OntologySummit2012: Media Kit (draft) (32CX)
This is the workspace for preparing the OntologySummit2012_MediaKit. (32CY)
... (content coming!) (32EI)
What is the Ontology Summit? (38YP)
The Ontology Summit is an annual series of events (first started by Ontolog and NIST in 2006) that involves the ontology community and communities related to each year's theme chosen for the summit. The Ontology Summit program is now co-organized by Ontolog, NIST, NCOR, NCBO, IAOA, NCO_NITRD along with the co-sponsorship of other organizations that are supportive of the Summit goals and objectives. (38YQ)
Each year's Summit comprises of a series of both virtual and face-to-face events that span about 3 months. These include a vigorous three-month online discourse on the theme of choice (for a particular summit), virtual panel discussions, research activities ... etc. which will culminate in a two-day face-to-face workshop and symposium at NIST (Gaithersburg, Maryland, USA.) The publication of a Summit Communique each year, at the end of the face-to-face symposium, to get an annual message from the participants to the world-at-large, has also been a signature activity of this Ontology Summit series. (38YR)
Ontology for Big Systems (38YS)
This year's Ontology Summit is titled "Ontology for Big Systems" and seeks to explore, identify and articulate how ontological methods can bring value to the various disciplines required to engineer a "big system." The term "big system" is intended to cover a large scope that includes many of the terms encountered in the media such as big data, complex techno-socio-economic systems, intelligent or smart systems, cloud computing, netcentricity and collective intelligence. Established disciplines that fall within the summit scope include (but not limited to) systems engineering, software engineering, information systems modelling, and data mining. (38YT)
The principal goal of the summit is to bring together and foster collaboration between the ontology community, systems community, and stakeholders of some of the "big systems." Together, the summit participants will exchange ideas on how ontological analysis and ontology engineering might make a difference, when applied in these "big systems." We will aim towards producing a series of recommendations describing how ontologies can create an impact; as well as providing illustrations where these techniques have been, or could be, applied in domains such as bioinformatics, electronic health records, intelligence, the smart electrical grid, manufacturing and supply chains, earth and environmental, e-science, cyberphysical systems and e-government. As is traditional with the Ontology Summit series, the results will be captured in the form of a communique¦, with expanded supporting material provided on the web. (38YU)
What Are Ontologies? (38ZZ)
Here’s a surprising fact - ontologies are all around us, we’re using them all the time. If this is the case, and ontologies are everywhere, why haven’t you heard of it? (38YW)
A big reason is that until recently, ontology was primarily of interest only to philosophers. A branch of metaphysics, Ontology in philosophy, is concerned with answering big questions such as “What exists?” (38YX)
While it might be interesting to ruminate on the nature of being, it’s just not that relevant to most people’s lives. Sure each and everyone us answer this question everyday, but we do it pragmatically, often intuitively and almost always implicitly. (38YY)
Whenever we engage with our world, whether it is eating a meal, going to work, socializing (anything really), we are in a very real sense, answering and living, “What exists?” While we might not consciously think about it, whenever we do anything, we’re acting on a vast body of implicit knowledge and belief about what we think exists and is real. (38YZ)
So when we’re watching a movie in a theatre, we do so in the belief that paying for a ticket gives us the right to sit and watch the theatre project a movie onto a screen. We abide by a convention that suggests we not disturb others. And we watch knowing that physical reality ensures that the dinosaur on screen can’t actually jump out beside me. (38Z0)
The same is true for many of the objects we use and the systems we interact with. The forks we use are designed based on assumptions about human mouths, hands and the types of foods we eat. A transit system is designed according to assumptions about population density, growth, usage and rates. Both of these carry with them an imprint of the beliefs of their designers. (38Z1)
In many ways, ontologies are like the air we breathe. We each act according to our own internal ontologies, but we rarely think about. When something unexpected happens, we learn and implicitly make adjustments. When we say something to a friend and they don’t understand, we elaborate and explain ourselves. For humans, we’re already at ease living with our many ontologies. (38Z2)
