Hostname: page-component-745bb68f8f-l4dxg Total loading time: 0 Render date: 2025-01-07T13:05:26.585Z Has data issue: false hasContentIssue false
  • English
  • Français

Interactive knowledge capture in the new millennium: how the Semantic Web changed everything

Published online by Cambridge University Press:  07 February 2011

Yolanda Gil
Yolanda Gil*
Affiliation:
Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Suite 1001, Marina del Rey, CA 90292, USA;e-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The Semantic Web has radically changed the landscape of knowledge acquisition research. It used to be the case that a single user would edit a local knowledge base, that the user would have domain expertise to add to the system, and that the system would have a centralized knowledge base and reasoner. The world surrounding knowledge-rich systems changed drastically with the advent of the Web, and many of the original assumptions were no longer a given. Those assumptions had to be revisited and addressed in combination with new challenges that were put forward. Knowledge-rich systems today are distributed, have many users with different degrees of expertise, and integrate many shared knowledge sources of varying quality. Recent work in interactive knowledge capture includes new and exciting research on collaborative knowledge sharing, collecting knowledge from Web volunteers, and capturing knowledge provenance.

Type
Articles
Information
The Knowledge Engineering Review , Volume 26 , Special Issue 1: 25th Anniversary Issue , 07 February 2011 , pp. 45 - 51
Copyright
Copyright © Cambridge University Press 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Artz, D., Gil, Y. 2007. A survey of trust in computer science and the Semantic Web. Journal of Web Semantics 5(2).CrossRefGoogle Scholar
Bada, M., Stevens, R., Goble, C., Gil, Y., Ashburner, M., Blake, J. A., Cherry, J. M., Harris, M., Lewis, S. 2004. A short study on the success of the gene ontology. Journal of Web Semantics 1(2).CrossRefGoogle Scholar
Blythe, J., Kim, J., Ramachandran, S., Gil, Y. 2001. An integrated environment for knowledge acquisition. In Proceedings of the ACM International Conference on Intelligent User Interfaces (IUI-01), Santa Fe, New Mexico.CrossRefGoogle Scholar
Chalupsky, H., Gil, Y., Knoblock, C. A., Lerman, K., Oh, J., Pynadath, D. V., Russ, T. A., Tambe, M. 2001. Electric elves: applying agent technology to support human organizations. In Proceedings of the Thirteenth Annual Conference of Innovative Applications of Artificial Intelligence (IAAI-2001), Seattle, WA.CrossRefGoogle Scholar
Chklovski, T., Gil, Y. 2005a. User interfaces with semi-formal representations: a study of designing argumentation structures. In Proceedings of the ACM International Conference on Intelligent User Interfaces (IUI-2005), San Diego, CA.CrossRefGoogle Scholar
Chklovski, T., Gil, Y. 2005b. An analysis of knowledge collected from volunteer contributors. In Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI-05), Pittsburgh, PA.Google Scholar
Chklovski, T., Gil, Y. 2005c. Improving the design of intelligent acquisition interfaces for collecting world knowledge from web contributors. In Proceedings of the Third International Conference on Knowledge Capture (KCAP-05), Banff, Alberta.CrossRefGoogle Scholar
Clark, P., Thompson, J., Barker, K., Porter, B., Chaudhi, V., Rodriguez, A., Thomere, J., Mishra, S., Gil, Y., Hayes, P., Reichherzer, T. 2001. Knowledge entry as graphical assembly of components. In Proceedings of the First International Conference on Knowledge Capture (K-CAP'01), Victoria, BC.CrossRefGoogle Scholar
Deelman, E., Blythe, J., Gil, Y., Kesselman, C., Mehta, G., Vahi, K., Blackburn, K., Lazzarini, A., Arbree, A., Cavanaugh, R., Koranda, S. 2003. Mapping abstract workflows onto grid environments. Journal of Grid Computing 1(1).CrossRefGoogle Scholar
Deelman, E., Singh, G., Su, M.-H., Blythe, J., Gil, Y., Kesselman, C., Mehta, G., Vahi, K., Berriman, G. B., Good, J., Laity, A., Jacob, J. C., Katz, D. S. 2005. Pegasus: a framework for mapping complex scientific workflows onto distributed systems. Scientific Programming 13(3).CrossRefGoogle Scholar
Ebiquity. 2006. Big OWL Documents on the Semantic Web. http://ebiquity.umbc.edu/blogger/?page_id=452Google Scholar
