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Answer Upon - Collaborative Modelling Using Ontologies
Driving under the influence (DUI), commonly known as drunk driving, means operating a motor vehicle in a public area while intoxicated. It is considered a criminal offence to drive a vehicle after consuming alcoholic beverages. DUIs are also considered an offense when boating and piloting aircraft.Driving under the influence of alcohol is a misdemeanor, and depending upon the nature of jurisdiction, it is sometimes referred to as driving while intoxicated (DWI), operating while intoxicated (OWI) or operating a motor vehicle while intoxicated (OMVI). DUI is perhaps one of the main reasons for motor vehicle crashes. The excess use of alcohol impairs the nervous system, thereby adversely affecting the balance of mind and body.While driving under the influence is regarded as a serious offense in almost all states, the punishment generally depends upon the DUI statute in the state. If you are charged with driving under the influence, blood and urine levels are first tested. Also, a breath test is administered. If it is proved that your blood alcohol content is above the legal limit set by the state’s statute, you will be arrested. In some states, if any one is proved to have a breath test of .08 or higher, they are said to be under the influence of alcohol.In all cases, if you are driving under the influence of alcohol, there is a good chance that you will lose your license, serve time in prison and serve a lengthy probation period. The charge could also affect future employment. DUI laws are quite complex, and offenders generally seek assistance of a competent and experienced lawyer to help them out.Offenders are generally charged large fines based on the court proceedings and test results. Usually, first-time offenders are charged a fine, and there are certain provisions to get keep punishments at a mimimum.
References
Cheung, W. M., Maropoulos, P. G., Gao, J. X., Aziz, H., 2005. Ontological Approach for Organisational Knowledge Re-use in Product Developing Environments. In: 11th International Conference on Concurrent Enterprising - ICE 2005, University BW Munich, Germany.
Cheung, W. M., Matthews, P. C., Gao, J. X., Maropoulos, P. G., 2007. Advanced product development integration architecture: an out-of-box solution to support distributed production networks. International Journal of Production Research March 2007.
Ciocoiu, M., Gruninger, M., Nau, D. S., 2000. Ontologies for Integrating Engineering Applications. Journal of Computing and Information Science in Engineering, 1(1) pp 12-22.
Crapo, A. W., Waisel, L. B., Wallace, W. A., Willemain, T. R., 2002. Visualization and Modelling for Intelligent Systems. In: C. T. Leondes, ed. Intelligent Systems: Technology and Applications, Volume I Implementation Techniques, 2002 pp 53-85.
Eng, N., Salustri, F. A., 2006. "Rugplot" Visualization for Preliminary Design. In: CDEN 2006 3rd CDEN/RCCI International Design Conference University of Toronto, Ontario, Canada.
Fensel, D. Van Harmelen, F. Horrocks, I. McGuinness, D. Patel-Schneider, P. F., 2001. OIL: An ontology infrastructure for the semantic web. IEEE Intelligent Systems, 16(2), pp 38-45.
Gruber, T. R. 1993. Toward Principles for the Design of Ontologies Used for Knowledge Sharing. In: N. Guarino and R. Poli, ed. Formal Ontology in conceptual Analysis and Knowledge Representation. Kluwer Academic Publishers.
Gruber, T.R. 1993. A Translation Approach to Portable Ontology Specifications, Knowledge Acquisition, vol 5 pp 199-220.
Hendler, J., 2001. Agents and the Semantic Web. IEEE Intelligent Systems Journal.
Horrocks, I., 2002. DAML+OIL: a Reason-able Web Ontology Language. In: proceedings of the Eighth Conference on Extending Database Technology (EDBT 2002) March 24-28 2002, Prague.
Johnson, P., 2004. Interactions, Collaborations and breakdowns. In: ACM International Conference Proceeding Series; Proceedings of the 3rd annual conference on Task models and diagrams Vol 86 Prague, Czech Republic.
McGuinness D. L., 2003. Ontologies Come of Age. In: Dieter Fensel, Jim Hendler, Henry Lieberman, and Wolfgang Wahlster, ed. Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential. MIT Press, 2003.
Nurminen, J. K., Karaonen, O., Hatonen, K., 2003. What makes expert systems survive over 10 years-empirical evaluation of several engineering applications. Expert Systems with Applications 24(2) pp 199-211.
Stanford University, 2007. Welcom
One very useful approach you can adopt when preparing to write an article is creating an outline first. Creating an outline for your article helps direct your writing. You start with an idea and structure its development by making a plan for the major points . This makes the job easier and faster and you are less likely to overlook key matters.An outline acts as the design or blueprint for your article. It will guide you in creating the introduction, body and conclusion of your article. For each point, you can write down some of the ideas and sentences that you feel will benefit your article. These can be some of the key supporting points that will help your article look more creative, interesting and appealing to a reader.A carefully planned and prepared approach will ensure fewer problems. Creating an outline for all your articles will help to generate ideas and overcome "writer's block". Here are a few tips and hints for doing this.First, do a bit of brainstorming and jot down possibly relevant ideas as they come to you. Don't worry about criticising these ideas at this stage; just jot them down. Also, consider some of the areas that would likely be of concern to your readers. Allow enough time to write down the ideas that you can use for your articles. Once you have done your research, review your ideas and all your notes; gain sufficient familiarity with your topic that writing later and expanding on the points will be easy for you.The next step is to discover your sub topic and sub titles. Provide a first sentence for your article, one that will immediately grab the attention of your reader. You will need some as well for your sub topics. To be concise and balanced, gather the facts that support your point, as well as those opposed.This is the frame or skeleton of your article. Now it's time to add
Database - ontology engine - ontology visualizer - calculation engine - inputs visualizer - results visualizer
The aim is to ensure ease of development and use of the software system by using applications that operate at one or more levels in a conceptual hierarchy, while still being able to communicate with the layers above and below in the hierarchy, and with other applications. McGuinness (2003) writes about how ease of use via conceptual modelling support and graphical browsing tools is essential if systems are to be usable for mainstream use. To facilitate this, open standard tools are used and communication tested within the overall system. The communication mechanism should be invisible to the end user who cannot be expected to consider such matters. This communication would involve large amounts of related information being translated and passed on in its entirety rather than just individual objects or messages. The intention for this main prototype is to facilitate full communication between software applications and so make it easier for engineers and others to collaborate and co-ordinate their product design and manufacture.
