Selected Readings on Linked Data and the Semantic Web

Curated on Posted on by Andrew Young

The Living Library’s Selected Readings series seeks to build a knowledge base on innovative approaches for improving the effectiveness and legitimacy of governance. This curated and annotated collection of recommended works on the topic of linked data and the semantic web was originally published in 2013.

Linked Data and the Semantic Web movement are seeking to make our growing body of digital knowledge and information more interconnected, searchable, machine-readable and useful. First introduced by the W3C, Sir Tim Berners-Lee, Christian Bizer and Tom Heath define Linked Data as “data published to the Web in such a way that it is machine-readable, its meaning is explicitly defined, it is linked to other external data sets, and can in turn be linked to from external datasets.” In other words, Linked Data and the Semantic Web seek to do for data what the Web did for documents. Additionally, the evolving capability of linking together different forms of data is fueling the potentially transformative rise of social machines – “processes in which the people do the creative work and the machine does the administration.”

Selected Reading List (in alphabetical order)

Annotated Selected Reading List (in alphabetical order)

Alani, Harith, David Dupplaw, John Sheridan, Kieron O’Hara, John Darlington, Nigel Shadbolt, and Carol Tullo. “Unlocking the Potential of Public Sector Information with Semantic Web Technology,” 2007. http://bit.ly/17fMbCt.

  • This paper explores the potential of using Semantic Web technology to increase the value of public sector information already in existence.
  • The authors note that, while “[g]overnments often hold very rich data and whilst much of this information is published and available for re-use by others, it is often trapped by poor data structures, locked up in legacy data formats or in fragmented databases. One of the great benefits that Semantic Web (SW) technology offers is facilitating the large scale integration and sharing of distributed data sources.”
  • They also argue that Linked Data and the Semantic Web are growing in use and visibility in other sectors, but government has been slower to adapt: “The adoption of Semantic Web technology to allow for more efficient use of data in order to add value is becoming more common where efficiency and value-added are important parameters, for example in business and science. However, in the field of government there are other parameters to be taken into account (e.g. confidentiality), and the cost-benefit analysis is more complex.” In spite of that complexity, the authors’ work “was intended to show that SW technology could be valuable in the governmental context.”

Berners-Lee, Tim, James Hendler, and Ora Lassila. “The Semantic Web.” Scientific American 284, no. 5 (2001): 28–37. http://bit.ly/Hhp9AZ.

  • In this article, Sir Tim Berners-Lee, James Hendler and Ora Lassila introduce the Semantic Web, “a new form of Web content that is meaningful to computers [and] will unleash a revolution of new possibilities.”
  • The authors argue that the evolution of linked data and the Semantic Web “lets anyone express new concepts that they invent with minimal effort. Its unifying logical language will enable these concepts to be progressively linked into a universal Web. This structure will open up the knowledge and workings of humankind to meaningful analysis by software agents, providing a new class of tools by which we can live, work and learn together.”

Bizer, Christian, Tom Heath, and Tim Berners-Lee. “Linked Data – The Story So Far.” International Journal on Semantic Web and Information Systems (IJSWIS) 5, no. 3 (2009): 1–22. http://bit.ly/HedpPO.

  • In this paper, the authors take stock of Linked Data’s challenges, potential and successes close to a decade after its introduction. They build their argument for increasingly linked data by referring to the incredible value creation of the Web: “Despite the inarguable benefits the Web provides, until recently the same principles that enabled the Web of documents to flourish have not been applied to data.”
  • The authors expect that “Linked Data will enable a significant evolutionary step in leading the Web to its full potential” if a number of research challenges can be adequately addressed, both technical, like interaction paradigms and data fusion; and non-technical, like licensing, quality and privacy.

Ding, Li, Dominic Difranzo, Sarah Magidson, Deborah L. Mcguinness, and Jim Hendler. Data-Gov Wiki: Towards Linked Government Data, n.d. http://bit.ly/1h3ATHz.

