UF Prospectus 5 Example systems
From Knowledge Federation
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5 Example systems
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Substantial work is being done or has been done outside of the knowledge federation community that need to be counted as knowledge federation resources and contributions. Here we outline a limited view and present the related work that has been done by the authors of this article. We emphasize that this exposition is far from exhausting the possble approaches. Our aim is only to point at the spectrum of possibilities, by showing that already within this small group diverse approches have been represented. A central task of the knowledge federation community will be to develop and present various approaches to federation, and allow them to cross-fertilize with each other, as well as with knowledge and ideas from other fields, thus leading to new directins and solutions for academic practice, media informing, technology and business. Meme mediaImagine that you are developing a compendium for a course, but instead of authoring the entire volume, you create the volume by combining pieces authored by the leading best experts in the field. Imagine further that those contributions are not fixed but fluid – they can automatically change, as the authors improve their knowledge and the way of expressing it. Or imagine that you are creating a Web service, but instead of writing code, you combine the existing services and programs as building blocks by cut-and-paste. This is roughly the idea behind the meme media technology, that has been developed by Yuzuru Tanaka since 1987 (...). The larger idea of meme media is to facilitate cultural evolution by providing a technical medium by which memes can easily be brought together and combined to produce new ones. Knowledge gardeningKnowledge gardening is an approach developed by Jack Park from the Open University (earlier SRI International). Here knowledge production and sharing is envisioned as interacting with a living system system, where knowledge is dynamically stored, grown and retrieved. The system informs us when the status of knowledge associated with one of our registgered subjects of interest has changed. When we discover something, or come up with a new idea, or a question, those are placed into the garden. At the limit, the knowledge gardeners are no longer writing books and articles. They interact directly with the garden, by planting questions and ideas, tending and fertilizing and foraging. The insights, the ideas and realizations grow organically, tended by human gardeners and intelligent software agents. Hypermedia discourseYou pick up a phone when you want to talk to a friend. You may even be using Skype. But what do you do when your intention is to understand the options for responding to climate change, and you want to speak to other experts, or potentially everyone on this planet who may want or be able to contribute? In hypermedia discourse, a general approach represented by Simon Buckingham Shum, is to facilitate suitable forms of discourse, for sense making and problem solving, by providing both media technology and forms of social interaction. This work is focused specifically on the form of discourse by which problems are framed, and meaning is constructed in teams of analysts, e.g. deliberation over alternatives, dialogue seeking common ground, or rational debate and argumentation. It draws on the conceptual foundations offered by fields such as argumentation, cognitive coherence relations and organisational sensemaking. Hypermedia points to the engineering and aesthetics of managing webs of meaningful connections as visualizable networks of claims, issues, potential solutions, evidence, and so forth. The tools include the Compendium, where a map-like representation turns a linear chronological sequence of a group conversation into a two-dimensional map or network of inter-dependent ideas (questions, answers, and pro- and con- arguments). Another is Cohere, by which a community can contribute facts and statements corresponding to an issue, and then use the tool for developing or cohering the meaning and the conclusions Fuzzzy.comFolksonomies such as del.ici.us allow for tagging knowledge objects so that they can be found – Here we link also tags together, we give the links meaning (for example ‘Mozart’ and ‘Magic Flute’ as tags are linked with ‘is composed by’ for further reference. Fuzzzy is an experimental platform, a semantic folksonomy with fuzziness (links have degrees etc.).It is developed by Roy Lachica from Bouvet (earlier University of Oslo). One of the points behind fuzzzy is that the knowledge is fuzzy, and so is our knowledge about knowledge. Hence links have strengths, users vote on them, and the links die or become more visible. Hence artifacts with more relevance gain visibility. Irrelevant connections and pieces of knowledge sink into oblivion (but are never completely abandoned, and can be discovered with more diligent search).
