Next-Gen E-Learning Ideas

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In a nutshell, this 'idea deck' describes how a (node-edge) graph and data model can, in addition to containing knowledge, can also include: 1) metadata to drive knowledge and collaboration UX …

In a nutshell, this 'idea deck' describes how a (node-edge) graph and data model can, in addition to containing knowledge, can also include: 1) metadata to drive knowledge and collaboration UX behavior, 2) content curation, 3) temporal knowledge, 4) collaborative voting, and 5) deep provenance of the statements contained in the knowledge graph.

Note: This slide deck contains ideas for 'reinventing' Education. In particular, a proposal I submitted in January-2010 to the MacArthur Foundation 'Reinvent Learning' RFP is included along with a handful of supplementary mockup screenshots.

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  • 1. Knowledge Base with 3D Graphical Environment for Learning, Distributed Collaboration and Critical Thinking Jan-2010 MacArthur Foundation Proposal + Supporting Mockup Diagrams + More... Richard Creamer 2to32minus1@gmail. com Copyright © 2010-2013 Richard Creamer All Rights Reserved Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved 1
  • 2. Why I’m publishing this slide deck I am currently in the middle of a job search (have any openings?) and want to place the ideas in this slide deck in the public domain prior to accepting a new position so that any IP contained herein is ‘clean’ as well as in the hope that I can find developers who would like to help me form an open source project to evolve and implement the ideas presented. I believe that with the right tweaks, these ideas can form the bulk of an ideal MOOC platform of the future. Also, this isn’t just about online/MOOC Education - I feel that there are also many potential applications for these ideas in both traditional Education as well as the Commercial sector. By the way, most of these ideas were previously publicly published on my personal website in January, 2010 and remained online for about two years as an unofficial supplement to a 300-word-limit proposal I submitted to a MacArthur Foundation RFP in January, 2010 with a ‘Reinvent Learning’ theme. (This proposal is included in this slide deck.) The bulk of the brainstorming which went into the mockup diagrams was done in about a 2-week period in January-2010. Richard Creamer October 22, 2013 PS: This deck was necessarily assembled in a hurry and as a result, is incomplete, has errors, and some redundancies. PPS: Some of the foundational ideas are derived from the W3C’s Semantic Web standards. PPPS: I can remove the copyright notices at any time - they are included now only as a preliminary precaution. Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved 2
  • 3. - Begin Intro Slides - Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved 3
  • 4. Preface Overview • • • • • • • • • There are many important problems in Education. Many of these problems, perhaps the most important problems, remain unrecognized or ignored. Hint: There are much more effective and expedient ways to present the material in many textbooks and videos. This under-development slide deck presents ideas which I hope uniquely integrate the following: • Knowledge representation and carefully-designed graphical visualization • Learning • Long-term memory retention of learned material • Collaboration and discourse • Computational argumentation • More A large node-edge graph and the integrated use of novel metadata coupled with new UI components, underlie the presented approach. Most of the UIs for this project are either graphical, or new forms of instrumented, multi-layer hypermedia. Important: The architecture described in this deck enables, via metadata, any form of external information, URI, highly-specialized learning apps, and even 3D immersive environments to be associated with, and launched from, any node in the graph. Most of these ideas were developed in Jan-2010 while developing a proposal for a MacArthur Foundation RFP. This technology has many important applications beyond Education. Goals • • • • • • • • Understanding: Provide technology which ensures a deep, cohesive, cross-disciplinary understanding of topic materials. Difficult Learners: Rapidly resolve students’ confusion on any topic, statement, problem, equation, symbol, etc. Time Efficiency: Enable substantially more time-efficient learning  learn more in less time  learn more Retained Knowledge: Solve the Forgetting Curve problem and develop efficient ways to graphically review, refresh, and maintain all salient knowledge from current and prior courses throughout one’s education and career. Collaboration: Greatly improve upon today’s poorly-indexed, disorganized, repetitive, non-scalable discussion thread models. Non-Technical Subjects: Develop technology allowing many non-technical subjects, such as History, to be effectively taught and tested/graded in online environments. UI/UX Presentation Components: Develop new, more powerful, UI components which can be instrumented to support new and existing types of hypermedia (in multiple layers). Community Contribution: Enable community voting on, and contributions to, problem sets, curriculum, and explanatory materials. Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved 4
  • 5. Projected Benefits Projected/Anticipated Benefits* • Double the amount of knowledge taught and assimilated in typical online (and traditional) courses. • Deepen the level of students' understanding of course materials, concepts, how they fit together, and are applied. • Dramatically reduce the time often spent by students resolving their confusion on difficult concepts such as mathematical formula notation and conventions. • Effectively solve the 'forgetting curve' problem. Initial thoughts: • Create a UI component which visualizes a complete, highly-minified sub-graph visualizing a complete subject in a single view where the nodes are just tiny dots • Color-code portions of this course-holistic sub-graph view according to a student’s mastery level of each course vicinity. • Employ a scheduler to queue/revisit each vicinity and test a student’s level of mastery until a high level of recall has been demonstrated the last n times. • Perhaps on an annual basis, re-activate the above steps to sample and refresh accordingly, course knowledge and keep it from fading over time. • This same UI component could also be used by students to review/prepare for tests and final exams. • Develop more efficient and precise curriculum authoring and curation tools. • Develop more effective presentation UX paradigms. • Develop a common foundational data model on which to base all: • Knowledge • Curriculum • Metadata: • Statement provenance • Interactive visualization component UX behavior • Content curation • And more • Problem sets and problem solutions • Collaboration/discourse/voting • Embed critical thinking, statement provenance, points of contention, and precise, scalable, distributed collaboration into MOOC platforms. • Integration of arbitrary, external topic-specific apps so that the best instructional tools can be utilized at any point. • This project can be implemented in an evolutionary manner. • A coherent, phased project plan could be designed to gradually incorporate various aspects of this project, so this path need not be ‘disruptive.’ • And more... *These are big claims, but my 20+ years of developing software and novel, effective user interfaces suggests to me that they are, in fact, achievable. Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved 5
