Semantic Gnosis Web

Did we tell you we love infographics?

Here is another one to savor, by Anna Vital, a presentation of how funding of start ups work

In the weeks since the Campaign to Stop Killer Robots launched, there has been a lot of media coverage.  The media coverage is very exciting and what I have found to be very interesting is the number of articles that refer to Isaac Asimov’s Three Laws of Robotics.

Now unless like me you grew up with a sci-fi geek for a father who introduced you to various fictional worlds like those in Star Wars, Star Trek and 2001: A Space Odyssey at a young age, you might not know who Isaac Asimov is, what his Three Laws of Robotics are and why these laws are relevant to the Campaign to Stop Killer Robots.

Isaac Asimov (1920-1992) was an American scientist and writer, best known for his science fiction writings especially short stories.  In his writings, Asimov created the Three Laws of Robotics which govern the action of his robot characters.  In his stories, the Three Laws were programmed into robots as a safety function.  The laws were first stated in the short story Runaround but you can see them in many of his other writings and since then they have shown up in other authors’ work as well.

The Three Laws of Robotics are:

A robot may not injure a human being or, through inaction, allow a human being to come to harm.A robot must obey the orders given to it by human beings, except where such orders would conflict with the First Law.A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.

After reading the Three Laws, it might be pretty clear why Mr. Asimov’s ideas are frequently mentioned in media coverage of our campaign to stop fully autonomous weapons.  A fully autonomous weapon will most definitely violate the first and second laws of robotics.

To me, the Three Laws seem to be pretty common sense guides for the actions of autonomous robots.  It is probably a good idea to protect yourself from being killed by your own machine – ok not probably – it is a good idea to make sure your machine does not kill you!  It also is important for us to remember that Asimov recognized that just regular robots with artificial intelligence (not even fully autonomous weapons) could pose a threat to humanity at large so he also added a fourth, or zeroth law, to come before the others:

0. A robot may not harm humanity, or, by inaction, allow humanity to come to harm.

“But Erin,” you say, “these are just fictional stories; the Campaign to Stop Killer Robots is dealing with how things really will be.  We need to focus on reality not fiction!”  I hear you but since fully autonomous weapons do not yet exist we need to take what we know about robotics, warfare and law and add a little imagination to foresee some of the possible problems with fully autonomous weapons.  Who better to help us consider the possibilities than science fiction writers who have been thinking about these types of issues for decades?

At the moment, Asimov’s Three Laws are currently the closest thing we have to laws explicitly governing the use of fully autonomous weapons.  Asimov’s stories often tell tales of how the application of these laws result in robots acting in weird and dangerous ways the programmers did not predict.  By articulating some pretty common sense laws for robots and then showing how those laws can have unintended negative consequences when implemented by artificial intelligence, Asimov’s writings may have made the first argument that a set of parameters to guide the actions of fully autonomous weapons will not be sufficient.  Even if you did not have a geeky childhood like I did, you can still see the problems with creating fully autonomous weapons.  You don’t have to read Asimov, know who HAL is or have a disliking for the Borg to worry that we won’t be able to control how artificial intelligence will interpret our commands and anyone who has tried to use a computer, a printer or a cell phone knows that there is no end to the number of ways technology can go wrong.  We need a pre-emptive ban on fully autonomous weapons before it is too late and that is what the Campaign to Stop Killer Robots will be telling the diplomats at the UN in Geneva at the end of the month.

Overview

The Web is entering a new phase of evolution. There has been much debate recently about what to call this new phase. Some would prefer to not name it all, while others suggest continuing to call it “Web 2.0”. However, this new phase of evolution has quite a different focus from what Web 2.0 has come to mean.

With the increase on ontology development and networked ontologies, both good ontology development and ontology matching for ontology linking and integration are becoming a more pressing issue. Many contributions have been proposed in these areas. One of the ideas to tackle both—supposedly in one fell swoop—is the use of a foundational ontology. A foundational ontology aims to (i) serve as a building block in ontology development by providing the developer with guidance how to model the entities in a domain, and  (ii) serve as a common top-level when integrating different domain ontologies, so that one can identify which entities are equivalent according to their classification in the foundational ontology. Over the years, several foundational ontologies have been developed, such as DOLCE, BFO, GFO, SUMO, and YAMATO, which have been used in domain ontology development. The problem that has arisen now, is how to link domain ontologies that are mapped to different foundational ontologies?

