2826645
PROFILE COMPILER: Ontology-based, community-grounded, multilingual online services to support collaborative decision making
In todaypsilas industries, ontologies and the modern semantic technologies are offered as the potential solutions to support mutual understanding in the collaborative settings. Currently, organizations are unable to cope with collaborative decision making in a particular domain. Mastering domain knowledge and sharing decision rules is a complex yet important issue. In this paper, we first design a generic framework for community-grounded, collaborative decision making enabled, ontology based systems. This framework is constructed with three main engineering tracks: the linguistic engineering track, the ontology engineering track and the group decision engineering track. Each engineering track has its own methodology supported by a tool. Then, we use Semantic Decision Tables to elaborate the knowledge blocks resulting from these three engineering tracks. Semantic Decision Tables are mainly used (1) to elaborate concepts of the domain ontologies, (2) to gather semantically grounded decision rules within a decision group and (3) to provide tabular reports to the users. Finally, an online multilingual knowledge platform - the PROFILE COMPILER is developed as the result. It allows small or medium-sized enterprises (SMEs) to retrieve and personalize existing domain ontology, design and share their decision rules. We illustrate in the domain of Human Resource Management (HRM).
15667862
Semantic Decision Tables: Self-organizing and Reorganizable Decision Tables
A Semantic Decision Table (SDT) provides a means to captureand examinedecision makers' concepts, as well as a tool for refiningtheir decision knowledge and facilitating knowledgesharingin a scalablemanner. One challenge SDT faces is to organize decision resources represented in a tabular format based on the user's needs at different levels. It is important to make it self organizedand automatically reorganizedwhen the requirements are updated. This paper describes the ongoing research on SDT and its tool that supports the self organizations and automatic reorganization of decision tables. We argue that simplicity, precision, and flexibilityare the key issues to respond to the paper challenge. We propose a novel combination of the principles of Decision Support and Database Modeling, together with the modern technologies in Ontology Engineering, in the adaptive s elf- o rganization and a utomatic r eorganization procedures (SOAR).
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.
16344510
On Constructing, Grouping and Using Topical Ontology for Semantic Matching
An ontology topic is used to group concepts from different contexts (or even from different domain ontologies). This paper presents a pattern-driven modeling methodology for constructing and grouping topics in an ontology (PAD-ON methodology), which is used for matching similarities between competences in the human resource management (HRM) domain. The methodology is supported by a tool called PAD-ON. This paper demonstrates our recent achievement in the work from the EC Prolix project. The paper approach is applied to the training processes at British Telecom as the test bed.
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.
8498633
The Data Scales in Multi-valued Context Based on Formal Concept Analysis
Formal Concept Analysis has very important meaning in mathematical methods for data mining and knowledge processing. In the face of the variety of the data structure, traditional methods can hardly meet decider’s need. This paper summarizes a few of data structure for the concept analysis and present the data scale methods for each other based on their own characters. It not only extends the application of the concept analysis, but also makes the data scale methods more efficiently.
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.
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
44335940
DRC-BK: Mining Classification Rules with Help of SVM
Currently, the accuracy of SVM classifier is very high, but the classification model of SVM classifier is not understandable by human experts. In this paper, we use SVM, which is applied with a Boolean kernel, to construct a hyper-plan for classification, and mine classification rules from this hyper-plane. In this way, we build DRC-BK, a decision rule classifier. Experiment results show that DRC-BK has a higher accuracy than some state-of-art decision rule (decision tree) classifiers, such as C4.5, CBA, CMAR, CAEP and so on.
40790501
A Practical Peer-Performance-Aware DHT
How to Build an efficient Distributed Hash Table (DHT) is a fundamental issue in Peer-to-Peer research field. Previous solutions ignore the heterogeneity of the large scale network. However, in practice, the fact is that the resource held by each peer in the Internet is extremely diverse. And the the willing to share local resources of each peer is also diverse. Therefore, the contribution for the system of a peer should depend on the resources it holds or how many resources it want to share, and should not be uniform. In this paper, we propose a Peer-Performance-Aware Distributed Hash Table (PPADHT) which aims to exploit the heterogeneity. It takes the performance difference of peers into consideration to construct a dynamic variation of wrapped butterfly to achieve the goal. We also show how to optimize the performance of PPADHT in the view of hop counts by random graphs. Our simulation results show that the average lookup hop counts of the PPADHT is approximately a log scale with constant out degrees. And it can achieve loadbalance in two ways: both the document load and message routing load, without introducing any additional load on the peer. Here, the load balance means the load is proportion to the performance of peer.