  • 6. Introduction • In the long term, express all knowledge as a node-edge graph using a 4-tuple graph data model [ subject, predicate, object, uuid ] called Quads. • This graph will include not only knowledge and concept-related data, but also novel types of useful metadata including: • Provenance, UX behavior, permissions, vote tallies, links to related materials, and the expansion predicate vocabularies available to a node/edge. • Typically, only one or two small, interactive salient subsets of this graph will be viewable at a once, usually via special-purpose knowledge viewers. • Even though a large node-edge graph underlies the knowledge, over-cluttered conventional visualizations of this graph will never be seen by students. • Courses will be defined as paths through this knowledge graph and pause points (‘vicinities’) corresponding to curriculum chapters/sections/sidebars. • Each vicinity may be viewed at user-controllable level-of-detail settings: Low, Medium, and High, and branches may be interactively expanded/collapsed. • Vicinities will be rendered differently for different grade levels (driven by metadata). E.g., a 5th grader may see factoring differently than a 10th grader. • This large underlying graph will drive multiple types of higher-level, interactive graphical UI components. These multi-layer UI components include: • Slide Deck Viewer • An advanced slide deck viewer which can be ‘instrumented’ to link to arbitrary graph vicinities, external hypermedia, animations, and more • Much of the narration and ‘scribbling’ in video lectures could be built into such a hypermedia component, again, via underlying graph metadata. • Knowledge Graph Viewers • Several highly interactive knowledge graph viewers which allows users to efficiently control the amount and type of information displayed as well as to explore ad hoc branches in the graph as-needed, for example, in order to learn more about a side topic often found in a different textbook. • Unlike traditional graph visualization, these viewers: • Will not overwhelm users with too much information - too many nodes and crossing edges • Will not simply display ‘pixels’ but rather, each node, edge, and node ‘grouping element’ will be interactive objects • Both 2D and 3D (including immersive 3D environment) viewers are anticipated. • Computer Screen Viewer (and Video Viewer) • A tool which allows a recording of a computer’s screen to be displayed, but which also supports interactive (id, dx, dy, dt) hot spots so that students can pause the viewer and overlay other viewers in order to explore supplementary/explanatory materials related to the in-view screen as well as make notes and sharable bookmarks. • Argument Diagram Viewer • An argument diagram viewer visually groups related graph statements (Quads) together to form premises which may then be linked into a formal, complete, discussion- and voting-enabled argument. All statements, premises (and their provenance) may be viewed, voted on, have adjacent vote tallies, and in general, be precisely critiqued in a collaborative environment. • Distributed Collaboration Environment • This component is similar to the Argument Diagram Viewer except that ad hoc sub-graphs, not necessarily part of a formal argument, may be viewed, discussed, and voted on (at the individual Quad level). • Storyboard Viewer • An advanced storyboard component will be developed which can be used to visually represent story-like subjects such as History which have timelines, scenes, actors, relationships and more. Storyboards might also offer a good way to portray temporally-varying data and processes such as chemical, political, and assembly/repair processes. • Tools • Finally, efficient content authoring and curation tools will be developed. This would include revisioning, metadata authoring, grading, etc. Note: I feel this direction has great potential and that a phased, evolutionary approach to implementing these ideas both in MOOCs and classrooms is possible. Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved 6
  • 7. Comments Time Efficiency • In today’s multi-disciplinary, lifelong learning world, both students and adults need to learn more skills/courses and even new careers. • But we still need to have a life and spend time with our children!  Therefore, we need to reduce the time needed to learn new subjects. • Too much of students’ time is unnecessarily wasted (a traditionally unrecognized area of potential improvement): • Text books are too verbose and disconnected (students must take lengthy tangents to learn related topics in other books). • Many university texts almost appear to have intentionally been made difficult to read. • Videos take too long to view, have low information density, and are just ‘pixels’ and audio narrative - not interactive, multi-layer hypermedia, but they do have their place. (Some people learn best from different presentation types than others.) • Many MOOC courses give difficult assignments without enough preparatory, illustrative examples. (Compare w/any Calculus book.) • For the most part, MOOC curriculum lecture slides rarely take advantage of even ordinary hypermedia such as lecturer pen scribbling, let alone the new, advanced hypermedia which would be possible with new presentation technologies. This: • Adds to the time it takes a student to understand a slide • Does not provide the deeper understanding and linkage to other related materials which advance hypermedia could provide • MOOC forums have very little organization and are poorly indexed making it difficult for students to navigate and find answers to their questions or find threads they have previously visited. • Student collaboration on MOOCs, based primarily upon forums, is cumbersome and slow because it is linear, unstructured text. Retained Knowledge • Textbooks contain a lot of material. If each page contains 10 units of knowledge and there are 500 pages in the book, then 5,000 units of knowledge are contained in the book. (A hypothetical, illustrative example which probably underestimates the fact count by 5x-10x.) • Think about what your brain actually retains after completing a course in, say, Algebra. Do you remember: • Every word on every page? No, of course not. • 5,000 things? No, of course not. • What your brain does store is a small collection of concepts, techniques, and knowledge of when and how to apply those techniques. • And even for engineers who use Algebra throughout their careers, this concise subset of retained knowledge is quite sufficient! • The brain distills those 500 pages of text into a small, concise subset of relevant material, logically organized as a network.  This suggests that it may be possible to condense and formulate curriculum into a form more akin to how it is stored in the brain. Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved 7
