SMART
System Model Acquisition from
Requirements Text
Technion – Israel Institute of Technology
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
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
SMART - System Diagram
SMART
OPCAT
System Requirements
Unstructured Text
Categorization
Engine
System Model
Acquisition
OPL Generator
System Architecting
Team
System Model
Technion – Israel Institute of Technology
System Model Acquisition In-zoomed
SMART
System Model
Acquisition
System Requirements
Unstructured Text
Category
Extraction
Categorization
Engine
Category List
raw
System Architecting
Team
edited
List Editing
Relation
Set
Relation
Formulating
OPL Generator
OPL Sentence
Generating
OPL Sentence
Set
OPCAT
System Model
OPD Constructing
Technion – Israel Institute of Technology
SMART – Procedural Steps
1. Automatic Extraction of Categories from
Unstructured Text
2. Manual Editing of Categories
3. Automatic Search of OPM Relations
4. Automatic Generation of OPL Sentences
5. Manual Editing of the Results
Technion – Israel Institute of Technology
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
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
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
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
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
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
Future Research Directions
• Tested on EEC IST-2001-38100 GRACE (Grid
Retrieval and Categorization Engine)
• To be utilized for system design in EEC IST202-507126 COCOON (Building Knowledgedriven and Dynamically Networked
Communities within European Healthcare
Systems)
• Looking for commercial pilot application
Technion – Israel Institute of Technology