Introduction to Enterprise Search
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Introduction to Enterprise Search. A two hour class to introduce Enterprise Search. It covers: The problems enterprise search can solve History of (web) search How we search and find? Current state of Enterprise Search + stats Technical concept Information quality Feedback cycle Five dimensions of Findability
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Introduction to Enterprise Search
- 1. INTRODUCTION TO ENTERPRISE SEARCH Kristian Norling
- 2. Introduction • Who is here? • Your expectations? • Kristian? • 2 hours, one break • Lifetime answer Guarantee on this class
- 3. Hikingartist
- 4. Agenda • Problem • History of (web) search • How we search and !nd? • Current state of Enterprise Search + stats • Technical concept • Information quality • Feedback cycle • Five dimensions of Findability
- 5. •List mrflip
- 6. nathansnider
- 7. erikref
- 8. The Problems • Growing amounts of Information • Changing patterns of information consumption • Information silos • Web like behaviour > Information !lters • Internal information use is still in the Digital Stone Age
- 9. History of Search In Academia search is called Information Retrieval. It is an old discipline, dating back thousands of years... Basic concepts in Information Retrieval: Recall and Precision, more later...
- 10. Directories vs. Search Engines • Directories are manually compiled taxonomies of websites • Directories are far more costly and time intensive to maintain • Directories lack coverage, although it provides an important alternative, especially for novice surfers • Search engines rely mainly on automated search algorithms • Search engines rank pages by popularity on the web, the more referrals (links) the more relevant
- 11. Early days of Web Search Yahoo – searchable directory (1994, ~10000 websites) • Integrates search over its directory. Organized by subject ma8ers. Sites can be suggested, but human editors control quality of directory (~100 dedicated editors) Ask – natural language search engine (1998) • used human editors to match popular queries. Tried different algorithms to rank pages by popularity Google – searchable index (1998) • Developed Pagerank, popularity algorithm that hides bad content. Set standards (spellchecking, query suggesIon, search results page design)
- 12. Web Search - evolution First generation (1995-97) – AltaVista, Excite, WebCrawler Uses mostly on-page data (text and formatting). Informational queries. Second generation (1998-2010) – Google, Yahoo Use o"-page, web-speci!c data: link analysis, anchor-text, click- through data. Informational and navigational queries. Third generation (2010-present) – Google, Wolfram-Alpha, Bing Blend data from many sources, tries to answer ‘‘the need behind the query’’: semantic analysis, context determination, dynamic database selection etc. Informational, navigational, and transactional queries.
- 13. Seeking information modes: Informational Find information assumed to be available on the web in a static form.
- 14. Seeking information modes: Navigational Reach a particular site that the user has in mind, either because they visited it in the past or because they assume that such a site exists. Have usually only one "right" result.
- 15. Seeking information modes: Transactional Reach a site where further interaction will happen. This interaction constitutes the transaction de!ning these queries. The main categories for such queries are shopping, !nding various web-mediated services, downloading various type of !le (images, songs, etc), accessing certain data-bases (e.g. Yellow Pages type data), !nding servers (e.g.for gaming) etc.
- 16. Four modes of seeking information Finding something when I know what I want and have words to describe it.
- 17. Four modes of seeking information Exploring when I only have some idea of what I want and may lack the words to articulate it.
- 18. Four modes of seeking information Finding relevant items when I don’t know what I need.
- 19. Four modes of seeking information Finding something I have seen before, but can’t remember where.
- 20. The State of Enterprise Search • Amount of information is growing everyday • What to Search for? • Where to Search? • How to Search? • Search is simple, complex and powerful • Findability Dimensions
- 21. STATS FROM THE “ENTERPRISE SEARCH AND FINDABILITY SURVEY 2012” SIGN-UP
- 22. HOW CRITICAL IS FINDING THE RIGHT INFORMATION TO BUSINESS GOALS AND SUCCESS?
- 23. EUROPE 76.5% IMPERATIVE/SIGNIFICANT
- 24. Zoom Zoom
- 25. IS IT EASY TO FIND THE RIGHT INFORMATION WITHIN YOUR ORGANISATION TODAY?
- 26. EUROPE 77% MODERATELY/VERY HARD
- 28. proimos
- 29. EUROPE 18.5% MOSTLY/VERY SATISFIED
- 30. WHAT ARE THE OBSTACLES TO FINDING THE RIGHT INFORMATION?