  • 8. Comments Knowledge Representation • The W3C’s Semantic Web has a fundamental data type called an RDF Triple: [subject, predicate, object], for example: (see slide 18 for more details) • [ rick, WeightInKg, 88.1826 ] (after his mid-day snack;-) • The above RDF statement states that ‘rick’ weighs 88 Kg. • ‘rick’ is the subject, ‘WeightInKg’ is the predicate (analogous to an attribute), and 88.1826 is the object (analogous to an attribute value). • ‘WeightInKg’ is the predicate and is similar to an attribute/property. • The three elements in actual RDF triples are URIs (like URLs) - globally unique IDs (‘object’ can also be a string literal, also see slide 18). • Since each tuple element is globally unique, ambiguity can be completely avoided. Think about how many different meanings exist for ‘thread.’ • An RDF triple is a ‘statement’ and collections of triples can be visualized as a node-edge graph/diagram. • Except for the smallest graphs, simple/naive visualization of such a graph is usually not effective. • Sometimes, you want to know information related to a statement called ‘metadata’ (see Provenance below). • To do this, you need to write a ‘statement about a statement’ formally called ‘reification.’ (also see slide 27) • With reification, anyone can state anything about anything. This is a very powerful capability supporting explanations, contention, uncertainty, etc. • The W3C provides a way to do reification, but a perhaps simpler way is to create 4-tuples (Quads): [ subject, predicate, object, uuid ]. • With this simple foundational data structure, arbitrarily complex node-edge graphs may be defined (networks). • This graph structure can encode any type of information: curriculum, problem sets, solution sets, multimedia, arguments, permissions, etc. • Not only can knowledge be represented in such a graph, but also any type of associated metadata (data about the data): • Provenance • Who or which entity made the statement? • For what moment or time span is this statement valid (e.g., temporal data such as city population or temperature)? • If the statement was a data measurement in a scientific experiment, what was the experiment ID, date-time, equipment, and uncertainty? • If the statement was made by a politician, what special interests groups which benefit from this statement contributed to this politician’s campaign? • If the statement was someone’s opinion/vote in an online student forum, what were the vote tallies? On which statements? • Contention - Show all perspectives on issues • Clearly/graphically display any individuals or groups who disagree with any statement. • With which specific statements do they disagree, and on what basis (links to additional statements and their provenance)? • Visualization - Graphical knowledge graph browser visualization/UX hints/behavior (explained later) • More - Additional metadata can be specified such as geolocation, associated statements (as in argument diagrams). • So, knowledge, its metadata, its visualization, interrelationships between the data, permissions for content curation, collaboration, voting, external Web links, problem set aggregation, problem solutions, solution explanations, and more can all be integrated into a single data model and network. Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved 8
  • 9. - End Intro Slides - Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved 9
  • 10. Begin January-2010 MacArthur Foundation Proposal Theme: ‘Reinvent Learning’ (Converted to 5-slide slide deck) Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved 10
  • 11. Knowledge Base with 3D Graphical Environment for Learning, Distributed Collaboration and Critical Thinking Richard Creamer 2to32minus1@gmail.com Copyright © Richard Creamer 2010 - All Rights Reserved Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved 11
  • 12. Brief Project Description Perform the R&D necessary to create a unique educational knowledge base enabling students, world-wide, to graphically explore most subjects, grades 4-16. The included 3D graphical environment will also enable distributed student groups to collaboratively view, compose, discuss, critique, and vote on: topics, arguments and premises, ideas and thought processes. Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved 12
  • 13. Primary Goals • Graphical Knowledge Exploration: Provide all children with a free, multilingual, complementary/supplementary educational knowledge repository enabling them to view and explore knowledge in the form of intuitive graphical diagrams (enhanced mathematical node-edge directed graphs). Graph nodes may contain text, charts, multimedia, HTML documents, etc. as well as virtual 3D environments and other graphs/diagrams. Students will be able to “jump into” many types of node content. Unlimited levels of supplementary/explanatory information such as Why, ExplainFurther, ExplainDifferently, BasedUpon, and RealWorldUses will be able to be associated with facts/statements and interactively navigated. • Collaborative Critical Thinking: Enable distributed student groups to collaboratively discuss, critique, and vote on: topics, arguments and premises, ideas and thought processes--all from within the 3D graphical environment. Argument premises will be clearly linked to their supportive facts in the same view. This will enable students to critically discuss/chat and vote on the validity and merit of any fact or premise. As a result, students will learn to judge the logical strength of arguments while collaborating on a variety of socially-relevant topics such as Global Warming. Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved 13
  • 14. Details • Data Model: For many reasons, the data model for this project will likely be based on the W3C Semantic Web’s RDF "triple" format. • Knowledge Base Content: The goal is to motivate scholars from around the world to contribute to the knowledge base’s content, in parallel. • Rationale for Graphical Visualization: • Clear, concise, relational depiction of knowledge, its categorization, and its provenance. • Efficient, selective navigation and display of information. • Ability to avoid disruptive context switches when browsing multiple side topics. • Graphical knowledge representation has been shown to improve student knowledge retention. • Provides skeletal framework/context for organizing/assimilating knowledge gained in coursework. • Expertise: Multiple expert academics/consultants will be engaged during this project. • Example preliminary diagrams: tinyurl.com/DMLExamples Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved (Note: this link no longer valid) 14
  • 15. Knowledge Base with 3D Graphical Environment for Learning, Distributed Collaboration and Critical Thinking Richard Creamer 2to32minus1@gmail.com Copyright © Richard Creamer 2010 - All Rights Reserved Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved 15