To be able to do this in a structured fashion, the foundational ontologies have to be matched somehow, and ideally have to have some software support for this. As early as 2003, this issue as foreseen already and the idea of a “WonderWeb Foundational Ontologies Library” (WFOL) proposed, so that—in the ideal case—different domain ontologies can to commit to different but systematically related (modules of) foundational ontologies [1]. However, the WFOL remained just an idea because it was not clear how to align those foundational ontologies and, at the time of writing, most foundational ontologies were still under active development, OWL was yet to be standardised, and there was scant stable software infrastructure. Within the Semantic Web setting, the solvability of the implementation issues is within reach yet not realised, but their alignment is still to be carried out systematically (beyond the few partial comparisons in the literature).

We’re trying to solve these theoretical and practical shortcomings through the creation of the first such online library of machine-processable, aligned and merged, foundational ontologies: the Repository of Ontologies for MULtiple USes ROMULUS. This version contains alignments, mappings, and merged ontologies for DOLCE, BFO, and GFO and some modularized versions thereof, as a start. It also has a section on logical inconsistencies; i.e., entities that were aligned manually and/or automatically and seemed to refer to the same thing—e.g., a mathematical set, a temporal region—actually turned out not to be (at least from a logical viewpoint) due to other ‘interfering’ axioms in the ontologies. What one should be doing with those, is a separate issue, but at least it is now clear where the matching problems really are down to the nitty-gritty entity-level.

During the last weeks I was working hard on the next release of RDFaCE . RDFaCE implements the WYSIWYM (What You See Is What You Mean) concept in order to facilitate the process of semantic content authoring.

The new version of RDFaCE is focused on Schema.org . You can create RDFa or Microdata annotations based on the schemas defined by schema.org. It will help to improve the SEO of your Website.

The main features of this new version include*:

Providing a flexible form-based approach to annotate the content by using schemas defined by schema.orgProviding a mechanism to select a subset of schemas defined by schema.orgProviding colors for schemasAutomatic content annotation

Links:

Video explaining the WYSIWYM conceptRDFaCE demoAnnotation steps in RDFaCERDFaCE plugin for WordPressMore information about RDFaCE and WYSIWYM concept

*Of course it is only a preview release and might have some bugs.Please report the possible bugs using our issue tracker. As our next step, we will integrate the functionality of previous versions of RDFaCE with support for schema.org.

“Simple models and a lot of data trump more elaborate models based on less data.”

With that line, the 2009 paper “The Unreasonable Effectiveness of Data” (co-authored by Google co-workers Alon Halevy and Fernando Pereira), Google Director of Research Peter Norvig all but guaranteed his status as one of most-quoted — or at least most-paraphrased — people in the world of big data. Last week, Norvig — as well as Google VP of Energy Arun Majumdar — was bestowed a slightly more-formal honor, as he was inducted into the American Academy of Arts and Sciences.

Norvig, who previously led Google’s search algorithms team and was head of computational sciences at the NASA Ames Research Center, is best known for his work in the realm of artificial intelligence. In fact, the above quote and the paper in which it appears are essentially a testament to the advances Google has been able to make in AI and machine learning thanks to the massive web page and search dataset that Google has amassed. The more examples it has of words and phrases used together in natural language, the better it can perform semantic analysis to determine what’s related to what.

Continuing the legacy of the New York Times Index, which stretches back nearly to the founding of the newspaper, The New York Times and The New York Times Company Research & Development Lab have adopted Linked Open Data to maintain and share the newspaper’s extensive holdings. The New York Times’ suite of Linked Open Data datasets, tools and APIs are based in large part on the newspaper’s 150 year old controlled vocabulary, which was released as 10,000 SKOS subject headings in January of 2010.

