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Ontological Navigation
 

Ontological Navigation

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Ontological navigation will continue as long as the presentation of relevant information can be refined to ‘Intuitive Search’ and ‘Data Driven Research’ for a business world that is Gasping in ...

Ontological navigation will continue as long as the presentation of relevant information can be refined to ‘Intuitive Search’ and ‘Data Driven Research’ for a business world that is Gasping in a Gulf of information.

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    Ontological Navigation Ontological Navigation Presentation Transcript

    • Ontological Navigation The landscape of ontology is an organic process in a collaborative sense and built around three broad processes (Input, Organization, and Output) with two actors in each (Human and Artificial). The content of these categories is constantly being updated in the open-source community and the business world is adopting these innovations as quickly as their clients can acclimate to this new found source of relevance. The process will continue as long as the presentation of relevant information can be refined to ‘Intuitive Search’ and ‘Data Driven Research’ for a business world that is Gasping in a Gulf of information. INPUT ORGANIZATION OUTPUT -HI (HUMAN INTELLIGENCE) -HI -HI -COMMUNITY GENERATED ONTOLOGY -Protégé ONTOLOGY EDITOR -Protégé VISUALIZATIONS -4D ONTOLOGY -COLLABORATIVE WHITE PAGE -WHITE PAGE PDF (INCLUDES TIME FROM EMERGING TO MAINSTREAM) -COMMUNITY SUGGESTED ONTOLOGY -NEEMEE COLLECTIONS -TAG CLOUD -AI (ARTIFICIAL INTELLIGENCE) -AI -AI -SEMANTIC SIGNATURES -SEMANTIC HACKER -WEIGHTED TAGS -QUINTURA -QUINTURA -KEYWORD CLOUD -ZEMANTA -CLUSTERING ALGORITHMS -SEMANTICALLY SIMILAR TAGS
    • HI- ONTOLOGY BEGINS WITH YOU MS -COMMUNITY GENERATED ONTOLOGY Community driven Ontology begins at the level of a thought capture. A community specific Ontology is organically cultivated by allowing users to direct thoughts, and their associated tags, to specific users. The icons shown can represent individuals, user groups, collections (as currently exists within Neemee), or Waves -4D ONTOLOGY CultureWaves tracks cultural evidence over time. By allowing users to identify evidence relative to G perceived cultural norms, a personal ontology COMMUNITY GENERATED based in time can emerge. This can be used to identify users that may be more or less mainstream across the whole of the Neemee ONTOLOGY BEGINS WITH community. By identifying the pervasiveness of a thought at the point of capture, a time based USERS MAKING CHOICES model of Neemee evidence can evolve across multiple CW quarterly reports THROUGH A COMMON -COMMUNITY SUGGESTED ONTOLOGY VOCABULARY AND Using ‘auto suggest’ technology as you type, alphabetical tag cloud design, and user-selected COLLABORATING tags, users can choose from multiple existing tags in Neemee. This reinforces Neemee’s existing ontological organization and fosters a THROUGH COMMON E sense of collaboration eliminating the burden of ‘tagging in the dark’ IDEAS
    • AI- ONTOLOGY IS ABOUT CHOICES FROM RELEVANT SOURCES Zemanta offers Neemee the opportunity to dynamically source the entire site for Ontological content and bring that content back to the level of a single thought. Users can add links and tags that they find relevant from the Neemee ontology or add their own. The Neemee ontology is dynamically sourced and kept up to date through community participation. -WEIGHTED TAG CLOUD A weighted tag cloud shows the prevalence of more frequent tags to thoughts and diminishes tags with fewer connections. Ontology is not a static view and needs constant reinforcement, keeping ontology graphics consistent across page views and user interfaces allows users to take a dashboard view of ontological relevance at every layer of Neemee -COMMUNITY LINKED THOUGHTS Community linked thoughts create semantic connections at the granular level of a single thought. Currently, a thought page has no other links outside of itself other than the tag list. By including thought links at the level of a thought, the ontologic web of Neemee is reinforced. These are dynamically updated every time a thought is viewed, thus updating the semantic integration of a thought regardless of the date of capture. -COMMUNITY UPDATED TAGS Community updated tags are dynamically sourced from within and outside of Neemee. These can reference thoughts ontologically related to the thought being viewed regardless of the date of capture. Users can choose which tags to add to the thought they are viewing or add their own. This continual update of the ontologic information in Neemee allows relevance to be built through meaning as well as the date of capture.
