions in society. It’s certainly a start. Exploring the 'Net and Star Trek with Pearltrees. Over the past few days, you may have noticed that we've embedded a new tool known as Pearltrees in certain articles on TG Daily. As you can see, Pearltrees embeds a significant amount of supplemental information related to a post in a way that is easy to navigate, while giving you a chance to preview content before you even click a link.
There's a lot more to Pearltrees, though. During a recent interview, the company told us they are engaged in building an expanding a comprehensive "social curation" community. What does this mean for you? Well, you can "team-up" with people who share your interests to curate a topic, thereby providing improved context, more depth and high-quality information. To give you an idea of how the tool works, check out this cool "pearltree" about the Star Trek universe. "Obviously, it would be pretty difficult to find all of this content in any reasonable amount of time using Google or another search tool," Pearltrees rep Oliver Starr told TG Daily.
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. 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 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 Utopia now! 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 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 XML Books | An Indexer’s Guide to the Internet, 2nd Edition. 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. 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. In the early days of the World Wide Web, the keywords meta element was used by web designers to tell web search engines what the web page was about, but these keywords were only visible in a web page's source code and were not modifiable by users.
"A Description of the Equator and Some ØtherLands", collaborative hypercinema portal, produced by documenta X, 1997. User upload page associating user contributed media with the term Tag. Knowledge management. 21 Pictures That Will Restore Your Faith in Humanity: How BuzzFeed makes viral hits in four easy steps. Museums and the Web 2010: Papers: Miller, E. and D. Wood, Recollection: Building Communities for Distributed Curation and Data Sharing. British Medical Journal: Statistics Notes.
Toward a Civilization of Collective Intelligence. CI capitalising on the crowd. Using the internet to harness the wisdom of the crowd - Technology Industry News | Market & Trends | The Irish Times - Thu, Dec 06. Local Harvest / Farmers Markets / Family Farms / CSA / Organic Food. Tester le rendu de la version mobile de son site Web depuis un ordinateur. | SysKB.com. Mentimeter audience response system - free. Jing.
Knowledge Is a Common Good - Transform Network. Open Data Institute | Knowledge for everyone. OPEN DATA. Share » Gossip Consensus Algorithm Based on Time-Varying Influence Factors and Weakly Connected Graph for Opinion Evolution in Social Networks. What’s the law around aggregating news online? A Harvard Law report on the risks and the best practices.