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Semantic Web SEO: Using Linked Data and schema.org to improve Library Reach and Digital Repository Access
- 1. Semantic Web SEO: Using Linked Data and schema.org to improve Library Reach and Digital Repository Access Kenning Arlitsch & Patrick OBrien DLF Fall – Denver, Colorado November 5, 2012
- 2. Today’s Objec.ves u Basic understanding of v Semantic Web SEO for digital repositories v How to get started incorporating Schema.org and linked data into a digital repository u Implement baseline metrics to support pre/post funding decisions of digital repositories v Simplify setup and administration of Google Analytics and Google Webmaster for an organization and its stakeholders v Implement Digital Repository SEO Google Analytics dashboard
- 3. Agenda u Why SEO & the Semantic Web Matters v Performance & Accountability v The semantics of what really matters today u How to Get Started v SEO Administration at an Institutional Scale v Enhance Your Data v Clean up You Data
- 4. You can not evaluate what you do not measure "We cannot call a digital-‐library or electronic-‐ publishing system a success if we cannot measure and interpret its use" -‐ -‐ Ann Peterson Bishop “Logins and Bailouts: Measuring Access, Use, and Success in Digital Libraries” The Journal of Electronic Publishing Volume 4, Issue 2, December, 1998
- 5. Funding providers want more accountability and demonstrated value* u “IMLS is focusing on areas where it can best effect change and measure its results.”** u The IMLS assessment model will “identify effective museum and library services through performance monitoring” among other things.** * ACRL Research Planning and Review Committee, “2010 top ten trends in academic libraries,” June 2010 **Institute of Museum and Library Services. 2011. “Creating a Nation of Learners; IMLS Five-Year Strategic Plan 2012–2016”
- 6. Accountability extends beyond gran.ng agencies u State Legislatures v Local tax payers u University administration u Library administration u Donors u Association of Research Libraries statistics
- 7. Accountability at the Ins.tu.onal level u Enable all your Stakeholders v Collection Managers v IT Personnel v Administrators u Avoid the free-‐for-‐all of silos u Establish an institutional master account v Administer rights v Everyone uses same baseline metrics and tools
- 8. 2010: began looking at proxy metrics for digital collec.on public accessibility and use u 12+ Billion v Number of search queries submitted to Google each month by Americans* u 12% v Percentage of our digital collection content in Google index u 0.5% v Percentage of our USpace IR scholarly papers accessible to researchers using Google Scholar * http://www.comscore.com/Press_Events/Press_Releases/2012/1/comScore_Releases_December_2011_U.S._Search_Engine_Rankings
- 9. Basic SEO has improved collec.on accessibility in Google across the board… Google Index Ratio - All Collections* 12% Average 51% 79% 37% High** 87% 100% 0% 25% 50% 75% 100% 07/05/10 04/04/11 11/30/11 * Google Index Ratio = URLs submitted / URLs Indexed by Google for about 150 collections containing ~170,00 URLs **Highest index ratio achieved for Collections with over 500 URLs submitted to Google
- 10. …almost 100% of USpace IR content is accessible to patrons using Google. Google Index Ratio 12% 07/05/10 ETD 1 69% 11/19/10 97% 10/16/11 0% ETD 2 68% 98% 23% UScholar Works 51% 98% 4% Board of Regents 47% 97% 0% 25% 50% 75% 100% *October 16, 2011 Weighted Average Google Index Ratio = 97.82% (10,306/10,536).