- 31. Globally 63.4% POOR SEARCH FUNCTIONALITY 52.1% DON'T KNOW WHERE TO LOOK 51.4% INCONSISTENCY IN HOW WE TAG CONTENT 50.0% LACK OF ADEQUATE TAGS 33.1% DON’T KNOW WHAT TO LOOK FOR
- 32. Wikipedia De!nition “Enterprise search is the practice of making content from multiple enterprise-type sources, such as databases and intranets, searchable to a de!ned audience.” http://en.wikipedia.org/wiki/Enterprise_search
- 33. The Concept of Enterprise Search: Precision In the !eld of information retrieval, precision is the fraction of retrieved documents that are relevant to the search. Precision takes all retrieved documents into account, but it can also be evaluated at a given cut-o" rank, considering only the topmost results returned by the system. This measure is called precision at n or P@n. Source: Wikipedia
- 34. The Concept of Enterprise Search: Recall Recall in information retrieval is the fraction of the documents that are relevant to the query that are successfully retrieved. For example for text search on a set of documents recall is the number of correct results divided by the number of results that should have been returned. Source: Wikipedia
- 35. Precision and Recall R number of M number of N number of retrieved documents relevant documents retrieved documents that are also relevant
- 36. Precision and Recall Recall = R / M = Number of retrieved documents that are also relevant / Total number of relevant documents. Precision = R / N = Number of retrieved documents that are also relevant / Total number of retrieved documents.
- 37. Relevance ...enterprises typically have to use other query- independent factors, such as a document's recency or popularity, along with query-dependent factors traditionally associated with information retrieval algorithms. Also, the rich functionality of enterprise search UIs, such as clustering and faceting, diminish reliance on ranking as the means to direct the user's attention. Source: Wikipedia
- 38. PageRank
- 39. Relevance We do not have PageRank... ...but we have social! Social Reconnects Enterprise Search Emails, People Catalogues, Connections, Tagging, Sharing etc.
- 40. The Concept of Enterprise Search
- 41. Search based Solutions Examples of implementations: - People Search - Product Search - Document Search - Intranet and Website Search - E-commerce - Dashboard / Search as a Service
- 42. Information / Content • Good Data/Information hygiene • Crap in = Crap out • Metadata is very important! • Taxonomy and Metadata demysti!ed • TetraPak example (video) • SimCorp example • VGR example (video)
- 43. •List yeraze
- 44. svenwerk
- 45. HCE (SWEDEN) DEWEY DECIMAL CLASSIFICATION
- 46. KristianNorling
- 47. Author: Douglas Coupland Title: Hej Nostradamus! Publisher: Norstedts Year: 2003 Printed by: Smedjebacken Printed: 2004 KristianNorling
- 48. Metadata Semantic KristianNorling
- 49. ESEO: Actionable activities Example: Ernst & Young • Metadata • Titles • Content Quality • Information Life Cycle Management
- 50. Show me the Money But, an average Search budget is 100K Euro • TCO • ROI • KPI Search Analytics is key
- 51. Search Analytics Important, delivers actionable to-dos quickly • 0-results • Top Terms Searched for Video: Search Analytics in Practice
- 52. User Satisfaction • Feedback form • KPI from Search Analytics • Session time x n:o sessions = Time spent on search x hourly price = Cost per “answer” • Add search re!nements + exit page (=is the right answer)
- 53. Findability by Findwise 1. BUSINESS Build solutions to support your business processes and goals 2. INFORMATION Prepare information to make it !ndable 3. USERS Build usable solutions based on user needs 4. ORGANISATION Govern and improve your solution over time 5. SEARCH TECHNOLOGY Build solutions based on state-of-the-art search technology
- 54. Business • Analyze how your business goals and strategies can be met by improved information access • Set Findability goals. Examples; increase the revenue on sales, raise productivity, improve knowledge sharing, better collaboration • Specify your requirements • De!ne KPI’s and measure the success of your investments
- 55. Information • Clean up and archive or delete outdated/ unrelevant information • Ensure good quality of information by adding structured and suitable metadata • Create and use information models and taxonomies • Tagging?
