This document discusses a case-based workflow composition assistant that helps users create workflows from existing ones, focusing on matching inputs and outputs using natural language processing (NLP) techniques. The approach aims to improve workflow composition efficiency by utilizing semantic annotations and keyword matching. Future work includes integrating standard semantic languages and evaluating the system's performance against existing methods.

1. CBR Based Workflow
Composition Assistant
Eran Chinthaka, Jaliya Ekanayake, David Leake, Beth Plale
School of Informatics, Indiana University
Bloomington, Indiana, USA.
{echintha, jekanaya, leake, plale}@cs.indiana.edu

2. Problem
 Composing workflows from scratch is hard
 Common case :
 User has inputs and expected outputs
 Need a way to get to output from Inputs
 What can help
 Use of existing workflows
 From user’s space
 From known places like myexperiments.org
 Enabling input and output descriptions using multiple
methods
 semantic annotations
 keywords

3. Suggested Solution
 Assumption
 “All workflows that are acyclic can be represented as a
graph”
 User describes the inputs and outputs of a
workflow/part of a workflow using keywords or
semantic annotations
 Use NLP techniques to match inputs and outputs
 Simplest solution uses Keyword matching with Lesk
Algorithm
 Use CBR based approach for retrieving similar
workflows
 Adapt existing workflow to new ones
 By down sizing existing ones
 Extending existing ones

4. System Flow

5. Matching Workflows
• Getting part of a workflow
• Adopt a part of a workflow

6. Key Features
• Better performance compared to heavy
knowledge based approach or manual
composition
• Independent of the workflow description
languages (WSDL, SCUFL, etc.,)
• Ability to extend similarity measures to use
semantic annotations or other NLP techniques
• Ability to provide threshold for similarity
check and to control number of cases
retrieved
• NLP based keyword checking

7. Future Work
 Reducing common workflow languages in to
graph representation
 Integration of standard semantic language like
OWL, RDF or WSDL-S
 Integration with a workflow composition tool
(eg: Xbaya, WF)
 Performance Evaluation with an existing
knowledge heavy system
 Evaluation of accuracy and/or usability of
suggested cases

8. Related Work
• D. Leake and J. Kendall-Morwick, “Towards case-based support for e-science
workflow generation by mining provenance information,” in Proceedings of the
Nineth European Conference on Case-Based Reasoning. Springer, 2008, in press.
• J. Kim, Y. Gil, and M. Spraragen, “A knowledge-based approach to interactive
workflow composition,” 2004.
• J. L. Ambite and D. Kapoor, “Automatically composing data workflows with
relational descriptions and shim services,” International Semantic Web
Conference, 2007.
• M. Carman, L. Serafini, and P. Traverso, “Web service composition as planning,” in
ICAPS03 International Conference on Automated Planning and Scheduling, 2003.
• E. Sirin, J. Hendler, and B. Parsia, “Semi-automatic composition of web services
using semantic descriptions,” in ICEIS-2003 Workshop on Web Services.

9. Thank You !!