Focused Crawls, Tunneling, and Digital Libraries
Crawling the Web to build collections of documents related to pre-specified topics became an active area of research during the late 1990’s, crawler technology having been developed for use by search engines. Now, Web crawling is being seriously considered as an important strategy for building large scale digital libraries. This paper covers some of the crawl technologies that might be exploited for collection building. For example, to make such collection-building crawls more effective, focused crawling was developed, in which the goal was to make a “best-first” crawl of the Web. We are using powerful crawler software to implement a focused crawl but use tunneling to overcome some of the limitations of a pure best-first approach. Tunneling has been described by others as not only prioritizing links from pages according to the page’s relevance score, but also estimating the value of each link and prioritizing them as well. We add to this mix by devising a tunneling focused crawling strategy which evaluates the current crawl direction on the fly to determine when to terminate a tunneling activity. Results indicate that a combination of focused crawling and tunneling could be an effective tool for building digital libraries.
Unable to display preview. Download preview PDF.
- 1.Lagoze (ed.), C., Arms, W., Gan, S., Hillmann, D., Ingram, C., Krafft, D., Marisa, R., Phipps, J., Saylor, J., Terrizzi, C.: Core services in the architecture of the National Digital Library for science education NSDL). In: Proceedings of the Second ACM/IEEE-CS Joint Conference on Digital Libraries, Portland, OR (2002)Google Scholar
- 2.Zia, L.L.: The NSF national science, technology, engineering, and mathematics education digital library (NSDL) program: New projects and a project report. D-Lib Magazine: The Magazine of Digital Library Research 7 (2001)Google Scholar
- 3.Arms, W.: Automated digital libraries: How effectively can computers be used for the skill tasks of professional librarianship. D-Lib Magazine: The Magazine of Digital Library Research (2000) http://www.dlib.org/dlib/july00/arms/07arms.html.
- 4.Bergmark, D.: Collection synthesis. In: Proceedings of the Second ACM/IEEECS Joint Conference on Digital Libraries, Portland OR (2002) Available: http://mercator.comm.nsdlib.org/CollectionBuilding/bergmark-paper.pdf.
- 5.Chakrabarti, S., van den Berg, M., Dom, B.: Focused crawling: a new approach to topic-specific Web resource discovery. In: Proceedings of the Eighth International World-Wide Web Conference., Toronto, Canada (1999) 545–562 Available: http://www8.org/w8-papers/5a-search-query/crawling/index.html and http://www.cs.berkeley.edu/soumen/doc/www99focus/ Current as of August 2001.
- 6.Belew, R.K.: Finding Out About. Cambridge Press (2001)Google Scholar
- 7.Salton, G.: Automatic Information Organization and Retrieval. McGraw-Hill, New York (1968)Google Scholar
- 8.Bergmark, D.: Using high performance systems to build collections for a digital library. In: Proceedings of the 2002 International Conference on Parallel Processing Workshops (ICPP 2002 Workshops), Vancouver, Canada (2002) Preprint available at http://mercator.comm.nsdlib.org/CollectionBuilding/DCADL_bergmark.ps.
- 9.Pirolli, P., Pitkow, J., Rao, R.: Silk from a sow’s ear: Extracting usable structures from the Web. (1996) Available: http://www.acm.org/pubs/articles/proceedings/chi/238286/p118-pirolli/p118-pirolli.html.
- 11.Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. In: Proceedings of the 7th International World Wide Web Conference (WWW7), Brisbane, Australia (1998) Available online at http://www7.scu.edu.au/programme/fullpapers/1921/com1921.htm, (current as of 28 Feb. 2001).
- 12.Gibson, D., Kleinberg, J., Raghavan, P.: Inferring Web communities from link topology. In: Proceedings of the 9th ACM Conference on Hypertext and Hypermedia: Links, Objects, Time and Space— Structure in Hypermedia Systems (hypertext’98, Pittsburg, PA). (1998) 225–234Google Scholar
- 13.Chakrabarti, S., van den Berg, M., Dom, B.: Distributed hypertext resource discovery through examples. In: Proceedings of the 25th VLDB Conference, Edinburgh,Scotland, Morgan-Kaufman (1999) 375–386Google Scholar
- 14.Rennie, J., McCallum, A.: Using reinforcement learning to spider the Web efficiently. In: Proceedings of the International Conference on Machine Learning (ICML). (1999)Google Scholar
- 15.Menczer, F., Belew, R.K. In: Adaptive Retrieval Agents: Internalizing Local Context and Scaling up to the Web. (1999) 1–45 Republished in Machine Learning, 39(2/3) pp. 203-242, 2000.Google Scholar
- 16.Menczer, F., Pant, G., Srinivasan, P.: Evaluating topic-driven Web crawlers. In: SIGIR’01, September 9–12, New Orleans, La. USA (2001)Google Scholar
- 17.Mukherjea, S.: WTMS: A system for collecting and analyzing topic-specific Web information. In: Proceedings of the 9th International World Wide Web Conference: The Web: The Next Generation, Amsterdam, Elsevier (2000) Available: http://www9.org/w9cdrom/293/293.html (current as of August 2001).
- 18.Chakrabarti, S.: Recent results in automatic Web resource discovery. ACM Computing Surveys (1999) Available: http://www.acm.org/pubs/articles/journals/surveys/1999-31-43es/a17-chakrabarti/a17-chakrabarti.pdf.
- 19.Diligenti, M., Coetzee, F., Lawrence, S., Giles, C., Gori, M.: Focused crawling using context graphs. In: Proceedings of the 26th International Conference on Very Large Databases. (2000)Google Scholar
- 20.Heydon, A., Najork, M.: Mercator: A scalable, extensible Web crawler. World Wide Web 2 (1999)Google Scholar
- 21.Najork, M., Heydon, A.: High-performance Web crawling. Technical Report Research Report 173, Compaq SRC (2001) Available at http://gatekeeper.research.compaq.com/pub/DEC/SRC/research-reports/abstracts/src-rr-173.html.
- 22.Davison, B.D.: Topical locality in the Web. In: Proceedings of the 23rd Annual International Conference on Research and Development in Information Retrieval (SIGIR 2000), Athens, Greece, ACM (2000)Google Scholar
- 23.Joachimes, T.: A support vector method for learning ranking functions in information retrieval (2002) Cornell University Colloqium.Google Scholar
- 24.Parsia, B.: A simple, prima facie argument in favor of the semantic web. MonkyFist (2002) Available: http://monkeyfist.com/articles/815.
- 25.Kluev, V.: Compiling document collections from the Internet. SIGIR Forum 34 (2000) Available at http://www.acm.org/sigir/forum/F2000/Kluev00.pdf.
- 26.Han, E.H.S., Karypis, G.: Centroid-based document classification: Analysis & experimental results. Technical Report 00-017, Computer Science, University of Minnesota (2000)Google Scholar
- 27.Katz, V., Li, W.S.: Topic distillation on hierarchically categorized Web documents. In: Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange, IEEE (1999)Google Scholar