The Semantic Web Science Association (SWSA) is seeking statements of interest from organizations or consortia interested in hosting the 11th International Semantic Web Conference, ISWC 2012. The conference series moves regularly between the Americas, Europe, and the Asia/Pacific region and we expect that the 2012 edition will be held in the US in late October or early November 2012.

Organizations wishing to host ISWC 2012 should contact SWSA President Professor James Hendler (swsa-president@aifb.uni-karlsruhe.de) who will work with the SWSA members who are co-ordinating the bidding process for ISWC 2012.

The process comprises two stages. During the first stage, statements of interest are solicited through an open call. Once the first phase is complete, SWSA will shortlist a number of applications, who will be invited to submit a full proposal, using a standard form and budget template. More information about the ISWC Conference Series and the bidding process for hosting a conference in the series can be found in the ISWC Conference Guide.

The important dates for applying to host a Conference in 2012 are:

• September 30, 2010: Deadline for receiving statements of interest
• November 15, 2010: Notifications to shortlisted bids are sent out
• January 15, 2011: Formal applications received from shortlisted bids
• March 1, 2011: SWSA decides on location for the 2012 Conference

Twitter’s planned shortening of all links via its t.co service is about to happen. The initial motivation was security, according to Twitter:

“Twitter’s link service at http://t.co is used to better protect users from malicious sites that engage in spreading malware, phishing attacks, and other harmful activity. A link converted by Twitter’s link service is checked against a list of potentially dangerous sites. When there’s a match, users can be warned before they continue.”

Declan McCullagh reports that Twitter announced in an email message that when someone click “on these links from Twitter.com or a Twitter application, Twitter will log that click.” Such information is extremely valuable. Give Twitter’s tens of millions of active users, just knowing how often certain URLs are clicked by people indicates what entities and topics are of interest at the moment.

“Our link service will also be used to measure information like how many times a link has been clicked. Eventually, this information will become an important quality signal for our Resonance algorithm—the way we determine if a Tweet is relevant and interesting.”

Associating the clicks with a user, IP address, location or device can yield even more information — like what you are interested in right now. Moreover, Twitter now has a way to associate arbitrary annotation metadata with each tweet. Analyzing all of this data can identify, for example, communities of users with common interests and the influential members within them.

Note that Twitter has not said it will do this or even that it will record and keep any user-identifiable information along with the clicks. They might just log the aggregate number of clicks in a window of time. But going the next step and capturing the additional information would be, in my mind, irresistible, even if there was no immediate plan to use it.

Search engines like Google already link clicks to users and IP addresses and use the information to improve their ranking algorithms and probably in many other ways. But what is troubling is the seemingly inexorable erosion of our online privacy. There will be no way to opt out of having your link wrapped by the t.co service and no announced way to opt out of having your clicks logged.

Microsoft’s Bing team announced on their blog that that the Bing search engine is “powering Yahoo!’s search results” in the US and Canada for English queries. Yahoo also has a post on their Yahoo! Search Blog.

The San Jose Mercury News reports:

“Tuesday, nearly 13 months after Yahoo and Microsoft announced plans to collaborate on Internet search in hopes of challenging Google’s market dominance, the two companies announced that the results of all Yahoo English language searches made in the United States and Canada are coming from Microsoft’s Bing search engine. The two companies are still racing to complete the transition of paid search, the text advertising links that run beside and above the standard search results, before the make-or-break holiday period — a much more difficult task.”

Combining the traffic from Microsoft and Yahoo will give the Bing a more significant share of the Web search market. That should help them by providing both companies with a larger stream of search related data that can be exploited to improve search relevance, ad placement and trend spotting. It will also help to foster competition with Google focused on developing better search technology.

Hopefully, Bing will be able to benefit from the good work done at Yahoo! on adding more semantics to Web search.

Analog computers were a hot idea — in the 1950s! But I find this intriguing because I’ve come around to the position that a lot of our human “intelligence” is the result of acquiring and using probabilistic models. So supporting this in hardware might be a big win, especially for low-cost, low-power devices. It will also support lots of other common tasks in social computing, image processing and language technology.

Technology review has a short article, A New Kind of Microchip, on computer chip being developed by Lyric Semiconductor that process signals representing probabilities rather than digital bits.

“A computer chip that performs calculations using probabilities, instead of binary logic, could accelerate everything from online banking systems to the flash memory in smart phones and other gadgets. … And because that kind of math is at the core of many products, there are many potential applications. “To take one example, Amazon’s recommendations to you are based on probability,” says Vigoda. “Any time you buy [from] them, the fraud check on your credit card is also probability [based], and when they e-mail your confirmation, it passes through a spam filter that also uses probability.”

All those examples involve comparing different data to find the most likely fit. Implementing the math needed to do this is simpler with a chip that works with probabilities, says Vigoda, allowing smaller chips to do the same job at a faster rate. A processor that dramatically speeds up such probability-based calculations could find all kinds of uses.”