    • AI- ONTOLOGY IS ABOUT THE CROWD TEACHING MACHINES Semantic Signatures provides quantifiable data through an AI ‘learning’ process that refines relevance through community participation. By editing articles from the web through the capture process, relevance is defined. AI can help refine and define relevance. Semantic Signatures uses an AI semantic training process with an existing ontological data base from Neemee. The algorithm can create tag strings, or ‘scaled language’, with the top line ontological category names taken from the tag data base and then drills down to the common descriptor tags. The trainable Semantic technology automatically generates a semantic data base during the training phase. This data base then contains the ontology known to be relevant to Neemee thoughts and automatically provides a “definition” of each term using tag strings. Tag Strings can be used to define high level concepts relative to thoughts and provide conceptual relevance for Waves, White papers, Consulting, etc. The resulting tag strings can then be tracked over time to provide a conceptual history of Waves and Human Truths. How do the consumer concepts related to “I want what I want, when I want it.” change over time and what kind of trends can our consulting group define as a history and a future. Manually, through conjecture, CW can interpret Neemee thoughts for our clients but collaborative relevance remains elusive. Is the thought to the left about health or finance? Both for sure, but what are the relativistic weights of those two concepts? Through an AI application of ontology interpretation, CW consulting can build verifiable data over time that brings credence to the Collaborative Ontology building in which Neemee is based. As the AI learns, we learn. As as we learn, the AI learns. AI provides a consistent verifiable record of our accuracy and helps us have confidence in our collaborative Ontology
    • AI- ONTOLOGY IS ABOUT VISUALIZING MEANING Quintura provides a dynamically sourced search cloud and search results that changes as Neemee users select one ontological term or another. This dynamic visualization encourages community participation with the ontology and refines relevance through selection. Quintura creates interactive site search and ontology navigation. The Search cloud engages users in site search more intuitively than regular tag clouds resulting in greater ontological relevance and deeper understanding of related search terms. The interactive search box provides dynamically changing results on A mouse over of terms resulting in fewer clicks and page views and the ability to intuitively search through ontologically related concepts Neemee users need to feel participation in their navigation through neemee. The experience is equal to the insights gleaned. Quintura offers a dynamically changing search cloud and results. The organization and search results offered by Quintura is based in ontology and relevance. The API is customizable and can be geared to return high level CW search results with the associated crowd sourced tags. If ontology is seen by Neemee users as a system of organization that is static, that they must ascribe to, then the relevance of the ontology will consistently be viewed as something outside their normal way of thinking. Ontology must be seen as a way of clarifying peoples normal method of thinking about things, a way of organizing their thoughts through a shared vocabulary. The visualization of ontology is as important as the architecture of the ontology itself. Dynamic visualization encourages community participation and it is through this participation that the ontology will organically become more relevant.
    • HI- ONTOLOGY IS ABOUT INTENTIONALLY PLACED MEANING Each Neemee user has a unique perspective that is unequally distributed and is weighted toward some things rather than others. This is Neemee’s strength and the power of mass collaboration. Given the ability to express this diversity and coupled with the ontological data that accompanies each Neemee thought, Neemee’s collaborative ontology is as intuitive as daily life and as informative as a conversation with a thousand experts. Polyvore combines collaborative data base storage with white page composition. Ontology is about relational meaning where the proximity of things defines the strength of connection. Allowing Neemee users to define, intuitively, the relative strength and weakness of the relations between Neemee evidence is at the heart of a collaborative ontology and Culturewaves insights. Neemee users ability to refine the evidence within a collection coupled with semantic data creates crafted and defined relevance. Ontology is a result meaningful relationships rather than a prescribed rule set to follow. It begins at the granular level with users making informed choices about what Neemee captures mean to them through a collaborative vocabulary. Ontology is not about singular definitions, it is the sum of the crowd that collectively defines meaning. Each thought in Neemee carries with it its own semantic signature and when intuitively and intentionally placed in relation to one another, these signatures add up to define high level ontologic concepts and insights. How is a ‘Human Truth’ defined when it’s translated into tangible evidence through Neemee evidence and how does this definition change over time. User created collaborative white pages bring the whole of the Neemee community to bear on these questions. Their answers then define the structure of the Neemee ontology and its relational architecture over time. The white page is where the ‘Human Truth’ meets the street, it is the Wave pages, and its where ontology takes on a human face.