  • In this paper, the authors “investigate the role of Semantic Web technologies in converting, enhancing and using linked government data” in the context of Data-gov Wiki, a project that attempts to integrate datasets found at Data.gov into the Linking Open Data (LOD) cloud.
  • The paper features discussion and “practical strategies” based on four key issue areas: Making Government Data Linkable, Linking Government Data, Supporting the Use of Linked Government Data and Preserving Knowledge Provenance.

Kalampokis, Evangelos, Michael Hausenblas, and Konstantinos Tarabanis. “Combining Social and Government Open Data for Participatory Decision-Making.” In Electronic Participation, edited by Efthimios Tambouris, Ann Macintosh, and Hans de Bruijn, 36–47. Lecture Notes in Computer Science 6847. Springer Berlin Heidelberg, 2011. http://bit.ly/17hsj4a.

  • This paper presents a proposed data architecture for “supporting participatory decision-making based on the integration and analysis of social and government data.” The authors believe that their approach will “(i) allow decision makers to understand and predict public opinion and reaction about specific decisions; and (ii) enable citizens to inadvertently contribute in decision-making.”
  • The proposed approach, “based on the use of the linked data paradigm,” draws on subjective social data and objective government data in two phases: Data Collection and Filtering and Data Analysis. “The aim of the former phase is to narrow social data based on criteria such as the topic of the decision and the target group that is affected by the decision. The aim of the latter phase is to predict public opinion and reactions using independent variables related to both subjective social and objective government data.”

Rady, Kaiser. Publishing the Public Sector Legal Information in the Era of the Semantic Web. SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, 2012. http://bit.ly/17fMiOp.

  • Following an EU directive calling for the release of public sector information by member states, this study examines the “uniqueness” of creating and publishing primary legal source documents on the web and highlights “the most recent technological strategy used to structure, link and publish data online (the Semantic Web).”
  • Rady argues for public sector legal information to be published as “open-linked-data in line with the new approach for the web.” He believes that if data is created and published in this form, “the data will be more independent from devices and applications and could be considered as a component of [a] big information system. That because, it will be well-structured, classified and has the ability to be used and utilized in various combinations to satisfy specific user requirements.”

Shadbolt, Nigel, Kieron O’Hara, Tim Berners-Lee, Nicholas Gibbins, Hugh Glaser, Wendy Hall, and m.c. schraefel. “Linked Open Government Data: Lessons from Data.gov.uk.” IEEE Intelligent Systems 27, no. 3 (May 2012): 16–24. http://bit.ly/1cgdH6R.

  • In this paper, the authors view Open Government Data (OGD) as an “opportunity and a challenge for the LDW [Linked Data Web]. The opportunity is to grow by linking with PSI [Public Sector Information] – real-world, useful information with good provenance. The challenge is to manage the sudden influx of heterogeneous data, often with minimal semantics and structure, tailored to highly specific task contexts.
  • As the linking of OGD continues, the authors argue that, “Releasing OGD is not solely a technical problem, although it presents technical challenges. OGD is not a rigid government IT specification, but it demands productive dialogue between data providers, users, and developers. We should expect a ‘perpetual beta,’ in which best practice, technical development, innovative use of data, and citizen-centric politics combine to drive data-release programs.”
  • Despite challenges, the authors believe that, “Integrating OGD onto the LDW will vastly increase the scope and richness of the LDW. A reciprocal benefit is that the LDW will provide additional resources and context to enrich OGD. Here, we see the network effect in action, with resources mutually adding value to one another.”

Vitale, Michael, Anni Rowland-Campbell, Valentina Cardo, and Peter Thompson. “The Implications of Government as a ‘Social Machine’ for Making and Implementing Market-based Policy.” Intersticia, September 2013. http://bit.ly/HhMzqD.