This system would manage software to be used in the following areas - Knowledge Management, Decision support, and Simulation. The system will provide automated translation from a model provided by the user, or by other systems into the software, ontology, and database representation. Any required calculations would then be made and translated to provide a model that can be interpreted by users. Johnson (2004) explains that successful interaction requires mapping between levels of abstractions and that translation between the levels of abstraction required by users and computers is difficult. He explains that this problem often means systems are created that make the user cope with the problems of this mis-translation. The research is intended to solve this problem by giving users more involvement in the translation process by letting them interactively model the problem themselves until they are satisfied with the solution. This allows the user to establish "common ground" with the computer, an expression used by Johnson. Nurminen et al (2003) evaluate a system called NICAD that used calculation rules in this manner. Nurminen et al emphasize that successful expert systems have in common that they put user needs at the centre of a fast and agile development process. The authors explain that users prefer usability over automation, and that users should drive the more difficult tasks where they are needed and leave routine tasks to the system. As well as translating between the user and computer systems it is necessary to provide translations between different computer systems. Ciocoiu et al (2000) make the point that as it becomes necessary to translate between more systems the number of paths for the translation increases exponentially. To improve interoperability it is therefore necessary to provide either a translator or multiple translators, and the translators would be based on taxonomies or ontologies.
The basis of this research is an ontology that can be visualized and edited in tree form. Gruber (1993a) defines and ontology - "An ontology is an explicit specification of a conceptualization. The term is borrowed from philosophy, where an Ontology is a systematic account of existence. For AI systems, what 'exists' is that which can be represented." Gruber goes on to explain design criteria for ontologies. Fensel et al (2001) explain Grubers' ontology definition. They explain that conceptualization refers to an abstract model of a phenomenon in the real world which identifies its relevant concepts. Explicit means the types of concepts and the constraints are explicitly defined. Fensel et al cite another Gruber paper where Gruber (1993b) defines the ontology as a formal explicit specification, and explains that formal means the ontology should be machine understandable. Cheung et al (2005) cite Davies et al (2002) in explaining that ontologies "provide a shared and common understanding of a domain that can be communicated between people and application systems". McGuinness (2003) explains with the aid of a diagram the level of definition in ontologies, from purely human readable to machine readable. A similar diagram with more specific computing technologies is included in Uscholds' presentation (2006) and his paper of 2003 which shows a continuum
Implicit - Informal (explicit) - Formal (for humans) - Formal (for machines)
Uschold states that "there is nothing inherently good about being further along the semantic continuum. In some cases there will be advantages; in other cases there will not. What is good is what works." In my research I have tended to move from left to right towards more formal representations but only as necessary to ensure the modelling approach works. Horrocks (2002) explains that "An ontology typically consists of a hierarchical description of important concepts in a domain, along with descriptions of the properties of each concept. The degree of formality employed in capturing these descriptions can be quite variable, ranging from natural language to logical formalisms, but increased formality and regularity clearly facilitates machine understanding."
The ontology created in Prot?g? for this research has formal definitions of is-a relationships, and formal instances. Therefore it is much more machine readable than those at the human readable only end of the ontology scale. In fact it is tested as machine readable by the automated conversion process from the Prot?g? representation to the decision support modelling system used for calculations. The decision support system can be made to read hierarchical trees from Prot?g? and it can split or combine these trees or branches as needed for the model being used, and in response to decisions made by the user. Logical constraints are informal and failures are caught by the modelling tool which will flag any illogical expressions. It would be better to represent such logical constraints in the ontology also, as is done in the most rigorous machine readable ontologies, this was not finished due to time constraints and because it was not necessary for validation of the concept.
In this research I attempt to take a problem, break it down into sub parts that can be represented by a number or equation, and then allow the user to see and interact with the whole solution as applicable to this persons' defined problem. Gruber examines how equations and quantities can be represented in an ontology. To achieve this I have investigated and/or created tools to allow visualization and interaction with models. This approach has also been taken by Crapo et al (2002) who explain "Models are artefacts used to understand our world. As such they are embedded in intelligent systems as representations of knowledge. In the context of mining data to create knowledge, the modeler is often faced with discovering and understanding relationships in data that have no apparent analog in the laws of physical science. Sketches and diagrams as aids in problem solving and as a means of communication are as old as recorded history. The question now is: Can visualization help us not only to discover the patterns and relationships in these data but also to use newly discovered knowledge to build computational models." Eng and Salustri (2006) discuss the role of computers in aiding decision making, and explain that human mind is the best tool for making decisions. They explain that visualization systems must help the user use the information access capabilities of computers. So the task is to enable the creation and sharing of these visualizations in all ways that can aid in the understanding of the problems to be modelled.