The New York Times publicizes their projects through their blog – Open: All the News That’s Fit to printf () – and through social media. In addition to creating prototype tools such as Who Went Where, The New York Times also promotes the use of their APIs and source code of their tools. Open has been a regularly updated blog since 2007 when the New York Times Company began its foray into the use and promotion of open source software.

 

The Dataset

The roots of The New York Times’ dataset is The New York Times Index, which was published quarterly beginning in 1913 and continues to be published today, although with less frequency. These red covered volumes contain a cross-referenced index of all of the names, articles and items that appear in the newspaper. Along with creating an authoritative controlled vocabulary, The New York Times Index also helped to launch The New York Times as a trusted research resource for students, scholars and librarians throughout the United States. As the New York Times continues to promote and develop their Linked Open Data assets they can continue their legacy as information innovators.

The New York Times began publishing their vocabulary as Linked Open Data in 2009 and by 2010 the vocabulary had grown to include 10,000 subject headings. Currently – as of March 2013 – the dataset includes the names of 4,978 people, 1,489 organizations, 1,910 locations and 498 descriptors for a total of 10,467 total tags. These tags are available as RDF documents and also as HTML. Individual data records can be browsed alphabetically, download in packages of SKOS files or queried using the 15 APIs available through The New York Times.

REST style, as defined by Roy T. Fielding

REST was formally defined in 2000 for systems that are both distributed and driven by hypermedia interactions with humans.

Fully embracing it requires you to respect the 5 mandatory constraints (plus an optional one), as described below.

Note that this summary contains large excerpts from Roy T. Fielding’s thesis:

1 – Client-Server constraintseparation of concernsclient = user interface concerns (better UI portability across platforms)server = data storage concerns (moresimple and scalable implementations)independent evolution of clients and servers at Internet scale2 – Stateless constrainteach client request must contain all the info necessary for the server to respondsession state is kept entirely on the client3 – Cache constraintallow client to reuse response data in a controlled mannersupport intermediaries such as proxies and shared caches4 – Uniform Interface constraintcentral and unique feature of RESTless efficient than a customized interface due to the standardization overheadefficient for large-grain hypermedia data transfersnot optimal for other forms of architectural interactionsub-constraintsidentification of resources via URIsmanipulation of resources through representations (HTML hyperlinks, forms or JavaScript/AJAX style)self-descriptive messageshypermedia as the engine of application state (HATEOAS)5 – Layered System constrainteach component only knows about the immediate component/layer it is interacting withfavor intermediary components (load-balancers, reverse proxies, shared caches)can add overhead and latencygives pipe-and-filter benefits (combined with Uniform Interface constraint)6 – Code on demand constraint [optional]client component doesn’t know how to process the resources it has access tocode representing this know-how is retrieved by the client and executed locallydynamic feature addition to deployed clientsimproved extensibility and configurabilitybetter user-perceived performance and flexibilityimproved server scalability (offload work to the client)lack of visibility requires client to trust the server (security sandboxing required)

In addition to these constraints, REST also defines as a set of architectural elements that directly abstract the semantics of the HTTP application protocol:

Google’s interface problem

There’s little reason to be concerned about a future LarryWorld at the moment. While Google is rapidly iterating its beta versions of the Government Remote Control Interface, the company is still trying to get a handle on what’s inside the black box we call Congress. Google’s leadership was politically tone-deaf early in the game. In 2008, Sergey Brin showed up in jeans and sneakers to lobby senators over network neutrality. He could find only a few willing to talk to him—none of them particularly influential.

Google has tried to hone its government hacking skills, spending an exponentially expanding amount of treasure on lobbying. In 2012, according to data compiled by OpenSecrets, Google spent $18,220,000 on campaign contributions and lobbying. That includes money spent directly and through 24 lobbyist firms from both sides of the political divide, including the Podesta Group and former Senator Dick Gephardt’s Gephardt Group on the left and Crossroad Strategies (who advised the campaign of Mitt Romney) on the right.

A lot of that money was spent to lobby against the Stop Online Privacy Act (SOPA) and Protect Intellectual Property Act (PIPA). But those dollars also tried to influence the various versions of cybersecurity legislation that have been launched in the past few years. Other big sinkholes for Google’s lobbying dollars included issues clustered around spectrum availability, “competition issues," and getting more H1B visas for engineers.