  • 16. End January-2010 MacArthur Foundation Proposal Note: Since this proposal was submitted, I believe that grades K-3 could also partially benefit from this approach. Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved 16
  • 17. Begin Preliminary Illustrative Diagram Screenshots and Explanations Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved 17
  • 18. Preface - A hastily-prepared primer for understanding the diagrams to follow Briefly: • First, this document is very under-developed and preliminary. Much refinement, evolution, and elaboration on benefits and motivations has yet to be developed. • What is not included are more recent ideas such as: • The utility for Storyboard applications (similar to how movies are initially planned) for converting subjects such as History into a graphical, interactive, electronic format. • Various ways in which traditional slide decks may be augmented and ‘instrumented’ to become a much more effective form of ‘hypermedia’. • All knowledge can be represented as a large, single network of statements, which in turn can be modeled as a node-edge graph. • Courses are simply paths through this network graph with stopping points along the way for viewing the local vicinity. • These local vicinities could, in general, represent a chapter/section/sidebar in a traditional text book. • Unlike a text book, if a math symbol or concept is not explained, the graph network will always include links/branches to other vicinities where these things are defined, recursively, if necessary. • Ultimately, a child may navigate all the way back to fundamental axioms of a Greek mathematician when exploring a side topic/area of confusion. • In most of the following static screenshot mockups, please realize that the amount of information in view at any time will be completely controlled by the viewer/student. The reader of this slide deck may feel these diagrams are too cluttered, but that is only because they have been substantially ‘expanded’ to illustratively show what branches may be available to be viewed. • I anticipate offering students the ability to set the level of detail in ‘vicinities’ to Low, Medium, and High, and allow them to toggle back-and-forth as desired. • To make a graphical learning experience as effective as possible, I have/will develop several advanced User Experience techniques to make the navigation of this graph very intuitive and efficient - much more efficient than current graph visualization tools. • The fact that I use a formal graph data model (RDF triple/quad) means that any statement can be ‘decorated’ with additional RDF statements/information. Here are but a few useful, illustrative ‘predicates’: • ExplainFurther, ExplainDifferently, RealWorldUses (these are self-explanatory, may have cardinality > 1, and may form arbitrarily long chains) • Provenance: Who made this statement • Validity: Who agrees that this statement is valid • ProbabilisticValidity: What is the probability that a statement is valid/factual (typically used for scientific or imperfect observations) • Contention: Who disagrees with a statement, and what are their reasons • GeoLocation: A geographical point or area associated with a statement or statement element • VoteTally: Students will be able to collaboratively vote on any statement and these results can be displayed in the graphical browser • Temporal History: Some knowledge/facts are not static - they change with time. Hence, graphical ‘time slider’ controls will be provided so that students can view information at different times. For example, a city’s population could be viewed at any time in the past or future (projected). • UX: Statements and groups thereof can be decorated with metadata hints for how the actual graphical knowledge browser should render the statement(s) and what operations they support (such as ‘jump into,’ available expansion predicate vocabularies, and display time slider). I hope this is enough to permit readers to better understand what follows and some of the motivation for why things were designed this way. Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved 18
  • 19. Introduction to RDF Triples* One of the simplest spoken language sentence structures is the pattern: Subject + Predicate + Object. For example, in the sentence “Jim lives in Seattle.”, we have: • Subject = Jim • Predicate = lives in • Object = Seattle This simple sentence structure is the basis for the unit of information used in the W3C’s Semantic Web Resource Description Framework (RDF). Any and all complex information can be decomposed into this basic, atomic format called an RDF “triple.” Often, a triple is visualized as a mathematical directed graph: Subject Predicate Object Or, for the above example: Jim lives in Seattle The above graph is a bit simplified. In the Semantic Web, the elements of a triple are semantically precise and unique strings** (URIs to be specific). For example, “Jim” might actually be defined as http://bigonto.org/earth/us/humans/093a5e21-988e-449fb89d-55b09d1c2b3b. Similarly, the predicate “lives in” might be defined as http://world.eduweb.org/predicates/human/geo/livesin. Fortunately, non-unique human-friendly labels may be associated with RDF resources to make graphs more readable. The basic idea is that globally-unique URIs can be assigned to concepts, such as “Jim” or “lives in.” This enables unambiguous statements to be asserted that refer to specific concepts using specific (globally-unique) predicates. Thus, using semantically-precise triples, intricate networks of semantically precise statements can be created to richly describe any area of knowledge. Due to the inherent tree structure of URIs, RDF triple elements are often derived from nodes in taxonomic classification trees. Benefits resulting from the selection of RDF triples as the basic unit of information for the educational knowledge base include: • Automatic addressability and incorporation of everything on the Internet • Intuitive visual/graphical representation of knowledge and information • Unrestricted decoration of knowledge to facilitate learning (e.g., predicates such as Why or ExplainDifferently) • Ability to represent and adapt to any information regardless of its structural characteristics (unlike DBMS schemas) • Basic multi-lingual support due to RDF’s support for language tags (“Biology”@en vs. “Biologie”@fr) • By using language tags and very brief text in graph nodes, translation to any language becomes much simpler. * Note: In this project, ‘Quads’, not Triples, are used, where a 4th tuple element (uuid) is added to support efficient reification (statement about a statement). ** The object element may be either a URI or a string literal such as “98.6 F”. Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved 19
