Evolution: It's a process
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Afternoon keynote given at the Web 3.0 Event in New York City, May 20, 2009
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Evolution: It's a process
- 1. Evolution: It’s a process Christine Connors Among other things: librarian, information scientist, semantic web advocate and Global Director, Semantic Technology Solutions, Dow Jones & Company Web 3.0, New York, NY, May 20th, 2009
- 2. What is the Semantic Web? ✤ A universal medium for exchanging information that can be processed electronically and still have meaning and relevance ✤ It provides a common, standardized framework that allows data to be shared and reused across applications, enterprises, and community boundaries. Why do we care about it for our solutions? ✤ We need to provide import and export support for the Semantic Web to enable easier data exchange ✤ Greater interoperability means better standardization and integration into Web based applications – more customers can use it!
- 3. Challenges of the Semantic Web ✤ Overcoming prior organizational “bad experiences” with integration, MDM, KM, metadata, taxonomies and related projects ✤ What vs. How ✤ Identify and prioritize solutions ✤ Do the heavy lifting to reduce complexity for our users ✤ Usability & interaction design ✤ Determining ROI/ROE ✤ Trust & Security ✤ Determining market strategy 3
- 4. The Myths of the Semantic Web ✤ There will be a handful of “killer apps” to replace Web2.0 giants ✤ The current web will be replaced by the semantic web ✤ Everything will have to be tagged ✤ It will be expensive to migrate everything ✤ We will experience instant gratification ✤ It’s hard to get started 4
- 5. Battlestar Galactica Science Fiction http://www.scifi.com/battlestar/video/widget.php http://en.battlestarwiki.org/wiki/Hera_Agathon
- 6. Astrolabes Man, machine, and data http://astrolabes.org/mariner.htm http://en.wikipedia.org/wiki/Mariner's_astrolabe
- 7. BBC MusicBeta Data from users of MusicBrainz and Wikipedia, with BBC editorial oversight. http://www.bbc.co.uk/music/
- 8. Text Predicate Subject Object Two views of the semantic web Machine learning, natural language processing, artificial intelligence and linked data Images from Wikipedia
- 9. Editorial Content Interfaces Monitoring & Alerting Information Providers Data Data Capture Normalizer Normalization Coding Quality Distribution Capture Control Manual Coding Entity Rules-based Expansion/ Queue Categorizer Extraction Coding Validation Dow Jones Intelligent Indexing Metadata Management Manual Coding Software Interface Hybrid Solution Humans build the architecture-hardware, software AND data; machines process efficiently www.factiva.com
- 15. The Continuum Thesaurus Ambiguity Control Folksonomy Synonym Ring Synonym Control Hierarchical Relationships Personalized Labels Synonym Associative Relationships Control Scope Note (Equivalency) (BT, NT, RT, USE, SeeAlso) Less Complexity More Taxonomy Ontology List Ambiguity Control Ambiguity Control Ambiguity Synonym Control Synonym Control Control Hierarchical Relationships Hierarchical Relationships (BT, NT) Associative Relationships Classes Properties Localization Annotation Reasoning “NOT” See NISO Z39.19-2005
- 16. The Continuum We are building more complex and powerful data architectures; all types are available for use on the semantic web
- 17. Ontology Thesaurus Taxonomy Power Synonym Ring List Folksonomy Complexity The Continuum We are building more complex and powerful data architectures; all types are available for use on the semantic web
- 21. Andorra Austria Belgium Denmark Finland France Germany Name: Hungary Address: Ireland City: Italy State/Province: Liechtenstein Country: Monaco Zip/Postal Code: Netherlands Norway Portugal Spain Sweden Switzerland United Kingdom
- 22. Andorra Austria Belgium Denmark Finland France Germany Name: Hungary Address: Ireland City: Italy Precision State/Province: Liechtenstein Country: Monaco Zip/Postal Code: Netherlands Norway Portugal Spain Sweden Switzerland United Kingdom
- 23. United Kingdom Synonym: UK Synonym: United Kingdom of Great Britain and Northern Ireland
- 24. United Kingdom Synonym: UK Synonym: United Kingdom of Great Britain and Northern Ireland Behind the scenes in search
- 25. United Kingdom Synonym: UK Synonym: United Kingdom of Great Britain and Northern Ireland Behind the scenes in search Recall
- 26. Europe NT ... United Kingdom NT England Scotland Wales Northern Ireland See http://www.nlsearch.com/rssearch.php
- 27. Europe NT ... United Kingdom Advanced Search NT England Scotland Wales Northern Ireland See http://www.nlsearch.com/rssearch.php
- 28. Europe NT Better ... Recall United Kingdom Advanced Search NT England Scotland Better Wales Precision Northern Ireland See http://www.nlsearch.com/rssearch.php
- 29. Europe NT ... United Kingdom NT England Scotland Wales Northern Ireland See www.endeca.com or www.newssift.com
