Ontological Knowledge Representation Research Articles

OGSS: An Ontology-Guided and Scheduled-Sampling Approach for Overlapping Event Extraction

Event extraction is a complex and challenging task in the field of information extraction. It aims to identify event types, triggers, and argument information from the text. In recent years, overlapping event extraction has attracted the attention of researchers because of its higher challenge and practicability, and some work has carried out in-depth research on overlapping event extraction and achieved remarkable results. But these works (1) ignore the role of ontology knowledge in event extraction; (2) use the same semantic encoding for multi-stage models, lacking consideration for the independent characteristics of extraction tasks such as event types, triggers, and arguments; and (3) face issues in the training process of multi-stage models, such as error cascading and slow convergence. To address the above issues, we propose an ontology-guided and scheduled-sampling approach for overlapping event extraction, termed as OGSS. First, we design a symmetric matrix for event ontology knowledge representation and integrate it into the semantic encoding process, infusing ontology knowledge into event extraction. Second, for extraction targets such as event types, triggers, and arguments, we process the semantic encoding according to the characteristics of each extraction target, obtaining semantic representations tailored for each subtask. Finally, we view multi-stage predictions as sequential outputs of a joint model, using a scheduled sampling strategy between subtasks to effectively mitigate the cascading propagation of errors during training and accelerate model convergence. We conduct extensive experiments on the FewFc event extraction benchmark dataset. The results show that OGSS achieves significant improvements in overlapping event extraction tasks compared to previous methods.

ORPP—An Ontology for Skill-Based Robotic Process Planning in Agile Manufacturing

Ontology plays a significant role in AI (Artificial Intelligence) and robotics by providing structured data, reasoning, action understanding, context awareness, knowledge transfer, and semantic learning. The structured framework created by the ontology for knowledge representation is crucial for enabling intelligent behavior in robots. This paper provides a state-of-the-art analysis on the existing ontology approaches and at the same time consolidates the terms in the robotic task planning domain. The major gap identified in the literature is the need to bridge higher-level robotic process management and lower-level robotic control. This gap makes it difficult for operators/non-robotic experts to integrate robots into their production processes as well as evaluate key performance indicators (KPI) of the processes. To fill the gap, the authors propose an ontology for skill-based robotics process planning (ORPP). ORPP not only provides a standardization in the robotic process planning in the agile manufacturing domain but also enables non-robotic experts to design and plan their production processes using an intuitive Process-Task-Skill-Primitive structure to control low-level robotic actions. On the performance level, this structure provides traceability of the KPIs down to the robot control level.

Онтолого-орiєнтована мультиагентна система для децентралiзованого керування групою БПЛА

Introduction. Today, UAVs are becoming an increasingly important tool for performing complex tasks in various fields of application, both civil (economic) and military, as they are particularly effective in dynamically uncertain environments with hard-to-reach areas. In addition, technological advances such as blockchain, artificial intelligence (AI) and machine learning have enabled the development of updated and improved UAV systems. To create and deploy a swarm of UAVs, coordinate actions, manage, and exchange data, a model of a multi-agent system (MAC) based on an ontological representation of knowledge is proposed. This model enables a swarm of UAVs to effectively make decisions in various situations while performing assigned tasks. This approach enables the safety, reliability, and efficiency of the tasks of the UAV group. The purpose of the paper is to develop further the theoretical and practical foundations of the integration of the multi-agent system (MAS) based on the ontological representation of knowledge with the UAV network. This involves the development of a MAS architecture and a hierarchical set of ontologies of different levels. The goal is to create a common data description lan guage, define data semantics to ensure data uniqueness and consistency, provide support for decision-making during UAV swarm management, and swarm survivability in the event of aircraft failures or loss. It is necessary to develop algorithms and a method of dividing a complex task into sub-tasks in a swarm of UAVs among all MAS agents. This is to ensure reliable exchange of messages (data) between agents during the joint performance of the assigned task, and the possibility of dynamic redistribution of roles between UAV agents as needed. Methods. During the research, the general theory of intelligent information technologies was applied; agent theory methods in particular intelligent BDI agents; methods of analyzing the performance of wireless data exchange networks; theory of combinatorial optimization for dividing tasks into subtasks; methods of ontological analysis and descriptive logic to create an ontological hierarchical model of the subject area; methods of enriching ontological models from external semantically marked information resources. Results. As a result of the performed scientific research, the MAS architecture was proposed and its main functions were determined for the decentralized control of a swarm of UAVs. A set of agents with assigned roles was formed, who jointly (cooperatively) perform tasks, exchanging messages, and information with each other, which ensures the survivability of the system (in case of a failure or loss of the device, its task must be distributed among other drones). Plans and scenarios of MAS actions for various situations and means of coordinating actions between agents have been developed to perform the mission by a swarm of UAVs. A hierarchical ontological model of the subject area related to the work of the UAV swarm has been created. The algorithms and methods were based on the integration of semantic technologies that support the MAS during the execution of the UAV swarm mission, decision-making, assessment of the dynamic environment, and response to its changes. Conclusions. An original approach, algorithms, and method for improving the system of decentralized control of a group of UAVs were proposed. Expanding the functionality of the system for maintaining the interaction of a swarm of unmanned systems based on MAS artificial intelligence was suggested. This system was based on ontological models. The models describe knowledge of the subject area, processes of UAV swarm operation, scenarios of actions in difficult situations, distribution of roles to agents, principles of planning, and coordination. The proposed MAS is integrated with the UAV swarm software platform, which makes it possible to improve the efficiency of the decentralized control system and adapt UAVs to dynamic changes in the environment. The practical result of the work will be a prototype of a software agent system that interacts with ontologies while performing simple tasks. The economic significance of the work consists of focusing on the creation of new intelligent information technologies, which were based on AI and knowledge of the subject area, and this significantly increases the efficiency of the functioning of modern systems. Keywords: multi-agent system, ontology, formalization of knowledge, UAV, drone, decentralized control, task allocation.