But all that lobbying had a limited impact on moving things Google’s way on issues like immigration reform and patent reform. There are two simple reasons for that: Google and the tech sector have failed to convince enough people that the things important to Google are important to them, and Google is being outspent in Washington by corporations and organizations hostile to Google's agenda (such as the telecom industry, the entertainment industry, and labor groups fighting H1B expansion). The TV, film, and recording industry collectively spent about $117 million in 2012. AT&T alone spent $24 million.

The changes Page wants require more than money. They require a change of culture, both political and national. The massively optimistic view that technology can solve all of what ails America—and the accompanying ideas on immigration, patent reform, and privacy—are not going to be so easy to force into the brains of the masses.

The biggest reason is trust. Most people trust the government because it's the government—a 226-year old institution that behaves relatively predictably, remains accountable to its citizens, and is governed by source code (the Constitution) that is hard to change. Google, on the other hand, is a 15-year old institution that is constantly shifting in nature, is accountable to its stockholders, and is governed by source code that is updated daily. You can call your Congressman and watch what happens in Washington on C-SPAN every day. Google is, to most people, a black box that turns searches and personal data into cash.

Before the IT industry can even begin to get headway toward anything like Page's view of a more hackable world, it has to overcome the issue of trust. There's a big difference between creating a brand that consumers "trust" and getting past the emotional and ideological baggage surrounding issues like immigration, privacy, and corporate power. So while organizations like Code for America try to show government how to do more with less, maybe Page and Google should invest less in lobbying government and more in educating the people who elect it.

Reader comments 43

Sean Gallagher / Sean is Ars Technica's IT Editor. A former Navy officer, systems administrator, and network systems integrator with 20 years of IT journalism experience, he lives and works in Baltimore, Maryland.

The Visual Semantic Browser

The Semantic Web not only covers ontology definitions, but also their relationships and instances. This work describes an adaptable tool for the visualization of all these Semantic Web elements. The tool includes a set of interfaces to enable the inclusion of different visualization tools as plug-ins. Thus, it is divided into four views: ontology groups, ontology mappings, ontologies and instances. Some algorithms are included but we are planning to develop new ones to improve the tool capabilities. The tool has also been successfully applied to develop a graphical query interface that takes advantage of the ontology and instance levels.

Amid a blizzard of announcements at its I/O conference, Google unveiled a major change to its core search product in singularly low-key fashion.

Amit Singhal, a senior Google executive in charge of its search efforts, said that Google search, starting today, wouldn't just answer the question implied in the words we type into that ubiquitous search box. Google, Singhal said, would display results that "anticipate the next question."

Singhal gave the example of a search for "the population of India." The searcher wants a number, yes—but it's likely that they're on the hunt for more information about India after that. There's no reason why Google shouldn't get a jump start on answering those questions.

That word Singhal used—"anticipate"—is a powerful one in the field of artificial intelligence. Anticipatory systems, as we've written, are a hot field that's been moving from theory to practice. There are apps today that will recommend a restaurant, a purchase, even a date. And we've noted how anticipatory systems are the future of search.

But with this latest update, Google is launching the biggest real-world experiment in anticipatory systems ever, with hundreds of millions of its search users getting a glimpse of the anticipatory future today.

Google Maps, too, is getting an anticipatory update. Search for the "Walt Disney Family Museum," a popular San Francisco institution, and you might get another kid-friendly museum suggested, like the Exploratorium, based on the reasonable assumption that you're looking for a place to go with your family.

Although visualization is hardly the most technologically challenging part of the data-analysis puzzle, it’s arguably the most important.

Storage, databases, query processing and algorithms are all extremely important — heck, visualization is next to nothing without them — but in a data-driven world where is obsessed with insights, they’re just the foundational layers. They are to big data what server and network configurations are to mobile-app development on platforms like Parse. If you’re going to find out new things from massive and highly complex data sets, or going to give new types of people the ability to analyze even simple data, the presentation of that data and the ability to create consumable presentations are critical.

