Ontology-based Decision Research Articles

A Decision Tool for Business Process Crowdsourcing: Ontology, Design, and Evaluation

As the crowdsourcing strategy becomes better known, the managerial decisions necessary to establish it as a viable business process are becoming increasingly important. However, a divide and conquer approach, currently dominant in the field, leads to scattered decision support for the crowdsourcing processes. We propose an ontology-based decision tool that supports the whole business process crowdsourcing. The advantage of the ontology approach is that it collects and consolidates knowledge from the existing literature to provide a solid knowledge base for the tool construction. Operationalising the ontology, the tool helps make the decision to crowdsource or not, and choose appropriate design alternatives for the crowdsourcing process. We evaluated the tool through a controlled experiment with 190 participants. The obtained results show that the tool is useful by significantly increasing: (1) the performance in making the decision to crowdsource or not, and (2) the design of crowdsourcing processes.

An Ontology-Based Decision Support System for the Diagnosis of Plant Diseases

There are several cities and countries whose population depends on agriculture. Crops demand close monitoring regarding diseases because these ones can affect significantly both production and post-harvest life. The identification of disease symptoms plays a crucial role in the successful cultivation of crops. The diagnosis of diseases is a challenging task since many symptoms should be considered, which makes a proper diagnosis becomes a knowledge handling problem. This paper specifies an ontology-based decision support system that promotes the knowledge of experts for the plant disease diagnosis to farmers. This system takes advantage of ontologies in two ways, to exploit the knowledge contained in the ontology for decision support purposes, in this case, the diagnosis of diseases, and to provide a standard vocabulary for integrating phytopathology data sources. The system was evaluated for the diagnosis of diseases presented in short-cycle and perennial crops achieving promising results based on the F-measure metric.

Ontology-based Decision Support System with Analytic Hierarchy Process for Tour Package Selection

The popularity of Internet and growing B2C electronic commerce nowadays make product or service information easy to be acquired. However, making an optimal choice from the various alternative products becomes a laborious process. In this paper, an ontology-based Decision Support System (DSS) with Analytic Hierarchy Process (AHP) was proposed for the specific application of tour package selection. The system is composed of two subsystems, the product gatherer and the decision maker, which are used to find out right products and make an expected choice respectively. In the product gatherer subsystem, an ontology-based web service architecture with Web Ontology Language (OWL) was established for the semantic content processing of product information. The Simple Object Access Protocol (SOAP) is utilized to establish the communication interface and gather XML-based contents through Remote Procedure Calls (RPC) between the system and the database servers of travel agencies. In the decision maker subsystem, the Analytic Hierarchy Process is utilized to make an optimal decision for satisfying the requirement given by the consumer. The system aims to help consumers to avoid falling into decision-making hesitation and get an expected choice from various and similar products.

Towards an ontological infrastructure for chemical process simulation and optimization in the context of eco-industrial parks

In this paper, we introduce the concept of constructing an ontology-based decision support system called J-Park Simulator for the design and operation of eco-industrial parks. It is inspired by Jurong industrial park in Singapore. A biodiesel plant is implemented into the system as a first step. OntoCAPE is adapted for the purposes of the biodiesel plant to establish a knowledge base, which is employed to carry out a number of applications via J-Park Simulator. Firstly, information query can be performed. Information of the biodiesel plant can be extracted through natural language query. Secondly, J-Park Simulator can be used to carry out process simulation. New process equilibrium can be evaluated after certain operation parameters change. Thirdly, process optimization can be realized through J-Park Simulator. Optimal operation condition under different market scenarios can be obtained for the biodiesel producing process in order to reduce the energy consumption and achieve maximal plant profit.

Towards ontology-based decision support systems for complex ultrasound diagnosis in obstetrics and gynecology