- 30. Europe England NT BT ... Britain United Kingdom Great Britain NT United Kingdom England BT Scotland European Union Wales Group of Eight Northern Ireland United Nations Security Council NATO See www.endeca.com or www.newssift.com
- 31. Europe England NT BT ... Britain United Kingdom Great Britain NT United Kingdom England BT Scotland European Union Wales Group of Eight Northern Ireland United Nations Security Council NATO Faceted or Parametric Search; Guided Navigation See www.endeca.com or www.newssift.com
- 32. Europe NT United Kingdom Scope Note Situated in north-west Europe, the island nation was formed January 1, 1801. Use For UK Use For United Kingdom of Great Britain and Northern Ireland See Also Great Britain See Also Britain See Also British Isles NT England Scotland Wales Northern Ireland
- 33. Europe NT United Kingdom Scope Note Situated in north-west Europe, the island nation was formed January 1, 1801. Categorization Use For UK Classification Use For United Kingdom of Search Great Britain and Northern Ireland Advanced Search See Also Great Britain Rules-based Coding See Also Britain See Also British Isles *Precision ? Recall ? NT England Scotland Wales Northern Ireland
- 37. Region 1 Region 2 100 70 75 52.5 50 35 25 17.5 0 0 2007 2008 2009 2010
- 38. NT England Britain BT NT NT BT BT Wales Great Britain NT NT BT Scotland BT United NT Northern Kingdom BT Ireland
- 39. NT England Britain BT God and my right NT NT BT BT Wales motto Great Britain NT NT BT Scotland BT flag United NT Northern God Save the Queen Kingdom BT Ireland anthem official English language capital currency legislature London pound sterling Parliament
- 40. OpenCyc Large ontology based on the Cyc Knowledge Base http://sw.opencyc.org/concept/Mx4rvViRhJwpEbGdrcN5Y29ycA
- 41. DBpedia A sizable ontology derived from data in Wikipedia http://dbpedia.org/page/United_kingdom http://wiki.dbpedia.org/Datasets
- 43. Richard Cyganiak, available from Richard Cyganiak and Chris Bizer
- 44. As of March 2008 Richard Cyganiak, available from Richard Cyganiak and Chris Bizer
- 45. As of March 2008 Richard Cyganiak, available from Richard Cyganiak and Chris Bizer
- 46. As of March 2008 Richard Cyganiak, available from Richard Cyganiak and Chris Bizer
- 47. Sindice Index to linked data: books, people, places, news, statistics, events, business, music ... http://sindice.com/map
- 50. Semantic Web Layer Cake Key components; time left to influence - publish your use cases http://www.w3.org/2007/03/layercake.png 33
- 51. Capabilities ✤ Business development - market analysis, use cases ✤ Technical development - servers, apps, web ✤ Information architects ✤ Information scientists - define, organize, link ✤ User interface and interaction designers - user studies, structural design
- 52. Metadata Management: A commitment to process
- 53. Metadata Management: A commitment to process Assess Design Build Maintain Business Goals Audience Entity Extraction Continuous Work- Segmentation & (machine and/or in-progress Content Definition human) Engage end-users IT Facet Analysis Content Tagging (query log Rules (machine analysis, focus Metadata Schema Information and/or human) groups, Architecture folksonomy) Taxonomy Controlled Editorial Vocabulary or Governance Standards & Best Guidelines & Knowledge Base Process Practices Workflow Construction & Mapping Users
- 54. Why do we care? Business Perspective ✤ Embed ability to manipulate data rather than expend effort scraping it back out ✤ Re-purpose data rather than re-create it ✤ Improve product development with a global business vocabulary that feeds right into downstream applications such as portals, reporting programs and CRMs ✤ Improved findability ✤ Improve analytical capabilities ✤ Increase online revenue and improve your customers’ online experience by cross- referencing industry classification codes and brand names ✤ Compliance ✤ Increase delivery channels for data and services 36
- 55. Information Overload Relevancy Overload What’s important to me right now Getting Past Relevancy Overload The more precise the concept’s URI, the more precise the results
- 56. Thank you Christine.Connors@dowjones.com FOAF: http://www.synapticacentral.com/foafs/christineconnors-foaf.rdf Nick: CJMConnors at Twitter, Slideshare, LinkedIn, Identi.ca et al SynapticaCentral.com CJMConnors.com
Notas do Editor
- When you markup your data semantically, you and anyone else can use that data to their best advantage. Unanticipated uses by unanticipated users. Enable your MacGyvers as Anthony Bradley of Gartner encouraged attendees at last Septembers Web Innovation Summit. Semantic Web is the internet’s equivalent of the Green Building movement: reduce, recycle, reuse. (Re-use, re-mix, = Mashup)
- The “semantic web” is not the answer - it is a potential solution for existing business problems. Consider a semantic solution just as you would consider any other solution.