Cheung et al (2005) explain that ontologies until recently have been predominately applied in the medical informatics field. Linking ontologies with modelling tools will also make ontologies very useful, in engineering and science, and mathematics whenever calculations are required. The open standard Stanford University (2007) ontology management tool Prot?g? has been used for this purpose, although there are other ontology tools that could have been used. This ontology can be translated into a Decision Support tool called Vanguard Studio. Vanguard (2007) are creating a modelling network where universities can share decision support models over a network.
Meta Programming, ontologies, and the Semantic Web
Uschold (2003) defines the Semantic Web as being machine usable and associated with more meaning. Semantic web technologies and the use of agents and ontologies are explained by Hendler (2001), Horrocks (2002), and Uschold (2003) who explains that "In order to carry out their required tasks, intelligent agents must communicate and understand meaning". Horrocks explain the use of meta-data annotations and how they can make resources accessible to agents. Meta programming is the writing of programs that write or manipulate other programs (or themselves) as their data. The idea behind the use of this technique is that instead of writing programs to do a task a domain expert needs the program for, the meta program developer creates an environment which all domain experts, in this and similar fields, can use to create their own solutions. The developer then only needs to maintain and improve this programming environment, and can concentrate on this task; the domain expert can concentrate on solving the problem at hand without having to ask the developer to create the code on his or her behalf. This can prevent problems of misunderstanding, delay, and expense that often result from communication of difficult concepts between people who are experts in different areas (domain expert and programming expert).
Meta programming is writing of programs by other programs. The purpose of this is to provide a cascading series of layers that translate a relatively easy to use visual representation of a problem to be modelled, into code that can be run by present day compilers and interpreters. This is to make it easier for computer literate non-programmers to specify instructions to a computer, without learning and writing code in computer languages. To achieve this, any layer of software or information must be able to read the code or the information represented in any other. Code and information are only separated out as a matter of design choice to aid human comprehension, they can be represented in the same way using the same kinds of open standard languages. Meta-programming can be used as a tool to produce software for linking ontologies, Semantic Web, modelling, and visualization.
References
Cheung, W. M., Maropoulos, P. G., Gao, J. X., Aziz, H., 2005. Ontological Approach for Organisational Knowledge Re-use in Product Developing Environments. In: 11th International Conference on Concurrent Enterprising - ICE 2005, University BW Munich, Germany.
Cheung, W. M., Matthews, P. C., Gao, J. X., Maropoulos, P. G., 2007. Advanced product development integration architecture: an out-of-box solution to support distributed production networks. International Journal of Production Research March 2007.
Ciocoiu, M., Gruninger, M., Nau, D. S., 2000. Ontologies for Integrating Engineering Applications. Journal of Computing and Information Science in Engineering, 1(1) pp 12-22.
Crapo, A. W., Waisel, L. B., Wallace, W. A., Willemain, T. R., 2002. Visualization and Modelling for Intelligent Systems. In: C. T. Leondes, ed. Intelligent Systems: Technology and Applications, Volume I Implementation Techniques, 2002 pp 53-85.
Eng, N., Salustri, F. A., 2006. "Rugplot" Visualization for Preliminary Design. In: CDEN 2006 3rd CDEN/RCCI International Design Conference University of Toronto, Ontario, Canada.
Fensel, D. Van Harmelen, F. Horrocks, I. McGuinness, D. Patel-Schneider, P. F., 2001. OIL: An ontology infrastructure for the semantic web. IEEE Intelligent Systems, 16(2), pp 38-45.
Gruber, T. R. 1993. Toward Principles for the Design of Ontologies Used for Knowledge Sharing. In: N. Guarino and R. Poli, ed. Formal Ontology in conceptual Analysis and Knowledge Representation. Kluwer Academic Publishers.
Gruber, T.R. 1993. A Translation Approach to Portable Ontology Specifications, Knowledge Acquisition, vol 5 pp 199-220.
Hendler, J., 2001. Agents and the Semantic Web. IEEE Intelligent Systems Journal.
Horrocks, I., 2002. DAML+OIL: a Reason-able Web Ontology Language. In: proceedings of the Eighth Conference on Extending Database Technology (EDBT 2002) March 24-28 2002, Prague.
Johnson, P., 2004. Interactions, Collaborations and breakdowns. In: ACM International Conference Proceeding Series; Proceedings of the 3rd annual conference on Task models and diagrams Vol 86 Prague, Czech Republic.
McGuinness D. L., 2003. Ontologies Come of Age. In: Dieter Fensel, Jim Hendler, Henry Lieberman, and Wolfgang Wahlster, ed. Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential. MIT Press, 2003.
Nurminen, J. K., Karaonen, O., Hatonen, K., 2003. What makes expert systems survive over 10 years-empirical evaluation of several engineering applications. Expert Systems with Applications 24(2) pp 199-211.