The field of ontology engineering became possible with the advent of computing. With the arrival of computers, it became possible to make ontologies accessible to machines. We could represent aspects of reality as logical theories which could be understood by computers. When machines need to talk to one another, or when we want to understand or use a system designed by another person (who might no longer be around), then those implicit assumptions suddenly matter a lot. (38Z3)
A fundamental task for ontology today is to make explicit the implicit assumptions that people or systems make about their relevant portion of the world. This can range from users independently, yet collaboratively creating tag clouds, to search engines providing directories or taxonomies, to organizations developing controlled vocabularies, deploying thesauri and to creating logical models of the world. (38Z4)
Making such background knowledge explicit in the form of an ontology allows our machines to “understand” that much more of our world. This makes what we believe accessible to others in a clear, precise way. Forcing us to consider our basic assumptions and bringing to light any subtle disagreements or indeed errors. (38Z5)
As Tom Gruber once wrote, “an ontology is a specification of a conceptualization.” In the modern, applied sense, it is about making explicit the assumptions we employ about what the world is. For a more thorough (and somewhat more technical) perspective on the exact nature of ontologies, the 2007 Ontology Summit discussed in great detail what computational ontologies can be. (38Z6)
Challenges for Big Systems (3900)
Over the past 100 years, we’ve entered the Cambrian age for information, knowledge and systems. The amount of knowledge that is produced, published and shared by humanity has been growing exponentially each year. In the past decade more data has been collected, more video has been produced and more information has been published than in all of previous human history. (38Z8)
At the same time, with the advent of the computers and the Internet, it has been possible to model more of the complexity of reality, connect more people and connect more systems. With all this new information and all these new systems, we have also seen a growth in the complexity of our systems, their size, their scale, their scope and their interdependence. (38Z9)
We need novel tools and approaches to address the new problems that have arisen during the Cambrian period of our knowledge. Some of the major challenges facing Big Systems include developing more robust models that represent these systems, which in turn help us tame Big Data. At the same time, there are novel challenges for Big Systems when different groups try to work together to a common goal (say understanding Climate Change), which means that we need better solutions for interoperability among federated systems and for fostering interdisciplinary collaboration. (38ZA)
Big Data (3901)
A key component of the current explosion of knowledge is the proliferation of vast amounts of data. With greater computing power, we’re able to encode anyone person’s DNA, track our internet usage, credit usage, the experiments at the Large Hadron Collider and so on, each of these activities creates a staggering amount of data. (38ZC)
While the sheer size and scale of these data sets presents a challenge, knowing how to intelligently combine the data means that we must accurately understand the world that this data represents. If we want to combine data from multiple sources, then it becomes all the more important that we understand what each source intended by the publication of the data. (38ZD)
To do this, we need theory. There are limits to blind statistical analysis. We need theory and statistical analysis together. Data publishers need to make explicit what their data represents, the systems that consume and transform To intelligently use this data and combine it for useful ends, involves developing theories about those relevant parts of the world. Especially if we want successful data reuse and adaptability. (38ZE)
There are a variety of groups working towards this vision. For example, the linked open data (URL) seeks to connect distributed data across the net. While there are many data sources available online today, that data is not readily accessible. The LOD cloud aims to create the requisite infrastructure to enable people to seamlessly build “mash-ups” by coming data from multiple sources. (38ZF)
Similarly, there has been a surge of work in bioinformatics, including the Open Biological and Biomedical Ontology, Gene Ontology and other sources which annotate big data with explicit semantics. These initiatives allow research groups to publish findings on genes, gene expression, proteins and so in a standardized consistent manner. (38ZG)
Another example is the FuturICT project funded by the European Union. Its ultimate goal is to understand and manage complex, global, socially interactive systems, with a focus on sustainability and resilience. FuturICT will build a Living Earth Platform, a simulation, visualization and participation platform to support decision-making of policy-makers, business people and citizens. (38ZH)
