17436215
Organizing Meaning Evolution Supporting Systems Using Semantic Decision Tables
DOGMA-MESS (Meaning Evolution Support System) is a system and methodology for supporting scalable, community-grounded ontology engineering. It uses a socio-technical process of meaning negotiation to tackle the scalability problems in ontology engineering. In order to improve the effectiveness of DOGMA-MESS, we adopt the idea of Semantic Decision Table (SDT). An SDT contains semantically rich decision rules that guide DOGMA-MESS micro-processes. It separates the decision rules from DOGMA-MESS. SDT with different decision rules results in different final decisions, which can be evaluated. In this paper, we illustrate how SDTs are used and apply our approach in the domain of Human Resource 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.
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.
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.
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.
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.
22140835
The Algorithm of R Peak Detection in ECG Based on Empirical Mode Decomposition
A novel automatic R peak detection algorithm that is based on EMD (empirical mode decomposition) and adaptive threshold technique is presented. The R peaks are then identified by computing the first threshold and applying a set of adaptive thresholds that are not limited to a strict range. The proposed algorithm was able to detect R peak with high sensitivity when compared to other algorithms used in Physionet ECG database. Additionally, the new algorithm can successfully detect R peak for a wide variety of ECG shapes.
14166643
3D Model Simplification Method Based on ROI
Based on edge-collapse algorithm and ROI (region of interest), an effective 3D mesh simplification method is proposed. During the simplification procedure, this method could successfully preserve a high resolution for the 3D part that the user is interested in while the rest parts could meet the basic requirement with lower resolution. The experiments support the conclusion.
12737531
Accelerating the 2D Mallat decomposition algorithm with cyclical convolution and FNTT
This paper presents a novel approach to accelerate the 2D Mallat decomposition algorithm. In particular the proposed approach performs the 2D Mallat decomposition algorithm by way of computing the 2D cyclical convolution, and further completes the 2D cyclical convolution with the fast number theoretical transform (FNTT). Therefore, the 2D Mallat decomposition algorithm can be speeded up. The results obtained from the theoretical analysis and the experiment show that the 2D Mallat decomposition algorithm is significantly improved.
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.