  • This report from the Australia and New Zealand School of Government (ANZSOG) explores the concept of government as a social machine. The authors draw on the definition of a social machine proposed by Sir Nigel Shadbolt et al. – a system where “human and computational intelligence coalesce in order to achieve a given purpose” – to describe a “new approach to the relationship between citizens and government, facilitated by technological systems which are increasingly becoming intuitive, intelligent and ‘social.'”
  • The authors argue that beyond providing more and varied data to government, the evolving concept of government as a social machine as the potential to alter power dynamics, address the growing lack of trust in public institutions and facilitate greater public involvement in policy-making.

Big Data

Curated on Posted on by Stefaan Verhulst

Special Report on Big Data by Volta – A newsletter on Science, Technology and Society in Europe:  “Locating crime spots, or the next outbreak of a contagious disease, Big Data promises benefits for society as well as business. But more means messier. Do policy-makers know how to use this scale of data-driven decision-making in an effective way for their citizens and ensure their privacy?90% of the world’s data have been created in the last two years. Every minute, more than 100 million new emails are created, 72 hours of new video are uploaded to YouTube and Google processes more than 2 million searches. Nowadays, almost everyone walks around with a small computer in their pocket, uses the internet on a daily basis and shares photos and information with their friends, family and networks. The digital exhaust we leave behind every day contributes to an enormous amount of data produced, and at the same time leaves electronic traces that contain a great deal of personal information….
Until recently, traditional technology and analysis techniques have not been able to handle this quantity and type of data. But recent technological developments have enabled us to collect, store and process data in new ways. There seems to be no limitations, either to the volume of data or technology for storing and analyzing them. Big Data can map a driver’s sitting position to identify a car thief, it can use Google searches to predict outbreaks of the H1N1 flu virus, it can data-mine Twitter to predict the price of rice or use mobile phone top-ups to describe unemployment in Asia.
The word ‘data’ means ‘given’ in Latin. It commonly refers to a description of something that can be recorded and analyzed. While there is no clear definition of the concept of ‘Big Data’, it usually refers to the processing of huge amounts and new types of data that have not been possible with traditional tools.

‘The new development is not necessarily that there are so much more data. It’s rather that data is available to us in a new way.’

The notion of Big Data is kind of misleading, argues Robindra Prabhu, a project manager at the Norwegian Board of Technology. “The new development is not necessarily that there are so much more data. It’s rather that data is available to us in a new way. The digitalization of society gives us access to both ‘traditional’, structured data – like the content of a database or register – and unstructured data, for example the content in a text, pictures and videos. Information designed to be read by humans is now also readable by machines. And this development makes a whole new world of  data gathering and analysis available. Big Data is exciting not just because of the amount and variety of data out there, but that we can process data about so much more than before.”

Making government simpler is complicated

Curated on Posted on by Stefaan Verhulst

Mike Konczal in The Washington Post: “Here’s something a politician would never say: “I’m in favor of complex regulations.” But what would the opposite mean? What would it mean to have “simple” regulations?

There are two definitions of “simple” that have come to dominate liberal conversations about government. One is the idea that we should make use of “nudges” in regulation. The other is the idea that we should avoid “kludges.” As it turns out, however, these two definitions conflict with each other —and the battle between them will dominate conversations about the state in the years ahead.

The case for “nudges”

The first definition of a “simple” regulation is one emphasized in Cass Sunstein’s recent book titled Simpler: The Future of Government (also see here). A simple policy is one that simply “nudges” people into one choice or another using a variety of default rules, disclosure requirements, and other market structures. Think, for instance, of rules that require fast-food restaurants to post calories on their menus, or a mortgage that has certain terms clearly marked in disclosures.

These sorts of regulations are deemed “choice preserving.” Consumers are still allowed to buy unhealthy fast-food meals or sign up for mortgages they can’t reasonably afford. The regulations are just there to inform people about their choices. These rules are designed to keep the market “free,” where all possibilities are ultimately possible, although there are rules to encourage certain outcomes.
In his book, however, Sunstein adds that there’s another very different way to understand the term “simple.” What most people mean when they think of simple regulations is a rule that is “simple to follow.” Usually a rule is simple to follow because it outright excludes certain possibilities and thus ensures others. Which means, by definition, it limits certain choices.