- We are not looking for a Google killer; there is a difference between documents on the web and data on the web. Google is a leader in providing access to documents and will likely remain so for some time. They will not be replaced, but there is opportunity for a “Google for data” to emerge. The user goals are different, and so the inputs and methods of analysis and retrieval will be different. The semantic web will co-exist with the current web; per TimBLs blog, there will be markets for both raw data and mashed up data Tagging everything does not scale. Everything old is new again – entity extraction and Natural Language processing tools will (are having) a renaissance. There are critical technical and editorial choices to make when employing those tools, but no, everything does NOT need to be tagged. $$$ - not really. Franz recently revealed they had converted 10B triples using Amazon’s EC2 service for just 2 days for only $192. Many semantic web technologies are being built by people passionate about their work, and they are making it open source. Enterprise level applications will want the security and stability of tested, supported systems that require investment – as well as the smart consultants to go along with it – but you can GET STARTED with open source tools while you make your case. It’s not hard to get started, and now we’re going to show you some simple things you can do. The best and most consistent advice I’ve received since becoming interested in the semantic web is this: take baby steps. Solve one discrete problem at a time. Don’t try to read the OWL spec and jump in with an OWL Full representation of your knowledge domain – you’ll drive yourself crazy. Work the model of your domain in small chunks, learn about how to make things disjoint when you have a need for it. Learn about domains and ranges when they come up. Don’t worry about first and second order logic until you’ve advanced to the point where it INTERESTS you and you NEED it. As I was thinking about how to begin this presentation, I mused over some ideas at home. It is human nature to reuse, to mash-up data. In early childhood we use the same tune to carry the lyrics for Baa, Baa Black Sheep, Twinkle Twinkle Little Star and the ABC song. Authors and playwrights are inspired by earlier myths - Shakespeare may have been inspired by Pyramus and Thisbe or a handful of other stories when he wrote Romeo & Juliet. West Side Story is another adaptation. Baz Luhrman, the film director, took a stab at it with Leonardo DiCaprio as Romeo, and then went on to produce Moulin Rouge, one of the most ambitious mashups of songs and stories seen in the film industry of late, combining snippets and full songs from David Bowie, Bono, Madonna, Elton John, Fatboy Slim, Rufus Wainwright, Labelle, Nirvana, Nat King Cole and many many more.
- http://www.scifi.com/battlestar/includes/untranscript.pdf The United Nations Department of Public Information And SCI FI Channel Present “Battlestar Galactica: A Retrospective” March 17, 2009 KIYOTAKA AKASAKA:To quote Isaac Asimov, famous science fiction author, science fiction writes foresee -- science fiction writers foresee the inevitable.
- The essential function of the device was to measure angles. Thus the instrument featured a ring graduated in degrees. In order to use the astrolabe, the navigator would hold the instrument by the ring at the top. This caused the instrument to remain in a vertical plane. He would align the plane of the astrolabe to the direction of the object of interest. The alidade was aligned to point at the object and the altitude was read off the outer degree scale. It was not possible to determine longitude at sea in the early days of transoceanic navigation, but it was quite easy to determine latitude. To go to a place of known latitude, the ship was sailed to that latitude and then sailed east or west along the latitude line until the place was reached. To find the latitude of the ship at sea, the noon altitude of the Sun was measured during the day or the altitude of a star of known declination was measured when it was on the meridian (due north or south) at night. The Sun's or star's declination for the date was looked up in an almanac. The latitude is then 90° - measured altitude + declination .
