Application Knowledge Engineering Methodology

 

AKEM stands for Application Knowledge Engineering Methodology. It is an knowledge engineering methodology practiced and evolved in FF POIROT. It is a collection of strategies and heuristics in knowledge capture, representation and application. It is devoted to the team work with members from multiple disciplines and different geographical locations. A key principle of its development is the ease of practice and adaptation with emphasis on low ceremony and agility, considering the features of knowledge engineering and its dynamic contexts.

 

 

 

 

 

Life cycle model                                               

AKEM development life cycle model inherits from the practical experience in software engineering and expert system development, Rational Unified Process in particular. It organises knowledge engineering projects through four phases: inception, elaboration, construction and transition. Each phase is one or more iteration of 8 activities with different degrees of emphasis and intensity: Problem Determination, Scoping, Analysis, Development, Deployment, Test and Validation, Documentation, Control. They are composed of 23 tasks with 21 specific deliverables and recommendations for implementation (17 principles, 50 best practices to follow, 11 prompt questions and 7 pitfalls). It recognises the importance of knowledge management in knowledge engineering and stresses the facility to trace back to the scope specification and conceptual context in order to recapture or re-examine the previous modelling decisions and builds the traceability into deliverables in AKEM to enable links among stories, knowledge analysis, ontology and deployment specification.

 

Activities

Tasks

Deliverables

Problem Determination

Problem definition

Vision statement

Solution prescription

Objective specification

Scoping

Scoping problem space

Knowledge resources

Scoping semantics

Story

Scoping tasks

Task specification

Analysis

Knowledge constituency analysis

Knowledge breakdown

Knowledge elaboration

Task hierarchy analysis

Task hierarchy

Development

Extraction

Highlights

Paraphrases

Abstraction

Lexons

Organization

Architecture of ontology

Groups of lexons

Deployment

Rule specification

Commitments to lexons

Test & Validate

Unit validation

Validated stories, task specification, knowledge constituency, lexon groups, commitments

Integrated validation

Validated scope, analysis, development and deployment

Unit test

Tested tasks, commitments

System test

Tested task hierarchy

User test

Tested stories

Documentation

Unit traceability

Links across versions and deliverables

System documentation

System architecture, functionality description

Control

Feasibility and risk management

 

Life cycle management

Work flow management

Quality management

Communication management

 

 

Scoping knowledge

Scoping on the semantic space is less tangible than on information system functionalities. It is not only to identify the part of the semantics proper to focus on, but also to convey its domain contexts. The scoping activity in the domain perspective in AKEM produces two main deliverables: knowledge resources (documents, interview proto-cols) and stories (knowledge use cases). The semantic scope under consideration at a given time of knowledge engineering is specified and documented by a story. It not only identifies the focus or boundary of attention, but also conveys the semantic context in which it stands. The Settings of the story describe the background information. The Characters the actors or objects involved. The Episodes describe either sets of objects or relationships in hierarchy or a sequence of events.

 

 

 

Analysing knowledge

The analysis activity produces the knowledge constituent model and task hierarchy. The knowledge constituent model consists of the knowledge breakdown and the elaboration of each constituent. The knowledge breakdown seeks to modularise knowledge in a hierarchical structure and the knowledge elaboration provides the description of each constituent in a programme specification language to capture the concerned business logic.

 

 

Ontology development

The knowledge artefact to develop is not only a of business logic and rules but also the underlying meta-knowledge in the form of lexons (context-term-role relations). The application specific constraints and rules are the special commitments to the lexons The purpose is to maximise the reusability and versatility of knowledge resources over different applications, time and versions. Ontology is extracted from knowledge resources, such as regulations, requirements specification, abstracted into term-role tuples and organized into an architecture reflecting the knowledge structure of the expert of the subject.

 

AKEM adopts an approach of machine-assisted human knowledge engineering. It is proven that in FF POIROT, the automatic knowledge discovery can be effectively used to perform the task of extraction. With the machine results, the task of abstraction and organization can be facilitated, especially when the coverage of the domain is large and complex.

 

 

 

Ontology deployment

The deployment of ontology considers system specific, application specific features to constrain lexons to produce commitments. The deployment consists of two tasks: the specification of the ontological commitments in the light of specific processing tasks and transformation of the commitments into a particular knowledge specification such as OWL, XTM, KIF.

 

FF POIROT experience

The ontology engineering activities in FF POIROT was planned and controlled following AKEM. Several iterations of major deliverables and small experiments were performed to augment its principles and best practices.

 

References