The case against “kludges”
This second definition of simple plays a key role in political scientist Steve Teles’ excellent recent essay, “Kludgeocracy in America.” For Teles, a “kludge” is a “clumsy but temporarily effective” fix for a policy problem. (The term comes from computer science.) These kludges tend to pile up over time, making government cumbersome and inefficient overall.
Teles focuses on several ways that kludges are introduced into policy, with a particularly sharp focus on overlapping jurisdictions and the related mess of federal and state overlap in programs. But, without specifically invoking it, he also suggests that a reliance on “nudge” regulations can lead to more kludges.
After all, non-kludge policy proposal is one that will be simple to follow and will clearly cause a certain outcome, with an obvious causality chain. This is in contrast to a web of “nudges” and incentives designed to try and guide certain outcomes.

Why “nudges” aren’t always simpler
The distinction between the two is clear if we take a specific example core to both definitions: retirement security.
For Teles, “one of the often overlooked benefits of the Social Security program… is that recipients automatically have taxes taken out of their paychecks, and, then without much effort on their part, checks begin to appear upon retirement. It’s simple and direct. By contrast, 401(k) retirement accounts… require enormous investments of time, effort, and stress to manage responsibly.”

Yet 401(k)s are the ultimately fantasy laboratory for nudge enthusiasts. A whole cottage industry has grown up around figuring out ways to default people into certain contributions, on designing the architecture of choices of investments, and trying to effortlessly and painlessly guide people into certain savings.
Each approach emphasizes different things. If you want to focus your energy on making people better consumers and market participations, expanding our government’s resources and energy into 401(k)s is a good choice. If you want to focus on providing retirement security directly, expanding Social Security is a better choice.
The first is “simple” in that it doesn’t exclude any possibility but encourages market choices. The second is “simple” in that it is easy to follow, and the result is simple as well: a certain amount of security in old age is provided directly. This second approach understands the government as playing a role in stopping certain outcomes, and providing for the opposite of those outcomes, directly….

Why it’s hard to create “simple” regulations
Like all supposed binaries this is really a continuum. Taxes, for instance, sit somewhere in the middle of the two definitions of “simple.” They tend to preserve the market as it is but raise (or lower) the price of certain goods, influencing choices.
And reforms and regulations are often most effective when there’s a combination of these two types of “simple” rules.
Consider an important new paper, “Regulating Consumer Financial Products: Evidence from Credit Cards,” by Sumit Agarwal, Souphala Chomsisengphet, Neale Mahoney and Johannes Stroebel. The authors analyze the CARD Act of 2009, which regulated credit cards. They found that the nudge-type disclosure rules “increased the number of account holders making the 36-month payment value by 0.5 percentage points.” However, more direct regulations on fees had an even bigger effect, saving U.S. consumers $20.8 billion per year with no notable reduction in credit access…..
The balance between these two approaches of making regulations simple will be front and center as liberals debate the future of government, whether they’re trying to pull back on the “submerged state” or consider the implications for privacy. The debate over the best way for government to be simple is still far from over.”

