Comparing Drools and Ontology Reasoning Approaches for Automated Monitoring in Telecommunication Processes

Ordóñez, Armando; Eraso, Luis; Ordóñez, Hugo; Merchán, Luis

doi:10.1016/j.procs.2016.09.345

Cited by 14 publications

(6 citation statements)

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“…Despite Drools is a good option to develop rule-based decision support systems, it does not allow the direct use of ontologies. Furthermore, Drools adopt an object-oriented approach, which is less expressive than OWL, which has a better representation and inference [31]. Also, in Drools, rules are written in DRL (Drools Rule Language) language, an open-source but non-standard language.…”

Section: Related Workmentioning

confidence: 99%

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AgriEnt: A Knowledge-Based Web Platform for Managing Insect Pests of Field Crops

et al. 2020

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In the agricultural context, there is a great diversity of insects and diseases that affect crops. Moreover, the amount of data available on data sources such as the Web regarding these topics increase every day. This fact can represent a problem when farmers want to make decisions based on this large and dynamic amount of information. This work presents AgriEnt, a knowledge-based Web platform focused on supporting farmers in the decision-making process concerning crop insect pest diagnosis and management. AgriEnt relies on a layered functional architecture comprising four layers: the data layer, the semantic layer, the web services layer, and the presentation layer. This platform takes advantage of ontologies to formally and explicitly describe agricultural entomology experts’ knowledge and to perform insect pest diagnosis. Finally, to validate the AgriEnt platform, we describe a case study on diagnosing the insect pest affecting a crop. The results show that AgriEnt, through the use of the ontology, has proven to produce similar answers as the professional advice given by the entomology experts involved in the evaluation process. Therefore, this platform can guide farmers to make better decisions concerning crop insect pest diagnosis and management.

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Section: Related Workmentioning

confidence: 99%

“…Also, in Drools, rules are written in DRL (Drools Rule Language) language, an open-source but non-standard language. In this sense, SWRL rules are simpler and easier to define than DRL rules [31]. On the other hand, works such as [12,24,28] relied on fussy technologies for developing the rules.…”

Section: Related Workmentioning

confidence: 99%

AgriEnt: A Knowledge-Based Web Platform for Managing Insect Pests of Field Crops

et al. 2020

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“…However, issues related to interdomain interactions must still be addressed. Ordóñez et al present an interesting idea, ie, introducing ontology and semantic‐based approaches to typical daily CSP activities, such as network monitoring and error detection. They compare the OWL approach to the DROOLS approach and conclude that despite the better performance of DROOLS on large data sets, OWL, a semantic approach, is more convenient because it can adopt new and already existing ontologies and offers better possibilities for expressing rules.…”

Section: Related Workmentioning

confidence: 99%

Extending a communication service provider's operations management based on knowledge granules and aspects derived from imperfect information

Dragun

Bogunović

2019

Int J Communication

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The core of a communication service provider's business revolves around servicerelated operations management, usually relying strongly on expert knowledge built inside the company. Knowledge representation in this environment inevitably requires addressing various domains. Team members working on common tasks bring domain diversity to their teams due to their various ontological areas of expertise. All team members must attain an acceptable level of knowledge regarding other domains to reach a team's desired level of effectiveness. This paper provides a novel way of linking divergent domain ontologies to facilitate and enrich service management in the communication service provider environment. The typical service provider infrastructure offers an extensive source of information for deriving knowledge regarding services, but the information is generally imperfect, inconsistent, and incomplete. Introducing a granular approach based on rough sets applicable to this imperfect information enables fractional management, which together with multiaspect formalisms enables the derivation of the specificity of a certain domain from a common source of knowledge without interference from other domains. A case study illustrates the implementation of an intelligent communication service and network management system based on the contexts used for assisting service strategies. The implementation empirically justifies the proposed approach.KEYWORDS aspects, imperfect information, knowledge granules, knowledge management, service operations management 1 | INTRODUCTION Communication service providers (CSPs) attempt to incorporate many disciplines into their daily service providing processes to differentiate themselves in the market. This incorporation involves broadening the set of standard telecomrelated domains by introducing new domains necessary to produce new content and to enrich existing portfolios. The final goal of these activities is to improve the CSP's service quality. However, despite many advantages of a multidisciplinary approach, stemming mostly from the fact that diversity results in greater versatility when providing services, substantial challenges exist under these conditions. Because introducing various disciplines requires bringing in experts from those disciplines to work together to provide the related services, functional teams covering all necessary domains