Ontology based autonomous robot task processing framework.

In recent years, the perceptual capabilities of robots have been significantly enhanced. However, the task execution of the robots still lacks adaptive capabilities in unstructured and dynamic environments. In this paper, we propose an ontology based autonomous robot task processing framework (ARTProF), to improve the robot's adaptability within unstructured and dynamic environments. ARTProF unifies ontological knowledge representation, reasoning, and autonomous task planning and execution into a single framework. The interface between the knowledge base and neural network-based object detection is first introduced in ARTProF to improve the robot's perception capabilities. A knowledge-driven manipulation operator based on Robot Operating System (ROS) is then designed to facilitate the interaction between the knowledge base and the robot's primitive actions. Additionally, an operation similarity model is proposed to endow the robot with the ability to generalize to novel objects. Finally, a dynamic task planning algorithm, leveraging ontological knowledge, equips the robot with adaptability to execute tasks in unstructured and dynamic environments. Experimental results on real-world scenarios and simulations demonstrate the effectiveness and efficiency of the proposed ARTProF framework. In future work, we will focus on refining the ARTProF framework by integrating neurosymbolic inference.

Developing a Quality Improvement Implementation Taxonomy for Organizational Employee Wellness Initiatives.

Standardized taxonomies (STs) facilitate knowledge representation and semantic interoperability within health care provision and research. However, a gap exists in capturing knowledge representation to classify, quantify, qualify, and codify the intersection of evidence and quality improvement (QI) implementation. This interprofessional case report leverages a novel semantic and ontological approach to bridge this gap. This report had two objectives. First, it aimed to synthesize implementation barrier and facilitator data from employee wellness QI initiatives across Veteran Affairs health care systems through a semantic and ontological approach. Second, it introduced an original framework of this use-case-based taxonomy on implementation barriers and facilitators within a QI process. We synthesized terms from combined datasets of all-site implementation barriers and facilitators through QI cause-and-effect analysis and qualitative thematic analysis. We developed the Quality Improvement and Implementation Taxonomy (QIIT) classification scheme to categorize synthesized terms and structure. This framework employed a semantic and ontological approach. It was built upon existing terms and models from the QI Plan, Do, Study, Act phases, the Consolidated Framework for Implementation Research domains, and the fishbone cause-and-effect categories. The QIIT followed a hierarchical and relational classification scheme. Its taxonomy was linked to four QI Phases, five Implementing Domains, and six Conceptual Determinants modified by customizable Descriptors and Binary or Likert Attribute Scales. This case report introduces a novel approach to standardize the process and taxonomy to describe evidence translation to QI implementation barriers and facilitators. This classification scheme reduces redundancy and allows semantic agreements on concepts and ontological knowledge representation. Integrating existing taxonomies and models enhances the efficiency of reusing well-developed taxonomies and relationship modeling among constructs. Ultimately, employing STs helps generate comparable and sharable QI evaluations for forecast, leading to sustainable implementation with clinically informed innovative solutions.

