24 results for cross pollination wikipedia

OCT 30th 2008

Cross-Pollinating DBpedia and FreebaseNow that Freebase is available as Linked Data a big question that comes to mind is whether these two major projects will move to assimilate one another. DBpedia and Freebase – two endeavors primarily focused on curating unstructured and semi-structured data about everything and releasing it back into the wild (with structure) – get the bulk of their information from Wikipedia, so the amount of topical overlap is assumed to be extremely high. DBpedia gains new information when it extracts data from the latest Wikipedia dump, whereas Freebase, in addition to Wikipedia extractions, gains new information through its userbase of editors.

Continue reading Cross-Pollinating DBpedia and Freebase

FEB 21st 2008

A lot of you emailed me asking where to find more videos, so I'm delivering the goods. I've expanded the previous list from a paltry 17 to a remarkable 302, and I've included podcasts this time! There were so many videos I had to break them up into different categories for easier skimming. There are no duplicates, however I did place some videos into more than one category when I felt it was appropriate. This list is monstrous, enjoy.

Continue reading 302 Semantic Web Videos and Podcasts!

True KnowledgeTrue Knowledge is a natural language search engine and question answering site, but to leave it at that would not do the site justice. What makes it stand out from similar sounding services like Powerset and Freebase? True Knowledge tackles natural language search and question answering (much like Powerset and Hakia), and it also maintains a knowledge base of facts about the world (similar to DBpedia and Freebase). However, what makes True Knowledge stand out is that they've combined these features and encourage their userbase to contribute facts and add new knowledge.

Continue reading True Knowledge: The Natural Language Question Answering Wikipedia for Facts

OCT 30th 2008

The Seesaw Effect of Algorithms vs. DataOver the years I've noticed that the importance of algorithms and data tends to shift back and forth, depending on which at the time is hardest to duplicate (often from a business perspective). This effect seems to be caused by the availability or demand of one side increasing or decreasing, shifting the balance of importance to the other. At one point the world of software was dominated by the proprietary. The organization with the best software (backend, algorithms, etc) was the dominant entity and data (from say, a Web 2.0 perspective) was generally not the focus. This may have partly been the responsibility of a mindset formed during an era with very little storage space and before mass user activity on the Web.

Continue reading Algorithms vs. Data: The Seesaw Effect

JAN 9th 2007

The journey from now to the Semantic Web is a long one. What we currently have on our hands with the current version of the Web are billions of documents totaling terabytes of data. This data is usually found within HTML pages comprised mainly of non-validating markup and very little, if any, meta data.

While there are billions of documents on the Web that contain no meta data whatsoever there is one shining star of hope: Natural Language Processing. NLP can be used to sift through the "garbage" data to extract coherent statements about the information held within.

Continue reading Natural Language Processing and the Semantic Web

JAN 20th 2007

Weekend Brain Dump of Ideas

Published 10 years ago by James Simmons

  • Semantic metadata for video and other multimedia?
  • Will a new platform away from the browser have huge success? (ala Joost)
  • How can video games benefit from what we're doing with the Semantic Web?
  • Is Wikipedia the best playground for natural language processors to test their ability?
  • Does the World Wide Web as we know it need to be replaced?
  • Is HTTP inadequate for the future of the Web where streaming and maintaining state are becoming increasingly important?
  • Are we entering another brutal browser war? Maybe this one will be different because we know the importance of compatability
  • Will RDF or RDF/a be adopted by mainstream Web developers to markup semantic metadata?
  • ...Or will something come along that's better suited and easier for beginners to pick-up
  • Are we making any progress as-is towards our goal, or do we need to look for a different approach?
  • Is the best course bottom-up (building the Semantic Web from the ground up by using semantic markup, microformats, RDF, etc) or is it top-down (using natural language processors to read the Web and make sense of it for us).
  • With the freedom to create any RDF vocabulary or any ontology for that matter, will the real power be in mapping my meaning to your meaning?
JAN 26th 2007

A mashup is a hybrid Web application that combines complementary elements from two or more sources to create one integrated experience. Content used in mashups is generally sourced from a third party via an API or from Web feeds (e.g. RSS or Atom). Basically, the point is to take multiple data sources or Web services and turn them into something useful. The idea of combining Web services is not a new one, but it has gained immense traction in recent times and will likely continue to grow in popularity. In this entry I will be discussing both the promising future mashups offer and also potential pitfalls.

Continue reading Mashups: Opportunity, Innovation, and Money

FEB 22nd 2007

The value of a dataset may be determined by any number of factors, however it can generally be agreed upon that the data's accuracy, how difficult it is to re-create, its source, and other important factors can affect the value of the data. However, as technology evolves to allow easier access to the information we require, the value of dataset may eventually decrease over time.

Continue reading The value of current datasets in the Semantic Web

MAR 11th 2007

For just about every area of research there exists documents online describing background information or techniques to accomplish a task in that domain of research. These documents are often referred to as white papers, provided their content is of technical or research orientation. The information held within white papers is essentially accessible by humans only because machines are not able to read and comprehend text in the same way humans can. If machines were able to read white papers and extract information in the same way humans can we would be able to store each fact and piece of knowledge from the documents. This method of indexing would facilitate much more detailed searches, allowing users to search by topic, theory, conclusion, methods, citations, references, etc.

Continue reading Extracting Information from White Paper Text

The Semantic WebThe World Wide Web has long been evolving towards the vision of the Semantic Web — an extension of the existing web through which machines are better able to interoperate and work on our behalf. It promises to infuse the Internet with a combination of metadata, structure, and various technologies so that machines can derive meaning from information, make more intelligent choices, and complete tasks with reduced human intervention. It is a dramatic vision that stands to transform the existing Web in devastatingly powerful ways.

Continue reading Introduction to the Semantic Web Vision and Technologies - Part 1 - Overview

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