1. System Model Acquisition from Requirements Text
SMART
System Model Acquisition from Requirements Text
Technion – Israel Institute of Technology

2. System Model Acquisition from Requirements Text
Operates on free text documentation, such as business process specifications or user requirements
Results depend critically on the quality of the processed documentation
Based on Object-Process Methodology (OPM) that has two semantically equivalent modalities:
Textual – Object-Process Language (OPL)
Graphic – Object-Process Diagram (OPD)
Technion – Israel Institute of Technology

3. System Model Acquisition from Requirements Text
Significantly reduces the quantity of material that needs to be processed manually
Reduces the initial level of conceptual complexity
Graphic manipulation (OPD) much easier than text editing
Quality, accuracy, and conciseness of the system architecture – higher due to the discipline OPM introduces
Capable of automatic generation of UML diagrams
Technion – Israel Institute of Technology

4. SMART - System Diagram SMART OPCAT Categorization Engine OPL Generator
System Model Acquisition
System Requirements Unstructured Text
System Architecting Team
System Model
Technion – Israel Institute of Technology

5. System Model Acquisition In-zoomed
SMART
System Requirements Unstructured Text
Category Extraction
Categorization Engine
Category List
raw
edited
System Architecting Team
List Editing
Relation Set
Relation Formulating
OPL Generator
OPL Sentence Generating
OPL Sentence Set
OPCAT
OPD Constructing
System Model
Technion – Israel Institute of Technology

6. SMART – Procedural Steps
Automatic Extraction of Categories from Unstructured Text
Manual Editing of Categories
Automatic Search of OPM Relations
Automatic Generation of OPL Sentences
Manual Editing of the Results
Technion – Israel Institute of Technology

7. Automatic Extraction of Categories from Unstructured Text
Categorization engine in Common LISP
Categories = idiomatic phrases (word sequence) reflecting the underlying topics in a given corpus of documents
Based on heuristics
Could combine external ontologies/taxonomies/thesauri
Technion – Israel Institute of Technology

8. Manual Editing of Categories
Selection of categories that can serve as things in the OPM model, and classifying them as either object or processes
Clustering of alternative formulations for the selected OPM things based on their semantic similarity
Optionally adding OPM things that did not show up among the extracted categories
Technion – Israel Institute of Technology

9. Automatic Search of OPM Relations
Utilizes a set of configurable, predefined templates:
Template consists of two things and the relation between them, expressed in alternative ways
Utilizes second order regular expressions defined on any lexical or grammatical attribute (part‑of‑speech, capitalization, punctuation)
Finite‑state automaton that operates on suffix‑tree index consisting of tokens
Instead of comparing character strings compares word sequences
Technion – Israel Institute of Technology

10. Automatic Generation of OPL Sentences
Every extracted natural language sentence straight‑forwardly translated into OPL
Reformulation of outcome to better reflect the underlying relations:
Custom relations transformed into processes (cached into => Caching)
Complex relations transformed into two equivalent simple sentences (Actual Documents Cached into Document Repositories => (1) Caching requires Actual Documents, (2) Caching yields Document Repositories)
Transformations do not modify the underlying semantics of the NL sentences
Technion – Israel Institute of Technology

11. Manual Editing of the Results
Non-semantic corrections – extraction did not depict all of the existing or implied relations
Additions and eliminations - semantically modify original output
Scaling applied to simplify results without losing details
Technion – Israel Institute of Technology

12. Benefits Significant cut-down in time and resources Minimizes efforts
Focus on the system overview ("big picture“)
High-quality results
Minimizes time-to-market
Technion – Israel Institute of Technology

13. Future Research Directions
Tested on EEC IST GRACE (Grid Retrieval and Categorization Engine)
To be utilized for system design in EEC IST COCOON (Building Knowledge-driven and Dynamically Networked Communities within European Healthcare Systems)
Looking for commercial pilot application
Technion – Israel Institute of Technology