Model Driven Engineering (3902)
One way to express a theory of (a part of) the world is to build a model. Concurrent with the increased use of computers, engineers and designers have come to rely on a variety of models to represent parts of their disciplines. Designing a car, a power plant, a transportation system or even the climate relies heavily on creating a computer model of the system. (38ZJ)
Different fields have models of varying sophistication, though in many the semantics - the meaning - of the parts of the model are governed by implicit or inconsistent convention. (38ZK)
First in engineering and slowly in other fields, we’re witnessing a gradual shift to explicit semantics. The various sub-disciplines within engineering have evolved from using informal modeling, to using formal languages to model their systems, to underpinning said languages with explicit semantics, to recognizing the importance of understanding the underlying ontology of the elements of the languages. (38ZL)
Current cutting edge research in ontology engineering involves teasing the ontological status of a system component. What does it mean for one to say that a car has a headlamp as a component? What happens to the component if the headlamp is broken or replaced? Is it the same headlamp, is it the same component? (38ZM)
Various standardization efforts are underway as well, from the development of ISO 15926, to providing formal semantics for the Unified Modeling Language. Similarly, groups are working to build repositories of ontologies, or libraries of ontology patterns - snippets that formalize important aspects of reality such as “part-of” or “is-a”. (38ZN)
Interoperability (3903)
The Internet means that it is far easier for different people in the different parts of the world to share and combine data, information and knowledge. If we want to realize the true potential of this interconnected world it means that we need to be able to combine not just our data, but also our models. (38ZP)
An initiative like Sage Bionetworks might allow a doctor in China to integrate diverse molecular mega-datasets, and reuse a predictive bionetworks built by a team in United States that deploys new insights into human disease biology by a team in France. Each different community views and prioritizes parts of the world according to their own viewpoints and interests. (38ZQ)
Similarly, within a single enterprise, the same product may be viewed differently by each of the marketing, engineering, manufacturing, sales and accounting departments. Making sure that these views are, if not harmonized, then aligned so that information can be successfully shared entails solving interoperability. (38ZR)
Semantic analysis is a fundamental, essential aspect of federation and integration. Building value by combining the views of different communities means solving interoperability, and that means negotiating the implicit meaning used by each of these groups. (38ZS)
The Object Modeling Group has recently put out a request for proposal to create a standard to address such issues. Similarly, within the systems engineering community, one examples is ISO 15926 which aims to federate CAD/CAM/PLM systems in industry, business and eco-system-wide (beyound boundary of enterprise) scales. (38ZT)
Interdisciplinary Collaboration (3904)
Similarly, as knowledge has become more specialized, different communities have developed their own bodies of knowledge. Bridging these gaps can unleash a lot of potential, foster innovation, reduce the reinvention of the wheel and accelerate the development of better tools. (38ZV)
While each specialization may use its own jargon and technical language, the underlying reality is the same. Ontologies, in the form of explicit statement of the assumptions in each sub-field can help identify points of overlap and interest between different communities. They can serve as tools to facilitate search and discovery. (38ZW)
The Linked Science effort is a project that aims to create an “executable paper.” It hopes to combine publication of scientific data, metadata, results, and provenance information using Linked Data principles, alongside open source and web-based environments for executing, validating and exploring research, using Cloud Computing for efficient and distributed computing and deploying Creative Commons for its legal infrastructure. (38ZX)
Another project, the iPlan Collaborative, is building the requisite cyberinfrastructure to help cross-disciplinary, community-driven groups publish and share information, build models and aid in search. The vision is to develop a cyberinfrastructure that is accessible to all levels of expertise, ranging from students to traditional biology researchers and computational biology experts. (38ZY)
-- maintained by the OntologySummit2012 Public Relations Champions: AliHashemi ... please do not edit (32CZ)