- Data from MusicBrainz and Wikipedia are combined - with a bit of editorial oversight - with playlists and story data from BBC properties
- There is a computationally complex view of the web that involves Boolean logic, Bayesian algorithms, syntax, pattern recognition, neural networks and more. There is another view that is concerned about meaning, categorization, classification and relationships. This view tends to require more human power. Neither is particularly practical – one requires heavy-duty processing and lots of monitoring. The other requires a great deal of handcrafting and maintaining. Using the best of each world will get you further in the long run. There are brilliant minds working in the artificial intelligence space, and we make great use of those tools in our own processing platform, but that’s not what we’re going to focus on today. Today, we’ll be talking about a web of data – linked data; the vision promoted by the world wide web consortium. The semantic web is NOT a new web, in fact the specifications are on average a decade old. It is an open framework designed to allow data to be shared by as many people, organizations and applications as is desired. Right now the majority of the data on the web is locked up in applications and markup languages that jumble the format, the style, delivery mechanism and the content all together. The semantic web is a group of standards that provide the common format for describing data so that data from different sources can easily be combined and integrated rather than siloed. http://en.wikipedia.org/wiki/File:Artificial_neural_network.svg http://en.wikipedia.org/wiki/File:Xbarst1.jpg http://en.wikipedia.org/wiki/Naive_Bayes_classifier
- i am editorial side not programming; semweb is about data; NLP etc have come a long way during web2 and will continue to be refined; XML - extensible, not interoperable -- not enough; grammar not meaning So, what powers the Dow Jones metadata platform? Human crafted taxonomies and ontologies, built using COTS software. Nothing new really, it’s another technique with a long tradition behind it.
- This is the card catalog room at the Sterling Memorial Library, Yale. Metadata goes back quite far, actually. In the British Museum are girginakku, Mesopotamian library boxes that have clay tablet labels on them - metadata. Go see David’s picture at http://www.flickr.com/photos/70494923@N00/2650269503/in/photostream/ SO what are taxonomies, ontologies etc? Let’s talk about it.
- A list can be a pick list, an index, an authority file Ambiguity Control Christine Connors vs. Christine Conners :( List of food We recently had a long holiday weekend, usually highlighted by barbecues, so let’s start with a list of food: hot dogs, hamburgers, buns, mustard, mayo, ketchup, onions, pickles, chips, salad, cookies, etc etc ----------- A synonym ring is what we think Roget’s Thesaurus is. Synonym Control (Equivalence Relationships) Ketchup or Catsup ---------- Hierarchical Relationships Is A, Part of type relationships Where would you put the poor tomato? Tomato - vegetable? Fruit? Both? It’s part of ketchup, should it be linked to ketchup under condiments? Mono-hierarchical vs. poly-hierarchical ------------ Associative Relationships - See Also Salt and Pepper - Spice? Condiment? Or would it be helpful to tell the user who is looking at Spices to also review Condiments? (or, do it for them -- Steve Krug’s Don’t Make Me Thnk) See NISO Z39.19-2005 BT = Broader Term NT = Narrower Term RT = Related Term (“See also”) SN = Scope Note UF = Used For USE = “See” (Refers reader from variant term to vocabulary term.) ------------ Get to define your own relationship types! Localization Annotation Reasoning “NOT” Ontology 101 by Natalya Foy and Deb McGuinnes Semantic Web for the Workind Ontologist by Dean Allemang and James Hendler ---------------------------- There is NO ONE RIGHT WAY to build any of these. They are an ART and a SCIENCE. The IA, UX, UI, etc - all human-computer interaction models for your system are important inputs to the design. How many of you shop for groceries? How many of you just go and walk up and down every aisle and grab what you like or think you need? How many of you just make a list as things run out, and then have to stop at the end of every aisle to look if there’s anything you need? How many of you have a list, organized according to your store’s layout?
