35426902
Towards Ontological Commitments with Omega -RIDL Markup Language
In the DOGMA (Developing Ontology-Grounded Methods and Applications) ontology engineering approach, ontology construction starts from an uninterpreted base of elementary fact types, called lexons, which are mined from linguistic descriptions. Applications that ontologically commit to such a lexon base are assigned a formal semantics by mapping the application symbols to paths in this lexon base. Besides specifying which concepts are used, we restrict how they may be used and queried with semantic constraints, or rules, based on the fact-based database modeling method NIAM/ORM. Such ontological commitments are specified in the Ω-RIDL1 language. In this paper we present the Ω -RIDL Markup Language and illustrate with a case from the field of Human Resources Management.
17885523
On Constructing Semantic Decision Tables
Decision tables are a widely used knowledge management tool in the decision making process. Ambiguity and conceptual reasoning difficulties arise while designing large decision tables in a collaborative environment. We introduce the notion of Semantic Decision Table (SDT), which enhances a decision table with explicit decision semantics by annotating it properly with a domain ontology. In this paper, we focus on the SDT construction process. First, we map decision items to the ontology by building a rooted tree of decision binary facts and visualize it in a scalable manner. Formal ontological roles are used during this mapping process. Then, we commit the decision rules to the mapping results with a high level pseudo-natural language to ground their semantic. We illustrate with an SDT example from the domain of human resource management.
1436959
Towards Semantically Grounded Decision Rules Using ORM +
Recently, ontologies are proposed for many purposes to assist decision making, such as representing terminology and categorizing information. Current ontology-based decision support systems mainly contain semantically rich decision rules. In order to ground the semantics, we formalize those rules by committing them to domain ontologies. Those semantically grounded decision rules can represent the semantics precisely, thus improve the functionalities of many available rule engines. We model and visualize the rules by means of a novel extension of ORM. These rules are further stored in an XML-based markup language, ORM+ ML, which is a hybrid language of Rule-ML and ORM-ML. We demonstrate in the field of on-line customer management.
32294667
Use Semantic Decision Tables to Improve Meaning Evolution Support Systems
Meaning Evolution Support Systems have been recently introduced as a real-time, scalable, community-basedcooperative systems to support the ontology evolution. In this paper, we intend to address the problems of accuracyand effectivenessin Meaning Evolution Support Systems in general. We use Semantic Decision Tables to tackle these problems. A Semantic Decision Table separates general decision rules from the processes, bootstraps policies and template dependencies in the whole system. Recently, DOGMA-MESS (" D eveloping O ntology G rounded M ethodology and A pplications" framework based " M eaning E volution S upport S ystems") is developed at VUB STARLab as a collection of meaning evolution support systems. We embed Semantic Decision Tables in DOGMA-MESS to illustrate our approach. Semantic Decision Tables play the roles in both top-downand bottom-upprocesses of the meaning evolution cycle. The decision rules that consist of templates dependency rules are mainly responsible for the top-down process execution. The bottom-up process execution relies on the ones that contain the concept lifting algorithms.
33017168
An ontology engineering methodology for DOGMA
Although ontologies occupy a central place in the Semantic Web and related research domains, there are currently not many fully fledged ontology engineering methodologies available. In this paper, we want to present an integrated methodology for ontology engineering from scratch, inspired by various scientific disciplines, in particular database semantics and natural language processing.
31156520
An Ontology-based Data Matching Framework: use Case Competency-based HRM
As part of the European PROLIX (Process Oriented Learning and Information eXchange) project, VUB STARLab designed a generic ontology-based data matching framework (ODMF). Within the project, the ODMF is used to calculate the similarity between data elements, e.g. competency, function, person, task, and qualification, based on competency-information. Several ontology-based data matching strategies were implemented and evaluated as part of the ODMF. In this article we describe the ODMF and discuss the implemented matching strategies.
9189850
The Parameter Optimization of Human-Simulated Intelligent Controller Based on Improved Genetic Algorithm
The human-simulated intelligent controller, using a hierarchical and multi-mode control structure, effectively solves the control difficult problems of many complex systems. But there are many parameters to be determined. In order to solve them, the paper introduces an improved genetic algorithm (IGA) which is combined with orthogonal design method. It is served as the simulation experiment platform by the cart-double pendulum. Through selected the reasonable fitness function and determined the appropriate parameters range, using the IGA, the parameters of the human-simulated intelligent controller are optimized. The simulation results show the IGA is better than the manual adjustment method. So it is proved that the IGA is more effective in solving the parameter optimization of human-simulated intelligent controller, and also promotes the more extended application space of intelligent control theory.
16895768
A Novel Stroke Extraction Model for Chinese Characters based on Steerable Filters
The technology of stroke extraction plays a key role in the off-line Chinese character recognition field. The accuracy and computing complexity of classifying depend on the output of stroke extraction. Based on the analysis of the existing methods, this paper proposed a novel stroke extraction model with steerable filters. The experiments indicate that this model can be used to extract strokes from both printed and handwritten Chinese characters. Meanwhile, it also can be applied to eliminate the redundant stroke connections impressively with considerable low computing complexity
38545491
Defining Re-usable Composite Aspect Patterns: An FDAF Based Approach
Architecting secure systems is an important and challenging problem. Solutions to model individual, or atomic, security capabilities have been proposed, including security patterns, component based, aspect-oriented, and service-oriented approaches. However, little work is available on how to model reusable compositions of security capabilities, where the security capabilities interact with each other and other parts of the system design. Here, an aspect-oriented approach to modeling composite aspects is presented. The approach is defined as an extension to the Formal Design Analysis Framework (FDAF). The FDAF metamodel is extended to support the static representation of composite aspects and an approach to defining the compositions is introduced. A composite aspect that provides Account Lockout with Selective Event Logging (ALSEL) capabilities is used as an example.
29100810
An Expert System Based Approach to Modeling and Selecting Requirement Engineering Techniques
The importance of requirements engineering (RE) has been raised numerous times in literatures. To choose suitable RE techniques for a particular project in a given situation is a challenging task, requiring substantial expertise and efforts. To help solving this problem, an expert system based approach is proposed. This expert system uses the knowledge from domain experts to model the causal factors of RE techniques. It can select suitable RE techniques for a software project. A web-based questionnaire is created in the first place to collect the expertise available in the community. The information collected by the questionnaire is analyzed and transformed into a new dataset for constructing a Bayesian Belief Network (BBN). The resulting BBN integrated with a GUI forms an expert system for RE techniques modeling and selection. Empirical study validates the transformed dataset and shows that the expert system outperforms other predictors in selecting suitable RE techniques in different RE phases.
16610906
EIRS: An Intelligent E-Commerce Application System for Small Retailers in China
E-commerce is a fast growing channel for new business in China. However, Chinese small retailers have many difficulties in e-commerce applications nowadays, such as lack of IT skills, expensive of operation cost, etc. In this paper we present an intelligent retail system named eIRS which can help small retailers to implement their e-commerce strategies. eIRS whose single terminal entity looks like a telephone is an e-commerce platform linking product producers and retailers. It can reduce the redundant courses between product producers and retailers and also can act as an efficient medium, providing service similar to ICP for both of them. The test results of eIRS indicate that it creates a new win-win e-commerce model for producers and small retailers in China. We also analyze the Development Obstacles and Challenges of eIRS finally.