ROMULUS is a foundational ontology repository aimed at improving semantic interoperability. Currently there are three foundational ontologies in the repository: DOLCE, BFO and GFO.

The Google Play Movies and TV app is debuting a new feature that gives greater insight into your favorite actors, films and shows — thanks to the search giant’s Knowledge Graph.

Google Play の Movies と TV のアプリで、あなたのお気に入りの俳優や、映画、そしてショーなどを調べるための、新しい機能がデビューした。このサーチ大手が開発してきた、Knowledge Graph の成果である。

If you’ve ever found yourself mulling over the name of an unidentified actor or song while watching a film, the app’s new "info cards" will provide facts related to any movie being watched on an Android tablet. To access them, users simply press pause, and the cards will pop up with information on the actors, soundtrack, among other details. They disappear when the movie continues.

映画を見ている最中に、俳優や楽曲の名前を思い出したくて、考えこんでしまったという経験があるなら、このアプリに新たに加わった「 Info Card 」が役に立つ。 つまり、再生中のあらゆる映画に関連する情報を、Androidタブレットから提供してくれるのだ。シンプルに Pause ボタンを押すだけで、この機能にアクセスできる。すると、俳優の名前や、サウンドトラックの楽曲名といった、詳細な情報を記したカードがポップアップされる。 そして、映画に戻れば、それらのカードは姿を消す。

"You can tap on an actor’s face to learn more about him, like his age, place of birth, his character in the movie, and his recent work, or scroll through the info cards to learn more about the movie or soundtrack," Ben Serridge, Google Play’s product manager, announced in a blog post Wednesday.

「その俳優の顔の部分をタップすれば、さまざまな情報が得られる。 たとえば、年齢／出身地／過去の作品／直近の作品などが表示される。さらに、カードをスクロールしていけば、その映画やサウンドトラックなどの詳細も表示 される」と、 Google Play の Product Manager である Ben Serridge が、水曜日（3/27）のブログポストで紹介している。

This information is similar to what appears in Google Search — that is, via Knowledge Graph, the technology that makes Google’s search-engine results more intuitive.

これらの情報は、Google Searchで表示されるものに類似している。すなわち、Google サーチ･エンジンの結果を、さらに直観的にする Knowledge Graph テクノロジーを介して、実現されているのだ。

Info cards will be available for hundreds of movies in Google Play, and more will be added "every day," Serridge said.

この Info Card は、Google Play 内の数百本の映画で、利用できるようになるだろう。 そして、さらに数多くの作品が、「毎日のように」加えられるだろうと、Serridge は発言している。

American users who have tablets that runs Android 4.0 (Ice Cream Sandwich) and higher, can download the newest version of the Movies and TV app from the Google Play store. Unfortunately, those who live outside the U.S. and don’t own an Android tablet will have to wait, as Google said it hopes to bring info cards to more countries and devices "soon."

Android 4.0（Ice Cream Sandwich）以降のバージョンを走らせる、タブレットを持っているアメリカのユーザーは、最新バージョンの Movies and TV アプリを、Google Play ストアか らダウンロードできる。 Google が Info Card を、より多くの国々や、各種のデバイスに対して提供すると言うように、アメリカ国外の住んでいる場合や、Android タブレットを所有しない人たちは、残念ながら、もう少し待たなければならないだろう。

What do you think of the app’s info cards feature? Tell us in the comments, below.

このアプリの Info Card について、どのように捉えるだろう？ コメントで知らせて欲しい。

Summary:

The Wikimedia Foundation’s first major new project in 7 years is now feeding the biggest project in that stable, Wikipedia itself. But anyone can take structured data from Wikidata, due to its open license.

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Wikidata, a centralized structured data repository for facts and Wikimedia’s first big new project in the last 7 years, is now feeding the foundation’s main project, Wikipedia.

The Wikidata project was kicked off around a year ago by the German chapter of Wikimedia, which is still steering its gradual development. For Wikipedia, the advantage is simple and powerful — if there’s a central, machine-readable source for facts, such as the population of a city, then any update to that data can be instantly reflected across all the articles in which the facts are included.