Stanford University, 2007. Welcome
Once upon a time I worked for a Vice President of Sales who made me want to be a better sales person. He was confident and he expounded from experience. When he spoke to you he looked you in eye. When he touted hard work as the one reliable indicator of success, you knew he meant it because he’d done it.Most of us have been fortunate to have spent a period of our professional lives with someone we would walk on hot coals to please. This particular gentleman, in my professional life, made we want to sell better and be better, at everything.Not all Vice Presidents of Sales are going to possess the natural leadership ability and intangible traits that made this person a perfect fit. There is a question we should ask however: Should they not all try?The Vice President of Sales is really the President of the country we sales people call “the sales force”. Mr. President of Sales Force America should treat his constituent salespeople much like the President of the United States treats the voting public. The President of the United States runs the government of the people and for the people. Then too, the Vice President of Sales should run the sales force of the salespeople and for the salespeople.The Vice President of Sales should become an advocate for an improved compensation plan not its inhibitor. He should stump from the podium and from the conference call and lead his team. He should be the best salesperson in the company and he should be selling! Who was the last quarterback who led his team to the Super Bowl from the bench? How can today’s organizations compete in our market of personal creativity and innovation with a President of Sales Force America who sells from the bench? The answer: they cannot.Leadership has never been more important than in today’s marketplace. And there is not a stron
The basis of this research is an ontology that can be visualized and edited in tree form. Gruber (1993a) defines and ontology - "An ontology is an explicit specification of a conceptualization. The term is borrowed from philosophy, where an Ontology is a systematic account of existence. For AI systems, what 'exists' is that which can be represented." Gruber goes on to explain design criteria for ontologies. Fensel et al (2001) explain Grubers' ontology definition. They explain that conceptualization refers to an abstract model of a phenomenon in the real world which identifies its relevant concepts. Explicit means the types of concepts and the constraints are explicitly defined. Fensel et al cite another Gruber paper where Gruber (1993b) defines the ontology as a formal explicit specification, and explains that formal means the ontology should be machine understandable. Cheung et al (2005) cite Davies et al (2002) in explaining that ontologies "provide a shared and common understanding of a domain that can be communicated between people and application systems". McGuinness (2003) explains with the aid of a diagram the level of definition in ontologies, from purely human readable to machine readable. A similar diagram with more specific computing technologies is included in Uscholds' presentation (2006) and his paper of 2003 which shows a continuum
Implicit - Informal (explicit) - Formal (for humans) - Formal (for machines)
Uschold states that "there is nothing inherently good about being further along the semantic continuum. In some cases there will be advantages; in other cases there will not. What is good is what works." In my research I have tended to move from left to right towards more formal representations but only as necessary to ensure the modelling approach works. Horrocks (2002) explains that "An ontology typically consists of a hierarchical description of important concepts in a domain, along with descriptions of the properties of each concept. The degree of formality employed in capturing these descriptions can be quite variable, ranging from natural language to logical formalisms, but increased formality and regularity clearly facilitates machine understanding."
The ontology created in Prot?g? for this research has formal definitions of is-a relationships, and formal instances. Therefore it is much more machine readable than those at the human readable only end of the ontology scale. In fact it is tested as machine readable by the automated conversion process from the Prot?g? representation to the decision support modelling system used for calculations. The decision support system can be made to read hierarchical trees from Prot?g? and it can split or combine these trees or branches as needed for the model being used, and in response to decisions made by the user. Logical constraints are informal and failures are caught by the modelling tool which will flag any illogical expressions. It would be better to represent such logical constraints in the ontology also, as is done in the most rigorous machine readable ontologies, this was not finished due to time constraints and because it was not necessary for validation of the concept.
In this research I attempt to take a problem, break it down into sub parts that can be represented by a number or equation, and then allow the user to see and interact with the whole solution as applicable to this persons' defined problem. Gruber examines how equations and quantities can be represented in an ontology. To achieve this I have investigated and/or created tools to allow visualization and interaction with models. This approach has also been taken by Crapo et al (2002) who explain "Models are artefacts used to understand our world. As such they are embedded in intelligent systems as representations of knowledge. In the context of mining data to create knowledge, the modeler is often faced with discovering and understanding relationships in data that have no apparent analog in the laws of physical science. Sketches and diagrams as aids in problem solving and as a means of communication are as old as recorded history. The question now is: Can visualization help us not only to discover the patterns and relationships in these data but also to use newly discovered knowledge to build computational models." Eng and Salustri (2006) discuss the role of computers in aiding decision making, and explain that human mind is the best tool for making decisions. They explain that visualization systems must help the user use the information access capabilities of computers. So the task is to enable the creation and sharing of these visualizations in all ways that can aid in the understanding of the problems to be modelled.
Cheung et al (2005) explain that ontologies until recently have been predominately applied in the medical informatics field. Linking ontologies with modelling tools will also make ontologies very useful, in engineering and science, and mathematics whenever calculations are required. The open standard Stanford University (2007) ontology management tool Prot?g? has been used for this purpose, although there are other ontology tools that could have been used. This ontology can be translated into a Decision Support tool called Vanguard Studio. Vanguard (2007) are creating a modelling network where universities can share decision support models over a network.
Meta Programming, ontologies, and the Semantic Web
Uschold (2003) defines the Semantic Web as being machine usable and associated with more meaning. Semantic web technologies and the use of agents and ontologies are explained by Hendler (2001), Horrocks (2002), and Uschold (2003) who explains that "In order to carry out their required tasks, intelligent agents must communicate and understand meaning". Horrocks explain the use of meta-data annotations and how they can make resources accessible to agents. Meta programming is the writing of programs that write or manipulate other programs (or themselves) as their data. The idea behind the use of this technique is that instead of writing programs to do a task a domain expert needs the program for, the meta program developer creates an environment which all domain experts, in this and similar fields, can use to create their own solutions. The developer then only needs to maintain and improve this programming environment, and can concentrate on this task; the domain expert can concentrate on solving the problem at hand without having to ask the developer to create the code on his or her behalf. This can prevent problems of misunderstanding, delay, and expense that often result from communication of difficult concepts between people who are experts in different areas (domain expert and programming expert).