- A list can be a pick list, an index, an authority file Ambiguity Control Christine Connors vs. Christine Conners :( List of food We recently had a long holiday weekend, usually highlighted by barbecues, so let’s start with a list of food: hot dogs, hamburgers, buns, mustard, mayo, ketchup, onions, pickles, chips, salad, cookies, etc etc ----------- A synonym ring is what we think Roget’s Thesaurus is. Synonym Control (Equivalence Relationships) Ketchup or Catsup ---------- Hierarchical Relationships Is A, Part of type relationships Where would you put the poor tomato? Tomato - vegetable? Fruit? Both? It’s part of ketchup, should it be linked to ketchup under condiments? Mono-hierarchical vs. poly-hierarchical ------------ Associative Relationships - See Also Salt and Pepper - Spice? Condiment? Or would it be helpful to tell the user who is looking at Spices to also review Condiments? (or, do it for them -- Steve Krug’s Don’t Make Me Thnk) See NISO Z39.19-2005 BT = Broader Term NT = Narrower Term RT = Related Term (“See also”) SN = Scope Note UF = Used For USE = “See” (Refers reader from variant term to vocabulary term.) ------------ Get to define your own relationship types! Localization Annotation Reasoning “NOT” Ontology 101 by Natalya Foy and Deb McGuinnes Semantic Web for the Workind Ontologist by Dean Allemang and James Hendler ---------------------------- There is NO ONE RIGHT WAY to build any of these. They are an ART and a SCIENCE. The IA, UX, UI, etc - all human-computer interaction models for your system are important inputs to the design. How many of you shop for groceries? How many of you just go and walk up and down every aisle and grab what you like or think you need? How many of you just make a list as things run out, and then have to stop at the end of every aisle to look if there’s anything you need? How many of you have a list, organized according to your store’s layout?
- A list can be a pick list, an index, an authority file Ambiguity Control Christine Connors vs. Christine Conners :( List of food We recently had a long holiday weekend, usually highlighted by barbecues, so let’s start with a list of food: hot dogs, hamburgers, buns, mustard, mayo, ketchup, onions, pickles, chips, salad, cookies, etc etc ----------- A synonym ring is what we think Roget’s Thesaurus is. Synonym Control (Equivalence Relationships) Ketchup or Catsup ---------- Hierarchical Relationships Is A, Part of type relationships Where would you put the poor tomato? Tomato - vegetable? Fruit? Both? It’s part of ketchup, should it be linked to ketchup under condiments? Mono-hierarchical vs. poly-hierarchical ------------ Associative Relationships - See Also Salt and Pepper - Spice? Condiment? Or would it be helpful to tell the user who is looking at Spices to also review Condiments? (or, do it for them -- Steve Krug’s Don’t Make Me Thnk) See NISO Z39.19-2005 BT = Broader Term NT = Narrower Term RT = Related Term (“See also”) SN = Scope Note UF = Used For USE = “See” (Refers reader from variant term to vocabulary term.) ------------ Get to define your own relationship types! Localization Annotation Reasoning “NOT” Ontology 101 by Natalya Foy and Deb McGuinnes Semantic Web for the Workind Ontologist by Dean Allemang and James Hendler ---------------------------- There is NO ONE RIGHT WAY to build any of these. They are an ART and a SCIENCE. The IA, UX, UI, etc - all human-computer interaction models for your system are important inputs to the design. How many of you shop for groceries? How many of you just go and walk up and down every aisle and grab what you like or think you need? How many of you just make a list as things run out, and then have to stop at the end of every aisle to look if there’s anything you need? How many of you have a list, organized according to your store’s layout?
- A list can be a pick list, an index, an authority file Ambiguity Control Christine Connors vs. Christine Conners :( List of food We recently had a long holiday weekend, usually highlighted by barbecues, so let’s start with a list of food: hot dogs, hamburgers, buns, mustard, mayo, ketchup, onions, pickles, chips, salad, cookies, etc etc ----------- A synonym ring is what we think Roget’s Thesaurus is. Synonym Control (Equivalence Relationships) Ketchup or Catsup ---------- Hierarchical Relationships Is A, Part of type relationships Where would you put the poor tomato? Tomato - vegetable? Fruit? Both? It’s part of ketchup, should it be linked to ketchup under condiments? Mono-hierarchical vs. poly-hierarchical ------------ Associative Relationships - See Also Salt and Pepper - Spice? Condiment? Or would it be helpful to tell the user who is looking at Spices to also review Condiments? (or, do it for them -- Steve Krug’s Don’t Make Me Thnk) See NISO Z39.19-2005 BT = Broader Term NT = Narrower Term RT = Related Term (“See also”) SN = Scope Note UF = Used For USE = “See” (Refers reader from variant term to vocabulary term.) ------------ Get to define your own relationship types! Localization Annotation Reasoning “NOT” Ontology 101 by Natalya Foy and Deb McGuinnes Semantic Web for the Workind Ontologist by Dean Allemang and James Hendler ---------------------------- There is NO ONE RIGHT WAY to build any of these. They are an ART and a SCIENCE. The IA, UX, UI, etc - all human-computer interaction models for your system are important inputs to the design. How many of you shop for groceries? How many of you just go and walk up and down every aisle and grab what you like or think you need? How many of you just make a list as things run out, and then have to stop at the end of every aisle to look if there’s anything you need? How many of you have a list, organized according to your store’s layout?