- 56. Users • Get to know your users and their needs • Make sure your solution is easy to use • Perform continuous usability evaluations, like usage tests and expert evaluations • Make sure users !nd what they are looking for • Enable feedback loops for complaints, feedback and praise
- 57. Organisation • Resources! • De!ne processes, roles and routines to govern the solution • Perform Search Analytics • Create easy to use administration interfaces • Perform training, technical and editorial • Help publishers get started with processes for better !ndability
- 58. Search Technology • Select a suitable search platform or make the most of your current solution • Design your architecture with search-as-a- service in mind • Utilise the full potential of the selected technology
- 59. Kristian Norling Kristian Norling LinkedIn @kristiannorling @!ndwise !ndwise.com Findability Blog Slideshare Vimeo Newsroom
Editor's Notes
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- What do you want to know?\n
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- We humans love to collect information, we have a harder time deleting/archiving.\nWhen we start valuing information correctly we can also motivate investments in search and put processes in place to keep information updated AND with high quality. \nInformation hygiene. Structure, metadata.\nInfonomics = information as an asset in the balance sheet. \n
- Is this how you feel information is organised and structured in your organsation?\n
- Is the information you need stored in a silo somewhere?\n
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- Yahoo: The directory is organized by subject matter, the top level containing categories such as Arts and Humanities, Business and Economy, Computers and the Internet, Education, Government, Health, News and Media, Recreation and Sports, Science, Society and Culture, and so on.\nThe natural hierarchical structure of the directory allows users easy navigation through and across its categories.\nThe directory is not strictly hierarchical, as it has many cross-references between categories from different parts of the hierarchy. For example, the subcategory Musicals under the Theater category, has a reference to the Movies and Film subcategory, which comes under Entertainment.\nWeb directories provide an important alternative to search engines, especially for novice surfers, as the directory structure makes it is easy to find relevant information provided when an appropriate category for the search query can be found. The fundamental drawback of directories is their lack of coverage.\nKnowing the category of a web page that a user clicked on is very indicative of the user's interests, and may be used to recommend to the user similar pages from the same or a related category. To solve the problem of how to automatically associate a web page with a category we need to make use of machine learning techniques for automatic categorization of web pages\n
- Yahoo: The directory is organized by subject matter, the top level containing categories such as Arts and Humanities, Business and Economy, Computers and the Internet, Education, Government, Health, News and Media, Recreation and Sports, Science, Society and Culture, and so on.\nThe natural hierarchical structure of the directory allows users easy navigation through and across its categories.\nThe directory is not strictly hierarchical, as it has many cross-references between categories from different parts of the hierarchy. For example, the subcategory Musicals under the Theater category, has a reference to the Movies and Film subcategory, which comes under Entertainment.\nWeb directories provide an important alternative to search engines, especially for novice surfers, as the directory structure makes it is easy to find relevant information provided when an appropriate category for the search query can be found. The fundamental drawback of directories is their lack of coverage.\nKnowing the category of a web page that a user clicked on is very indicative of the user's interests, and may be used to recommend to the user similar pages from the same or a related category. To solve the problem of how to automatically associate a web page with a category we need to make use of machine learning techniques for automatic categorization of web pages\n
- Yahoo: The directory is organized by subject matter, the top level containing categories such as Arts and Humanities, Business and Economy, Computers and the Internet, Education, Government, Health, News and Media, Recreation and Sports, Science, Society and Culture, and so on.\nThe natural hierarchical structure of the directory allows users easy navigation through and across its categories.\nThe directory is not strictly hierarchical, as it has many cross-references between categories from different parts of the hierarchy. For example, the subcategory Musicals under the Theater category, has a reference to the Movies and Film subcategory, which comes under Entertainment.\nWeb directories provide an important alternative to search engines, especially for novice surfers, as the directory structure makes it is easy to find relevant information provided when an appropriate category for the search query can be found. The fundamental drawback of directories is their lack of coverage.\nKnowing the category of a web page that a user clicked on is very indicative of the user's interests, and may be used to recommend to the user similar pages from the same or a related category. To solve the problem of how to automatically associate a web page with a category we need to make use of machine learning techniques for automatic categorization of web pages\n