Meta programming is writing of programs by other programs. The purpose of this is to provide a cascading series of layers that translate a relatively easy to use visual representation of a problem to be modelled, into code that can be run by present day compilers and interpreters. This is to make it easier for computer literate non-programmers to specify instructions to a computer, without learning and writing code in computer languages. To achieve this, any layer of software or information must be able to read the code or the information represented in any other. Code and information are only separated out as a matter of design choice to aid human comprehension, they can be represented in the same way using the same kinds of open standard languages. Meta-programming can be used as a tool to produce software for linking ontologies, Semantic Web, modelling, and visualization.
References
Cheung, W. M., Maropoulos, P. G., Gao, J. X., Aziz, H., 2005. Ontological Approach for Organisational Knowledge Re-use in Product Developing Environments. In: 11th International Conference on Concurrent Enterprising - ICE 2005, University BW Munich, Germany.
Cheung, W. M., Matthews, P. C., Gao, J. X., Maropoulos, P. G., 2007. Advanced product development integration architecture: an out-of-box solution to support distributed production networks. International Journal of Production Research March 2007.
Ciocoiu, M., Gruninger, M., Nau, D. S., 2000. Ontologies for Integrating Engineering Applications. Journal of Computing and Information Science in Engineering, 1(1) pp 12-22.
Crapo, A. W., Waisel, L. B., Wallace, W. A., Willemain, T. R., 2002. Visualization and Modelling for Intelligent Systems. In: C. T. Leondes, ed. Intelligent Systems: Technology and Applications, Volume I Implementation Techniques, 2002 pp 53-85.
Eng, N., Salustri, F. A., 2006. "Rugplot" Visualization for Preliminary Design. In: CDEN 2006 3rd CDEN/RCCI International Design Conference University of Toronto, Ontario, Canada.
Fensel, D. Van Harmelen, F. Horrocks, I. McGuinness, D. Patel-Schneider, P. F., 2001. OIL: An ontology infrastructure for the semantic web. IEEE Intelligent Systems, 16(2), pp 38-45.
Gruber, T. R. 1993. Toward Principles for the Design of Ontologies Used for Knowledge Sharing. In: N. Guarino and R. Poli, ed. Formal Ontology in conceptual Analysis and Knowledge Representation. Kluwer Academic Publishers.
Gruber, T.R. 1993. A Translation Approach to Portable Ontology Specifications, Knowledge Acquisition, vol 5 pp 199-220.
Hendler, J., 2001. Agents and the Semantic Web. IEEE Intelligent Systems Journal.
Horrocks, I., 2002. DAML+OIL: a Reason-able Web Ontology Language. In: proceedings of the Eighth Conference on Extending Database Technology (EDBT 2002) March 24-28 2002, Prague.
Johnson, P., 2004. Interactions, Collaborations and breakdowns. In: ACM International Conference Proceeding Series; Proceedings of the 3rd annual conference on Task models and diagrams Vol 86 Prague, Czech Republic.
McGuinness D. L., 2003. Ontologies Come of Age. In: Dieter Fensel, Jim Hendler, Henry Lieberman, and Wolfgang Wahlster, ed. Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential. MIT Press, 2003.
Nurminen, J. K., Karaonen, O., Hatonen, K., 2003. What makes expert systems survive over 10 years-empirical evaluation of several engineering applications. Expert Systems with Applications 24(2) pp 199-211.
Stanford University, 2007. Welcom
1. Talk to your audience, not the screen. Trust the image behind you. Look at the laptop screen in front of you, if you have to.2. Stand centre stage and put the screen off to the side.3. Place the screen at a slight angle on the left side of the room or stage (and to your right). Adult learning research shows that people use the left-brain to process data, so put PowerPoint words, statistics, and graphs on the audience’s left.4. Learn to command your equipment. When in ‘slideshow’, the B key toggles to a blank screen (or W for white) when visuals aren’t required5. A number followed by ENTER takes you to that slide. Keep a clearly numbered paper copy (6 slides per page) of your presentation so you can be flexible6. Set up PC so that left mouse button takes you forward, the right button takes you back a slide.7. Finish with blank slide at end8. Put only enough data on each slide to jog your memory. The content should be in your head, not on the slides. Keep it simple.9. If you have a busy set of slides which you have to use, then create an executive summary set to use whilst presenting and keep the busy set as handouts.10. Avoid stock templates that just look cheap11. Use a wireless remote to give you freedom of movement. Buy the smallest you can and learn to keep it in the centre of your palm so as not to fiddle with it. Alternatively put it in your back pocket when not using.12. Definitely no fancy movement or sound effects. These are so embarrassing13. If you just have to use a busy slide set then use TTT (touch, turn, talk) to isolate the section you want and maybe adapt a new slide which just shows that section blown up. This way you audience knows that the slide with the detail is still there as a handout.14. Have each bullet point slide onto the s
The ontology created in Prot?g? for this research has formal definitions of is-a relationships, and formal instances. Therefore it is much more machine readable than those at the human readable only end of the ontology scale. In fact it is tested as machine readable by the automated conversion process from the Prot?g? representation to the decision support modelling system used for calculations. The decision support system can be made to read hierarchical trees from Prot?g? and it can split or combine these trees or branches as needed for the model being used, and in response to decisions made by the user. Logical constraints are informal and failures are caught by the modelling tool which will flag any illogical expressions. It would be better to represent such logical constraints in the ontology also, as is done in the most rigorous machine readable ontologies, this was not finished due to time constraints and because it was not necessary for validation of the concept.