Boosting autonomous process design and intensification with formalized domain knowledge

Conceptual process design deals with searching for optimal process flowsheets in a large design space. Effective approaches benefit from sensible search space restrictions, commonly carried out by a knowledgeable expert in the form of a superstructure optimization or heuristic rules. To achieve the goal of autonomous process design, knowledge has to be formalized in a machine-readable format. This contribution aims to incorporate an ontological representation of fundamental process knowledge to empower general-purpose design procedures while respecting problem-specific variability. Specifically, the presented framework leverages an ontology to express declarative knowledge (what-is) of processes, phenomena and design tasks to set up the search space and boost a hierarchical reinforcement learning agent which learns the required procedural knowledge (how-to) in order to find an optimal solution. The work is applied in a case study of an intensified steam methane reforming process. Results show that the automated treatment of domain knowledge allows for dynamic search space reduction and achieves better computational efficiency and solution quality, highlighting its potential in autonomous process design approaches.

An interval type‐2 fuzzy ontological model: Predicting water quality from sensory data

SUMMARYWith the advent of break‐through sensing technology, performing data capturing and analysis for knowledge engineering has become more opportunistic. The task of efficiently analyzing sensor based data for effective decision making poses a significant challenge. Conventional prediction and recommender systems lack comprehensive analysis of all parameters and aspects, thus compromising prediction results. At the decision‐making level, traditional knowledge driven prediction systems deploy classical ontology for knowledge representation and analysis. However, classical ontologies are not considered as powerful tools due to their inability to handle vagueness in data for real‐world applications. On the contrary, fuzzy ontology deals with the issue of hazy and uncertain data for effective analysis to give promising results. This work presents interval type 2 fuzzy ontological knowledge model that predicts water quality of sensor based water samples and providing solutions with respect to the corresponding quality state. The proposed knowledge model constitutes of two newly developed ontologies: water sensor observations ontology (crisp ontology to model sensor observational data) and water quality ontology (interval type 2 fuzzy ontology for modeling the water quality prediction process). The inference mechanism is based on interval type‐2 fuzzy partitioning and computation. Besides water quality prediction and providing solutions, the proposed model handles the issue of interoperability and exchange of consensual knowledge among multiple disciplines. The proposed knowledge model is validated with real‐life water sensor based parameterized data captured from various geographically dispersed monitoring stations with approximately 50,000 samples at each station.

The OpenBiodiv Knowledge Graph Rebuilt: A semantic hub on top of the ARPHA-published content and the Biodiversity Literature Repository

OpenBiodiv is a complex ecosystem of tools and services for RDF conversion of XML narratives of biodiversity articles including Darwin Core data into Linked Open Data (LOD), running on top of a graph database. OpenBiodiv provides four main types of services: Searching named entities (e.g., taxon names, taxon concepts, treatments, specimens, occurrences, gene sequences, bibliographic information, institutions, persons) in context, within and between articles. Answering questions based on the presence of certain named entities within specific article sections (e.g., titles, abstracts, introduction or other sections, taxon treatments). Identifying article sections for further text processing (NLP) and providing contextual information, stored in MongoDB. Federating the SPARQL endpoint with other triple stores to enrich the discovered knowledge. Searching named entities (e.g., taxon names, taxon concepts, treatments, specimens, occurrences, gene sequences, bibliographic information, institutions, persons) in context, within and between articles. Answering questions based on the presence of certain named entities within specific article sections (e.g., titles, abstracts, introduction or other sections, taxon treatments). Identifying article sections for further text processing (NLP) and providing contextual information, stored in MongoDB. Federating the SPARQL endpoint with other triple stores to enrich the discovered knowledge. Conversion of such data into RDF follows a general semantic model expressed in the OpenBiodiv-O ontology, an extension of the Treatment Ontology for knowledge representation of current and legacy biodiversity publications (Senderov et al. 2018) and uses two main sources, the full-text article XML published on the ARPHA Publishing Platform and the taxon treatments extracted by Plazi’s TreatmentBank from more than 100 biodiversity journals, stored in the Biodiversity Literature Repository at Zenodo. To ensure efficiency, quality control and fast tracking of all stages of the entire process of extraction, conversion to RDF and indexing of the content has been re-built on the Apache Kafka event streaming platform (Fig. 1). In this new format, OpenBiodiv provides not only a GraphDB SPARQL query endpoint but also indexes the named entities through Elasticsearch and additional provision of data to end users through a RESTful API and a number of user applications. OpenBiodiv is designed for a wide range of users who are interested in a deep-level bibliographic exploration, an ontology-linked search of various data elements (e.g., specimens, sequences, taxon concepts, persons), or co-existence of named entities (e.g., taxon names with a possible biotic relationships between them, or taxon names and potential habitats of occupation) in pre-defined sections of the articles. The SPARQL endpoint allows complex queries of various kinds (Dimitrova et al. 2021).