- Yahoo: The directory is organized by subject matter, the top level containing categories such as Arts and Humanities, Business and Economy, Computers and the Internet, Education, Government, Health, News and Media, Recreation and Sports, Science, Society and Culture, and so on.\nThe natural hierarchical structure of the directory allows users easy navigation through and across its categories.\nThe directory is not strictly hierarchical, as it has many cross-references between categories from different parts of the hierarchy. For example, the subcategory Musicals under the Theater category, has a reference to the Movies and Film subcategory, which comes under Entertainment.\nWeb directories provide an important alternative to search engines, especially for novice surfers, as the directory structure makes it is easy to find relevant information provided when an appropriate category for the search query can be found. The fundamental drawback of directories is their lack of coverage.\nKnowing the category of a web page that a user clicked on is very indicative of the user's interests, and may be used to recommend to the user similar pages from the same or a related category. To solve the problem of how to automatically associate a web page with a category we need to make use of machine learning techniques for automatic categorization of web pages\n
- Yahoo: The directory is organized by subject matter, the top level containing categories such as Arts and Humanities, Business and Economy, Computers and the Internet, Education, Government, Health, News and Media, Recreation and Sports, Science, Society and Culture, and so on.\nThe natural hierarchical structure of the directory allows users easy navigation through and across its categories.\nThe directory is not strictly hierarchical, as it has many cross-references between categories from different parts of the hierarchy. For example, the subcategory Musicals under the Theater category, has a reference to the Movies and Film subcategory, which comes under Entertainment.\nWeb directories provide an important alternative to search engines, especially for novice surfers, as the directory structure makes it is easy to find relevant information provided when an appropriate category for the search query can be found. The fundamental drawback of directories is their lack of coverage.\nKnowing the category of a web page that a user clicked on is very indicative of the user's interests, and may be used to recommend to the user similar pages from the same or a related category. To solve the problem of how to automatically associate a web page with a category we need to make use of machine learning techniques for automatic categorization of web pages\n
- Yahoo: The directory is organized by subject matter, the top level containing categories such as Arts and Humanities, Business and Economy, Computers and the Internet, Education, Government, Health, News and Media, Recreation and Sports, Science, Society and Culture, and so on.\nThe natural hierarchical structure of the directory allows users easy navigation through and across its categories.\nThe directory is not strictly hierarchical, as it has many cross-references between categories from different parts of the hierarchy. For example, the subcategory Musicals under the Theater category, has a reference to the Movies and Film subcategory, which comes under Entertainment.\nWeb directories provide an important alternative to search engines, especially for novice surfers, as the directory structure makes it is easy to find relevant information provided when an appropriate category for the search query can be found. The fundamental drawback of directories is their lack of coverage.\nKnowing the category of a web page that a user clicked on is very indicative of the user's interests, and may be used to recommend to the user similar pages from the same or a related category. To solve the problem of how to automatically associate a web page with a category we need to make use of machine learning techniques for automatic categorization of web pages\n
- Yahoo: The directory is organized by subject matter, the top level containing categories such as Arts and Humanities, Business and Economy, Computers and the Internet, Education, Government, Health, News and Media, Recreation and Sports, Science, Society and Culture, and so on.\nThe natural hierarchical structure of the directory allows users easy navigation through and across its categories.\nThe directory is not strictly hierarchical, as it has many cross-references between categories from different parts of the hierarchy. For example, the subcategory Musicals under the Theater category, has a reference to the Movies and Film subcategory, which comes under Entertainment.\nWeb directories provide an important alternative to search engines, especially for novice surfers, as the directory structure makes it is easy to find relevant information provided when an appropriate category for the search query can be found. The fundamental drawback of directories is their lack of coverage.\nKnowing the category of a web page that a user clicked on is very indicative of the user's interests, and may be used to recommend to the user similar pages from the same or a related category. To solve the problem of how to automatically associate a web page with a category we need to make use of machine learning techniques for automatic categorization of web pages\n