- 11. …resul.ng in more referrals and visitors 12 week comparison 2010 vs. 2012
- 12. Agenda u Why SEO & the Semantic Web Matters v Performance & Accountability v The semantics of what really matters today u How to Get Started v SEO Administration at an Institutional Scale v Enhance Your Data v Clean up You Data
- 13. Today’s Key Premise, Concepts & Focus u SEO Goals are to increase access, visibility and use by patrons that value our content u Semantic Web is a framework of standards and technologies to share, integrate and represent data as concepts across different content, information and system boundaries. u Semantic Search incorporates the Semantic Web to understand the context and intent of users seeking information and the concepts contained within a document
- 14. Why seman.c search is useful u Perfect application for research & discovery of concepts v Apple Siri v IBM Watson v Google Knowledge Graph u Making content Search Engine Readable & semantically Understandable can increase v click though rates (CTR) by 15%* v organic trafjic by 30%* * http://searchengineland.com/how-to-get-a-30-increase-in-ctr-with-structured-markup-105830
- 15. Seman.c implies “meaning” or “understanding”
- 16. Seman.c implies “meaning” or “understanding” u Why would I search for “historic landmarks in Denver”? u Anticipates what information I want?
- 17. Seman.c implies “meaning” or “understanding” u Why would I search for “historic landmarks in Denver”? u Anticipates what information I want?
- 18. Seman.c implies “meaning” or “understanding”
- 19. 4 Major SE’s commiZed to Schema.org as their seman.c model
- 20. The 4 major SE’s have commiZed Schema.org as their Seman.c model u SE Understandable v Schema.org is a mechanism (i.e., ontology) to communicate the meaning of your data u SE Readable v Microdata and RDFa are the preferred way SE’s read your data u US submits 19 Billion queries per month to 3 of these SE’s* u We have not found any tools within reach of typical Library budgets, or skill sets, that are easily implementable * http://www.comscore.com/Press_Events/Press_Releases/2012/1/comScore_Releases_December_2011_U.S._Search_Engine_Rankings
- 21. Agenda u Why SEO & the Semantic Web Matters v Performance & Accountability v The semantics of what really matters today u How to Get Started v SEO Administration at an Institutional Scale v Enhance Your Data v Clean up You Data
- 22. Created a SEO Scorecard designed to support pre / post funding decisions u Assembled Team of v Collection Managers v Business School Group Project v 2nd Year MBA Team u Focused on the 10 Google Analytics features that support v IMLS & NEH strategic plan v SEO Collection Manager Goals
- 23. Created a SEO Scorecard designed to support pre / post funding decisions
- 24. Workshop Process u Diagrams and Process of what we did at Utah u Live Demo Using Montana State (MSU) u Information that would be helpful today v Access to your organization’s Admin Accounts (i.e., User ID & password) n Google Analytics n Google Webmaster Tools v An internal list server for your organizations Managers responsible for making pre / post funding digital repository decisions
- 25. Diagram of problem domain
- 26. Steps for se]ng up Measurement & Evalua.on for your Ins.tu.on and Staff 1. Associate a Google Account with your Institution 2. Staff create their own Google Account using their Institution email address 3. Activate Google Services using your Institution Google Account v Google Analytics v Google Webmaster Tools 4. Add Staff to Google Services using their Institution email addresses
- 27. Diagram of what it all looks like 2 1
- 28. Diagram of what it all looks like 4 3
- 29. Step 1: Associate a Google Account* (Master) with your Ins.tu.on u Use an internal list server e.g., seo@utah.edu u Include managers who are responsible for administration v Google Analytics v Google Web Master Tools * https://accounts.google.com/NewAccount