IntroductionWe have developed a new knowledge base intelligent system for obstetrics and gynecology ultrasound imaging, based on an ontology and a reference image collection. This study evaluates the new system to support accurate annotations of ultrasound images. We have used the early ultrasound diagnosis of ectopic pregnancies as a model clinical issue. Material and methodsThe ectopic pregnancy ontology was derived from medical texts (4260 ultrasound reports of ectopic pregnancy from a specialist center in the UK and 2795 Pubmed abstracts indexed with the MeSH term “Pregnancy, Ectopic”) and the reference image collection was built on a selection from 106 publications. We conducted a retrospective analysis of the signs in 35 scans of ectopic pregnancy by six observers using the new system. ResultsThe resulting ectopic pregnancy ontology consisted of 1395 terms, and 80 images were collected for the reference collection. The observers used the knowledge base intelligent system to provide a total of 1486 sign annotations. The precision, recall and F-measure for the annotations were 0.83, 0.62 and 0.71, respectively. The global proportion of agreement was 40.35% 95% CI [38.64–42.05]. DiscussionThe ontology-based intelligent system provides accurate annotations of ultrasound images and suggests that it may benefit non-expert operators. The precision rate is appropriate for accurate input of a computer-based clinical decision support and could be used to support medical imaging diagnosis of complex conditions in obstetrics and gynecology.

Automatic classification of histopathological diagnoses for building a large scale tissue catalogue

In this paper an automatic classification system for pathological findings is presented. The starting point in our undertaking was a pathologic tissue collection with about 1.4 million tissue samples described by free text records over 23 years. Exploring knowledge out of this “big data” pool is a challenging task, especially when dealing with unstructured data spanning over many years. The classification is based on an ontology-based term extraction and decision tree build with a manually curated classification system. The information extracting system is based on regular expressions and a text substitution system. We describe the generation of the decision trees by medical experts using a visual editor. Also the evaluation of the classification process with a reference data set is described. We achieved an F-Score of 89,7% for ICD-10 and an F-Score of 94,7% for ICD-O classification. For the information extraction of the tumor staging and receptors we achieved am F-Score ranging from 81,8 to 96,8%.

An Ontology-based System for Building Individualized Education Plans for Students with Special Educational Needs

This paper presents the results of a two years research project aimed at adopting semantic web technology to draft the IEP (Individualized Education Plan) for pupils with special educational needs in school. It includes a report of lessons learned through the collaborative building of an ontology in a concrete and multidisciplinary context, as well as in developing an ontology-based decision support system.

OntoDiabetic: An Ontology-Based Clinical Decision Support System for Diabetic Patients

Clinical decision support systems (CDSS) assist medical practitioners in their daily work, thereby enhancing the quality of care given to a patient. It supports them in the decision-making process and suggests appropriate treatments. The use of the ontology to build knowledge-driven decision support systems is widely adopted. Ontology is best suited to encapsulate the concepts and relationships of terms associated with the medical domain. It is suitable for capturing medical knowledge in a formal way, allowing sharing and reusing it whenever necessary. All concepts and relationships detailed in clinical guidelines can be implemented using Web Ontology Language (OWL). The reasoning mechanism is vital in any knowledge-based system. Ontology can be reasoned to recommend the suitable treatment for a patient by considering the current medical status of the patient. OntoDiabetic, an ontology-based decision support system is developed to assess the risk factors and provide appropriate treatment suggestions for diabetic patients. This paper focuses on the modeling and implementation of clinical guidelines using OWL2 rules and the reasoning process of the OntoDiabetic system. The case study is conducted for patients having the risk of overt cardiovascular disease, diabetic nephropathy and hypertension in primary health centers of Oman.

An ontology-based decision support tool for optimizing domestic solar hot water system selection

In an effort to tackle climate change most countries utilize renewable energy sources. This is more pronounced in the building sector, which is currently one of the major consumers of energy, mostly in the form of heat. In order to further promote the use of domestic solar hot water systems in buildings, an ontology-based decision support tool has been developed and is presented in this paper. The proposed tool aids non-technical consumers to select a domestic solar hot water system tailored to their needs, containing up-to-date information on its components and interrelationships, installation costs etc., in the form of an ontology formulated in OWL (Web Ontology Language). The optimum system configurations are computed based on various specific parameters, such as number of occupants, daily hot water requirements and house location. The backbone of the proposed system is an ontology that represents the application domain and contains information regarding the various domestic solar hot water system components along with their interrelationships. Ontologies are a rapidly evolving knowledge representation paradigm that offers various advantages and, when deployed specifically in the domestic solar hot water systems domain, deliver improved representation, sharing and re-use of the relevant information. As a conclusion, this paper presents an ontology-driven decision support system for facilitating the selection of domestic solar hot water system, which delivers certain advantages, such as sustainability of the decision support system itself, due to its open and interoperable knowledge-base, and its adaptability/flexibility in decision making policies, due to is semantic (ontological) nature.