A Description Logic Based Ontology for Knowledge Representation in Process Planning for Laser Powder Bed Fusion

Laser powder bed fusion (LPBF) provides a rapid and cost-effective solution for fabricating metallic parts with near full density and high precision, strength, and stiffness directly from metallic powders. In LPBF, process variables are widely recognised as fundamental factors that have important effect on the quality of the built parts. However, activity of designing process variables for LPBF, i.e., process planning for LPBF, still heavily depends on knowledge from domain experts. This necessitates a knowledge base that enables the capture, representation, inference, and reuse of existing knowledge. In this paper, a description logic (DL) based ontology for knowledge representation in process planning for LPBF is presented. Firstly, a set of top-level DL entities and specific DL entities and semantic web rule language (SWRL) rules for part orientation, support generation, model slicing, and path planning are created to construct the ontology. The application of the ontology is then illustrated via process planning on an LPBF part. Finally, the benefits of the ontology are demonstrated through a few examples. The demonstration results show that the ontology has rigorous computer-interpretable semantics, which provides a semantic enrichment model for LPBF process planning knowledge and enables automatic consistency checking of the ontology, knowledge reasoning on the ontology, and semantic query from the ontology. This would lay solid foundation for development of a process planning tool with autonomous decision-making capability.

ViLO — A Core Ontology of Vietnamese Legal Documents

Legal ontologies play a key role in various legal applications and have been broadly used by many stakeholders. Innovative systems and ontologies in the law hold potential to conduct legal research. With the needs for legal information management in smart applications, especially for Vietnamese law, it is vitally important to construct core legal ontologies for knowledge representation. This study proposes a core ontology for Vietnamese legal documents which covers general legal domain called as ViLO. The ViLO ontology mainly consists of related institutions of Vietnamese political system, types and structures of legal documents. The method of the NeOn-based collaborations among domain experts and ontology engineers was conducted to build up the ViLO ontology. Through FOCA-based validation results, the proposed method was shown to be effective and efficient. The resulting ontology was demonstrated to be reliable and enriched. The ViLO ontology is supposed to be a basis for further constructions of domain ontologies and artificial intelligence applications in Vietnamese law.

Ontology-Based Recommender System for a Learning Sequence in Programming Languages

The same learning process in educational systems could be boring and time consuming for some learners. This problem arises from the lack of personalized learning sequence for learners with different knowledge level. Recommender systems play an important role in assisting the learners to find suitable learning materials and personalized learning sequence. Use of ontology for knowledge representation in knowledge-based recommender systems would facilitate sharing, reuse and common terminology. Since programming concepts have logical relationships among together so, traditional education systems are more stressful and very time-consuming. This paper aims to propose an ontology based recommender system to present a Personalized Learning Sequence in Programming (PLSP) domain which is depended to learner's knowledge level. A recommender module and, the knowledge base module are integrated together in the proposed framework. The recommender module as the main module in the framework, has three stages which is working based on semantic rules and ontology representation. Evaluation of the system was carried out by comparing the non-recommender system (web-based search) using 32 ICT respondents. Results demonstrate that the participants who used the proposed system spent 1119 seconds to find the suitable learning path in comparison to those who used a non-recommender system (3480 seconds) in the same learning material. It means that learners who follow learning path with PLSP, are more suitable for them. Furthermore, the average mean value of usability test is 4.47, (5 maximum scale) which indicates that the system proved to be useful, was easy to use, and satisfied the users.