- Navigational queries. reach a particular site that the user has in mind, either because they visited it in the past or because they assume that such a site exists. have usually only one "right" result.\nInformational queries. find information assumed to be available on the web in a static form. \nTransactional queries. reach a site where further interaction will happen. This interaction constitutes the transaction defining these queries. The main categories for such queries are shopping, finding various web-mediated services, downloading various type of file (images, songs, etc), accessing certain data-bases (e.g. Yellow Pages type data), finding servers (e.g.for gaming) etc.\n \n 2nd gen. - Google, first engine to use link analysis as a primary ranking factor and DirectHit concentrated on click-through data. By now, all major engines use all these types of data. Link analysis and anchortext seems crucial for navigational queries.\n3rd gen. - For instance on a query like San Francisco the engine might present direct links to a hotel reservation page for San Francisco, a map server, a weather server, etc.\nRapidly changing landscape\n
- Navigational queries. reach a particular site that the user has in mind, either because they visited it in the past or because they assume that such a site exists. have usually only one "right" result.\nInformational queries. find information assumed to be available on the web in a static form. \nTransactional queries. reach a site where further interaction will happen. This interaction constitutes the transaction defining these queries. The main categories for such queries are shopping, finding various web-mediated services, downloading various type of file (images, songs, etc), accessing certain data-bases (e.g. Yellow Pages type data), finding servers (e.g.for gaming) etc.\n \n 2nd gen. - Google, first engine to use link analysis as a primary ranking factor and DirectHit concentrated on click-through data. By now, all major engines use all these types of data. Link analysis and anchortext seems crucial for navigational queries.\n3rd gen. - For instance on a query like San Francisco the engine might present direct links to a hotel reservation page for San Francisco, a map server, a weather server, etc.\nRapidly changing landscape\n
- Navigational queries. reach a particular site that the user has in mind, either because they visited it in the past or because they assume that such a site exists. have usually only one "right" result.\nInformational queries. find information assumed to be available on the web in a static form. \nTransactional queries. reach a site where further interaction will happen. This interaction constitutes the transaction defining these queries. The main categories for such queries are shopping, finding various web-mediated services, downloading various type of file (images, songs, etc), accessing certain data-bases (e.g. Yellow Pages type data), finding servers (e.g.for gaming) etc.\n \n 2nd gen. - Google, first engine to use link analysis as a primary ranking factor and DirectHit concentrated on click-through data. By now, all major engines use all these types of data. Link analysis and anchortext seems crucial for navigational queries.\n3rd gen. - For instance on a query like San Francisco the engine might present direct links to a hotel reservation page for San Francisco, a map server, a weather server, etc.\nRapidly changing landscape\n
- Navigational queries. reach a particular site that the user has in mind, either because they visited it in the past or because they assume that such a site exists. have usually only one "right" result.\nInformational queries. find information assumed to be available on the web in a static form. \nTransactional queries. reach a site where further interaction will happen. This interaction constitutes the transaction defining these queries. The main categories for such queries are shopping, finding various web-mediated services, downloading various type of file (images, songs, etc), accessing certain data-bases (e.g. Yellow Pages type data), finding servers (e.g.for gaming) etc.\n \n 2nd gen. - Google, first engine to use link analysis as a primary ranking factor and DirectHit concentrated on click-through data. By now, all major engines use all these types of data. Link analysis and anchortext seems crucial for navigational queries.\n3rd gen. - For instance on a query like San Francisco the engine might present direct links to a hotel reservation page for San Francisco, a map server, a weather server, etc.\nRapidly changing landscape\n
- Navigational queries. reach a particular site that the user has in mind, either because they visited it in the past or because they assume that such a site exists. have usually only one "right" result.\nInformational queries. find information assumed to be available on the web in a static form. \nTransactional queries. reach a site where further interaction will happen. This interaction constitutes the transaction defining these queries. The main categories for such queries are shopping, finding various web-mediated services, downloading various type of file (images, songs, etc), accessing certain data-bases (e.g. Yellow Pages type data), finding servers (e.g.for gaming) etc.\n \n 2nd gen. - Google, first engine to use link analysis as a primary ranking factor and DirectHit concentrated on click-through data. By now, all major engines use all these types of data. Link analysis and anchortext seems crucial for navigational queries.\n3rd gen. - For instance on a query like San Francisco the engine might present direct links to a hotel reservation page for San Francisco, a map server, a weather server, etc.\nRapidly changing landscape\n