Information Now: Open Access and the Public Good

Curated on Posted on by Stefaan Verhulst

Podcast from SMARTech (Georgia Tech): “Every year, the international academic and research community dedicates a week in October to discuss, debate, and learn more about Open Access. Open Access in the academic sense refers to the free, immediate, and online access to the results of scholarly research, primarily academic, peer-reviewed journal articles. In the United States, the movement in support of Open Access has, in the last decade, been growing dramatically. Because of this growing interest in Open Access, a group of academic librarians from the Georgia Tech library, Wendy Hagenmaier (Digital Collections Archivist), Fred Rascoe (Scholarly Communication Librarian), and Lizzy Rolando (Research Data Librarian), got together to talk to folks in the thick of it, to try and unravel some of the different concerns and benefits of Open Access. But we didn’t just want to talk about Open Access for journal articles – we wanted to examine more broadly what it means to be “open”, what is open information, and what relationship open information has to the public good. In this podcast, we talk with different people who have seen and experienced open information and open access in practice. In the first act, Dan Cohen from the DPLA speaks about efforts to expand public access to archival and library collections. In the second, we’ll hear an argument from Christine George about why things sometimes need to be closed, if we want them to be open in the future. Third, Kari Watkins speaks about specific example of when a government agency decided, against legitimate concerns, to make transit data open, and why it worked for them. Fourth, Peter Suber from Harvard University will give us the background on the Open Access movement, some myths that have been dispelled, and why it is important for academic researchers to take the leap to make their research openly accessible. And finally, we’ll hear from Michael Chang, a researcher who did take that leap and helped start an Open Access journal, and why he sees openness in research as his obligation.”

See also Personal Guide to Open Access

The "crowd computing" revolution

Curated on Posted on by Stefaan Verhulst

Michael Copeland in the Atlantic: “Software might be eating the world, but Rob Miller, a professor of computer science at MIT, foresees a “crowd computing” revolution that makes workers and machines colleagues rather than competitors….
Miller studies human-computer interaction, specifically a field called crowd computing. A play on the more common term “cloud computing,” crowd computing is software that employs a group of people to do small tasks and solve a problem better than an algorithm or a single expert. Examples of crowd computing include Wikipedia, Amazon’s Mechanical Turk (where workers outsource projects that computers can’t do to an online community) a Facebook’s photo tagging feature.
But just as humans are better than computers at some things, Miller concedes that algorithms have surpassed human capability in several fields. Take a look at libraries, which now have advanced digital databases, eliminating the need for most human reference librarians. There’s also flight search, where algorithms are much better than people at finding the cheapest fare.
That said, more complicated tasks even in those fields can get tricky for a computer.
“For complex flight search, people are still better,” Miller says. A site called Flightfox lets travelers input a complex trip while a group of experts help find the cheapest or most convenient combination of flights. “There are travel agents and frequent flyers in that crowd, people with expertise at working angles of the airfare system that are not covered by the flight searches and may never be covered because they involve so many complex intersecting rules that are very hard to code.”
Social and cultural understanding is another area in which humans will always exceed computers, Miller says. People are constantly inventing new slang, watching the latest viral videos and movies, or partaking in some other cultural phenomena together. That’s something that an algorithm won’t ever be able to catch up to. “There’s always going to be a frontier of human understanding that leads the machines,” he says.
A post-employee economy where every task is automated by a computer is something Miller does not see happening, nor does he want it to happen. Instead, he considers the relationship between human and machine symbiotic. Both machines and humans benefit in crowd computing, “the machine wants to acquire data so it can train and get better. The crowd is improved in many ways, like through pay or education,” Miller says. And finally, the end users “get the benefit of a more accurate and fast answer.”
Miller’s User Interface Design Group at MIT has made several programs illustrating how this symbiosis between user, crowd and machine works. Most recently, the MIT group created Cobi, a tool that taps into an academic community to plan a large-scale conference. The software allows members to identify papers they want presented and what authors are experts in specific fields. A scheduling tool combines the community’s input with an algorithm that finds the best times to meet.
Programs more practical for everyday users include Adrenaline, a camera driven by a crowd, and Soylent, a word processing tool that allows people to do interactive document shortening and proofreading. The Adrenaline camera took a video and then had a crowd on call to very quickly identify the best still in that video, whether it was the best group portrait, mid-air jump, or angle of somebody’s face. Soylent also used users on Mechanical Turk to proofread and shorten text in Microsoft Word. In the process, Miller and his students found that the crowd found errors that neither a single expert proofreader nor the program—with spell and grammar check turned on—could find.
“It shows this is the essential thing that human beings bring that algorithms do not,” Miller said.
That said, you can’t just use any crowd for any task. “It does depend on having appropriate expertise in the crowd. If [the text] had been about computational biology, they might not have caught [the error]. The crowd does have to have skills.” Going forward, Miller thinks that software will increasingly use the power of the crowd. “In the next 10 or 20 years it will be more likely we already have a crowd,” he says. “There will already be these communities and they will have needs, some of which will be satisfied by software and some which will require human help and human attention. I think a lot of these algorithms and system techniques that are being developed by all these startups, who are experimenting with it in their own spaces, are going to be things that we’ll just naturally pick up and use as tools.”