  • 20. Example: Browsing First Grade Curriculum (extremely preliminary) (This is not what a child would see - this design work is under construction.) Numbers Handwriting Category Alphabet First Grade Subject[ ] Category Category Arithmetic Letter Sounds Lessons[ ] ... Upper-Case Lower-Case Reading Hours Spelling Telling Time Lessons[ ] ... Minutes Lessons[ ] ... Category Category [1] Category Hours & Minutes Lessons[ ] [2] In general, Predicates will be blue. ... ... Predicates corresponding to a collection of statements (a ‘bag’) will sometimes be displayed as a node with [ ] brackets. Clicking on this node launches an external, custom, highly-specialized time -telling practice application (Nodes can contain text, URLs, HTML, applications, multimedia, etc.) Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved 20
  • 21. Example: Astronomy/Solar System Browsing Session (simplified, intentionally incomplete, under-construction, very preliminary) Astronomical Body definition “A naturally-occurring, gravitationally bound aggregation of matter located in space“@en is-a definition Star is-a The Sun “An astronomical body that radiates energy resulting from sustained, internal thermonuclear reactions” @en definition meanDiameter Major Planet 1,390,000 km is-a [1] is-a [2] majorPlanet [] “An astronomical body orbiting a star, or n-ary star system, which is not itself a star, has sufficient mass to have a nearly round shape, and has cleared the neighborhood around its orbit” @en [3] The Earth moon The Moon meanDiameter [4] 3,474 km meanOrbitalRadius 12,742 km meanOrbitalRadius [5] meanDiameter Moon 384,403 km 149,600,000 km [6] [7] [8] planetarySummary minorPlanet [] [1] Pluto is-a Minor Planet definition “An astronomical body orbiting a star, or n-ary star system, which is not itself a star and has sufficient mass to have a nearly round shape” @en [2] ... [Tabular summary of selected planetary data] Please note the use of @en tags using language tags will make translation of graph data much simpler than translating entire textbooks. In all diagrams, students interactively collapse and expand the graph branches/nodes to quickly view just the content of interest. Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved 21
  • 22. Example: Interactive browsing a topic with distributed group voting and collaboration (very preliminary) Global Warming – Synopsis  X Interactive hovering and node expansion user session... Earth – Temperature  Increasing Global Warming – Synopsis  X Voting... Mouse hover popup displays available predicates Caused By... (Optionally, user could first select desired predicate vocabulary) Open in new panel... Earth – Temperature  Increasing Basis... Distributed Collaboration and Decision Making (group) Effects... Contention... Global Warming – Synopsis  X Agree Voting Data Earth – Temperature  Increasing Note: RDF triples do not necessarily have to be rendered as a distinct node-edgenode. Voting Action Voting Action Caused By Earth – Atmosphere.CO2  Increasing Caused By Vote Tally Disagree Unsure Vote Agree 84 3 1 Vote Disagree User Actions Humans – Generate  CO2 Popup GUI Controller Caused By Humans – Engage In  Deforestation Relevance Type [] Source [] (weighted set) 1900 1950 2000 2050 2100 Relevance Burning – Reduces  Tree Population Harvesting – Reduces  Tree Population Humans.Power Plants 33% 22% Temporal Data (Displayed information interactively changes in real-time based on selected year via slider) Humans .[ Factories, Home Heating ] Humans .[ Cars , Trucks ] 12% Burning – Produces  CO2 Scope of slider(s) could encompass entire graph or subset Year: 2000 33% Trees – Convert  CO2 Tally nodes could be linked to interactive chat session view so voters could debate premise. Humans.Major Transportation Ref umich.edu/~gs265/society/greenhouse.htm Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved 22
  • 23. Example of Social/Scientific Topic RDF Graph (extremely preliminary) Purpose: Show that graphs/diagrams can be very useful vs. narrative text/HTML Global Warming Related To Synopsis Known As Solutions Sources Global CO2 Reform Policies Improved Energy Efficiency Asserted By 1900 1950 2000 2050 2100 Projected Effects Solutions Increase in global temperatures primarily caused by increases in atmospheric CO2 levels arising from multiple human activities. Year: 2100 Climate Change Best Case Set [ ] Major Sources Basis Targets Category Category Intermediate ... Worst Case Industries [ ] Country Greenhouse Effect IPCC Technology (2100) 3.2° F surface temperature rise 7.8” ocean level rise (2100) 5.0 ° F surface temperature rise 31.1” ocean level rise (2100) 6.48 ° F surface temperature rise 54.3” ocean level rise Coal Burning Technology Could easily be line graphs depending on visualization metadata Set [ ] Oil Burning Electric Cars Lighting China Deforestation US Type Incandescent Disadvantage Energy Inefficient Type Type CFL Lights Disadvantage Advantage Safe Possibly Unsafe: Mercury is released into homes and environment, high cost Relevance Increased probability of toddler neurodevelopmental diseases (autism, Down syndrome) LED Lights Advantage 4X > efficiency vs. incandescent lighting Disadvantage Higher initial cost, more R&D needed An example of a statement ‘grouping element’ Advantage Safe, 10X > efficiency vs. incandescent lighting Contention Validity of research concluding released mercury levels are safe is currently disputed. Relevance Current autism rate in U.S. alone > 29,000 children per year Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved 23
  • 24. Example Knowledge Graph - Argument Graph Hybrid (very preliminary) Argument: World should decrease its consumption of fossil fuel B F D Infrared Light Absorbed By Re-Emits Atmosphere.CO2 E D G Observed increase in global surface temperature Infrared Light To Earth.Surface Warms • Sunlight warms Earth’s surface • Earth’s surface radiates infrared light • The infrared light is absorbed by the atmosphere’s CO2 • The atmosphere’s CO2 re-emits the infrared light • ...to the Earth’s surface • ...which warms the Earth’s surface • ...which contributes to Global Warming C C Radiates Earth.Surface Graph should be read as: A A Warms Sunlight Earth.Surface Global Warming Contributes To ftp://ftp.ncdc.noaa.gov/pub/data/anomalies/monthly.land.9 0S.90N.df_1901-2000mean.dat Ref Graph B Note: At this very early stage, it is thought that the above type of argument diagram will support inplace expand/collapse of nodes to permit exploration of hidden, hierarchically-contained subargument elements, their premises, and their underlying facts and associated metadata (voting, uncertainty, etc.) Home Heating Defined As C Gradual increase in Atmospheric CO2 Caused By Deforestation Human Activity Factories Type [] Fossil Fuel Consumption Type [] Cars, Trucks Major Transportation Caused By Power Plants Global Warming E Arctic Ice Melt Projected Effect [] * F 1 Meter Ocean Rise by 2100 Species Extinction Major Coastline Recession This slide also illustrates how the (many) corresponding pages of Wikipedia’s unstructured text may be compressed into a single diagram which allows ‘bigger picture’ structure and interrelationships to be displayed. Validity Endorsed By Solution [] 10% Humans Lose Housing by 2100 Temperature Rise > 10 °F 40+ Scientific Societies World Consumption of Fossil Fuel Should Decrease G Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved Distributed student groups would be able to vote on any fact statement and cite other facts, arguments or argument premises, as the basis for their vote. Distributed students could then discuss their votes and the underlying premises in a virtual group environment. Essentially all nodes have further expansion predicates available to ultimately get to the root validity, pedigree, basis, or origin of the fact/premise under discussion/referenced. *Some illustrative statements are approximations 24