- 30. Step 1: Associate a Google Account (Master) with your Ins.tu.on
- 31. Step 2: Staff create their own Google Account* (Master) using Ins.tu.on email * https://accounts.google.com/NewAccount
- 32. Step 3: Ac.vate Google Services using your Ins.tu.on Google Account (Master)
- 33. Step 4: Add Staff to Google Services using their Ins.tu.on email addresses
- 34. Step 3 & 4: Successful Google Analy.cs
- 35. Step 3 & 4: Successful Google Webmaster Tools
- 36. Next steps are to test scalable tools and repeatable process u Found issues with most Analytics conjigurations u We Need study participants to evaluate and test accuracy of additional analytics tools being developed under IMLS Grant program
- 37. What type of web analy.cs socware does your IR use? A. Analytics Service B. Log Files C. Don't Know D. None IR HTML Page Tagging B A {JavaScript} Log Files Analytics Service
- 38. Both types have poten&al accuracy issues for IRs A. Analytics Services v Under count non-‐HTML (e.g., PDF) jile downloads B. Log Files v Over count visits & downloads due to spiders, etc. IR v Under count page views due to web caching – upto 30% HTML Page Tagging B A {JavaScript} Log Files Analytics Service
- 39. Analy.cs Services do not track non-‐HTML downloads out of the box Special Config Non-HTML HTML Page Tagging A {JavaScript} Analytics Service
- 40. Analy.cs Services do not track non-‐HTML file downloads via direct external links Non-HTML HTML Page Tagging A {JavaScript} Analytics Service
- 41. Agenda u Why SEO & the Semantic Web Matters v Performance & Accountability v The semantics of what really matters today u How to Get Started v SEO Administration at an Institutional Scale v Enhance Your Data v Clean up You Data
- 42. Tradi.onal SEO is s.ll very important, but not today’s focus. u Descriptive Page Titles, anchor text, descriptions, etc. u Easy & Intuitive Site Navigation u Submit sitemaps/conjigure robots.txt jile u Monitor/address errors u Inform staff & assign ownership u Clean metadata u Upgrade repository software
- 43. Recommended Background informa.on u Ronallo, Jason. "HTML5 Microdata and Schema. org." Code4Lib Journal (2012). http://journal.code4lib.org/articles/6400 u Arlitsch, Kenning, and Patrick OBrien. "Invisible Institutional Repositories: Addressing the Low Indexing Ratios of IRs in Google Scholar." Library Hi Tech 30, no. 1 (2012): 60-‐81. http://www.emeraldinsight.com/journals.htm?articleid=17020806 u Arlitsch, Kenning, and Patrick OBrien. "Search Engine Optimization (SEO) for Institutional Repositories." In Technical Advances for Innovation in Cultural Heritage Institutions (TAI CHI) Webinar Series; 2012 Mar 16; pp. 1-‐48. OCLC Research, Online Computer Library Center, Inc. (OCLC), 2012. http://www.oclc.org/resources/research/events/20120316seo.pdf u Arlitsch, Kenning, and Patrick OBrien. "Search engine optimization (SEO) for digital repositories." In Coalition for Networked Information (CNI) Spring 2011 Membership Meeting; 2011 Apr 4-‐5; San Deigo, California, USA; pp. 1-‐25. J. Willard Marriott Library, University Libraries, University of Utah, 2011. http://content.lib.utah.edu/utils/getfile/collection/uspace/id/1976/filename/713.pdf
- 44. Challenge is presen.ng structured data SE’s can iden.fy, parse and digest Human Readable Woljinger, N. H., & McKeever, M. (2006, July). Thanks for nothing: changes in income and labor force participation for never-‐married mothers since 1982. In 101st American Sociological Association (ASA) Annual Meeting; 2006 Aug 11-‐14; Montreal, Canada (No. 2006-‐07-‐04, pp. 1-‐42). Institute of Public & International Affairs (IPIA), University of Utah. Machine Understandable
- 45. Google Scholar can read and understand! Google Scholar
- 46. However, Google can not understand or read any of our “structured data” nd able de rsta No t Un rg = ad able em a.o t Re Sch No N o DFa= R data or icro N oM
