1. Exploring Bridges Between Open and Vetted Information Domains By Julie Mason, Data Fountains Service Manager Johannes Ruscheinski, Lead Programmer Wagner Truppel, Programmer

2. SECTION I: Background and Context of Our Projects SECTION II: Approaches to System Building SECTION III: Classification SECTION IV: Preview of Data Fountains Interface INFOMINE, iVia, Data Fountains

3. SECTION I: Background and Context of Our Projects INFOMINE, iVia, Data Fountains

4. If you build it… …they will come.

5. The Evolution of INFOMINE 1994-2004 1994-2004 Guiding Principle… To build a collaborative public domain academic Internet resources finding tool through creative application of technologies.

6. INFOMINE, iVia, Data Fountains - INFOMINE is a virtual library containing over 100,000 links (A hybrid collection containing 26,000 librarian created links and 75,000 plus robot/crawler created links). - Founded in January of 1994 it is one of the first Web- based services offered by a library anywhere. - It is a cooperative effort of librarians from UC Riverside, other UCs (including UCLA, UCSC and the UC Shared Cataloging Project), three California State Universities, Wake Forest University and the University of Detroit. Special cooperative efforts are in process with the Library of Congress and NSDL.

7. INFOMINE, iVia, Data Fountains Our Technologies and Architecture: New and Interactive Collection Building Technologies to Amplify Expert Effort / New Uses of Expertise to Refine Collection Building Technologies 1.) Focused crawling 2.) Rich Text Identification and Harvest 3.) Machine-based Classification 4.) Foundation Record 5.) Hybrid Collections Architecture 6.) Usage in Existing, Closed, Relatively Homogeneous Collections 7.) Cooperative Technology 8.) Appropriately Scaled and Modularly Designed

8. INFOMINE, iVia, Data Fountains

9. http://infomine.ucr.edu/iVia/ The new open source software platform designed to help INFOMINE and other virtual libraries scale well in terms of amplifying expert effort to enable the development of better and more representative collections. Automated and semi-automated Internet resource identification and collection is made possible through focused crawling software. Automated and semi-automated indexing or metadata generation is made possible through classifier software. A hybrid, two-tiered collection is supported. The first tier are our expert created records and the second tier is made up of machine created records. Brings together some of the best of expert created virtual library approaches with the best of automated approaches to collection building

10. INFOMINE, iVia, Data Fountains

14. Data Fountains (under development this year) A cooperative, cost-recovery based metadata generation service that will be an array of iVias, one for each participating project or subject community, and which will create metadata records for the participants. A big emphasis, in addition to fully-automated resource discovery and metadata generation, will be on semi-automated approaches that strongly involve and amplify the efforts of collection experts. They, in turn, work to refine and perfect machine approaches and processes. The metadata created can be bundled in differing “products” according to differing participant needs in terms of amount of metadata needed, type (natural language or controlled terminology), degree of relevance or comprehensiveness desired (highly relevant records or moderately relevant).

15. INFOMINE, iVia, Data Fountains

17. Modular Architecture that Supports a Federated Array of Subject Specific Focused Crawlers and Classifiers

25. If This Was of Interest to You, Consider Joining Our Data Fountains Founders Group A group of 10-20 collections or portal projects cooperatively defining products that will be generated from the Data Fountains service. If interested contact: Julie Mason, jmason@ucr.edu

29. A Brief Overview Of The iVia And Data Fountains Architectures Dr. Johannes Ruscheinski ruschein@infomine.ucr.edu

30. iVia & Data Fountains Overview What are iVia and Data Fountains (DF)? Why do we need iVia and DF? Architecture overview of iVia Architecture overview of DF INFOMINE, iVia, Data Fountains

31. 1. What are iVia and Data Fountains? Tools to find resources on the Web and to assign metadata to them, both manually and automatically A public Web interface to search a database of URL’s based on various metadata and full text A family of crawlers (spiders) that automatically and semi- automatically retrieve Web pages and classify them A sophisticated Web interface for human classification experts for assigning metadata to Web documents INFOMINE, iVia, Data Fountains

32. 2. Why do we need iVia and DF? We already have powerful search engines (e.g., Google) so why do we need yet more and much smaller ones? Precision is abysmal Researchers claim that even the largest search engines cover a shrinking percentage of the Web iVia offers highly specific search and browse capabilities allowing for higher search precision Both iVia and DF use focused crawling to find authoritative pages on indicated topics INFOMINE, iVia, Data Fountains

33. 3. Architecture overview of iVia INFOMINE, iVia, Data Fountains

34. 3.1 Master Database MySQL-based SQL database Contains both expert and robot generated records Contains metadata (URL’s, subjects, keywords, authors, titles, …) Contains full text in the form of compressed Web page content INFOMINE, iVia, Data Fountains

35. 3.2 The Adder Interface Sophisticated Web interface for expert classification of Web pages Password-protected with varying privilege levels Allows both adding of new resources and editing of already existing ones Has automatic resource duplicate checking Contains an automatic metadata extractor Configurable via a preferences screen INFOMINE, iVia, Data Fountains