Why Crowdsourcing is the Next Cloud Computing

Curated on Posted on by Stefaan Verhulst

Alpheus Bingham, co-founder and a member of the board of directors at InnoCentive, in Wired: “But over the course of a decade, what we now call cloud-based or software-as-a-service (SaaS) applications has taken the world by storm and become mainstream. Today, cloud computing is an umbrella term that applies to a wide variety of successful technologies (and business models), from business apps like Salesforce.com, to infrastructure like Amazon Elastic Compute Cloud (Amazon EC2), to consumer apps like Netflix. It took years for all these things to become mainstream, and if the last decade saw the emergence (and eventual dominance) of the cloud over previous technologies and models, this decade will see the same thing with crowdsourcing.
Both an art and a science, crowdsourcing taps into the global experience and wisdom of individuals, teams, communities, and networks to accomplish tasks and work. It doesn’t matter who you are, where you live, or what you do or believe — in fact, the more diversity of thought and perspective, the better. Diversity is king and it’s common for people on the periphery of — or even completely outside of — a discipline or science to end up solving important problems.
The specific nature of the work offers few constraints – from a small business needing a new logo, to the large consumer goods company looking to ideate marketing programs, or to the nonprofit research organization looking to find a biomarker for ALS, the value is clear as well.
To get to the heart of the matter on why crowdsourcing is this decade’s cloud computing, several immediate reasons come to mind:
Crowdsourcing Is Disruptive
Much as cloud computing has created a new guard that in many ways threatens the old guard, so too has crowdsourcing. …
Crowdsourcing Provides On-Demand Talent Capacity
Labor is expensive and good talent is scarce. Think about the cost of adding ten additional researchers to a 100-person R&D team. You’ve increased your research capacity by 10% (more or less), but at a significant cost – and, a significant FIXED cost at that. …
Crowdsourcing Enables Pay-for-Performance.
You pay as you go with cloud computing — gone are the days of massive upfront capital expenditures followed by years of ongoing maintenance and upgrade costs. Crowdsourcing does even better: you pay for solutions, not effort, which predictably sometimes results in failure. In fact, with crowdsourcing, the marketplace bears the cost of failure, not you….
Crowdsourcing “Consumerizes” Innovation
Crowdsourcing can provide a platform for bi-directional communication and collaboration with diverse individuals and groups, whether internal or external to your organization — employees, customers, partners and suppliers. Much as cloud computing has consumerized technology, crowdsourcing has the same potential to consumerize innovation, and more broadly, how we collaborate to bring new ideas, products and services to market.
Crowdsourcing Provides Expert Services and Skills That You Don’t Possess.
One of the early value propositions of cloud-based business apps was that you didn’t need to engage IT to deploy them or Finance to help procure them, thereby allowing general managers and line-of-business heads to do their jobs more fluently and more profitably…”