  • 25. Visualization of Rationale for Voting w/Discussion (very preliminary) Major benefits: 1) specificity (precise statement discussed), and 2) aggregation of many equivalent opinions (scalability) IPCC 40 Scientific Societies Agrees With Trend Agrees With Earth.Temperature Trend Caused By Increasing Graph Increasing Natural Geologic Fluctuation Disagrees With Chat Group: G&G 120B X Ian: Mike, I disagree—I think GW is the result of natural environmental fluctations. See my thought bubble. Mike: Ian, whether or not that’s the case, don’t you think we should be reducing our carbon emissions? The polar ice is melting and the sea levels are going to rise regardless of whether GW is caused by humans or due to natural causes. Ian: Mike, you have a point, the cause is unimportant – we need to take action, and now. Human.Activity Caused By Unsure Vote Tally Voting Data 1 Agree Vote Agree Voting Action 3 Disagree Undecided About Note: Student name labels in thought bubbles correspond to names and colors in chat window. 84 Vote Disagree Voting Action Agrees With User Actions - Alternate Idea (Graphical Thought Bubbles) IPCC Ian Agrees With Trend 40 Scientific Societies Agrees With Earth.Temperature X Increasing Caused By Trend Graph Human.Activity Caused By Natural Geologic Fluctuation Increasing Disagrees With Caused By Distributed student groups would be able to vote on any fact statement and cite other facts, arguments or argument premises, as the basis for their vote. Distributed students could then discuss their votes and the underlying premises in a virtual group environment. Essentially all nodes have further expansion predicates available to ultimately get to the root validity, pedigree, basis, or origin of the fact/premise under discussion/referenced. Voting Data Voting Action Human.Activity Vote Tally Vote Agree Disagree Unsure Agree 3 ? 1 84 Mike Voting Action Vote Disagree User Actions Increasing Caused By Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved Trend Human.Activity 25 X
  • 26. RDF Graph for Example Algebra Problem (very preliminary) Solve This shows the result of a user interacting with and exploring the problem/graph by successively expanding desired predicates. This verbose detail (and more) would be available if the user elects to display it. 2x – 3 = 5 Note: Advanced mouse hovering techniques will be used to make navigation and expand/collapse of graph nodes efficient and intuitive. Problem Answer x=4 Solution Solution 1 View Options Step Expansion Labels:  Operation  Before  During  After Control Graph Visualization This, for example, would allow a user to see only During predicates for steps, if desired. Author Steps ... Set [ ] Add 3 to both sides Operation Step 1 Before During 2x – 3 = 5 Why To move the 3 to the opposite side of equal sign. Why To solve Algebra problems, one needs to get x on one side of the equal sign by itself. Explain Further 2x – 3 + 3 = 5 + 3 After 2x = 8 Allowable Operations Divide both sides by 2. Operation Step 2 Before During Algebraic equations are solved by manipulating the equation until the variable, x, is on one side of the equal sign while the numeric values are placed on the opposite side. This is done by performing operations, one at a time, on both sides of the equal sign so that equality is preserved. By doing this step-by-step, one gradually simplifies the equation until the last operation yields the answer. + - 2x = 8 x 2x / 2 = 8 / 2 After ÷ Exception x=4 It is illegal to divide by zero. Logarithm Raise to Exception Power Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved It is illegal to take square root of negative number. 26
  • 27. Older Example of Previous Algebra Problem Exception Begin Solution Exception 2x – 3 = 5 It is illegal to take the square root of a negative number. It is illegal to divide by zero. Valid Operations +, -, /, x, raise to power, logarithm Step 1 Operation 2x – 3 + 3 = 5 + 3 Add 3 to both sides. As long as you do the same operation to both sides of an equation, both sides will remain equal. Explain Step Algebraic equations are solved by manipulating the equation until the variable, x, is on one side of the equal sign while the numeric values are placed on the opposite side. This is done by performing operations, one at a time, on both sides of the equal sign. By doing this step-by-step, you gradually simplify the equation until the last operation yields the answer. Why Step 1 Result Step 2 2x = 8 2x / 2 = 8 / 2 Operation Explain Why Step 2 Result Divide both sides by 2. Expand… Expand… Note: Hovering the cursor over a predicate will show a popup Collapse… button. x=4 Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved 27
  • 28. Preliminary Thoughts on Graph Data & Metadata Model Predicate Subject Single, logical data structure type Object •Subjects can be associated with zero or more types. •Subjects (and Types) can be associated with zero or more predicate vocabularies. •Predicate vocabularies allow filtered graphical expansion of graphs and easier query formulation GUIs. Notation for displaying predicates which denote collections of related objects Subject Predicate[] [0] Object ( Composed Subject) Metadata/reification: Statement about Statement aka: Composition of Subject Predicate Subject Predicate Object Object (All metadata are triples as well) 1240463339800 rdf:statement rdf:type Unique Identifiers (uuids) enable metadata rdf:subject rdf:predicate (statement ) uuid x:MeasurementTime subject uuid (statement ) uuid1 predicate uuid x:AssertedBy rdf:object (This is how data will tentatively be stored “under the hood.”) NOAA [0] [0] [1] type1 0.27 Example metadata statements describe statement object uuid or value Vocabulary Definition: A group of related predicates. x:Uncertainty type2 Domain [] Range[] type5 type3 type6 type4 Examples: Fetus, Infant, Child, Human, Mammal [0] [1] [0] [2] Type[] Object Predicate Subject MemberOfVocabulary Polymorphic Type uuids (Allow Polymorphic Queries/Patterns) [0] vocabulary1 [1] vocabulary2 [2] Associated Predicate Vocabularies[] [1] Type[] [0] Elements[] vocabulary3 Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved [1] … 28