In this research I attempt to take a problem, break it down into sub parts that can be represented by a number or equation, and then allow the user to see and interact with the whole solution as applicable to this persons' defined problem. Gruber examines how equations and quantities can be represented in an ontology. To achieve this I have investigated and/or created tools to allow visualization and interaction with models. This approach has also been taken by Crapo et al (2002) who explain "Models are artefacts used to understand our world. As such they are embedded in intelligent systems as representations of knowledge. In the context of mining data to create knowledge, the modeler is often faced with discovering and understanding relationships in data that have no apparent analog in the laws of physical science. Sketches and diagrams as aids in problem solving and as a means of communication are as old as recorded history. The question now is: Can visualization help us not only to discover the patterns and relationships in these data but also to use newly discovered knowledge to build computational models." Eng and Salustri (2006) discuss the role of computers in aiding decision making, and explain that human mind is the best tool for making decisions. They explain that visualization systems must help the user use the information access capabilities of computers. So the task is to enable the creation and sharing of these visualizations in all ways that can aid in the understanding of the problems to be modelled.
Cheung et al (2005) explain that ontologies until recently have been predominately applied in the medical informatics field. Linking ontologies with modelling tools will also make ontologies very useful, in engineering and science, and mathematics whenever calculations are required. The open standard Stanford University (2007) ontology management tool Prot?g? has been used for this purpose, although there are other ontology tools that could have been used. This ontology can be translated into a Decision Support tool called Vanguard Studio. Vanguard (2007) are creating a modelling network where universities can share decision support models over a network.
Meta Programming, ontologies, and the Semantic Web
Uschold (2003) defines the Semantic Web as being machine usable and associated with more meaning. Semantic web technologies and the use of agents and ontologies are explained by Hendler (2001), Horrocks (2002), and Uschold (2003) who explains that "In order to carry out their required tasks, intelligent agents must communicate and understand meaning". Horrocks explain the use of meta-data annotations and how they can make resources accessible to agents. Meta programming is the writing of programs that write or manipulate other programs (or themselves) as their data. The idea behind the use of this technique is that instead of writing programs to do a task a domain expert needs the program for, the meta program developer creates an environment which all domain experts, in this and similar fields, can use to create their own solutions. The developer then only needs to maintain and improve this programming environment, and can concentrate on this task; the domain expert can concentrate on solving the problem at hand without having to ask the developer to create the code on his or her behalf. This can prevent problems of misunderstanding, delay, and expense that often result from communication of difficult concepts between people who are experts in different areas (domain expert and programming expert).
Meta programming is writing of programs by other programs. The purpose of this is to provide a cascading series of layers that translate a relatively easy to use visual representation of a problem to be modelled, into code that can be run by present day compilers and interpreters. This is to make it easier for computer literate non-programmers to specify instructions to a computer, without learning and writing code in computer languages. To achieve this, any layer of software or information must be able to read the code or the information represented in any other. Code and information are only separated out as a matter of design choice to aid human comprehension, they can be represented in the same way using the same kinds of open standard languages. Meta-programming can be used as a tool to produce software for linking ontologies, Semantic Web, modelling, and visualization.
References
Cheung, W. M., Maropoulos, P. G., Gao, J. X., Aziz, H., 2005. Ontological Approach for Organisational Knowledge Re-use in Product Developing Environments. In: 11th International Conference on Concurrent Enterprising - ICE 2005, University BW Munich, Germany.
Cheung, W. M., Matthews, P. C., Gao, J. X., Maropoulos, P. G., 2007. Advanced product development integration architecture: an out-of-box solution to support distributed production networks. International Journal of Production Research March 2007.
Ciocoiu, M., Gruninger, M., Nau, D. S., 2000. Ontologies for Integrating Engineering Applications. Journal of Computing and Information Science in Engineering, 1(1) pp 12-22.
Crapo, A. W., Waisel, L. B., Wallace, W. A., Willemain, T. R., 2002. Visualization and Modelling for Intelligent Systems. In: C. T. Leondes, ed. Intelligent Systems: Technology and Applications, Volume I Implementation Techniques, 2002 pp 53-85.
Eng, N., Salustri, F. A., 2006. "Rugplot" Visualization for Preliminary Design. In: CDEN 2006 3rd CDEN/RCCI International Design Conference University of Toronto, Ontario, Canada.
Fensel, D. Van Harmelen, F. Horrocks, I. McGuinness, D. Patel-Schneider, P. F., 2001. OIL: An ontology infrastructure for the semantic web. IEEE Intelligent Systems, 16(2), pp 38-45.
Gruber, T. R. 1993. Toward Principles for the Design of Ontologies Used for Knowledge Sharing. In: N. Guarino and R. Poli, ed. Formal Ontology in conceptual Analysis and Knowledge Representation. Kluwer Academic Publishers.
Gruber, T.R. 1993. A Translation Approach to Portable Ontology Specifications, Knowledge Acquisition, vol 5 pp 199-220.
Hendler, J., 2001. Agents and the Semantic Web. IEEE Intelligent Systems Journal.
Horrocks, I., 2002. DAML+OIL: a Reason-able Web Ontology Language. In: proceedings of the Eighth Conference on Extending Database Technology (EDBT 2002) March 24-28 2002, Prague.
Johnson, P., 2004. Interactions, Collaborations and breakdowns. In: ACM International Conference Proceeding Series; Proceedings of the 3rd annual conference on Task models and diagrams Vol 86 Prague, Czech Republic.
McGuinness D. L., 2003. Ontologies Come of Age. In: Dieter Fensel, Jim Hendler, Henry Lieberman, and Wolfgang Wahlster, ed. Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential. MIT Press, 2003.