- A list can be a pick list, an index, an authority file Ambiguity Control Christine Connors vs. Christine Conners :( List of food We recently had a long holiday weekend, usually highlighted by barbecues, so let’s start with a list of food: hot dogs, hamburgers, buns, mustard, mayo, ketchup, onions, pickles, chips, salad, cookies, etc etc ----------- A synonym ring is what we think Roget’s Thesaurus is. Synonym Control (Equivalence Relationships) Ketchup or Catsup ---------- Hierarchical Relationships Is A, Part of type relationships Where would you put the poor tomato? Tomato - vegetable? Fruit? Both? It’s part of ketchup, should it be linked to ketchup under condiments? Mono-hierarchical vs. poly-hierarchical ------------ Associative Relationships - See Also Salt and Pepper - Spice? Condiment? Or would it be helpful to tell the user who is looking at Spices to also review Condiments? (or, do it for them -- Steve Krug’s Don’t Make Me Thnk) See NISO Z39.19-2005 BT = Broader Term NT = Narrower Term RT = Related Term (“See also”) SN = Scope Note UF = Used For USE = “See” (Refers reader from variant term to vocabulary term.) ------------ Get to define your own relationship types! Localization Annotation Reasoning “NOT” Ontology 101 by Natalya Foy and Deb McGuinnes Semantic Web for the Workind Ontologist by Dean Allemang and James Hendler ---------------------------- There is NO ONE RIGHT WAY to build any of these. They are an ART and a SCIENCE. The IA, UX, UI, etc - all human-computer interaction models for your system are important inputs to the design. How many of you shop for groceries? How many of you just go and walk up and down every aisle and grab what you like or think you need? How many of you just make a list as things run out, and then have to stop at the end of every aisle to look if there’s anything you need? How many of you have a list, organized according to your store’s layout?
- A list can be a pick list, an index, an authority file Ambiguity Control Christine Connors vs. Christine Conners :( List of food We recently had a long holiday weekend, usually highlighted by barbecues, so let’s start with a list of food: hot dogs, hamburgers, buns, mustard, mayo, ketchup, onions, pickles, chips, salad, cookies, etc etc ----------- A synonym ring is what we think Roget’s Thesaurus is. Synonym Control (Equivalence Relationships) Ketchup or Catsup ---------- Hierarchical Relationships Is A, Part of type relationships Where would you put the poor tomato? Tomato - vegetable? Fruit? Both? It’s part of ketchup, should it be linked to ketchup under condiments? Mono-hierarchical vs. poly-hierarchical ------------ Associative Relationships - See Also Salt and Pepper - Spice? Condiment? Or would it be helpful to tell the user who is looking at Spices to also review Condiments? (or, do it for them -- Steve Krug’s Don’t Make Me Thnk) See NISO Z39.19-2005 BT = Broader Term NT = Narrower Term RT = Related Term (“See also”) SN = Scope Note UF = Used For USE = “See” (Refers reader from variant term to vocabulary term.) ------------ Get to define your own relationship types! Localization Annotation Reasoning “NOT” Ontology 101 by Natalya Foy and Deb McGuinnes Semantic Web for the Workind Ontologist by Dean Allemang and James Hendler ---------------------------- There is NO ONE RIGHT WAY to build any of these. They are an ART and a SCIENCE. The IA, UX, UI, etc - all human-computer interaction models for your system are important inputs to the design. How many of you shop for groceries? How many of you just go and walk up and down every aisle and grab what you like or think you need? How many of you just make a list as things run out, and then have to stop at the end of every aisle to look if there’s anything you need? How many of you have a list, organized according to your store’s layout?