Lyric’s chip is called LEC and is 30 times smaller in size than current digital error correction technology according to Wired. Although small it yields “a Pentium’s worth of computation,” according to Lyric CEO Vigoda. His 2003 dissertation at MIT was on a related topic, Analog Logic: Continuous-Time Analog Circuits for Statistical Signal Processing.

Privacy continues to be an important topic surrounding social media systems. A big part of the problem is that virtually all of us have a difficult time thinking about what information about us is exposed and to whom and for how long. As UMBC colleague Zeynep Tufekci points out, our intuitions in such matters come from experiences in the physical world, a place whose physics differs considerably from the cyber world.

Bruce Schneier offered a taxonomy of social networking data in a short article in the July/August issue of the IEEE Security & Privacy. A version of the article, A Taxonomy of Social Networking Data, is available on his site.

“Below is my taxonomy of social networking data, which I first presented at the Internet Governance Forum meeting last November, and again — revised — at an OECD workshop on the role of Internet intermediaries in June.

• Service data is the data you give to a social networking site in order to use it. Such data might include your legal name, your age, and your credit-card number.
• Disclosed data is what you post on your own pages: blog entries, photographs, messages, comments, and so on.
• Entrusted data is what you post on other people’s pages. It’s basically the same stuff as disclosed data, but the difference is that you don’t have control over the data once you post it — another user does.
• Incidental data is what other people post about you: a paragraph about you that someone else writes, a picture of you that someone else takes and posts. Again, it’s basically the same stuff as disclosed data, but the difference is that you don’t have control over it, and you didn’t create it in the first place.
• Behavioral data is data the site collects about your habits by recording what you do and who you do it with. It might include games you play, topics you write about, news articles you access (and what that says about your political leanings), and so on.
• Derived data is data about you that is derived from all the other data. For example, if 80 percent of your friends self-identify as gay, you’re likely gay yourself.”

I think most of us understand the first two categories and can easily choose or specify a privacy policy to control access to information in them. The rest however, are more difficult to think about and can lead to a lot of confusion when people are setting up their privacy preferences.

As an example, I saw some nice work at the 2010 IEEE International Symposium on Policies for Distributed Systems and Networks on “Collaborative Privacy Policy Authoring in a Social Networking Context” by Ryan Wishart et al. from Imperial college that addressed the problem of incidental data in Facebook. For example, if I post a picture and tag others in it, each of the tagged people can contribute additional policy constraints that can narrow access to it.

Lorrie Cranor gave an invited talk at the workshop on Building a Better Privacy Policy and made the point that even P3P privacy policies are difficult for people to comprehend.

Having a simple ontology for social media data could help us move forward toward better privacy controls for online social media systems. I like Schneier’s broad categories and wonder what a more complete treatment defined using Semantic Web languages might be like.

The number of citations a paper receives is generally thought to be a good and relatively objective measure of its significance and impact.

Researchers naturally are interested in knowing how to attract more citations to their papers. Publishing the results of good work helps of course, but everyone knows there are many other factors. Nature news reports on research by Gregory Webster that analyzed the 53,894 articles and review articles published in Science between 1901 and 2000.

The advice the study supports is “cite and you shall be cited”.

A long reference list at the end of a research paper may be the key to ensuring that it is well cited, according to an analysis of 100 years’ worth of papers published in the journal Science.
     The research suggests that scientists who reference the work of their peers are more likely to find their own work referenced in turn, and the effect is on the rise, with a single extra reference in an article now producing, on average, a whole additional citation for the referencing paper.
     ’There is a ridiculously strong relationship between the number of citations a paper receives and its number of references,” Gregory Webster, the psychologist at the University of Florida in Gainesville who conducted the research, told Nature. “If you want to get more cited, the answer could be to cite more people.’
…
A plot of the number of references listed in each article against the number of citations it eventually received reveal that almost half of the variation in citation rates among the Science papers can be attributed to the number of references that they include. And — contrary to what people might predict — the relationship is not driven by review articles, which could be expected, on average, to be heavier on references and to garner more citations than standard papers.

Seen on the Web: “Swoogle is an alien from outer space send out to spy on the modnation circuit. He got five faces so he can watch them from all angles without turning his head. However only his front shows many emotions. His right face is always angry, his left face is always in awe for some reason.”

Washington Technology, which describes itself as “the online authority for government contractors and partners”), has an article by Carlos A. Soto on 5 technologies that will change the market. They are:

1. Mobile
2. Search and the Semantic Web
3. Search and the Semantic Web
4. Virtualization and cloud computing
5. Virtualization and cloud computing

These are reasonable choices, thought I’ve have not done the double counting and added “machine learning applied to the massive amounts of Web data now available” and “social computing”.

But it’s gratifying to see the Semantic Web in the list. Here’s some of what he he has to say about search and the Semantic Web.