Gil, Y. 2006. Workflow composition. In Workflows for e-Science, Gannon, D., Deelman, E., Shields, M. & Taylor, I. (eds). Springer Verlag.Google Scholar
Gil, Y. 2009. From data to knowledge to discoveries: scientific workflows and artificial intelligence. Scientific Programming 17(3).CrossRefGoogle Scholar
Gil, Y. (ed.). 2010. Report of the W3C Provenance Incubator Group, World Wide Web Consortium. http://www.w3.org/2005/Incubator/prov/wiki/Google Scholar
Gil, Y., Artz, D. 2006. Towards content trust of web resources. In Proceedings of the Fifteenth International World Wide Web Conference, (WWW-06), Edinburgh, Scotland.CrossRefGoogle Scholar
Gil, Y., Artz, D. 2007. Towards content trust of web resources. Journal of Web Semantics 5(4).CrossRefGoogle Scholar
Gil, Y., Kim, J. 2002. Interactive knowledge acquisition tools: a tutoring perspective. In Proceedings of the 24th Annual Meeting of the Cognitive Science Society (CogSci), Fairfax, VA.Google Scholar
Gil, Y., Melz, E. 1996. Explicit representations of problem-solving strategies to support knowledge acquisition. In Proceedings of the Thirteen National Conference on Artificial Intelligence (AAAI-96), Portland, OR.Google Scholar
Gil, Y., Ramachandran, S. 2001. PHOSPHORUS: a task-based agent matchmaker. In Proceedings of the Fifth International Conference on Autonomous Agents (Agents'01), Montreal, Canada.CrossRefGoogle Scholar
Gil, Y., Ratnakar, V. 2002a. Trusting information sources one citizen at a time. In Proceedings of the First International Semantic Web Conference (ISWC), Sardinia, Italy.CrossRefGoogle Scholar
Gil, Y., Ratnakar, V. 2002b. TRELLIS: an interactive tool for capturing information analysis and decision making. In Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management (EKAW), Siguenza, Spain.CrossRefGoogle Scholar
Gil, Y., Ratnakar, V. 2008. Automating to-do lists for users: interpretation of to-dos for selecting and tasking agents. In Proceedings of the Twenty-Third Conference of the Association for the Advancement of Artificial Intelligence (AAAI-08), Chicago, IL.Google Scholar
Gil, Y., Tallis, M. 1997. A script-based approach to modifying knowledge bases. In Proceedings of the Fourteenth National Conference on Artificial Intelligence (AAAI-97), Providence, RI.Google Scholar
Gil, Y., Ratnakar, V., Fritz, C. 2011. TellMe: learning procedures from tutorial instruction. In Proceedings of the ACM International Conference on Intelligent User Interfaces (IUI-11), Palo Alto, CA.CrossRefGoogle Scholar
Gil, Y., Ratnakar, V., Deelman, E., Mehta, G., Kim, J. 2007a. Wings for Pegasus: creating large-scale scientific applications using semantic representations of computational workflows. In Proceedings of the 19th Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-07), Vancouver, British Columbia, Canada.Google Scholar
Gil, Y., Deelman, E., Ellisman, M., Fahringer, T., Fox, G., Gannon, D., Goble, C., Livny, M., Moreau, L., Myers, J. 2007b. Examining the challenges of scientific workflows. IEEE Computer 40(12), 24–32.CrossRefGoogle Scholar
Gil, Y., Kim, J., Florez, G., Ratnakar, V., Gonzalez Calero, P. A. 2009. Workflow matching using semantic metadata. In Proceedings of the Fifth International Conference on Knowledge Capture (K-CAP), Redondo Beach, CA.CrossRefGoogle Scholar
Gil, Y., Kim, J., Spraragen, M. 2010a. Principles for interactive acquisition and validation of workflows. Journal of Experimental and Theoretical Artificial Intelligence 22(2).Google Scholar
Gil, Y., Gonzalez-Calero, P. A., Kim, J., Moody, J., Ratnakar, V. 2010b. Automatic generation of computational workflows from workflow templates using distributed data and component catalogs. Journal of Experimental and Theoretical Artificial Intelligence (In Press).Google Scholar
Groth, P., Gil, Y. 2009a. A scientific workflow construction command line. In Proceedings of the ACM International Conference on Intelligent User Interfaces (IUI-09), Sanibel, FL.CrossRefGoogle Scholar
Groth, P., Gil, Y. 2009b. Scaffolding instructions to learn procedures from users. In AAAI Spring Symposium on Agents that Learn from Human Teachers, Stanford, CA.Google Scholar
Hall, M., Gil, Y., Lucas, R. 2008. Self-configuring applications for heterogeneous systems: program composition and optimization using cognitive techniques. In Proceedings of the Institute of Electrical and Electronics Engineers, Special Issue on Edge Computing.CrossRefGoogle Scholar
