Semantic eScience Class Fall 2008

Instructors: Professor Deborah McGuinness and Professor Peter Fox
Meeting times: Monday afternoons 1:00 pm - 3:50 pm. Low Center for Industrial Innovation Room 4040 initially; Winslow 1129 (Focus Group Room) from Sept 8 on
Office Hours: Tuesdays 11-12 in Winslow 3129
phone: 276-4404
Class Listing: 28165 CSCI-6962-01 SEMANTIC E-SCIENCE M 1:00 3:50PM McGuinness LOW 4040


Science has fully entered a new mode of operation. E-science, defined as a
combination of science, informatics, computer science, cyberinfrastructure and
information technology is changing the way all of these disciplines
do both their individual and collaborative work.

Scientists are facing global problems of a magnitude, complexity and
interdisciplinary nature that progress is limited by a trained
and agile workforce.

At present, there is a lack formal training in the key cognitive and
skill areas that would enable graduates to become key participants
in e-science collaborations. The purpose is to teach methodologies,
and provide application experience and skill-sets in an
inter-disciplinary forum to students and interested participants.

As semantic technologies have been gaining momentum in various e-Science
areas (for example, W3C's new interest group for semantic web health care
and life science), it is important to offer semantic-based methodologies,
tools, middleware to facilitate scientific knowledge modeling, logical-based
hypothesis checking, semantic data integration and application composition,
integrated knowledge discovery and data analyzing for different e-Science

Partially influenced by the Artificial Intelligence community, the Semantic Web
researchers have largely focused on formal aspects of semantic representation
languages or general-purpose semantic application development, with inadequate
consideration of requirements from specific science areas. On the other hand,
general science researchers are growing ever more dependent on the web, but they have no coherent agenda for exploring the emerging trends on the semantic web technologies. It urgently requires the development of a multi-disciplinary field
to foster the growth and development of e-Science applications based on the
semantic technologies and related knowledge-based approaches.

Goals: to fill the gaps that are currently present in the integrative
nature of informatics for the translation of science into requirements
for the underlying and largely syntactic e-infrastructure.


Topics for Semantic e-Science/ Foundations:

  • Semantic Methodologies
  • Knowledge Representation for e-Science
  • Ontology Engineering and Re-Use for e-Science
  • Knowledge Integration for e-Science
  • Semantic Data Integration
  • Semantic Web Languages, Tools and Services
  • Semantic Infrastructure and Architecture for e-Science
  • Semantic Grid Middleware
  • Ontology Evolution for e-Science
  • Knowledge Management for e-Science
  • e-Science Workflow Management
  • Data life-cycle for e-Science
  • Data Mining and Knowledge Discovery

Semantic Web Applications and Ontologies for:

  • Semantic Web for Health Care and Life Science
  • Semantic Web for Bio-Med-informatics
  • Semantic Web for System and Integrated Biology
  • Semantic Web for Sun, Earth, Environment and Climate
  • Semantic Web for Chemistry, Physics and Astronomy
  • Semantic Web for Engineering
  • Semantic Web and Digital Libraries and Scientific Publications

Semantic e-Science Project options

  • Configuration and Deployment of Semantic Virtual Observatories
  • Ontology Merging and Validation Test-bed
  • Semantic Language and Tool Use and Evaluation
  • Data Life-Cycle Studies
  • Data and Information Product Generation and Explanation
  • Semantic Collaboration Case Studies
  • Semantic Application Development and Demonstration

Class Calendar


For complete reading citation with link(s) to papers, see reference list below.

Ontologies 101, Semantic Web, e-Science, RDFS, Common Logic
reading: OWL Guide,
assignment 1: Representing Knowledge and Understanding Representations (version with working hyperlinks)
reading: Use Cases
reading: Ontology Tool Summary, Pellet, OWL-S, Wine Agent
assignment 2: Use-case Driven Knowledge Encoding Part I
reading: Ontology Evolution
Use case template
Partial use case example 1
Partial use case example 2
OWL-S editor tutorial
OWL-S and WSDL references
reading: no new reading - use case preparation
  • Class 7: Class Presentation I: Use Cases - Part II of Assignment 2
  • Class 8: Class exercise II: Use Case Implementation
reading: IAAI VSTO, Semantic eScience Web Services, Computers and Geoscience
reading: Evaluation
additional material: Summative versus Formative evaluation
additional material: Example of evaluation
additional material: Template example for Evaluation
reading: semantic integration
assignment 3: Team Use Case Implementation
reading: no new reading
  • Class 12: Class Presentation II: Use Case Implementation
reading: TBD
Term assignment: Use Case: Iterating and Evolving, Lessons Learn; Present in Class 14 on December 1, written report due December 1
reading: no new required reading (there is optional reading)
  • Class 14: Class Presentation III: Term assignment - Project Outcome

Academic Integrity

Student-teacher relationships are built on trust. For example, students must trust that teachers have made appropriate decisions about the structure and content of the courses they teach, and teachers must trust that the assignments that students turn in are their own. Acts, which violate this trust, undermine the educational process. The Rensselaer Handbook of Student Rights and Responsibilities defines various forms of Academic Dishonesty and you should make yourself familiar with these. In this class, all assignments that are turned in for a grade must represent the student’s own work. In cases where help was received, or teamwork was allowed, a notation on the assignment should indicate your collaboration. Submission of any assignment that is in violation of this policy will result in a penalty.
If found in violation of the academic dishonesty policy, students may be subject to two types of penalties. The instructor administers an academic (grade) penalty, and the student may also enter the Institute judicial process and be subject to such additional sanctions as: warning, probation, suspension, expulsion, and alternative actions as defined in the current Handbook of Student Rights and Responsibilities.
of an academic grade penalty or . If you have any question concerning this policy before submitting an assignment, please ask for clarification.