- A list can be a pick list, an index, an authority file Ambiguity Control Christine Connors vs. Christine Conners :( List of food We recently had a long holiday weekend, usually highlighted by barbecues, so let’s start with a list of food: hot dogs, hamburgers, buns, mustard, mayo, ketchup, onions, pickles, chips, salad, cookies, etc etc ----------- A synonym ring is what we think Roget’s Thesaurus is. Synonym Control (Equivalence Relationships) Ketchup or Catsup ---------- Hierarchical Relationships Is A, Part of type relationships Where would you put the poor tomato? Tomato - vegetable? Fruit? Both? It’s part of ketchup, should it be linked to ketchup under condiments? Mono-hierarchical vs. poly-hierarchical ------------ Associative Relationships - See Also Salt and Pepper - Spice? Condiment? Or would it be helpful to tell the user who is looking at Spices to also review Condiments? (or, do it for them -- Steve Krug’s Don’t Make Me Thnk) See NISO Z39.19-2005 BT = Broader Term NT = Narrower Term RT = Related Term (“See also”) SN = Scope Note UF = Used For USE = “See” (Refers reader from variant term to vocabulary term.) ------------ Get to define your own relationship types! Localization Annotation Reasoning “NOT” Ontology 101 by Natalya Foy and Deb McGuinnes Semantic Web for the Workind Ontologist by Dean Allemang and James Hendler ---------------------------- There is NO ONE RIGHT WAY to build any of these. They are an ART and a SCIENCE. The IA, UX, UI, etc - all human-computer interaction models for your system are important inputs to the design. How many of you shop for groceries? How many of you just go and walk up and down every aisle and grab what you like or think you need? How many of you just make a list as things run out, and then have to stop at the end of every aisle to look if there’s anything you need? How many of you have a list, organized according to your store’s layout?
- A list can be a pick list, an index, an authority file Ambiguity Control Christine Connors vs. Christine Conners :( List of food We recently had a long holiday weekend, usually highlighted by barbecues, so let’s start with a list of food: hot dogs, hamburgers, buns, mustard, mayo, ketchup, onions, pickles, chips, salad, cookies, etc etc ----------- A synonym ring is what we think Roget’s Thesaurus is. Synonym Control (Equivalence Relationships) Ketchup or Catsup ---------- Hierarchical Relationships Is A, Part of type relationships Where would you put the poor tomato? Tomato - vegetable? Fruit? Both? It’s part of ketchup, should it be linked to ketchup under condiments? Mono-hierarchical vs. poly-hierarchical ------------ Associative Relationships - See Also Salt and Pepper - Spice? Condiment? Or would it be helpful to tell the user who is looking at Spices to also review Condiments? (or, do it for them -- Steve Krug’s Don’t Make Me Thnk) See NISO Z39.19-2005 BT = Broader Term NT = Narrower Term RT = Related Term (“See also”) SN = Scope Note UF = Used For USE = “See” (Refers reader from variant term to vocabulary term.) ------------ Get to define your own relationship types! Localization Annotation Reasoning “NOT” Ontology 101 by Natalya Foy and Deb McGuinnes Semantic Web for the Workind Ontologist by Dean Allemang and James Hendler ---------------------------- There is NO ONE RIGHT WAY to build any of these. They are an ART and a SCIENCE. The IA, UX, UI, etc - all human-computer interaction models for your system are important inputs to the design. How many of you shop for groceries? How many of you just go and walk up and down every aisle and grab what you like or think you need? How many of you just make a list as things run out, and then have to stop at the end of every aisle to look if there’s anything you need? How many of you have a list, organized according to your store’s layout?
- A list can be a pick list, an index, an authority file Ambiguity Control Christine Connors vs. Christine Conners :( List of food We recently had a long holiday weekend, usually highlighted by barbecues, so let’s start with a list of food: hot dogs, hamburgers, buns, mustard, mayo, ketchup, onions, pickles, chips, salad, cookies, etc etc ----------- A synonym ring is what we think Roget’s Thesaurus is. Synonym Control (Equivalence Relationships) Ketchup or Catsup ---------- Hierarchical Relationships Is A, Part of type relationships Where would you put the poor tomato? Tomato - vegetable? Fruit? Both? It’s part of ketchup, should it be linked to ketchup under condiments? Mono-hierarchical vs. poly-hierarchical ------------ Associative Relationships - See Also Salt and Pepper - Spice? Condiment? Or would it be helpful to tell the user who is looking at Spices to also review Condiments? (or, do it for them -- Steve Krug’s Don’t Make Me Thnk) See NISO Z39.19-2005 BT = Broader Term NT = Narrower Term RT = Related Term (“See also”) SN = Scope Note UF = Used For USE = “See” (Refers reader from variant term to vocabulary term.) ------------ Get to define your own relationship types! Localization Annotation Reasoning “NOT” Ontology 101 by Natalya Foy and Deb McGuinnes Semantic Web for the Workind Ontologist by Dean Allemang and James Hendler ---------------------------- There is NO ONE RIGHT WAY to build any of these. They are an ART and a SCIENCE. The IA, UX, UI, etc - all human-computer interaction models for your system are important inputs to the design. How many of you shop for groceries? How many of you just go and walk up and down every aisle and grab what you like or think you need? How many of you just make a list as things run out, and then have to stop at the end of every aisle to look if there’s anything you need? How many of you have a list, organized according to your store’s layout?
