Twitter turned on its API for geotagging tweets yesterday, as announce in in a post on their blog, Think Globally, Tweet Locally. Currently, geographic information will only be associated with your tweets if you use an application that adds it and will only be used to display your tweets when viewed with an application that can exploit it. Here’s the way Twitter described it.

“This release is unique in that it’s API-only which means you won’t see any changes on twitter.com, yet. Instead, Twitter applications like Birdfeed, Seesmic Web, Foursquare, Gowalla, Twidroid, Twittelator Pro and others are already supporting this new functionality (go try them out now!) in interesting ways that include geotagging your tweets and displaying the location from where a tweet was posted.”

Examining Twitter’s status update API description shows how one associates a location with a Tweet. Pretty simple.

Since disclosing your location raises privacy concerns, Twitter has made geotagging an opt-in service and also allows users to delete all of the location information associated with their tweets. Moreover, their policy, as described here, says

“We require application developers to be upfront and obvious about when they are Geotagging an update. If you ever find that an application is doing it without notifying you, please let us know.”

Twitter has updated its privacy policy to cover location information.

You can read more on ReadWriteWeb and Techcrunch.

Wikipedia has an interesting RFC on approaches to achieve and maintain better coherence in its infobox templates. This is significant because Wikipedia is becoming the new CYC — a broad, practical KB filled with general purpose background knowledge. The RFC was kicked off by discussions on dbpedia template annotations. The RFC defines the problem as:

“Wikipedia uses hundreds of infobox templates for describing various entity types like NFL teams, schools in Canada, train stations etc. These infoboxes are separated and do not use a common vocabulary. Several different spellings of attributes are used for them, which all stand for the same meaning (e.g. birth_place, birthPlace, origin). This poses limitations to checking consistency within Wikipedia infoboxes, amongst different language editions, and it makes it hard for external tools to reuse the information in infoboxes.”

The goals mentioned in the RFC include (1) establishing the currently missing links between synonymous template attributes, (2) enabling authors to use template annotations to check for for factual inconsistencies (e.g., outdated population figures), and (3) providing consensus about which properties should be used in templates and what data they should contain.

Yong Yu and Rudi Studer are editing a special issue of the Journal of Web Semantics on semantic search that will appear in the summer 2010. The special issue will cover interdisciplinary topics between Semantic Web and search. See the call for papers for a list of relevant topics and details on how to submit papers, which are due by 20 January 2010

PCWorld has a story, Google VP Mayer Describes the Perfect Search Engine, with some interesting comments on semantic search from Marissa Mayer, Google’s vice president of Search Products & User Experience.

“IDGNS: What’s the status of semantic search at Google? You have said in the past that through “brute force” — analyzing massive amounts of queries and Web content — Google’s engine can deliver results that make it seem as if it understood things semantically, when it really functions using other algorithmic approaches. Is that still the preferred approach?

Mayer: We believe in building intelligent systems that learn off of data in an automated way, [and then] tuning and refining them. When people talk about semantic search and the semantic Web, they usually mean something that is very manual, with maps of various associations between words and things like that. We think you can get to a much better level of understanding through pattern-matching data, building large-scale systems. That’s how the brain works. That’s why you have all these fuzzy connections, because the brain is constantly processing lots and lots of data all the time.

IDGNS: A couple of years ago or so, some experts were predicting that semantic technology would revolutionize search and blindside Google, but that hasn’t happened. It seems that semantic search efforts have hit a wall, especially because semantic engines are hard to scale.

Mayer: The problem is that language changes. Web pages change. How people express themselves changes. And all those things matter in terms of how well semantic search applies. That’s why it’s better to have an approach that’s based on machine learning and that changes, iterates and responds to the data. That’s a more robust approach. That’s not to say that semantic search has no part in search. It’s just that for us, we really prefer to focus on things that can scale. If we could come up with a semantic search solution that could scale, we would be very excited about that. For now, what we’re seeing is that a lot of our methods approximate the intelligence of semantic search but do it through other means.”

I interpret these comments to mean that Google’s management still views the concept of semantic search (and the Semantic Web) as involving better understanding of the intended meaning of text in documents and queries. The W3C’s web of data model is still not on their radar.