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“…A rule-based system is a form of an expert system which usually uses an ontology-based representation to codify the information into a knowledge-base that contains facts which can be used later in the reasoning stage [26]. A rule-based system depends on two kinds of memory as it is shown in Figure 4a.…”

Section: Rule-based Systemmentioning

confidence: 99%

An Ontology-Based Approach to Enable Knowledge Representation and Reasoning in Worker–Cobot Agile Manufacturing

Sadik

Urban

2017

Future Internet

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Abstract:There is no doubt that the rapid development in robotics technology has dramatically changed the interaction model between the Industrial Robot (IR) and the worker. As the current robotic technology has afforded very reliable means to guarantee the physical safety of the worker during a close proximity interaction with the IR. Therefore, new forms of cooperation between the robot and the worker can now be achieved. Collaborative/Cooperative robotics is the new branch of industrial robotics which empowers the idea of cooperative manufacturing. Cooperative manufacturing significantly depends on the existence of a collaborative/cooperative robot (cobot). A cobot is usually a Light-Weight Robot (LWR) which is capable of operating safely with the human co-worker in a shared work environment. This is in contrast with the conventional IR which can only operate in isolation from the worker workspace, due to the fact that the conventional IR can manipulate very heavy objects, which makes it so dangerous to operate in direct contact with the worker. There is a slight difference between the definition of collaboration and cooperation in robotics. In cooperative robotics, both the worker and the robot are performing tasks over the same product in the same shared workspace but not simultaneously. Collaborative robotics has a similar definition, except that the worker and the robot are performing a simultaneous task. Gathering the worker and the cobot in the same manufacturing workcell can provide an easy and cheap method to flexibly customize the production. Moreover, to adapt with the production demands in the real time of production, without the need to stop or to modify the production operations. There are many challenges and problems that can be addressed in the cooperative manufacturing field. However, one of the most important challenges in this field is the representation of the cooperative manufacturing environment and components. Thus, in order to accomplish the cooperative manufacturing concept, a proper approach is required to describe the shared environment between the worker and the cobot. The cooperative manufacturing shared environment includes the cobot, the co-worker, and other production components such as the product itself. Furthermore, the whole cooperative manufacturing system components need to communicate and share their knowledge, to reason and process the shared information, which eventually gives the control solution the capability of obtaining collective manufacturing decisions. Putting into consideration that the control solution should also provide a natural language which is human readable and in the same time can be understood by the machine (i.e., the cobot). Accordingly, a distributed control solution which combines an ontology-based Multi-Agent System (MAS) and a Business Rule Management System (BRMS) is proposed, in order to solve the mentioned challenges in the cooperative manufacturing, which are: manufacturing knowledge representation, sharing, and reasoning.

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Comparing Drools and Ontology Reasoning Approaches for Automated Monitoring in Telecommunication Processes

Cited by 14 publications

References 14 publications

AgriEnt: A Knowledge-Based Web Platform for Managing Insect Pests of Field Crops

AgriEnt: A Knowledge-Based Web Platform for Managing Insect Pests of Field Crops

Extending a communication service provider's operations management based on knowledge granules and aspects derived from imperfect information

An Ontology-Based Approach to Enable Knowledge Representation and Reasoning in Worker–Cobot Agile Manufacturing

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