Intelligent machine for ontological representation of massive pedagogical knowledge based on neural networks

Higher education is increasingly integrating free learning management systems (LMS). The main objective underlying such systems integration is the automatization of online educational processes for the benefit of all the involved actors who use these systems. The said processes are developed through the integration and implementation of learning scenarios similar to traditional learning systems. LMS produce big data traces emerging from actors’ interactions in online learning. However, we note the absence of instruments adequate for representing knowledge extracted from big traces. In this context, the research at hand is aimed at transforming the big data produced via interactions into big knowledge that can be used in MOOCs by actors falling within a given learning level within a given learning domain, be it formal or informal. In order to achieve such an objective, ontological approaches are taken, namely: mapping, learning and enrichment, in addition to artificial intelligence-based approaches which are relevant in our research context. In this paper, we propose three interconnected algorithms for a better ontological representation of learning actors’ knowledge, while premising heavily on artificial intelligence approaches throughout the stages of this work. For verifying the validity of our contribution, we will implement an experiment about knowledge sources example.

Разработка технологии объектно-ориентированной классификации цветных аэрокосмических изображений, основанной на онтологическом представлении знаний

Предложена технология объектно-ориентированной классификации цветных аэрокосмических изображений, основанная на онтологическом представлении знаний. Отличительной особенностью разработанной технологии является формализованное представление семантической информации, используемой в процессе классификации. Показано, что данная технология может быть реализована с помощью двух авторских компьютерных программ: «Гео-ПК» и «OntoExplorer». Результаты наглядно показывают, что у разработанной технологии имеется большой потенциал для решения задач классификации цветных аэрокосмических изображений высокого и сверхвысокого разрешения.

A semantic web rule and ontologies based architecture for diagnosing breast cancer using select and test algorithm

Background and ObjectiveBreast cancer is widely known as the most lethal and chronic disease among women. Computational techniques have been applied to support and aid early detection of the disease. Methods such as image analysis, knowledgebase systems, machine learning, reasoning algorithm and natural language processing techniques. Approaches aimed at leveraging the availability of patient electronic records have been investigated to support formalisation of medical reasoning thereby achieving early detection of breast cancer. However, computational performance of these approaches is often limited largely due to formalism used for representing the records. We consider that a body of information silo in patient record systems, when accurately formalized, could be efficiently used for improving the performance of computational diagnostic systems which detects breast cancer. The objective of this study is the use of an accurate method for representation and formalisation of text-based patient records and domain knowledge to provide support to medical reasoning algorithm aimed at diagnosing breast cancer. MethodsWe propose an architecture which supports formalism of knowledgebase that is applied to reasoning algorithm used for diagnosing breast cancer. The method applied allow for the use of ontologies for the formalisation of both patient records and domain knowledge. Diagnostic procedure and guidelines in the domain were represented using rules based on semantic web rule language (SWRL). Furthermore, we applied the formalized ontologies and rules to Select and Test (ST) medical reasoning algorithm. ResultsExperimentation was done using records of ten (10) patient collected from University Teaching Hospital. Result obtained showed that our proposed system achieved accuracy gain of 23.5% and AUC of (0.49, 1.0). ConclusionThe impressive performance of the proposed architecture demonstrates the effectiveness of using rules and ontologies for knowledge representation. In addition, we found the interesting performance of the applied ST algorithm as a pointer that it is a potential algorithm in modeling computer aided diagnostic systems (CADs) for detection of breast cancer.

Semantically Enriched Crop Type Classification and Linked Earth Observation Data to Support the Common Agricultural Policy Monitoring

During the last decades, massive amounts of satellite images are becoming available that can be enriched with semantic annotations for the creation of value-added earth observation products. One challenge is to extract knowledge from the raw satellite data in an automated way and to effectively manage the extracted information in a semantic way, to allow fast and accurate decisions of spatiotemporal nature in a real operational scenario. In this work, we present a framework that combines supervised learning for crop type classification on satellite imagery time-series with semantic web and linked data technologies to assist in the implementation of rule sets by the European common agricultural policy (CAP). The framework collects georeferenced data that are available online and satellite images from the Sentinel-2 mission. We analyze image time-series that cover the entire cultivation period and link each parcel with a specific crop. On top of that, we introduce a semantic layer to facilitate a knowledge-driven management of the available information, capitalizing on ontologies for knowledge representation and semantic rules, to identify possible farmers noncompliance according to the Greening 1 (crop diversification) and SMR 1 rule (protection of waters against pollution caused by nitrates) rules of the CAP. Experiments show the effectiveness of the proposed integrated approach in three different scenarios for crop type monitoring and consistency checking for noncompliance to the CAP rules: the smart sampling of on-the-spot checks; the automatic detection of CAP's Greening 1 rule; and the automatic detection of susceptible parcels according to the CAP's SMR 1 rule.

