Semantic eScience Class Fall 2010

Instructors: Professor Deborah McGuinness and Professor Joanne Luciano with Peter Fox guest lecturing.
Meeting times: Monday afternoons 1:00 pm - 3:50; Winslow 1140
Office Hours: By appointment and walk-in in Winslow 2104 (Professor McGuinness) and Winslow 2143 (Professor Luciano)
phone: 276-4404 (Professor McGuinness) and 276-4939 (Professor Luciano)
Class Listing: SEMANTIC E-SCIENCE CLASS WILL MEET IN WINSLOW BUILDING -- CSCI 6962 - 01 and ITWS 6960 01 1:00 3:50PM McGuinness Semantic E-Science 67023 (CS) and 67345 (ITWS)

Table of Contents


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 applications.

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
    • Oceanography, astronomy, geology
  • Ontology Merging and Validation Test-bed
  • Semantic Language and Tool Use and Evaluation
  • Semantic eScience Implementation Evaluation
  • Semantic Collaboration Case Studies
  • Semantic Application Development and Demonstration

Course Calendar

  • Class 1: Monday, August 30
  • NO CLASS on Labor Day September 6
  • Class 2: Monday, September 13
  • Class 3: Monday, September 20
  • Class 4: Monday, September 27
  • Class 5: Monday, October 4
  • Class 6: TUESDAY, October 12
  • Class 7: Monday, October 18
  • Class 8: Monday, October 25
  • Class 9: Monday, November 1
  • Class 10: Monday November 8
  • Class 11: Monday, November 15
  • Class 12: Monday November 22
  • Class 13: Monday November 29
  • Class 14: Monday December 6

Course Syllabus

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

readings: Ontologies 101, Semantic Web, e-Science, RDFS, OWL Guide.
assignment: Assignment 0: Turn in a ONE PAGE description of the reading you liked best, two main points, and why you thought the points were interesting and useful.
reading: Semantic Web for the Working Ontologist (first few chapters). Alternate reading - OWL Pizza Tutorial.
assignment 1: Representing Knowledge and Understanding Representations - Extending the VSTO
reading: Use Cases
Use case template
Partial use case example 1
Partial use case example 2
reading: Ontology Tool Summary, Pellet, OWL-S, SAWSDL, Wine Agent
assignment 2: Use-case Driven Knowledge Encoding Part I
reading: Ontology Evolution
OWL-S editor tutorial
OWL-S and WSDL references
CMAP download
  • Class 6: Class Presentation I: Tuesday October 12, 2010 Use Cases - Part II of Assignment 2
reading: no new reading
assignment 3: Team Use Case Implementation
WHOI Use Cases: Use Cases from WHOI
WHOI Presentation by Andrew Maffei Slides
Enabling Incremental Enhancement of Provenance Records via User Annotation James Michaelis, presented by Deborah McGuinness
reading: PML, Inference Web, IAAI VSTO, Semantic eScience Web Services, Computers and Geoscience


additional materials Orchard Irrigation and Diabetic Exchange
reading: Evaluation
additional material: Summative versus Formative evaluation
additional material: Example of evaluation
additional material: Template example for Evaluation
reading: semantic integration
  • Class 12: Class Presentation II: Team Use Case Implementation, November 22, 2010
reading: no new reading
Term assignment: Use Case: Iterating and Evolving, Lessons Learned;

Extra Slides November 29, 2010

reading: Provenance/ PML
  • Class 14: Class Presentation III: Term assignment - Project Outcome, December 6, 2010

Course Learning Objective

  • Ontology Development, Merging and Validation
  • Semantic Language and Tool Use and Evaluation
  • Use Case Development and Elaboration
  • Semantic eScience Implementation and Evaluation via Use Cases
  • Semantic Application Development and Demonstration
  • Group Project and Team Development, Use Case Implementation and Evaluation
  • TBC

Assessment Criteria

  • Via written assignments with specific percentage of grade allocation provided with each assignment
  • Via oral presentations with specific percentage of grade allocation provided
  • Via group presentations
  • Via participation in class (not to exceed 10% of total)
  • Late submission policy: first time with valid reason – no penalty, otherwise 20% of score deducted each late day

Suggested Prerequisites

  • Knowledge such as that gained in a Semantic Web class (e.g., CSCI-6961)
  • 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:

  • Semantic Web:
T Berners-Lee, J Hendler, O Lassila. The Semantic Web. Scientific American, 2001. alternative link -
Grigoris Antoniou and Frank van Harmelen. Semantic Web Primer
  • [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 2: Reading Assignment:

Alternate reading -

  • Alan Rector, Nick Drummond, Matthew Horridge, Jeremy Rogers, Holger Knublauch, Robert Stevens, Hai Wang, Chris Wroe. OWL Pizzas: Practical Experience of Teaching OWL-DL: Common Errors & Common Patterns. EKAW 2004.

Optional reading -

Class 3: Reading Assignment:

  • Use Cases:



Class 4: Reading Assignment:

  • Wine Agent:

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


Class 6: Reading Assignment:

Class 7: Reading Assignment:

Class 8: Reading Assignment:

  • 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 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.
  • 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: none

Class 12: Reading Assignment:

Class 13: Reading Assignment: None.

Attendance Policy

Enrolled students may miss at most one class without permission of the instructor. Once one class has been missed (with permission) no additional classes may be missed without permission.

Course: Semantic eScience

Date: to