Nurminen, J. K., Karaonen, O., Hatonen, K., 2003. What makes expert systems survive over 10 years-empirical evaluation of several engineering applications. Expert Systems with Applications 24(2) pp 199-211.
Stanford University, 2007. Welcom
Below I will describe three basic principles that may come in handy for currency traders. They are very easy to implement and potentially take advantage of as you will see.Principle 1Some currency traders find that it is useful to always trade a given currency pair at the very same time every day. The reasoning for this is that most of the other traders buying or selling that currency pair may also trade at the same time. Major trading pits may also be working the exact same shift every day. This technique may be especially useful for currency traders who exploit technical analysis. Again, the reasoning for this is that it may be possible to standardize the trading conditions if one trades during the same time frame every day, if only for a very little bit. However, that small bit of standardization may yield several pips worth of profit. Nevertheless, it is readily obvious that the foreign exchange market can be very volatile and random.Principle 2Certain currencies trade with a certain volatility at a certain time. Once you've finished practicing your trading skills on a demo account and you decide to test the waters using your own investment capital, you may want to minimize the amount of liquidity and volatility to hedge your risk. Alternatively, you may want to increase the risk involved, and potentially increase your profit potential. (It should be noted that very heavy risk is involved under any circumstances.)The foreign exchange market follows the sun around the world moving from the United States to Australia and New Zealand to the Far East, to Europe and finally back to the United States. Overall foreign currency trading volume is determined by which markets are open and the overlap in the times that these markets are open. Currency trading volume is relatively high 24 hours a day, but there are c
Cheung et al (2005) explain that ontologies until recently have been predominately applied in the medical informatics field. Linking ontologies with modelling tools will also make ontologies very useful, in engineering and science, and mathematics whenever calculations are required. The open standard Stanford University (2007) ontology management tool Prot?g? has been used for this purpose, although there are other ontology tools that could have been used. This ontology can be translated into a Decision Support tool called Vanguard Studio. Vanguard (2007) are creating a modelling network where universities can share decision support models over a network.
Meta Programming, ontologies, and the Semantic Web
Uschold (2003) defines the Semantic Web as being machine usable and associated with more meaning. Semantic web technologies and the use of agents and ontologies are explained by Hendler (2001), Horrocks (2002), and Uschold (2003) who explains that "In order to carry out their required tasks, intelligent agents must communicate and understand meaning". Horrocks explain the use of meta-data annotations and how they can make resources accessible to agents. Meta programming is the writing of programs that write or manipulate other programs (or themselves) as their data. The idea behind the use of this technique is that instead of writing programs to do a task a domain expert needs the program for, the meta program developer creates an environment which all domain experts, in this and similar fields, can use to create their own solutions. The developer then only needs to maintain and improve this programming environment, and can concentrate on this task; the domain expert can concentrate on solving the problem at hand without having to ask the developer to create the code on his or her behalf. This can prevent problems of misunderstanding, delay, and expense that often result from communication of difficult concepts between people who are experts in different areas (domain expert and programming expert).
Meta programming is writing of programs by other programs. The purpose of this is to provide a cascading series of layers that translate a relatively easy to use visual representation of a problem to be modelled, into code that can be run by present day compilers and interpreters. This is to make it easier for computer literate non-programmers to specify instructions to a computer, without learning and writing code in computer languages. To achieve this, any layer of software or information must be able to read the code or the information represented in any other. Code and information are only separated out as a matter of design choice to aid human comprehension, they can be represented in the same way using the same kinds of open standard languages. Meta-programming can be used as a tool to produce software for linking ontologies, Semantic Web, modelling, and visualization.
References
Cheung, W. M., Maropoulos, P. G., Gao, J. X., Aziz, H., 2005. Ontological Approach for Organisational Knowledge Re-use in Product Developing Environments. In: 11th International Conference on Concurrent Enterprising - ICE 2005, University BW Munich, Germany.
Cheung, W. M., Matthews, P. C., Gao, J. X., Maropoulos, P. G., 2007. Advanced product development integration architecture: an out-of-box solution to support distributed production networks. International Journal of Production Research March 2007.
Ciocoiu, M., Gruninger, M., Nau, D. S., 2000. Ontologies for Integrating Engineering Applications. Journal of Computing and Information Science in Engineering, 1(1) pp 12-22.
Crapo, A. W., Waisel, L. B., Wallace, W. A., Willemain, T. R., 2002. Visualization and Modelling for Intelligent Systems. In: C. T. Leondes, ed. Intelligent Systems: Technology and Applications, Volume I Implementation Techniques, 2002 pp 53-85.
Eng, N., Salustri, F. A., 2006. "Rugplot" Visualization for Preliminary Design. In: CDEN 2006 3rd CDEN/RCCI International Design Conference University of Toronto, Ontario, Canada.
Fensel, D. Van Harmelen, F. Horrocks, I. McGuinness, D. Patel-Schneider, P. F., 2001. OIL: An ontology infrastructure for the semantic web. IEEE Intelligent Systems, 16(2), pp 38-45.
Gruber, T. R. 1993. Toward Principles for the Design of Ontologies Used for Knowledge Sharing. In: N. Guarino and R. Poli, ed. Formal Ontology in conceptual Analysis and Knowledge Representation. Kluwer Academic Publishers.
Gruber, T.R. 1993. A Translation Approach to Portable Ontology Specifications, Knowledge Acquisition, vol 5 pp 199-220.