Suggested Prerequisites

  • Knowledge such as that gained in a Semantic Web class (e.g., CSCI-6962)
  • Knowledge such as that gained in a Web Science class (e.g., CSCI-xx)
  • or permission of the instructors

Reference List

Class 1 Reading Assignment:


T Berners-Lee, J Hendler, O Lassila. The Semantic Web. Scientific American, 2001.
Grigoris Antoniou and Frank van Harmelen. Semantic Web Primer




Class 2: Reading Assignment:

  • [OWL Guide] Michael K. Smith, Chris Welty, and Deborah L. McGuinness. OWL Web Ontology Language Guide. World Wide Web Consortium (W3C) Recommendation. February 10, 2004.


Class 3: Reading Assignment:

  • Use Cases:



  • ,





  • RDFS:

Class 4: Reading Assignment:

  • Ontology Tool Summary: Michael Denny. Ontology Tools Survey, Revisited.
  • Pellet: web page:
  • Pellet: Evren Sirin and Bijan Parsia and Bernardo Cuenca Grau and Aditya Kalyanpur and Yarden Katz, Pellet: a practical owl-dl reasoner. Journal of Web Semantics.
  • OWL-S: David Martin, Mark Burstein, Drew McDermott, Deborah L. McGuinness, Sheila McIlraith, Massimo Paolucci, Evren Sirin, Naveen Srinivasan, and Katia Sycara. Bringing Semantics to Web Services with OWL-S. World Wide Web Journal, Volume 10, Number 3, pp 243-277. Also, Stanford KSL Technical Report KSL-06-21.


  • Wine Agent:
* Eric Hsu, and Deborah L. McGuinness. KSL Wine Agent: Semantic Web Testbed Application, Proceedings of the 2003 International Workshop on Description Logics (DL2003). Rome, Italy, September 5-7, 2003.
* James Michaelis, Li Ding, Deborah McGuinness. The TW Wine Agent A Social Semantic Web Demo. ISWC 2008 Poster and Demo Track, 2008.

Class 5: Reading Assignment:

  • [Ontology Evolution] Deborah L. McGuinness, Richard Fikes, James Rice, and Steve Wilder.


An Environment for Merging and Testing Large Ontologies. In Proceedings of the 7th International Conference on Principles of Knowledge Representation and Reasoning (KR2000), Breckenridge, Colorado, USA 12-15 April 2000


  • Aseem Das, Wei Wu, and Deborah L. McGuinness. ``Industrial Strength Ontology Management. Stanford Knowledge Systems Laboratory Technical Report KSL-01-09 2001. In the Proceedings of the International Semantic Web Working Symposium. Stanford, CA, July 2001. Also published in In Isabel Cruz, Stefan Decker, Jerome Euzenat, and Deborah L. McGuinness, eds. The Emerging Semantic Web. (Book available from IOS Press, 2002.

  • Prompt -


Class 6: Reading Assignment:

Class 7: Reading Assignment:

  • PML -McGuinness, Ding, Pinheiro da Silva, Chang. PML 2: A Modular Explanation Interlingua. AAAI 2007 Workshop on Explanation-aware Computing, Vancouver, Can., 7/07. Stanford Tech report KSL-07-07.


  • Inference Web - McGuinness and Pinheiro da Silva. Explaining Answers from the Semantic Web: The Inference Web Approach. Web Semantics: Science, Services and Agents on the World Wide Web Special issue: International Semantic Web Conference 2003 - Edited by K.Sycara and J.Mylopoulis. Volume 1, Issue 4. Journal published Fall, 2004
  • McGuinness, D.L.; Zeng, H.; Pinheiro da Silva, P.; Ding, L.; Narayanan, D.; Bhaowal, M. Investigations into Trust for Collaborative Information Repositories: A Wikipedia Case Study. The Workshop on the Models of Trust for the Web (MTW'06), Edinburgh, Scotland, May 22, 2006. 2006.

Class 8: Reading Assignment:


Class 9: Reading Assignment:


Class 10: Reading Assignment:

  • Integration:
  • Fox, P.; McGuinness, D.L.; Raskin, R.; Sinha, K. A Volcano Erupts: Semantically Mediated Integration of Heterogeneous Volcanic and Atmospheric Data. Proceedings of the First Workshop on Cyberinfrastructure: Information Management in eScience, co-located with the ACM Conference on Information and Knowledge Management, Lisbon, Portugal, November 9, 2007.
  • Sunil Movva, Rahul Ramachandran, Xiang Li, Phani Cherukuri, Sara Graves. Noesis: A Semantic Search Engine and Resource Aggregator for Atmospheric Science. NSTC2007.
  • Boyan Brodaric and Florian Probst. Enabling Cross-Disciplinary e-Science by Integrating Geoscience Ontologies with DOLCE. Under Review. 2008.
  • Yolanda Gil, Ewa Deelman, Mark Ellisman, Thomas Fahringer, Geoffrey Fox, Dennis Gannon, Carole Goble, Miron Livny, Luc Moreau, Jim Myers, "Examining the Challenges of Scientific Workflows," Computer , vol. 40, no. 12, pp. 24-32, December, 2007.

Class 11: Reading Assignment:

Class 12: Reading Assignment:

Class 13: Reading Assignment:

Attendance Policy

Enrolled students may miss at most one class without permission of the instructor.

Course: Semantic eScience

Date: to