36. 3.3 Crawlers Add robot records to the master database Assign metadata to crawled records Three different types of crawlers in iVia/DF Expert-guided crawler with drill-down and drill-out to crawl single sites Vlcrawler to crawl virtual libraries Nalanda iVia Focused Crawler (NIFC) to crawl Web communities defined around a given topic INFOMINE, iVia, Data Fountains

37. 3.4 Search Engine Public search interface (e.g. http://infomine.ucr.edu) Based on inverted index databases built from the contents of the master database on a nightly basis Supports sophisticated searching through metadata and full-text Nested boolean queries, truncated searches, word proximity searches, etc Search results can be displayed in a wide variety of different themes (skins) that allow collaborating institutions to brand their interface INFOMINE, iVia, Data Fountains

38. 4. Architecture overview of DF INFOMINE, iVia, Data Fountains

39. 4.1 Seed Set Generator Seed sets are sets of URL’s that define a topic of interest Seed sets can be supplied in various formats by a client (e.g. simple text file with a list of URL’s) Typically need around 200 highly topic-specific URL’s Problem: most users would come up with only a few dozen Solution: scout module uses a search engine such as Google to fatten up the user-provided initial set INFOMINE, iVia, Data Fountains

40. 4.2 Nalanda iVia Focused Crawler Primarily developed by Dr. Soumen Chakrabarti (IIT Bombay), a leading crawler researcher Sophisticated focused crawler using document classification methods and Web graph analysis techniques to stay on topic Supports user interaction via URL pattern blacklisting etc Uses an apprentice classifier to prioritize links that should be followed Returns a list of URL’s likely to be on the initial seed set topic INFOMINE, iVia, Data Fountains

41. 4.3 Distiller Attempts to rank URL’s returned by the NIFC according to their relevance to the client-provided topic Uses improved Kleinberg-like Web graph analysis to assign hub and authority values to each URL Returns scores for each provided URL INFOMINE, iVia, Data Fountains

42. 4.4 Metadata Exporter Final stage of DF Provides clients with convenient data formats to incorporate the best on-topic URL’s into their own databases Allows different amounts/quality of metadata to be exported based on the client’s selected service model Supports various export types and file formats (simple URL lists, delimiter-separated file formats, XML file formats, MARC records and export via OAI-PMH) INFOMINE, iVia, Data Fountains

43. Automatic Document Classification Wagner Truppel wagner@infomine.ucr.edu

44. Document Classification What is it? –Doing to documents on the Internet what we already do to books in a library Why? –To better organize information –To speed up searches –To rank search results INFOMINE, iVia, Data Fountains

45. Example Subject Categories LCC: Library of Congress Categories LCSH: Library of Congress Subject Headings Infomine Subject Categories –Biological, Agricultural, and Medical Sciences –Business and Economics –Cultural Diversity –Electronic Journals –Government Info –Maps and Geographical Information Systems –Physical Sciences, Engineering, and Mathematics –Social Sciences and Humanities –Visual and Performing Arts INFOMINE, iVia, Data Fountains

46. Example INFOMINE, iVia, Data Fountains

47. Example: Korea Rice Genome Database Is it about… –Geography ? –Agriculture ? –Genetics ? Which Infomine category do we put it in ? –Biological, Agricultural, and Medical Sciences Pretty obvious, right ? –For humans, yes. But how do we automate it ? INFOMINE, iVia, Data Fountains

48. Automating Document Classification We need a way to measure document similarity Each document is basically just a list of words, so we can count how frequently each word appears in it These word frequencies are one of many possible document attributes Document similarity is mathematically defined in terms of document attributes INFOMINE, iVia, Data Fountains

49. Automating Document Classification The previous slide contains 51 words –document6 –word, of 3 each –we, a, in, is, each 2 each –All other words1 each Note that we consider words such as word and words to be the same We also don’t care about capitalization In general, we’d also ignore non-descriptive words such as we, a, of, the, and so on INFOMINE, iVia, Data Fountains

50. Automating Document Classification Not an easy task –The distribution of words shows that the slide in question is not very rich in content The most frequent word (document) is not very descriptive The most descriptive word (classification) does not appear very frequently in the slide –How descriptive and how frequent a word should be depends on the category The task is easier when: –we have a large number of content-rich documents –categories are characterized by very specific words which don’t appear very frequently in other categories INFOMINE, iVia, Data Fountains

51. Automating Document Classification Two documents sharing a large number of category-specific words are considered to be very similar to each other Document similarity can thus be quantified and computed automatically Documents can then be ranked by their similarity to each other A large group of documents that are all very similar to each other can then be considered to define the category they belong to (the set of all such groups is called the Training Corpus) One way to classify a document is then to put it in the same category as that of the training document that it’s most similar to INFOMINE, iVia, Data Fountains

52. Automating Document Classification The classification method just described is known as the Nearest Neighbor method There are other methods, which may be more suited for the classification of documents from the Internet –Naïve Bayes –Support Vector Machine (SVM) –Logistic Regression Infomine uses a flexible approach – supporting all of these methods – in an attempt to produce highly-accurate classifications INFOMINE, iVia, Data Fountains