- 47. Work Shop Excercise Meta Tag Working Paper 1 -‐ citation_author Arlitsch, Kenning; OBrien, Patrick 2 -‐ citation_date 2011-‐04-‐05 3 -‐ citation_title Search engine optimization (SEO) for digital repositories 6 -‐ citation_volume 7 -‐ citation_issue 8 -‐ citation_jirstpage 1 9 -‐ citation_lastpage 25 10 -‐ citation_doi 13 -‐ citation_keywords SEO Tips, Special Collections, Digital Collection, Institutional Repository, Digital Repository 16 -‐ citation_technical_report_institution University of Utah 17 -‐ citation_technical_report_number 18 -‐ citation_language en 19 -‐ citation_conference_title Coali'on for Networked Informa'on (CNI) Spring 2011 Membership Mee'ng; 201 Apr 4-‐5; San Diego, California, USA 21 -‐ citation_pdf_url http://content.lib.utah.edu/utils/getfile/collection/uspace/id/1976/filename/713.pdf 22 -‐ citation_abstract_html_url http://content.lib.utah.edu/cdm/ref/collection/uspace/id/1976 23 – University University of Utah 24 – College University Libraries 25 – Department J. Willard MarrioO Library 26 – subject.LCSH Web search engines; Web sites-‐-‐Registra'on with search engines; Digital libraries-‐-‐Collec'on development
- 48. Describe concepts using Schema.org to help SE understand your repository u Answer Questions v What type of WebPage? v What content / data does the page contain? v Who was involved? n Organizations? n People? v Where is it? u Look at the properties to see if the concept applies
- 49. WebPage concepts relevant to digital repositories u Creative Works > WebPage* u WebPage Classes u Important Properties v SearchResultsPage v description v CollectionPage v breadCrumb n ImageGallery n VideoGallery v isPartOf v ItemPage v signijicantLink v signijicantLinks * http://schema.org/WebPage
- 50. Typical Digital Repository Content u CreativeWorks Classes v Article > ScholarlyArticle v Book v Map u Important Properties v Painting v publisher v Photograph v sourceOrganization v MediaObject v contentLocation n AudioObject v copyrightHolder n ImageObject v author n MusicVideoObject n VideoObject * http://schema.org/ScholarlyArticle
- 51. Organiza.ons might be relevant u Organization* v EducationalOrganization n CollegeOrUniversity v LocalBusiness u Important Properties n Library** v member v employee v contactPoint * http://schema.org/Organization ** http://schema.org/Library
- 52. What People might be relevant u Person* u Important Properties v memberOf v worksFor v jobTitle v email v afjiliation v alumniOf * http://schema.org/Person
- 53. What loca.ons might be relevant? u Place* v LandmarksOrHistoricalBuildings u Intangible > StructuredValue v GeoCoordinates u Important Properties v geo v photo v address v containedIn * http://schema.org/Place
- 54. Check your work using Google Rich Snipet Tool <title>Search engine optimization (SEO) for digital repositories</title> <body itemscope itemtype="http://schema.org/WebPage"> <div itemprop="breadcrumb"> <a href="category/ir.html">Uspace Instutional Repository</a> > <a href="category/CollegeofSocialBehavioralScience.html">University Libraries</a> > <a href="category/books-literature.html">J. Willard Marriott Library</a> > </div> <div itemscope itemtype="http://schema.org/ScholarlyArticle"> <span itemprop="name">Search engine optimization (SEO) for digital repositories</span> <div itemscope itemtype="http://schema.org/Person"> <span itemprop="name">Patrick OBrien</span> <a href="http://www.linkedin.com/in/obrienpatricks" itemprop="url">Patrick OBrien Resume</a> <span itemprop="jobTitle">Semantic Web Research Director</span> <div itemprop="affiliation" itemscope itemtype="http://schema.org/CollegeOrUniversity"> <span itemprop="name">Montana State University Library</span> </div> <div itemprop="affiliation" itemscope itemtype="http://schema.org/Organization"> <a href="http://www.RevXcorp.com" itemprop="name">RevX Corporation</a> </div> </div> </div> </body>
- 55. Ques.ons & Study Par.cipa.on? Kenning Arlitsch Dean of the Library at Montana State University kenning.arlitsch@montana.edu Patrick OBrien Semantic Web Research Director patrick.obrien4@montana.edu