To posit a morbid example: a singer may have dozens or even hundreds of language versions of her Wikipedia entry and, if she were to die, the addition of a date of death to the Wikidata database would immediately propagate across all those versions, with no need to manually update each one (yes, I can also see how this might go horribly wrong).

Indeed, Wikidata is now being used as a common data source for all 286 Wikipedia language versions. Here’s the under-development “item” page for Russia, if you want to see what Wikidata looks like in practise.

Yesterday the internet was alight with celebrations of the World Wide Web‘s 20 year jubilee.

I marked it with a moment of silence in memoriam for the internet that might have been (and possibly could still be).

Don’t get me wrong – I’m a big fan of the web and the irony of the fact that I’m using it to publish this essay is not lost on me.  As a matter of fact, I’m going to be making liberal use of a lot of the techniques I’m going to be complaining about, so this is not an ideological manifesto.

It all boils down to one thing: The Web is not the internet; it’s just something that runs over the internet.

Johann Stan focuses on semantic web and social data engineering. As a knowledge management expert, he is passionate about building context-aware social applications that help people in their information needs. In his research on a semantic framework for social search, he explored how social content sites, such as Facebook or Twitter, have significantly changed the way people organize, share information and interact with peers.

Organizing this huge quantity of social information is one of the major challenges of such collaborative environments. Traditional information retrieval techniques are not well suited for querying such corpus, because of the short size of the shared content, the uncontrolled vocabulary used by authors and because these techniques don’t take in consideration the ties in-between people. More concretely, these techniques are not tailored to systems that integrate both content and social information.

Thank you Google for the new homework assignment: Hangouts vs. Chat

Summary: Google today broke Google Chat by forcing Hangouts on all of us. David Gewirtz gets all cranky about the change. It's not pretty.

By David Gewirtz for DIY-IT |May 16, 2013 -- 15:19 GMT (08:19 PDT)

Google I/OGoogle I/O 2013: Building better e-commerce experiences on AndroidGoogle presses algorithm, cloud advantage vs. Apple, rivalsGoogle developer tool releases include new Maps, Games, Google+ APIsGoogle CEO Page: 'We're only at one percent of what's possible'Samsung Galaxy S4 gets Android Nexus treatment for $649Google sets up to challenge Amazon Web ServicesI/O 2013: Google's location APIs likely to fuel Google Glass apps

I really dislike software updates. It didn't used to be that way. It used to be I looked forward to the new features, new capabilities, new toys.

But that was before I had a life and responsibilities. Now I have both, and I find myself to be somewhat more change-averse.

Today, a day I had a lot of other more important things to do, Google decided to break one of my most-relied-upon resources: Google Chat.

Let me be clear: I use Google Chat for work. I talk to many of my colleagues about work-related activities, about ZDNet editorial, and about projects I'm working on.

I don't hang out.

I don't use Google Chat to make new friends or to share small details about the danish I had for breakfast or hear about new TV shows. I use it to coordinate meeting schedules, deliverables, titles, abstracts, article drop times, and the like.

My Google Chat list is carefully curated. There are about 30 people on it, all of whom have something to do with work

My Google Chat list is carefully curated. There are about 30 people on it, all of whom have something to do with work. Robert Scoble, as much as I like Robert, has never been on my Google Chat list, because I don't work with him.

Today, all that changed. My Chat list is gone. In fact, my nice little Google Chrome extension for Chat is gone as well. Poof. Stolen, like the time stolen from me today trying to recover my work messaging system.

In its place is Hangouts (and Robert is on the list). I had a British professor who used to titter every time someone mentioned hanging out, because to him, hanging out meant, well, something very inadvisable in public.

I don't want to hang out (either using my professor's definition or the more mundane one) with my work associates. I want to send a quick chat and get back to work. So do they.

This morning, however, I have to figure out what happened to my list of work associates, figure out how to get Chat back, figure out how to stop everyone in my Google Plus (which today is such a minus!) from hanging out on my desktop, and then wonder if every other work associate I've got has the same issue and whether all our productivity for the next week has just gone down the chute.

So, thanks Google. Thank you so plus'n much.