Semantic Research For The Rest of Us

Popego
Image by magerleagues via Flickr

While stumbling through the reader tonight, I tripped upon an article by Dana Oshiro at ReadWriteWeb about a new semantic search engine called Meaningtool and a semantically-inclined feed generator called Popego.

These tools will help you cut down the noise and pull in the signal based on your interests and intended targets. After completing a profile, Popego provides you with semantic recommendations based on your on-line activity and social circles. Once you generate what Popego calls your “interest platform”, you can find more quickly relevant content and connect more readily with others based on your interests. Popego’s customized feeds can be widgetized and shared on your other sites and blogs.

Meaningtool will analyze any website, pull out the relevant terms and create categories based on these terms, generating a tag cloud. You can click on any of the categorizes and further refine and “tree” the resulting information. Meaningtool recognizes most of the popular Western languages. Meaningtool’s Category Manager allows you to “train” your semantic feed via relevant RSS feeds to get better results.

Meaningtool is currently being used by marketing firms to better target customers with advertising campaigns, publishers looking to improve their search engine ranking, and semantic Web developers.

Like my6sense, the iPhone tool that pulls the cream from your streams to the top, you need to train Popego and Meaningtool to get the most out of them. With the dizzyingly large amount of content on the Web, a little training goes a long way to bringing you want you want to see.

For laughs, I fed my own website’s URL into Meaningtool to see what would happen. After the engine finished thinking, it showed this graphic:

Scrolling down, the results expanded to a tag cloud and suggestions to improve my SEO (which desperately needs improving):

Finally, the bottom of the screen showed the most relevant terms according to the listed classifiers from services like the New York Times, LA Times, Reuters, and Digg:

For certain applications, I can see this being a very effective tool.

Check out the Meaningtool video here:

Popego’s demo video follows:

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Have You Heard The News? Google Living Stories

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Google brings forth yet another interesting way to view and follow news and it is called Google Living Stories. It is a collaboration between Google, The New York Time and The Washington Post. GLS builds on articles found in these papers, providing information related to the stories as it develops on a single web page devoted to the story. If you navigate to the Google Labs GLS page here, you will see links to the included stories. Click on a link and see a very slick page with its own URL that includes an overview of the main article, a time line of developments pertaining to the story, interactive tables showing related information (in the case of health care reform, costs and impact on the deficit), links to related stories and pertinent background information, pictures, video, graphics, opinions, and all sorts of goodies fleshing out the various dimensions of a news article.

Updates are highlighted when you return to the page and older stories are summarized. There is a link for comments, as well as email or RSS subscription to the particular story’s new developments. What a great alternative to other collected resources on newsworthy topics! Do I wish I had a resource like this when I was still a school girl writing current events reports!

Check out the quick vid from Google describing Living Stories:

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To The Google Scholar User: Buyer Beware

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Here is a bit of common sense from real life example: when using Google Scholar for your legal research, use care in making sure that versions of a case match. Legal Writing Prof Blog has a post about an attorney preparing a brief for filing who noted a discrepancy in the footnote numbering between the official Wisconsin Reporter version of a case and the Google Scholar version. The Blog quotes the attorney’s findings as follows:

The source of the discrepancy quickly became apparent.  In the official version of the case (as in all official versions of Wisconsin cases), the filing of a petition for review in the Wisconsin Supreme Court gets noted in the caption with a footnote placed at the end of the name of the party that filed the petition.  The symbol for this footnote is a dagger, not a number.  Google Scholar, however, designates this footnote with a number (in this instance, the dagger became “1”) and renumbers the remaining footnotes accordingly.  Where there’s more than one footnote attached to the caption – e.g., Ellsworth v. Schelbrock, 229 Wis. 2d 542, 600 N.W.2d 247 (Ct. App. 1999) – Google Scholar shifts the footnote numbers even more:  in Ellsworth, the caption has two footnotes, so the numbered footnotes shifted by two as well, making footnote 1 in the official version into footnote 3 in the Google Scholar version.

My thinking on the proper role of Google Scholar is this: the greatest cost in using the paid databases is the time spent poking around looking for the main cases on a point of law. Once you have identified those cases, the costs of pulling them down out of the paid databases is relatively inexpensive. I see Google Scholar as an effective (but not sole) tool for the former task. When writing an appellate brief to any court, I would not feel the slightest bit comfortable relying on Google Scholar’s version. At that point, I would be pulling the actual cases from the paid databases. While these sources are far from infallible, they do have a longer track record with respect to accuracy, as well as complete citations and the ability to Keycite or Shepardize, a must for briefs to be filed in any court.

So there you have it. Use the free resources with your eyes wide open to their possible shortcomings, and you should not go far wrong.

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