  • 29. Example Astronomy RDF Ontology (with 3 instances: Sun, Earth, and Moon) (simplified, intentionally incomplete, under-construction, very preliminary) Type (Class) Definitions (RDF Triples) Property (Predicate) Definitions (RDF Triples) Instance Definitions (RDF Triples) // Type: Astronomical Body kb.astro.o:AstronomicalBody, rdf:type, rdfs:Class kb.astro.o:AstronomicalBody, rdfs:label, “Astronomical Body” @en kb.astro.o:AstronomicalBody, kb.gen:definition, “A naturally-occurring, gravitationally bound aggregation of matter located in space“@en // Property: majorPlanet kb.astro.o:majorPlanet, rdf:type, rdf:Property kb.astro.o:majorPlanet, rdfs:label, “majorPlanet” @en kb.astro.o:majorPlanet, rdfs:domain, kb.astro.o:AstronomicalBody kb.astro.o:majorPlanet, rdfs:range, kb.astro.o:MajorPlanet // Instance: The Sun kb.astro.i:Sun, rdf:type, kb.astro.o:Star kb.astro.i:Sun, rdfs:label, “The Sun” @en kb.astro.i:Sun, kb:meanDiameter, “1,390,000 km” @en kb.astro.i:Sun, kb.astro.o:majorPlanet, kb.astro.i.Earth // Type: Star extends Astronomical Body kb.astro.o:Star, rdf:type, rdfs:Class kb.astro.o:Star, rdfs:subClassOf, kb.astro.o:AstronomicalBody kb.astro.o:Star, rdfs:label, “Star” @en kb.astro.o:Star, kb.gen:definition, “An astronomical body that radiates energy resulting from sustained, internal thermonuclear reactions” @en // Property: moon kb.astro.o:moon, rdf:type, rdf:Property kb.astro.o:moon, rdfs:label, “moon” @en kb.astro.o:moon, rdfs:domain, kb.astro.o:AstronomicalBody kb.astro.o:moon, rdfs:range, kb.astro.o:Moon // Instance: The Earth kb.astro.i:Earth, rdf:type, kb.astro.o:MajorPlanet kb.astro.i:Earth, rdfs:label, “The Earth” @en kb.astro.i:Earth, kb.astro.o:meanDiameter, “12,742 km” @en kb.astro.i:Earth, kb.astro.o:meanOrbitalRadius, “149,600,000 km” @en kb.astro.i:Earth, kb.astro.o:moon, kb.astro.i:Moon // Type: Major Planet extends Astronomical Body kb.astro.o:MajorPlanet, rdf:type, rdfs:Class kb.astro.o:MajorPlanet, rdfs:subClassOf, kb.astro.o:AstronomicalBody kb.astro.o:MajorPlanet, rdfs:label, “Major Planet” @en kb.astro.o:MajorPlanet, kb.gen:definition, “An astronomical body orbiting a star, or n-ary star system, which is not itself a star, has sufficient mass to have a nearly round shape, and has cleared the neighborhood around its orbit” @en // Type: Minor Planet extends Astronomical Body kb.astro.o:MinorPlanet, rdf:type, rdfs:Class kb.astro.o:MinorPlanet, rdfs:subClassOf, kb.astro.o:AstronomicalBody kb.astro.o:MinorPlanet, rdfs:label, “Minor Planet” @en kb.astro.o:MinorPlanet, kb.gen:definition, “An astronomical body orbiting a star, or n-ary star system, which is not itself a star and has sufficient mass to have a nearly round shape” @en // Type: Moon extends Astronomical Body kb.astro.o:Moon, rdf:type, rdfs:Class kb.astro.o:Moon, rdfs:subClassOf, kb.astro.o:AstronomicalBody kb.astro.o:Moon, rdfs:label, “Moon” @en kb.astro.o:Moon, kb.gen:definition, “An astronomical body orbiting a planet” @en // Property: definition kb.gen:definition, rdf:type, rdfs:Property kb.gen:definition, rdfs:Label, “definition” @en // Property: meanDiameter kb.astro.o:meanDiameter, rdf:type, rdfs:Property kb.astro.o:meanDiameter, rdfs:Label, “meanDiameter” @en kb.astro.o:meanDiameter, rdfs:domain, kb.astro.o:AstronomicalBody kb.astro.o:meanDiameter, rdfs:range, rdfs:Literal rdf:type The Sun meanDiameter 1,390,000 km rdfs:subClassOf Star Major Planet Minor Planet Moon [0] [2] [3] majorPlanet [] Astronomical Body // Property: meanOrbitalRadius kb.astro.o:meanOrbitalRadius, rdf:type, rdfs:Property kb.astro.o:meanOrbitalRadius, rdfs:Label, “meanOrbitalRadius” @en kb.astro.o:meanOrbitalRadius, rdfs:domain, kb.astro.o:AstronomicalBody kb.astro.o:meanOrbitalRadius, rdfs:range, rdfs:Literal rdf:type [1] Star // Instance: The Moon kb.astro.i:Moon, rdf:type, kb.astro.o:Moon kb.astro.i:Moon, rdfs:label, “The Moon” @en kb.astro.i:Moon, kb:meanDiameter, “3,474 km” @en kb.astro.i:Moon, kb.astro.o:meanOrbitalRadius, “384,403 km” @en The Earth moon Major Planet The Moon meanDiameter meanOrbitalRadius rdf:type meanDiameter Moon 3,474 km meanOrbitalRadius 12,742 km 384,403 km [4] 149,600,000 km [5] [6] [7] Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved 29
  • 30. Example Astronomy RDF Ontology – Actual RDF/XML <?xml version="1.0"?> <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:kb="http://dmlkb.org/kb/gen#" xmlns:kb.astro.o="http://dmlkb.org/kb/astro.o#" xmlns:kb.astro.i="http://dmlkb.org/kb/astro.i#"> <rdfs:Class rdf:about="http://dmlkb.org/kb/astro.o#AstronomicalBody"> <rdfs:label xml:lang="en">Astronomical Body</rdfs:label> <kb.astro.o:definition xml:lang="en">A naturally-occurring, gravitationally bound aggregation of matter located in space</kb.astro.o:definition> </rdfs:Class> <rdfs:Class rdf:about="http://dmlkb.org/kb/astro.o#Star"> <rdfs:subClassOf rdf:resource="http://dmlkb.org/kb/astro.o#AstronomicalBody" /> <rdfs:label xml:lang="en">Star</rdfs:label> <kb.astro.o:definition xml:lang="en">An astronomical body that radiates energy resulting from sustained, internal thermonuclear reactions</kb.astro.o:definition> </rdfs:Class> <rdfs:Class rdf:about="http://dmlkb.org/kb/astro.o#MajorPlanet"> <rdfs:subClassOf rdf:resource="http://dmlkb.org/kb/astro.o#AstronomicalBody" /> <rdfs:label xml:lang="en">Major Planet</rdfs:label> <kb.astro.o:definition xml:lang="en">An astronomical body orbiting a star, or n-ary star system, which is not itself a star, has sufficient mass to have a nearly round shape, and has cleared the neighborhood around its orbit</kb.astro.o:definition> </rdfs:Class> <rdfs:Class rdf:about="http://dmlkb.org/kb/astro.o#MinorPlanet"> <rdfs:subClassOf rdf:resource="http://dmlkb.org/kb/astro.o#AstronomicalBody" /> <rdfs:label xml:lang="en">Minor Planet</rdfs:label> <kb.astro.o:definition xml:lang="en">An astronomical body orbiting a star, or n-ary star system, which is not itself a star and has sufficient mass to have a nearly round shape</kb.astro.o:definition> </rdfs:Class> <rdfs:Class rdf:about="http://dmlkb.org/kb/astro.o#Moon"> <rdfs:subClassOf rdf:resource="http://dmlkb.org/kb/astro.o#AstronomicalBody" /> <rdfs:label xml:lang="en">Moon</rdfs:label> This is the actual XML needed to assert the triples in the previous slide. <kb.astro.o:definition xml:lang="en">An astronomical body orbiting a planet</kb.astro.o:definition> </rdfs:Class> If you are unfamiliar with RDF, try this: <rdfs:Property rdf:about="http://dmlkb.org/kb/astro.o#majorPlanet"> <rdfs:domain rdf:resource="http://dmlkb.org/kb/astro.o#Star" /> <rdfs:range rdf:resource="kb.astro.o:MajorPlanet" /> 1. Copy the XML text from here. (PDF text on this page loses its formatting when copied.) <rdfs:label xml:lang="en">majorPlanet</rdfs:label> 2. Paste it into the Check by Direct Input text area on the W3C RDF Validator web page </rdfs:Property> <rdfs:Property rdf:about="http://dmlkb.org/kb/astro.o#meanDiameter"> (http://www.w3.org/RDF/Validator/). <rdfs:domain rdf:resource="kb.astro.o:AstronomicalBody" /> 3. In the Display Result Options drop-down list, select: Triples and Graph. <rdfs:range rdf:resource="rdfs:Literal" /> 4. Click on the Parse RDF button. <rdfs:label xml:lang="en">meanDiameter</rdfs:label> </rdfs:Property> 5. Browse the resulting parsed RDF triples as well as the (large) generated graph image. <rdfs:Property rdf:about="http://dmlkb.org/kb/astro.o#meanOrbitalRadius"> <rdfs:domain rdf:resource="kb.astro.o:AstronomicalBody" /> <rdfs:range rdf:resource="rdfs:Literal" /> <rdfs:label xml:lang="en">meanOrbitalRadius</rdfs:label> </rdfs:Property> <rdfs:Property rdf:about="http://dmlkb.org/kb/gen#definition"> <rdfs:label xml:lang="en">definition</rdfs:label> <rdfs:range rdf:resource="rdfs:Literal" /> </rdfs:Property> <kb.astro.o:Star rdf:about="http://dmlkb.org/kb/astro.i#Sun"> <rdfs:label xml:lang="en">The Sun</rdfs:label> <kb.astro.o:meanDiameter>1,390,000 km</kb.astro.o:meanDiameter> <kb.astro.o:majorPlanet rdf:resource="http://dmlkb/kb/astro.i#Earth" /> </kb.astro.o:Star> <kb.astro.o:MajorPlanet rdf:about="http://dmlkb.org/kb/astro.i#Earth"> <rdfs:label xml:lang="en">The Earth</rdfs:label> <kb.astro.o:meanDiameter>12,742 km</kb.astro.o:meanDiameter> <kb.astro.o:meanOrbitalRadius>149,600,000 km</kb.astro.o:meanOrbitalRadius> <kb.astro.o:moon rdf:resource="http://dmlkb/kb/astro.i#Moon" /> </kb.astro.o:MajorPlanet> <kb.astro.o:Moon rdf:about="http://dmlkb.org/kb/astro.i#Moon"> <rdfs:label xml:lang="en">The Moon</rdfs:label> <kb.astro.o:meanDiameter>3,474 km</kb.astro.o:meanDiameter> <kb.astro.o:meanOrbitalRadius>384,403 km</kb.astro.o:meanOrbitalRadius> </kb.astro.o:Moon> </rdf:RDF> Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved 30