    • HI- ONTOLOGY IS ABOUT HUMANS STRUCTURING KNOWLEDGE Ontology editors provide an environment where a community of users devoted to making meaning can cultivate a crowd sourced body of evidence to create collaborative connections. The Neemee ontology editing suite is the space of innovation and insight where seemingly unrelated concepts become codified through ‘Red Thread’ relationships. An intuitive interface combined with data driven structure creates a space where scientists work with artists, where analysts work with the ‘youniverse’. Protégé ontology editor is a collaborative tool for managing ontological concepts and collaborative data bases. Neemee currently exists as a static structure built by individuals. Incorporating a collaborative tool for managing that structure creates an environment where users have a level of participation in the ontology that goes beyond merely being observers. An environment where the dynamic and organically grown Neemee ontological structure can be edited by the mass of Neemee users will attract members of the Knowledge Management community to cultivate ontological meaning. Protégé allows users edit structures of meaning. Given a high level editor, those users with the intention and desire to participate in an intuitive and science based insights tool will dive in and begin to use a crowd sourced body of knowledge to develop a semantic web of relevance and meaning. This level of contributors will drive consulting dollars where clients’ raw data can be modeled through to traceable evidence based insights.
    • HI- Protégé AND THE NEEMEE ONTOLOGY Protégé is an ontology editor used for editing and modeling concepts and their semantic relationships. The Protégé editing suite allows for web based semantic data to be imported from Neemee’s relational database defined by specific relationships. In short, Neemee’s tag database can be imported into the Protégé ontology editor, the relationships between the tags can be defined, and those relationships can be visualized for client specific needs or CultureWaves data modeling.
    • STATIC MODEL OF THE NEEMEE ONTOLOGY AS A STATIC MODEL, THE SIGNIFICANCE OF THE NEEMEE ONTOLOGY IS HOW ALL OF THE PARTS ARE CONNECTED ENTERTAINMENT TECHNOLOGY DESIGN WELL BEING PHYSIOLOGICAL SELF ACTUALIZATION SAFETY ESTEEM BELONGING WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE lifestyle(522) light(113) lighting(140) limited edition(96) literature(100) living(101) living vicariously(977) local(212) location(79) locomotion(130) london(214) los angeles(92) love(1452) loyalty(151) luxury(695) mac(97) machine(71) magazine(109) management(93) manly(122) manufacturing(82) maps(71) marijuana(106) market(160) marketing(1305) marriage(200) mars(68) mash up(90) mccain(188) mcdonald's(73) mcdonalds(147) meals(102) meaning(71) meat(207) media(469) medical(193) medicine(245) memories(87) memory(141) men(300) mental health(78) menu(165) message(77) mexican(72) mexico(1244) microblogging(87) microsoft(408) military(263) milk(100) mind(66) mobile(376) mobile phone(125) mobility(75) moda(99) model(94) modern(171) mom(93) moment momentum(508) moms(113) money(755) monitor(73) motorcycle(72) movie(247) movies(347) mp3(90) murder(67) museum(74) music(1256) musica(77) myspace(174) nasa(160) natural(364) nature(259) network(121) networking(121) new(172) new media(86) new york(317) new york city(101) news(317) newspaper(107) nike(97) nintendo(236) nostalgia(140) novelty(88) nutrition(322) nyc(127) obama(963) obesity(366) ocean(79) office(118) oil(154) online(817) ontology(119)
    • DYNAMIC MODEL OF THE NEEMEE ONTOLOGY AS A DYNAMIC MODEL, THE SIGNIFICANCE OF THE NEEMEE ONTOLOGY IS WHY THE PARTS ARE CONNECTED. TECHNOLOGY DESIGN ENTERTAINMENT WELL BEING PHYSIOLOGICAL SELF ACTUALIZATION SAFETY ESTEEM BELONGING WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE WAVE THE INDIVIDUAL CONCEPTS IN AN WAVE THE SEMANTIC MODEL OF AN ONTOLOGY CAN BE DEFINED THROUGH WAVE ONTOLOGY CAN BE MAPPED OVER A COMMON VOCABULARY TIME THROUGH ORGANIC GROWTH WAVE lifestyle(522) light(113) lighting(140) limited edition(96) literature(100) living(101) living vicariously(977) local(212) location(79) locomotion(130) london(214) los angeles(92) love(1452) loyalty(151) luxury(695) mac(97) machine(71) magazine(109) management(93) manly(122) manufacturing (82) maps(71) marijuana(106) market(160) marketing (1305) marriage(200) mars(68) mash up(90) mccain(188) mcdonald's(73) mcdonalds(147) meals(102) meaning(71) meat(207) media(469) medical(193) medicine(245) memories(87) memory(141) men(300) mental health(78) menu(165) message(77) mexican(72) mexico(1244) microblogging(87) microsoft(408) military(263) milk(100) mind(66) mobile(376) mobile phone(125) mobility(75) moda(99) model(94) modern(171) mom(93) moment momentum(508) moms(113) money(755) monitor(73) motorcycle(72) movie(247) movies(347) mp3(90) murder(67) museum(74) music(1256) musica(77)