Integration of ontology-based decision support and a cognitive map approach to predict flat-screen TV market trends

In previous studies of the ontological approach to context modeling, two issues have remained unresolved: (1) how to update an ontology to adapt to changes in context, and (2) how to make proper inferences from the ontology established. To address the first issue, this study proposed the use of an ontology and cognitive map-based decision-support mechanism that emphasizes the competitive relationships between LCD (liquid crystal display) and PDP (plasma display panel) TVs. We investigated the dynamic competitive relationship between these TVs using ontological and cognitive map approaches by suggesting a robust form of context modeling that adapts to changes in context. This study also addressed how to facilitate the prediction of market trends for LCD and PDP TVs from the ontology established and proposed a new ontological approach to context modeling of the LCD and PDP TV markets.

The Priority of Decision Criteria why do College Students Play MMORPGs or Not: by Ontology-Based Ethnographic Decision Tree Model

The Priority of Decision Criteria why do College Students Play MMORPGs or Not: by Ontology-Based Ethnographic Decision Tree Model

Multi-criteria decision making in ontologies

Decision support is one of the main objectives of ontology-based knowledge management systems. However, there is no standard method that would define how to model decisions in ontologies. Despite many research efforts and established methods for decision modelling and support, they have not yet been systematically applied to the field of ontologies. This paper proposes an ontology based multi-criteria decision making method that enables one to define decision models using ontology as the base construct. It structures decision models in such a way that the problem solution can be obtained by reasoning upon the ontology. We propose a generic approach that can be applied to an arbitrary domain. The proposed method is based on qualitative multi-criteria decision making, which is applied to the field of ontologies. OWL is used as the ontology representation language. As a proof of concept we have developed an ontology-based decision support system for an electric power transmission company. The proposed method represents an important step forward in the field of ontologies. Its main advantages are: a higher level of decision support provided by the ontology, direct use of information captured in ontology for decision making, a higher level of business process automation, and reuse of decision model concepts in definitions of more complex ontology concepts.

The Development of a Product Ontology Information System for Similarity Measurement

Recently, the amount of multimedia content is increasing considerably. Several concurrent trends made this happen. Current software made creating, sharing, and editing of multimedia resources a common and easy task. In this paper we present a formal and implemented generic framework for building ontology-based information systems. The recall and precise of product ontology similarity measurement were higher than that of the traditional similarity measurement. In order to address this research issue, an ontology-based multi criteria decision model is proposed. The experimental results indicate that this method has great promise.

Practice-centred work analysis: towards ontology-based decision support in cross-boundary e-health

Ontologies are increasingly used as artefacts for representing human knowledge for problem solving and decision making. Studies have shown that the actual knowledge used in problem solving and decision-making often occurs at the work practice level – the level that typically goes beyond functional notions of work processes to include a wide range of contextual, socio-cultural, situated and dynamically changing factors through which individuals, in getting work done, seek to construct meaning out of their experiences and engagement with the environment. However, it still remains an open question how much of the issues encompassed by a notion of work practice is accommodated in current ontology design approaches in system design. In this paper, the creation and use of ontologies is analysed from a practice-theoretic perspective in order to understand the processes of shared conceptualisation and role of ontologies in explicating knowledge about actual clinical work processes and problem situations. We illustrate this using a clinical work practice ontology, which we refer to as WOPRON, and show its capability for representing clinical work practice knowledge for cross-boundary decision support in e-health.

Ontology-based decision support system for semiconductors EDS testing by wafer defect classification

Wafer failures have be analyzed and managed to improve the performance of semiconductor EDS testing. Thereby, in this paper, we propose an ontology-based decision support system to predict which defects may happen in a given condition by classifying previous wafer failure patterns. Especially, ontologies are exploited to represent and maintain (i.e., classify) those wafer failures, so that heterogeneity problem among multiple end users (e.g., in the clean room) can be dealt with. More importantly, this ontology-based scheme can build a system entity structure (SES) that contains knowledge of decomposition, taxonomy, and coupling relationships of a system necessary to direct model synthesis. Also, given a certain wafer failure, the SES can be reduced into a substructure, called PES, by removing irrelevant entities. Consequently, this system can support the end users to efficiently evaluate and monitor semiconductor data by (i) analyzing failures to find out the corresponding causes and (ii) managing historical data resulting into such failures. Therefore, this study contributes to the increase in product quality and wafer yield.