The small-world effect is a modern phenomenon

Curated on Posted on by Stefaan Verhulst

New paper by Seth A. Marvel, Travis Martin, Charles R. Doering, David Lusseau, M. E. J. Newman: “The “small-world effect” is the observation that one can find a short chain of acquaintances, often of no more than a handful of individuals, connecting almost any two people on the planet. It is often expressed in the language of networks, where it is equivalent to the statement that most pairs of individuals are connected by a short path through the acquaintance network. Although the small-world effect is well-established empirically for contemporary social networks, we argue here that it is a relatively recent phenomenon, arising only in the last few hundred years: for most of mankind’s tenure on Earth the social world was large, with most pairs of individuals connected by relatively long chains of acquaintances, if at all. Our conclusions are based on observations about the spread of diseases, which travel over contact networks between individuals and whose dynamics can give us clues to the structure of those networks even when direct network measurements are not available. As an example we consider the spread of the Black Death in 14th-century Europe, which is known to have traveled across the continent in well-defined waves of infection over the course of several years. Using established epidemiological models, we show that such wave-like behavior can occur only if contacts between individuals living far apart are exponentially rare. We further show that if long-distance contacts are exponentially rare, then the shortest chain of contacts between distant individuals is on average a long one. The observation of the wave-like spread of a disease like the Black Death thus implies a network without the small-world effect.”

Facilitating scientific discovery through crowdsourcing and distributed participation

Curated on Posted on by Stefaan Verhulst

Antony Williams in  EMBnet. journal:” Science has evolved from the isolated individual tinkering in the lab, through the era of the “gentleman scientist” with his or her assistant(s), to group-based then expansive collaboration and now to an opportunity to collaborate with the world. With the advent of the internet the opportunity for crowd-sourced contribution and large-scale collaboration has exploded and, as a result, scientific discovery has been further enabled. The contributions of enormous open data sets, liberal licensing policies and innovative technologies for mining and linking these data has given rise to platforms that are beginning to deliver on the promise of semantic technologies and nanopublications, facilitated by the unprecedented computational resources available today, especially the increasing capabilities of handheld devices. The speaker will provide an overview of his experiences in developing a crowdsourced platform for chemists allowing for data deposition, annotation and validation. The challenges of mapping chemical and pharmacological data, especially in regards to data quality, will be discussed. The promise of distributed participation in data analysis is already in place.”

Smart Machines: IBM's Watson and the Era of Cognitive Computing

Curated on Posted on by Stefaan Verhulst
New book from Columbia Business School Publishing: “We are crossing a new frontier in the evolution of computing and entering the era of cognitive systems. The victory of IBM’s Watson on the television quiz show Jeopardy! revealed how scientists and engineers at IBM and elsewhere are pushing the boundaries of science and technology to create machines that sense, learn, reason, and interact with people in new ways to provide insight and advice.
In Smart Machines, John E. Kelly III, director of IBM Research, and Steve Hamm, a writer at IBM and a former business and technology journalist, introduce the fascinating world of “cognitive systems” to general audiences and provide a window into the future of computing. Cognitive systems promise to penetrate complexity and assist people and organizations in better decision making. They can help doctors evaluate and treat patients, augment the ways we see, anticipate major weather events, and contribute to smarter urban planning. Kelly and Hamm’s comprehensive perspective describes this technology inside and out and explains how it will help us conquer the harnessing and understanding of “big data,” one of the major computing challenges facing businesses and governments in the coming decades. Absorbing and impassioned, their book will inspire governments, academics, and the global tech industry to work together to power this exciting wave in innovation.”
See also Why cognitive systems?

And Data for All: On the Validity and Usefulness of Open Government Data

Curated on Posted on by Stefaan Verhulst

Paper presented at the the 13th International Conference on Knowledge Management and Knowledge Technologies: “Open Government Data (OGD) stands for a relatively young trend to make data that is collected and maintained by state authorities available for the public. Although various Austrian OGD initiatives have been started in the last few years, less is known about the validity and the usefulness of the data offered. Based on the data-set on Vienna’s stock of trees, we address two questions in this paper. First of all, we examine the quality of the data by validating it according to knowledge from a related discipline. It shows that the data-set we used correlates with findings from meteorology. Then, we explore the usefulness and exploitability of OGD by describing a concrete scenario in which this data-set can be supportive for citizens in their everyday life and by discussing further application areas in which OGD can be beneficial for different stakeholders and even commercially used.”