The relationship between search technology and the Semantic Web is a perfect illustration of how a small sustaining technology, such as a basic search feature on an operating system, will eventually be eaten up by a larger disruptive technology, such as the Semantic Web. The Semantic Web has the potential of acting like a red giant star by expanding at exponential rates, swallowing whole planets of existing technology in the process.

The technology started as a simple group of secure, trusted, linked data stores. Now Semantic Web technologies enable people to create data stores on the Web and then build vocabularies or write rules for handling the data. Because all the data by definition is trusted, security is often less of a problem.

The task of turning the World Wide Web into a giant dynamic database is causing a shift among traditional search engines because products such as Apture, by Apture Inc. of San Francisco, Calif., let content publishers include pop-up definitions, images or data whenever a user scrolls over a word on a Web site. The ability to categorize content in this manner could have significant implications not only for Web searches but also for corporate intranets and your desktop PC.

These types of products will continue to expand, initially in the publishing industry and then to most industries on the Web in the next two to three years.

For example, human resources sites could use them to pop up a picture and a résumé blip when a recruiter drags a mouse over an applicant’s name. Medical and financial sites such as the National Institutes of Health could use it to break down jargon and help with site exploration.
…
Government sites around the world, such as Zaragoza, Spain, and medical facilities, such as the Cleveland Medical Clinic, are using the vocabulary features of the Semantic Web to create search engines that reach across complex jargon and tech silos to offer a high degree of automation, full integration with external systems and various terminologies, in addition to the ability to accurately answer users’ queries.
…”

(h/t @FrankVanHarmele)

Google Chrome has been showing me a malware warning page today as I try to visit normally trusted and benign sites. I got this one just now as I tried to got to Planet RDF.

Warning: Visiting this site may harm your computer!

The website at planetrdf.com contains elements from the site bin.clearspring.com, which appears to host malware – software that can hurt your computer or otherwise operate without your consent. Just visiting a site that contains malware can infect your computer.

For detailed information about the problems with these elements, visit the Google Safe Browsing diagnostic page for bin.clearspring.com.

Learn more about how to protect yourself from harmful software online.

[ ] I understand that visiting this site may harm my computer. PROCEED

Clearspring claims it’s a technical problem, although they admit they were using a service that was compromised with files redirecting users to a certain malware domain. I’m a bit fuzzy on what clearspring does and where they are being used on the Planet RDF site. I don’t see it in the page source, for example.

update: Maybe the problem stems from flash cookies in blog content being syndicated by Planet RDF that have flash objects mediated by clearspring.

The W3C has published a second working draft of EmotionML, or the emotion markup language, Here’s how it’s described.

As the web is becoming ubiquitous, interactive, and multimodal, technology needs to deal increasingly with human factors, including emotions. The present draft specification of Emotion Markup Language 1.0 aims to strike a balance between practical applicability and scientific well-foundedness. The language is conceived as a “plug-in” language suitable for use in three different areas: (1) manual annotation of data; (2) automatic recognition of emotion-related states from user behavior; and (3) generation of emotion-related system behavior.

Unfortunately EmotionML is not built on RDF. If it were, I would have marked up this post in RDFa using it!

The working draft identifies concrete examples where EmotionML might be useful including as a markup or representation for systems that do opinion mining, sentiment analysis, affect monitoring, and emotion recognition. A list of 39 individual use cases for EmotionML are given in an appendix.

EmotionML markup explicitly refers to one or more separate vocabularies used for representing emotion-related states. However, the group has defined some default vocabularies that can be used. An example is the Ekman “big six” basic emotions (anger, disgust, fear, happiness, sadness, and surprised). Another is the a set of appraisal terms defined by Ortony et al. (desirability, praiseworthiness, appealingness,, desirability-for-other, deservingness, liking, likelihood, effort, realization, strength-of-identification, expectation-of-deviation and familiarity)

Here’s an example from the working draft where a static image is annotated with several emotion categories with different intensities.

<emotionml xmlns="http://www.w3.org/2009/10/emotionml"
               xmlns:meta="http://www.example.com/metadata"
               category-set="http://www.example.com/custom/
                    hall-matsumoto-emotions.xml">
       <info>
          <meta:media-type>image</meta:media-type>
          <meta:media-id>disgust</meta:media-id>
          <meta:media-set>JACFEE-database</meta:media-set>
          <meta:doc>Example adapted from (Hall and Matsumoto 2004) 
    
    http://www.davidmatsumoto.info/Articles/
    
              2004_hall_and_matsumoto.pdf
          </meta:doc>
       </info>
    
       <emotion>
           <category name="Disgust"/>
           <intensity value="0.82"/>
       </emotion>
       <emotion>
           <category name="Contempt"/>
           <intensity value="0.35"/>
       </emotion>
       <emotion>
           <category name="Anger"/>
           <intensity value="0.12"/>
       </emotion>
       <emotion>
           <category name="Surprise"/>
           <intensity value="0.53"/>
       </emotion>
    </emotionml>
    

rdfs:seeAlso the short article by InqoQ on the EmotionML working draft.