  • 31. Example Astronomy RDF Ontology – Actual RDF Triples (After parsing the previous slide’s XML on the W3C RDF Validator web page) Number 1 2 3 4 5 6 Subject http://dmlkb.org/kb/astro.o#AstronomicalBody http://dmlkb.org/kb/astro.o#AstronomicalBody http://dmlkb.org/kb/astro.o#AstronomicalBody http://dmlkb.org/kb/astro.o#Star http://dmlkb.org/kb/astro.o#Star http://dmlkb.org/kb/astro.o#Star Predicate http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2000/01/rdf-schema#label http://dmlkb.org/kb/astro.o#definition http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2000/01/rdf-schema#subClassOf http://www.w3.org/2000/01/rdf-schema#label 7 http://dmlkb.org/kb/astro.o#Star http://dmlkb.org/kb/astro.o#definition 8 9 10 http://dmlkb.org/kb/astro.o#MajorPlanet http://dmlkb.org/kb/astro.o#MajorPlanet http://dmlkb.org/kb/astro.o#MajorPlanet http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2000/01/rdf-schema#subClassOf http://www.w3.org/2000/01/rdf-schema#label 11 http://dmlkb.org/kb/astro.o#MajorPlanet http://dmlkb.org/kb/astro.o#definition 12 13 14 http://dmlkb.org/kb/astro.o#MinorPlanet http://dmlkb.org/kb/astro.o#MinorPlanet http://dmlkb.org/kb/astro.o#MinorPlanet http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2000/01/rdf-schema#subClassOf http://www.w3.org/2000/01/rdf-schema#label 15 http://dmlkb.org/kb/astro.o#MinorPlanet http://dmlkb.org/kb/astro.o#definition 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 http://dmlkb.org/kb/astro.o#Moon http://dmlkb.org/kb/astro.o#Moon http://dmlkb.org/kb/astro.o#Moon http://dmlkb.org/kb/astro.o#Moon http://dmlkb.org/kb/astro.o#majorPlanet http://dmlkb.org/kb/astro.o#majorPlanet http://dmlkb.org/kb/astro.o#majorPlanet http://dmlkb.org/kb/astro.o#majorPlanet http://dmlkb.org/kb/astro.o#meanDiameter http://dmlkb.org/kb/astro.o#meanDiameter http://dmlkb.org/kb/astro.o#meanDiameter http://dmlkb.org/kb/astro.o#meanDiameter http://dmlkb.org/kb/astro.o#meanOrbitalRadius http://dmlkb.org/kb/astro.o#meanOrbitalRadius http://dmlkb.org/kb/astro.o#meanOrbitalRadius http://dmlkb.org/kb/astro.o#meanOrbitalRadius http://dmlkb.org/kb/gen#definition http://dmlkb.org/kb/gen#definition http://dmlkb.org/kb/gen#definition http://dmlkb.org/kb/astro.i#Sun http://dmlkb.org/kb/astro.i#Sun http://dmlkb.org/kb/astro.i#Sun http://dmlkb.org/kb/astro.i#Sun http://dmlkb.org/kb/astro.i#Earth http://dmlkb.org/kb/astro.i#Earth http://dmlkb.org/kb/astro.i#Earth http://dmlkb.org/kb/astro.i#Earth http://dmlkb.org/kb/astro.i#Earth http://dmlkb.org/kb/astro.i#Moon http://dmlkb.org/kb/astro.i#Moon http://dmlkb.org/kb/astro.i#Moon http://dmlkb.org/kb/astro.i#Moon http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2000/01/rdf-schema#subClassOf http://www.w3.org/2000/01/rdf-schema#label http://dmlkb.org/kb/astro.o#definition http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2000/01/rdf-schema#domain http://www.w3.org/2000/01/rdf-schema#range http://www.w3.org/2000/01/rdf-schema#label http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2000/01/rdf-schema#domain http://www.w3.org/2000/01/rdf-schema#range http://www.w3.org/2000/01/rdf-schema#label http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2000/01/rdf-schema#domain http://www.w3.org/2000/01/rdf-schema#range http://www.w3.org/2000/01/rdf-schema#label http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2000/01/rdf-schema#label http://www.w3.org/2000/01/rdf-schema#range http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2000/01/rdf-schema#label http://dmlkb.org/kb/astro.o#meanDiameter http://dmlkb.org/kb/astro.o#majorPlanet http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2000/01/rdf-schema#label http://dmlkb.org/kb/astro.o#meanDiameter http://dmlkb.org/kb/astro.o#meanOrbitalRadius http://dmlkb.org/kb/astro.o#moon http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2000/01/rdf-schema#label http://dmlkb.org/kb/astro.o#meanDiameter http://dmlkb.org/kb/astro.o#meanOrbitalRadius Object http://www.w3.org/2000/01/rdf-schema#Class "Astronomical Body"@en "A naturally-occurring, gravitationally bound aggregation of matter located in space"@en http://www.w3.org/2000/01/rdf-schema#Class http://dmlkb.org/kb/astro.o#AstronomicalBody "Star"@en "An astronomical body that radiates energy resulting from sustained, internal thermonuclear reactions"@en http://www.w3.org/2000/01/rdf-schema#Class http://dmlkb.org/kb/astro.o#AstronomicalBody "Major Planet"@en "An astronomical body orbiting a star, or n-ary star system, which is not itself a star, has sufficient mass to have a nearly round shape, and has cleared the neighborhood around its orbit"@en http://www.w3.org/2000/01/rdf-schema#Class http://dmlkb.org/kb/astro.o#AstronomicalBody "Minor Planet"@en "An astronomical body orbiting a star, or n-ary star system, which is not itself a star and has sufficient mass to have a nearly round shape"@en http://www.w3.org/2000/01/rdf-schema#Class http://dmlkb.org/kb/astro.o#AstronomicalBody "Moon"@en "An astronomical body orbiting a planet"@en http://www.w3.org/2000/01/rdf-schema#Property http://dmlkb.org/kb/astro.o#Star kb.astro.o:MajorPlanet "majorPlanet"@en http://www.w3.org/2000/01/rdf-schema#Property kb.astro.o:AstronomicalBody rdfs:Literal "meanDiameter"@en http://www.w3.org/2000/01/rdf-schema#Property kb.astro.o:AstronomicalBody rdfs:Literal "meanOrbitalRadius"@en http://www.w3.org/2000/01/rdf-schema#Property "definition"@en rdfs:Literal http://dmlkb.org/kb/astro.o#Star "The Sun"@en "1,390,000 km" http://dmlkb/kb/astro.i#Earth http://dmlkb.org/kb/astro.o#MajorPlanet "The Earth"@en "12,742 km" "149,600,000 km" http://dmlkb/kb/astro.i#Moon http://dmlkb.org/kb/astro.o#Moon "The Moon"@en "3,474 km" "384,403 km" Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved 31
  • 32. The End (for now) This is the end of this quick snapshot in time of my ideas on ‘reinventing learning.’ I have many other ideas but haven’t the time to evolve them or write them down. If you want to help, feel free to contact me at: Richard Creamer 2to32minus1@gmail.com Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved 32