    • Protégé OPPORTUNITIES AND APPLICATIONS THROUGH AN ONTOLOGY EDITOR NEEMEE MAINTAINED AND MODELED FOR MANY DIFFERENT APPLICATIONS Language and Spelling: Identify conceptually similar terms and merge them to avoid reducing relevance through multiple tags with the same meaning. Apply language dictionaries to translate Neemee.com into multiple languages and collaboratively refine definitions across languages. Implement dictionaries and thesauruses to automatically correct misspelled tags to avoid multiple instances of commonly misspelled words and unique words that are difficult to spell correctly. Collaboration and Software Platforms: Through the Protégé collaborative ontology Wiki, word definitions can be edited by groups of users to refine meaning and generate a common vocabulary. A structured ontology can be exported to multiple software platforms such as Excell, XML, RDF, OWL, Analytics software, and various other report generating plugins in the ProtegePluginsLibrary Clients can create their own ontology that is interpretable by and relevant to the Neemee ontology, clients can then create applications that address specific search functions and data exports for client specific ontologies. Visualization and Reporting: A structured ontology can be exported in real time to the many visualization plugins developed by the Protégé community. A visually modeled ontology defines organically changing relationships and relevancies within the Neemee ontology as they change. A structured ontology can be exported to multiple software platforms such as Excell, XML, RDF, OWL, Analytics software, and various other report generating plugins in the ProtegePluginsLibrary Clients can create their own ontology that is interpretable by and relevant to the Neemee ontology, clients can then create applications that address specific search functions and data exports for client specific ontologies.
    • Protégé LANGUAGE AND SPELLING MANY DEVELOPERS ARE CREATING PLUGINS FOR Protégé TO TRANSLATE LANGUAGES AND CORRECT SPELLING In the Entity Uniform Resource Identifier (URI) pane, select auto ID. When you create a new class, property or individual P4 will give it a meaningless URI and a readable label. That way if you exchange ontologies, correcting spelling mistakes (by merely changing labels) won’t cause the links between the ontologies to break. -Protege Jess Mapping Tab (PJMappingTab) Each terminology of this ontology is designed language dependent and for each language one separated class is defined as shown (Fig 1). Word attributes of term sets are gathered using dictionaries and thesauruses of different languages. Multilingual equivalents of each word are identified, regardless of ambiguity of synonym in different languages. Fig 3 shows a snapshot of the translation graph in protégé tool using TGviz plug-in. This approach helps to define and implement each word independent of a language and as a bridge between other languages. -Protégé Tool and Development of Multilingual Ontology
    • Protégé COLLABORATION AND SOFTWARE EXPORT MANY DEVELOPERS ARE CREATING PLUGINS FOR Protégé COLLABORATIVE EDITING AND SOFTWARE EXPORT You can export the query results to a tab structured file (the tabs are configurable) by clicking on the E icon at the top of the search result list. The exported file will contain a row for each exported entity. The row will contain the values for the exported slots/properties separate by the slot delimiter. It can be opened either with a text editor, or spreadsheet software (e.g., Open Office Spreadsheet, or Microsoft Office Excel). The result in Open Office will look as follows (similar also in Excel): The, Annotations tab shown in Figure 1 allows a user to annotate the selected tags in the ontology tree. Users may decide to start a new discussion thread related to the selected tags, or to reply to an existing comment. We also support different types of annotations (comment, questions, example, etc.) that can be selected from the combo­box at the upper right corner of the collaborative pane.