- Navigational queries. reach a particular site that the user has in mind, either because they visited it in the past or because they assume that such a site exists. have usually only one "right" result.\nInformational queries. find information assumed to be available on the web in a static form. \nTransactional queries. reach a site where further interaction will happen. This interaction constitutes the transaction defining these queries. The main categories for such queries are shopping, finding various web-mediated services, downloading various type of file (images, songs, etc), accessing certain data-bases (e.g. Yellow Pages type data), finding servers (e.g.for gaming) etc.\n \n 2nd gen. - Google, first engine to use link analysis as a primary ranking factor and DirectHit concentrated on click-through data. By now, all major engines use all these types of data. Link analysis and anchortext seems crucial for navigational queries.\n3rd gen. - For instance on a query like San Francisco the engine might present direct links to a hotel reservation page for San Francisco, a map server, a weather server, etc.\nRapidly changing landscape\n
- Navigational queries. reach a particular site that the user has in mind, either because they visited it in the past or because they assume that such a site exists. have usually only one "right" result.\nInformational queries. find information assumed to be available on the web in a static form. \nTransactional queries. reach a site where further interaction will happen. This interaction constitutes the transaction defining these queries. The main categories for such queries are shopping, finding various web-mediated services, downloading various type of file (images, songs, etc), accessing certain data-bases (e.g. Yellow Pages type data), finding servers (e.g.for gaming) etc.\n \n 2nd gen. - Google, first engine to use link analysis as a primary ranking factor and DirectHit concentrated on click-through data. By now, all major engines use all these types of data. Link analysis and anchortext seems crucial for navigational queries.\n3rd gen. - For instance on a query like San Francisco the engine might present direct links to a hotel reservation page for San Francisco, a map server, a weather server, etc.\nRapidly changing landscape\n
- Navigational queries. reach a particular site that the user has in mind, either because they visited it in the past or because they assume that such a site exists. have usually only one "right" result.\nInformational queries. find information assumed to be available on the web in a static form. \nTransactional queries. reach a site where further interaction will happen. This interaction constitutes the transaction defining these queries. The main categories for such queries are shopping, finding various web-mediated services, downloading various type of file (images, songs, etc), accessing certain data-bases (e.g. Yellow Pages type data), finding servers (e.g.for gaming) etc.\n \n 2nd gen. - Google, first engine to use link analysis as a primary ranking factor and DirectHit concentrated on click-through data. By now, all major engines use all these types of data. Link analysis and anchortext seems crucial for navigational queries.\n3rd gen. - For instance on a query like San Francisco the engine might present direct links to a hotel reservation page for San Francisco, a map server, a weather server, etc.\nRapidly changing landscape\n
- Navigational queries. reach a particular site that the user has in mind, either because they visited it in the past or because they assume that such a site exists. have usually only one "right" result.\nInformational queries. find information assumed to be available on the web in a static form. \nTransactional queries. reach a site where further interaction will happen. This interaction constitutes the transaction defining these queries. The main categories for such queries are shopping, finding various web-mediated services, downloading various type of file (images, songs, etc), accessing certain data-bases (e.g. Yellow Pages type data), finding servers (e.g.for gaming) etc.\n \n 2nd gen. - Google, first engine to use link analysis as a primary ranking factor and DirectHit concentrated on click-through data. By now, all major engines use all these types of data. Link analysis and anchortext seems crucial for navigational queries.\n3rd gen. - For instance on a query like San Francisco the engine might present direct links to a hotel reservation page for San Francisco, a map server, a weather server, etc.\nRapidly changing landscape\n
- Navigational queries. reach a particular site that the user has in mind, either because they visited it in the past or because they assume that such a site exists. have usually only one "right" result.\nInformational queries. find information assumed to be available on the web in a static form. \nTransactional queries. reach a site where further interaction will happen. This interaction constitutes the transaction defining these queries. The main categories for such queries are shopping, finding various web-mediated services, downloading various type of file (images, songs, etc), accessing certain data-bases (e.g. Yellow Pages type data), finding servers (e.g.for gaming) etc.\n \n 2nd gen. - Google, first engine to use link analysis as a primary ranking factor and DirectHit concentrated on click-through data. By now, all major engines use all these types of data. Link analysis and anchortext seems crucial for navigational queries.\n3rd gen. - For instance on a query like San Francisco the engine might present direct links to a hotel reservation page for San Francisco, a map server, a weather server, etc.\nRapidly changing landscape\n
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- Information silos. They are everywhere. \nEnterprise Search can “integrate” them.\n
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- On intranets or our web site search we do not have the equivalent of PageRank.\nWe can’t use the amount of inbound link as a factor for relevancy. \nWe have to find other ways...\n
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