Hendler, J., 2001. Agents and the Semantic Web. IEEE Intelligent Systems Journal.
Horrocks, I., 2002. DAML+OIL: a Reason-able Web Ontology Language. In: proceedings of the Eighth Conference on Extending Database Technology (EDBT 2002) March 24-28 2002, Prague.
Johnson, P., 2004. Interactions, Collaborations and breakdowns. In: ACM International Conference Proceeding Series; Proceedings of the 3rd annual conference on Task models and diagrams Vol 86 Prague, Czech Republic.
McGuinness D. L., 2003. Ontologies Come of Age. In: Dieter Fensel, Jim Hendler, Henry Lieberman, and Wolfgang Wahlster, ed. Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential. MIT Press, 2003.
Nurminen, J. K., Karaonen, O., Hatonen, K., 2003. What makes expert systems survive over 10 years-empirical evaluation of several engineering applications. Expert Systems with Applications 24(2) pp 199-211.
Stanford University, 2007. Welcom
Imagine, you have created something that is state of the art. The envy of the industry. You spared no expense and focused on every detail. Everyone says It’s a can’t miss smash success! Everyone applauds your launch, customers wishing they could be the first to use your product, and you are ready to make money by the vault-load. What could go wrong?Myopic Madness is what could go wrong. Your inability to see out in front of you causes you to crash into an avoidable obstacle and your project becomes the poster child for failure, in fact they make a movie about it and everyone enjoys watching your failure unfold in real time. This is the story if the Titanic.But it could be the story (up to the movie part) of many business ventures that failed to look far enough into the future. Myopia is commonly known as near-sightedness or the inability to see clearly into the distance. American business has never been so myopic in its vision as it is today and the madness it creates is frustrating managers across the country.Myopic Madness is creating work atmospheres that are so short-term bottom line focused, managers are no longer properly training newly-hired employees, are employing bad work practices in order to boost end of the month numbers to make a report look healthier than it really is, and exploring offshore options to save money while ignoring the long term effects of all of these practices.Want to stop the Madness?1. Look to the FutureLeaders need to be able to see the future today and drive the organization toward that destination. We would never ignore a map, get on a highway and ride it until it stops and say this is the destination we wanted for our vacation all along! So why do we do this with our businesses? Kodak ignored the future of digital photography and finally announced film would not b
References
Cheung, W. M., Maropoulos, P. G., Gao, J. X., Aziz, H., 2005. Ontological Approach for Organisational Knowledge Re-use in Product Developing Environments. In: 11th International Conference on Concurrent Enterprising - ICE 2005, University BW Munich, Germany.
Cheung, W. M., Matthews, P. C., Gao, J. X., Maropoulos, P. G., 2007. Advanced product development integration architecture: an out-of-box solution to support distributed production networks. International Journal of Production Research March 2007.
Ciocoiu, M., Gruninger, M., Nau, D. S., 2000. Ontologies for Integrating Engineering Applications. Journal of Computing and Information Science in Engineering, 1(1) pp 12-22.
Crapo, A. W., Waisel, L. B., Wallace, W. A., Willemain, T. R., 2002. Visualization and Modelling for Intelligent Systems. In: C. T. Leondes, ed. Intelligent Systems: Technology and Applications, Volume I Implementation Techniques, 2002 pp 53-85.
Eng, N., Salustri, F. A., 2006. "Rugplot" Visualization for Preliminary Design. In: CDEN 2006 3rd CDEN/RCCI International Design Conference University of Toronto, Ontario, Canada.
Fensel, D. Van Harmelen, F. Horrocks, I. McGuinness, D. Patel-Schneider, P. F., 2001. OIL: An ontology infrastructure for the semantic web. IEEE Intelligent Systems, 16(2), pp 38-45.
Gruber, T. R. 1993. Toward Principles for the Design of Ontologies Used for Knowledge Sharing. In: N. Guarino and R. Poli, ed. Formal Ontology in conceptual Analysis and Knowledge Representation. Kluwer Academic Publishers.
Gruber, T.R. 1993. A Translation Approach to Portable Ontology Specifications, Knowledge Acquisition, vol 5 pp 199-220.
Hendler, J., 2001. Agents and the Semantic Web. IEEE Intelligent Systems Journal.
Horrocks, I., 2002. DAML+OIL: a Reason-able Web Ontology Language. In: proceedings of the Eighth Conference on Extending Database Technology (EDBT 2002) March 24-28 2002, Prague.
Johnson, P., 2004. Interactions, Collaborations and breakdowns. In: ACM International Conference Proceeding Series; Proceedings of the 3rd annual conference on Task models and diagrams Vol 86 Prague, Czech Republic.
McGuinness D. L., 2003. Ontologies Come of Age. In: Dieter Fensel, Jim Hendler, Henry Lieberman, and Wolfgang Wahlster, ed. Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential. MIT Press, 2003.
Nurminen, J. K., Karaonen, O., Hatonen, K., 2003. What makes expert systems survive over 10 years-empirical evaluation of several engineering applications. Expert Systems with Applications 24(2) pp 199-211.
Stanford University, 2007. Welcome to prot?g? http://protege.stanford.edu/
Sternemann, K. H., Zelm, M., 1999. Context sensitive provision and visualisation of enterprise information with a hypermedia based system, Computers in Industry Vol 40 (2) pp 173-184.
Uschold, M., 2003. Where are the semantics in the semantic web? AI Magazine Vol 24 (3) pp 25-36.
Vanguard Studio, 2007. Vanguard Studio http://www.vanguardsw.com/products/vanguard-studio/
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