The Economist has been running a series of online Oxford Union style debates on topical issues — CEO pay, healthcare, climate change, etc. The latest one is on the cloud computing: This house believes that the cloud can’t be entirely trusted.

In his opening remarks, moderator Ludwig Siegele says

“The participants in this debate, including the three guest speakers, all agree that computing is moving into the cloud. “We are experiencing a disruptive moment in the history of technology, with the expansion of the role of the internet and the advent of cloud-based computing”, says Stephen Elop, president of Microsoft’s business division, which generates about a third of the firm’s revenues ($13 billion) and more than half of its profits ($4.5 billion) in the most recent quarter. Marc Benioff, chief executive of Salesforce.com, the world’s largest SaaS provider with over $1.2 billion in sales in the past 12 months, is no less bullish: ‘Like the shift [from the mainframe to the client/server architecture] that roiled our industry in decades past, the transition to cloud computing is happening now because of major discontinuities in cost, value and function.’”

While the debate’s proposition suggests that security or privacy is its focus, it’s really a broader argument about how software services will be delivered in the future in which security is just one aspect.

“Whether and to what extent companies and consumers elect to hand their computing over to others, of course, depends on how much they trust the cloud. And customers still have many questions. How reliable are such services? What about privacy? Don’t I lose too much control? What if Salesforce.com, for instance, changes its service in a way I do not like? Are such web-based services really cheaper than traditional software? And how easy is it to get my data if I want to change providers? Are there open technical standards that would make this easier?”

The Journal of Web Semantics now has a facebook page and a Twitter account to augment its blog. All three will be used for news and announcements of call for papers, special issues, availability of new papers, etc. As you might expect, the tweets will be terse items, the facebook updates longer notes and the blog posts full of details. Those who are interested can follow @journalWebSem on Twitter, become a fan of the JWS on facebook, and subscribe to the blog’s feed.

UMBC alumnus Joab Jackson has an article in Government Computer News, Tim Berners-Lee: Machine-readable Web still a ways off, reporting on the International Semantic Web Conference help outside of Washington DC at the end of October. The article uses data.gov to illustrate the challenges and opportunities for the Semantic Web. Data.gov is a site whose purpose “is to increase public access to high value, machine readable datasets generated by the Executive Branch of the Federal Government.”

Jackson quotes Tim Berners-Lee

“When you look at putting government data on the Web, one of the concerns is … to not just put it out there on Excel files on Data.gov,” he said. “You should put these things in” the Resource Description Framework.

and later describes a project at RPI to republish information from data.gov in RDF leaded by another UMBC alumnus, Li Ding.

“Our goal is to make the whole thing shareable and replicable for others to re-use,” said project researcher Li Ding. By rendering data into RDF, it can be more easily interposed with other sets of data to create entirely new datasets and visualizations, Ding said. He showed a Google Map-based graphic that interposed RDF-versions of two different data sources from the Environmental Protection Agency, originally rendered in CSV files.

Google added a great new service, Dashboard, that summarizes data stored for a Google account — see MY ACCOUNT>PERSONAL SETTINGS>DASHBOARD.

“Designed to be simple and useful, the Dashboard summarizes data for each product that you use (when signed in to your account) and provides you direct links to control your personal settings. Today, the Dashboard covers more than 20 products and services, including Gmail, Calendar, Docs, Web History, Orkut, YouTube, Picasa, Talk, Reader, Alerts, Latitude and many more. The scale and level of detail of the Dashboard is unprecedented, and we’re delighted to be the first Internet company to offer this — and we hope it will become the standard.”

This is a good move on Google’s part. But while there’s a lot of information included, it’s not everything that Google knows about you — e.g., data in cookies, click throughs data from search results and information from companies it’s acquired, like Doublclick. Still, it is a big step in a positive direction.

Like many newspapers, the New York Times links the first mention of well known entitles in its articles to a reference page. For example, a mention of Barack Obama links to a page which is a collection of basic information on President Obama and links to relevant stories and other resources that the Times has created.