Key point dialogs(Dino: I will develop this. For the time being pls. look up the motivation in my blog entry 'What keeps us from responding') One kind was – to synthesize the HL view from LL ones. The goal is to move from observed symptoms to structural causes. Used, for example in a municipality for following procedure: Begin from problems (ex. youth drug abuse, or high sickness absence). Find higher level causes (for ex. lack of inspiring role models, or nature and social activities). Used, still only as experiments, in Norwegian municipalities. The idea is to empower the contingency to change lifestyle. The question is exactly the power structure. The second kind was an enhancement of original Bohm’s idea. The point is to create a transformative circle, where the people can cohere their way of thinking and co-create a vision. Here the model is enhanced in two ways: First, by providing a an energy field that energizes the dialog. The context legitimizes it, by showing. Hence here we bring together a variety of ideas, a context, strike a match and – have the circle move. The movement then spreads through the Internet and media like waves in the water. MooM - Management of Ontology Mapping ManagementOntologies are an important component for the implementation of the Semantic Web vision [An04]. The promise of ontologies is to enable the sharing of a common understanding of a domain that can be flexibly communicated between users and applications [Gr94]. However, the actual conceptualization of a domain and the subsequent explication in an ontology language is a very heterogeneous process [Co05]. For example conceptual heterogeneity between ontologies arises due to the natural human diversity involved in modelling a domain [Eu04], e.g. two ontologies could differ because they provide a more or less detailed description of the same domain [Mi06]. The different levels of heterogeneities are major obstacles to the promise of interoperability of knowledge based on ontologies [La08]. A common approach to mitigate the effect of heterogeneity is to discover the specific correspondences between related ontologies and to document these correspondences using an appropriate ontology mapping expression [Eu06]. In recent years the research on ontology mappings has made remarkable progress [Os07] but the creation and application of ontology mappings is still a complex and time-consuming task [Mo09]. Instead of recreating the same or similar mappings repeatedly it may be more beneficial to discover existing mappings and if appropriate to reuse them [Bo05]. To decide, if an ontology mapping can be reused, it is essential to understand how the mapping was created. An analysis of the lifecycle of a particular ontology mapping [Th09b] helps to understand involved decisions and used information, e.g. used matching algorithms . Meta-data can be used to document the lifecycle and thus to make this kind of information retrievable in a structured and predictable way [Fu93]. Therefore meta-data is important to facilitate the discovery and reuse of ontology mappings. However, in current ontology mapping applications and format the ontology mapping lifecycle is only fragmentary documented and a comprehensive meta-data structure has not emerged as yet. In general only basic meta-data are provided identifying the subjects of mappings, but details of the generation process (who, how, why) are minimal [Th09b]. This makes it very difficult for an ontology engineer to reconstruct how a particular mapping was created and applied. In summary the sharing of a common understanding of an ontology mapping and thus the reuse and management of ontology mappings is not sufficiently supported yet. In order to improve the mapping discovery, management and reuse Hendrik Thomas will develop a framework for “Management of Ontology Mappings” (MooM). It will consist of a ontology-based meta-data model to represent the ontology mapping lifecycle in a semantically rich, comprehensive and unambiguous way. The meta-data needs to describe the initial creation process of an ontology mapping, but also the reuse iterations (see section 2.5), because mappings can depend on another, e.g. “if X is Y, then A is B” [Eu06]. In order to expose the semantics of the meta-data, it is necessary to define the requirements for such an unambiguous ontology-based modelling approach, e.g. URIs, human-readable definition, subject indicators. In addition, existing meta-data standards and schemas (see section 2.7) will be analysed in order to identify those which can be reused in order to represent meta-data in a rich and interoperable fashion, e.g. Dublin Core or FOAF . In addition MooM will provide a tool set for creation, valuation and maintenance of ontology mapping meta-data. The objective is to develop tools for the automated or semi-automated extraction and processing of relevant meta-data from the ontologies and the mappings. A modular architecture based on web services combined with the editing functions of an off-the-shelf content management system (e.g. Drupal) will be used to ensure that the system can be flexibly extended and is easy to integrate. The mapping management tool will support the discovery of existing mappings, the evaluation of an ontology mappings as well as the decision process if a mapping should be reused or not. References:
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