- This is like going to the store with no list. There are some staples that everyone needs, but everything is kind of random.
- This is like going to the store with an unorganized list. Everything on the list can be grouped according to the task, but the list itself could be random or alphabetical. Typically used in forms.
- This is like going to the store with an unorganized list. Everything on the list can be grouped according to the task, but the list itself could be random or alphabetical. Typically used in forms.
- This is like going to the store with an unorganized list. Everything on the list can be grouped according to the task, but the list itself could be random or alphabetical. Typically used in forms.
- This is like going to the store with an unorganized list. Everything on the list can be grouped according to the task, but the list itself could be random or alphabetical. Typically used in forms.
- This is like going to the store with an unorganized list. Everything on the list can be grouped according to the task, but the list itself could be random or alphabetical. Typically used in forms.
- This is like going to the store with an unorganized list. Everything on the list can be grouped according to the task, but the list itself could be random or alphabetical. Typically used in forms.
- This is like going to the store with an unorganized list. Everything on the list can be grouped according to the task, but the list itself could be random or alphabetical. Typically used in forms.
- This is like going to the store with an unorganized list. Everything on the list can be grouped according to the task, but the list itself could be random or alphabetical. Typically used in forms.
- This is like going to the store with an unorganized list. Everything on the list can be grouped according to the task, but the list itself could be random or alphabetical. Typically used in forms.
- This is like going to the store with an unorganized list. Everything on the list can be grouped according to the task, but the list itself could be random or alphabetical. Typically used in forms.
- This is like going to the store with an unorganized list. Everything on the list can be grouped according to the task, but the list itself could be random or alphabetical. Typically used in forms.
- This is like going to the store with an unorganized list. Everything on the list can be grouped according to the task, but the list itself could be random or alphabetical. Typically used in forms.
- This is like going to the store with an unorganized list. Everything on the list can be grouped according to the task, but the list itself could be random or alphabetical. Typically used in forms.
- This is like going to the store with an unorganized list. Everything on the list can be grouped according to the task, but the list itself could be random or alphabetical. Typically used in forms.
- This is like going to the store with an unorganized list. Everything on the list can be grouped according to the task, but the list itself could be random or alphabetical. Typically used in forms.
- This is like going to the store with an unorganized list. Everything on the list can be grouped according to the task, but the list itself could be random or alphabetical. Typically used in forms.
- This is like going to the store with an unorganized list. Everything on the list can be grouped according to the task, but the list itself could be random or alphabetical. Typically used in forms.
- This is like going to the store with an unorganized list. Everything on the list can be grouped according to the task, but the list itself could be random or alphabetical. Typically used in forms.
- This is like going to the store with an unorganized list. Everything on the list can be grouped according to the task, but the list itself could be random or alphabetical. Typically used in forms.
- This is like going to the store with an unorganized list. Everything on the list can be grouped according to the task, but the list itself could be random or alphabetical. Typically used in forms.
- This is like going to the store with an unorganized list. Everything on the list can be grouped according to the task, but the list itself could be random or alphabetical. Typically used in forms.
- This is what happens when what you want at the store is in a couple of different places - think about the featured products at the end of the aisles.
- This is what happens when what you want at the store is in a couple of different places - think about the featured products at the end of the aisles.
- This is what happens when what you want at the store is in a couple of different places - think about the featured products at the end of the aisles.
- isA, kindOf, partOf
- isA, kindOf, partOf
- Enterprise Search, content portals
- why do this findability reuse share but most importantly to NEXT (analyze)
- why do this findability reuse share but most importantly to NEXT (analyze)