Now the Times is also using RDF to publish some of information as linked open data. Yesterday the Times announced the publication of an LOD collection covering about 5,000 people at http://data.nytimes.com/ under under a Creative Commons 3.0 Attribution License and plan to put their full collection of 30K topics online soon.

“Over the last several months we have manually mapped more than 5,000 person name subject headings onto Freebase and DBPedia. And today we are pleased to announce the launch of http://data.nytimes.com and the release of these 5,000 person name subject headings as Linked Open Data.
…
Over the next several months, we plan to expand http://data.nytimes.com to include each of the nearly 30,000 subject headings we use to power Times Topics pages, a collection that includes locations, organizations and descriptors in addition to person names.”

OWL 2, the new version of the Web Ontology Language, officially became a W3C standard yesterday. From the W3C press release:

“Today W3C announces a new version of a standard for representing knowledge on the Web. OWL 2, part of W3C’s Semantic Web toolkit, allows people to capture their knowledge about a particular domain (say, energy or medicine) and then use tools to manage information, search through it, and learn more from it. Furthermore, as an open standard based on Web technology, it lowers the cost of merging knowledge from multiple domains.”

Wolfram|Alpha is an interesting query answering system developed by Wolfram Research that is a blend of a question answering system and a Semantic Web alternative. It tries to interpret and answer queries expressed as a sequence of words from a large collection of interlinked tables. Oh, and Mathematica is in thrown in for free. A free Web version was released last Spring.

The news today is that Wolfram|Alpha has released an API, as noted in their blog:

“The API allows your application to interact with Wolfram|Alpha much like you do on the web—you send a web request with the same query string you would type into Wolfram|Alpha’s query box and you get back the same computed results. It’s just that both are in a form your application can understand. There are plenty of ways to tweak and control the results, as well.”

The pricing plan runs from $60/month for 1000 (6 cents a query) queries to $220K for up to 10M queries/month (2.2 cents a query). programming language bindings are available for Java, PHP, Perl, Python, Ruby and .NET.

Their original web interface remains free, but the TOS specifies that it “may be used only by a human being using a conventional web browser to manually enter queries one at a time.”

In the Fall of 2007, two MIT students carried out a class project exploring how presumably private data could be inferred from an online social networking system. Their experiment was to predict the sexual orientation of Facebook users who make their basic information public by analyzing friendship associations. As reported in the Boston Globe last month, the students’ had not yet published their results.

Well, now they have — in the October issue of the First Monday, “one of the first openly accessible, peer–reviewed journals on the Internet”.

Carter Jernigan and Behram F.T. Mistree, Gaydar: Facebook friendships expose sexual orientation, First Monday, v14, n10, October 2009.

The paper has a lot of detail on the methodology for collecting the data and how it was analyzed. Here’s the abstract.

“Public information about one’s coworkers, friends, family, and acquaintances, as well as one’s associations with them, implicitly reveals private information. Social networking Web sites, e–mail, instant messaging, telephone, and VoIP are all technologies steeped in network data — data relating one person to another. Network data shifts the locus of information control away from individuals, as the individual’s traditional and absolute discretion is replaced by that of his social network. Our research demonstrates a method for accurately predicting the sexual orientation of Facebook users by analyzing friendship associations. After analyzing 4,080 Facebook profiles from the MIT network, we determined that the percentage of a given user’s friends who self–identify as gay male is strongly correlated with the sexual orientation of that user, and we developed a logistic regression classifier with strong predictive power. Although we studied Facebook friendship ties, network data is pervasive in the broader context of computer–mediated communication, raising significant privacy issues for communication technologies to which there are no neat solutions.”

As we had previously noted, this datamining exercise only accesses information that Facebook users explicitly choose to make public. The authors note that their analysis “relies on public self–identification of same–gender interest in Facebook profiles as a sentinel value for LGB identity”. The privacy vulnerability is that the default setting for a Facebook account is that friendship relations are public and you can not control the privacy settings of your friends. So if your leave your friend list public and many of your Facebook friends open up their profiles, it may be possible to draw reasonable inferences about your age, gender, political leanings, sexual preferences and other attributes.