    • Protégé VISUALIZATION AND REPORTING MANY DEVELOPERS ARE CREATING PLUGINS FOR Protégé ONTOLOGY VISUALIZATION AND DATA REPORTING LOCATIONS OF RELEVANCE CONVERGENCE OF RELEVANCE CONNECTIONS OF RELEVANCE Ve n n d i a g r a m s c a n r e p r e s e n t overlapping similarities across the Neemee evidence bank. This diagram visualizes the CONVERGENCE OF TGVizTab is a plugin for Protégé which RELEVANCE. allows visualizing ontologies using the TouchGraph library. TouchGraph provides a li b rar y fo r re n d e r i n g n et w o r k s a s interactive moveable graphs. These graphs A TagCloud can give you insight into top line aspects of your re pre s e nt t h e C O N N EC T I O N S O F ontology. Ratings are based on RELEVANCE between individual pieces of DATA OF RELEVANCE values such as frequency of evidence. usage, quantity of connections, etc. The bigger the name the h i g h e r t h e rat i n g , s o r t alphabetically, or by rating, and Data windows of the Neemee ontology can filter out low ranking entities be generated to display a myriad of easily. The TagCloud gives you statistical analysis and corresponding views. T h e DAT A O F R E L E VA N C E g i v e s a LOCATIONS OF RELEVANCE perspective of the Neemee ontology that is varied and multi faceted.
    • Protégé VISUALIZATION AND REPORTING MANY DEVELOPERS ARE CREATING PLUGINS FOR Protégé ONTOLOGY VISUALIZATION AND DATA REPORTING OVERVIEW OF RELEVANCE DETAIL OF RELEVANCE Visualizing the ontology as a whole gives clients the ability to trace the entire structure of relevance from higher level concepts to the micro level evidence. The over view answers question of ‘where did this come from?’ and ‘how do these go together?’ It is the OVERVIEW OF RELEVANCE form the top to the bottom DISTRIBUTION OF RELEVANCE Visualizing the ontology as a graph or pie chart gives clients the ability to see the relative quantities of ontological concepts across the spectrum of their subject. The DISTRIBUTION OF RELEVANCE answers question of ‘what is important?’ and ‘how important is it?’ In a collaborative ontology, the meaning o f te r m s m u st h a v e a co m m o n u n d e r sta n d i n g . T h e D E T A I L O F RELEVANCE provides the framework sharing definitions and lineage of terms within the ontology.
    • Protégé VISUALIZATION AND REPORTING MANY DEVELOPERS ARE CREATING PLUGINS FOR Protégé ONTOLOGY VISUALIZATION AND DATA REPORTING COLLABORATIVE ONTOLOGY COMMUNITY OF CONCEPTS The Protégé editor can correlate users and their tags to visualize clusters of users and their common interests. The mass collaborative a s p e ct o f N e e m e e c r e ate s a COLLABORATIVE ONTOLOGY that can be used to search users for relevances to particular concepts COMMUNITY OF RELEVANCE In the spirit of Facebook and Switching between views of users and tags Tw itte r fr ien d visualizat io ns, creates a COMMUNITY OF CONCEPTS form Neemee can model its community to which groups can arrange relevant search reveal users that have a great deal ter ms to fin d u sers vice versa. The in common through their tags but visualization reinforces the personal nature of may not know how closely related Neemee’s insights. they are. Building a COMMUNITY O F R E L E VA N C E b e yo n d t h e confines of client groups is at the core of Neemee’s social insights methodology.
    • Protégé NEXT STEPS 1. Generate documentation of existing Neemee code Document the existing Relational Data Base Management System (RDBMS) Define table names and, within each table, the field names and any other other metadata (eg, the field type and size). 2. Import Neemee data into Protégé and produce ontological modeling Using the DataMaster plug-in, import schema structure from the RDBMS. Extend the current database (eg, the field type and size) to support the kind and type of ontology modeling to build the reporting and data visualizations that grow Neemee a n d C u lt u r e W a v e s p r o d u c t capabilities.