Kim, J., Gil, Y. 1999. Deriving expectations to guide knowledge base creation. In Proceedings of the Fourteenth National Conference on Artificial Intelligence (AAAI-99), Orlando, FL.Google Scholar
Kim, J., Gil, Y. 2000. Acquiring problem-solving knowledge from end users: putting interdependency models to the test. In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-2000), Austin, TX.Google Scholar
Kim, J., Gil, Y. 2001. Knowledge analysis on process models. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-2001), Seattle, WA.Google Scholar
Kim, J., Gil, Y. 2002. Deriving acquisition principles from tutoring principles. In Proceedings of the Intelligent Tutoring Systems Conference (ITS), Biarritz, France.CrossRefGoogle Scholar
Kim, J., Gil, Y. 2003. Proactive acquisition from tutoring and learning principles. In Proceedings of the 11th International Conference on Artificial Intelligence in Education (AI-ED), Sydney, Australia.Google Scholar
Kim, J., Gil, Y. 2007. Incorporating tutoring principles into interactive knowledge acquisition. International Journal of Human-Computer Studies 65(10).CrossRefGoogle Scholar
Kim, J., Gil, Y., Ratnakar, V. 2006. Semantic metadata generation for large scientific workflows. In Proceedings of the Fifth International Semantic Web Conference (ISWC-06), Athens, GA.CrossRefGoogle Scholar
Kim, J., Myers, K., Gervasio, M., Gil, Y. 2010. Goal-directed meta-control for integrated procedure learning. In Metareasoning: Thinking about Thinking, Cox, M. T. & Raja, A. (eds). MIT Press.Google Scholar
Kim, J., Deelman, E., Gil, Y., Mehta, G., Ratnakar, V. 2008. Provenance trails in the wings/pegasus workflow system. Concurrency and Computation: Practice and Experience, Special Issue on the First Provenance Challenge 20(5).CrossRefGoogle Scholar
Kumar, V., Kurc, T., Ratnakar, V., Kim, J., Mehta, G., Vahi, K., Nelson, Y. L., Sadayappan, P., Deelman, E., Gil, Y., Hall, M., Saltz, J. 2010. Parameterized specification, configuration, and execution of data-intensive scientific workflows. Cluster Computing Journal 13.CrossRefGoogle ScholarPubMed
Kurc, T., Hastings, S., Kumar, V., Langella, S., Sharma, A., Pan, T., Oster, S., Ervin, D., Permar, J., Narayanan, S., Gil, Y., Deelman, E., Hall, M., Saltz, J. 2009. High performance computing and grid computing for integrative biomedical research. Journal of High Performance Computing Applications.CrossRefGoogle ScholarPubMed
Maechling, P., Chalupsky, H., Dougherty, M., Deelman, E., Gil, Y., Gullapalli, S., Gupta, V., Kesselman, C., Kim, J., Mehta, G., Mendenhall, B., Russ, T., Singh, G., Spraragen, M., Staples, G., Vahi, K. 2005. Simplifying construction of complex workflows for non-expert users of the southern california earthquake center community modelling environment. ACM SIGMOD Record, Special Issue on Scientific Workflows 34(3).Google Scholar
Marcus, S., McDermott, J. P. 1989. SALT: a knowledge acquisition language for propose-and-revise systems. Artificial Intelligence 39(1), 1–37.CrossRefGoogle Scholar
Moreau, L., Clifford, B., Freire, J., Futrelle, J., Gil, Y., Groth, P., Kwasnikowska, N., Miles, S., Missier, P., Myers, J., Plale, B., Simmhan, Y., Stephan, E., Van den Bussche, J. 2010. The open provenance model core specification (v1.1). Future Generation Computer Systems, July 2010.Google Scholar
Scaffidi, C., Shaw, M., Myers, B. 2005. Estimating the numbers of end users and end user programmers. In Proceedings of the 2005 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC 2005), Dallas, TX.Google Scholar
Tallis, M., Gil, Y. 1999. Designing scripts to guide users in modifying knowledge-based systems. In Proceedings of the Fourteenth National Conference on Artificial Intelligence (AAAI-99), Orlando, FL.Google Scholar
Tallis, M., Kim, J., Gil, Y. 2001. User studies of knowledge acquisition tools: methodology and lessons learned. Journal of Experimental and Theoretical AI 13, 359–378.CrossRefGoogle Scholar
Vrandečić, D., Ratnakar, V., Krötzsch, M., Gil, Y. 2010. Shortipedia: Aggregating and Curating Semantic Web Data. Third Prize in the Semantic Web Challenge, 2010. http://challenge.semanticweb.org/CrossRefGoogle Scholar
Woollard, D., Medvidovic, N., Gil, Y., Mattmann, C. 2008. Scientific software as wokflows: from discovery to distribution. IEEE Software, Special Issue on Developing Scientific Software.CrossRefGoogle Scholar