Departing the Ontology Layer Cake

2015 ◽  
pp. 167-203 ◽  
Author(s):  
Abel Browarnik ◽  
Oded Maimon
Keyword(s):  
Time Complexity ◽  
Alternative Model ◽  
Linguistic Knowledge ◽  
Ontology Learning ◽  
Learning Framework ◽  
Layer Cake ◽  
Learning From Text ◽  
Scientific Papers ◽  
Entity Relationship ◽  
Entity Relationship Diagrams

In this chapter we analyze Ontology Learning and its goals, as well as the input expected when learning ontologies - peer-reviewed scientific papers in English. After reviewing the Ontology Learning Layer Cake model's shortcomings we suggest an alternative model based on linguistic knowledge. The suggested model would find the meaning of simple components of text – statements. From them it is easy to derive cases and roles that map the reality as a set of entities and relationships or RDF triples, somehow equivalent to Entity-relationship diagrams. Time complexity for the suggested ontology learning framework is constant (O(1)) for a sentence, and O(n) for an ontology with n sentences. We conclude that the Ontology Learning Layer Cake is not adequate for Ontology Learning from text.

Ontology Learning from Text

2015 ◽  
Vol 4 (2) ◽  
pp. 1-14 ◽  
Author(s):  
Abel Browarnik ◽  
Oded Maimon
Keyword(s):  
Machine Learning ◽  
Linguistic Knowledge ◽  
Ontology Learning ◽  
Layer Cake ◽  
Learning From Text ◽  
Alternative Approaches ◽  
Or Applications

The goal of Ontology Learning from Text is to learn ontologies that represent domains or applications that change often. Manually learning and updating such ontologies is too expensive. This is the reason for the Ontology Learning discipline's emergence. The leading approach to Ontology Learning from Text is the Ontology Learning Layer Cake. This approach splits the task into four or five sequential tasks. Each of the tasks may use diverse methods, ranging from uses of Linguistic knowledge to Machine Learning. The authors review the shortcomings of the Ontology Learning Layer Cake approach and conclude that the approach is not viable for Ontology Learning from Text. They suggest alternative approaches that may help learning ontologies in an efficient, effective way.

The Problem of Equivalence for Entity-Relationship Diagrams

1983 ◽  
Vol SE-9 (5) ◽  
pp. 617-630 ◽  
Author(s):  
S. Jajodia ◽  
P.A. Ng ◽  
F.N. Springsteel
Keyword(s):  
Entity Relationship ◽  
Entity Relationship Diagrams

A METRIC-BASED APPROACH FOR PREDICTING CONCEPTUAL DATA MODELS MAINTAINABILITY

2001 ◽  
Vol 11 (06) ◽  
pp. 703-729 ◽  
Author(s):  
MARIO PIATTINI ◽  
MARCELA GENERO ◽  
LUIS JIMÉNEZ
Keyword(s):  
Structural Complexity ◽  
Data Models ◽  
Major Step ◽  
Development Life Cycle ◽  
Novel Method ◽  
Is Development ◽  
Entity Relationship ◽  
Conceptual Data ◽  
Entity Relationship Diagrams

It is generally accepted in the information system (IS) field that IS quality is highly dependent on the decisions made early in the development life cycle. The construction of conceptual data models is often an important task of this early development. Therefore, improving the quality of conceptual data models will be a major step towards the quality improvement of the IS development. Several quality frameworks for conceptual data models have been proposed, but most of them lack valid quantitative measures in order to evaluate the quality of conceptual data models in an objective way. In this article we will define measures for the structural complexity (internal attribute) of entity relationship diagrams (ERD) and use them for predicting their maintainability (external attribute). We will theoretically validate the proposed metrics following Briand et al.'s framework with the goal of demonstrating the properties that characterise each metric. We will also show how it is possible to predict each of the maintainability sub-characteristics using a prediction model generated using a novel method for induction of fuzzy rules.

Information Technology Transfer in Econometric Forecasting: A Pictorial Approach

1993 ◽  
Vol 8 (1) ◽  
pp. 3-13
Author(s):  
P. Pete Chong ◽  
Ye-Sho Chen ◽  
James M. Pruett
Keyword(s):  
Information Technology ◽  
Technology Transfer ◽  
Information Exchange ◽  
Management Tool ◽  
Task Management ◽  
System Maintenance ◽  
Concise Information ◽  
Entity Relationship ◽  
Entity Relationship Diagrams

Successful information technology transfer requires effective communication and clear, concise information exchange. This paper, using the Louisiana econometric model as a case study, proposes a pictorial approach to present and manage complex factors essential to information technology transfer. The approach utilizes multi-layer entity-relationship diagrams to provide a meaningful framework for the entire forecasting process, provide clarity to ensure better model maintenance when changes in social/economic structures require reformulations, and provide a procedural and data dictionary for clear documentation. The pictorial approach is both intuitive and readable, capable of serving as a task management tool, a model implementation aid, and a system maintenance resource.

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