Ontological methods and tools for semantic extension of the media WIKI technology

Practical aspects of ontological approach to organization of intelligent Wiki-based information resources (IR) are considered. We analyze the main features, capabilities and limitations of MediaWiki as a technological platform for development of the Web-based information resource and suggest main directions of its refinement. We propose an abstract model of MediaWiki architecture that formalizes relations between the main components of this software environment and analyze the ways of its semantic extensions based on ontological representation of domain knowledge. An original algorithm of semantic Wiki pages matching with domain ontology is developed. We propose an ontological model of IR that formalizes its knowledge base structure and explicitly performs main features of typical information objects (TIO) of this IR. Such TIOs depend on domain specifics and purposes of IR, therefore their development has to involve domain experts and knowledge engineers. Use of ontology corresponding to the set of Wiki pages (either with semantic markup or without it) provides new IR functions associated with semantic search and navigation. Other important aspect of intelligent Wiki resource development deals with adaptation of user interface to the specifics of IR: enabling various tools of navigation, visualization and content analysis by processing of TIO features enriches IR functionality, reduces access time to information and makes usage of IR more efficient. Developing additional MediaWiki functionality with new requests to the MediaWiki API using TIO templates, extends data analysis and integration capabilities, and offers different, user-focused, IR content views expands the possibilities of data integration and proposes various user-oriented representations of IR content. Wiki resource semantization allows the use knowledge acquired from such IR by external application, or example, by search engines for intelligent Web retrieval. Domain ontologies based on various subsets of the Wiki pages and generated by them thesauri can be used by various Semantic Web applications, both independently or in general technological chain for personified retrieval focused on individual users and their tasks. Approbation of this approach is demonstrated by MAIPS retrieval system. We consider the use semantic similarity of concepts represented by Wiki-pages of IR as an additional way of intelligent navigation between these pages. Such approach allows to group Wiki pages according to user interests by different aspects of their content and structure. Wiki ontologies are considered as the basis for estimation of semantic similarity between domain concepts pertinent to user task. Such elements of Wiki ontology as classes, property values of class instances and relations between them are used as parameters for the quantitative assessment of semantic similarity of Wiki pages. We propose to use local similarity and generate the sets of semantically similar concepts (SSC) that takes into account some subset of page properties and categories defined by user needs. Such sets of SSCs can be considered as user task thesauri for other applications. In addition, we propose to enrich the basic tools of MediaWiki used for access management to the IR content with specialized software code that performs content classification that take into consideration separate namespaces, categories, templates and semantic properties of TIO acquired from Wiki markup. We demonstrate the software implementation of proposed solutions by developing of portal version of the Great Ukrainian Encyclopedia (e-VUE) that contains heterogeneous multimedia content with complex structure. We analyze the specifics of e-VUE knowledge system and develop its formalized TIO representation based on Semantic Web technologies and ontological analysis. Ontological model of e-VUE and original methods of its processing used for this project extend the functionality of the portal in the area of search, navigation, integration and protection of content based on background domain knowledge. In addition, original user interface of e-VUE is developed with an allowance for Encyclopedia knowledge specifics, substantially differs from the standard Wiki, meets the requirements, goals and objectives of this IR and provides a lot of additional features.

Peran Ontologi dalam Pengembangan Sistem Rekomendasi pada Domain Online Learning

As online learning resources increase exponentially on the World Wide Web, online students have difficulty choosing the most suitable and relevant learning material that meets their learning needs due to information overload. Online learning recommendation system is used to predict the preferences or ranking of learners' targets on learning objects for the purpose of generating recommendations. However, the Recommendation system is considered to lack the ability to resolve semantic interoperability issues with heterogeneous sources of information. The purpose of this study is to discuss the role of ontology in the development of recommendation systems in the online learning domain. There are four electronic journal databases selected as references, namely IEEE, Science Direct, Springer Link, and ACM Digital Library. This study obtained 9 articles that were synthesized to answer research questions. This study shows that the involvement of ontology for knowledge representation in the recommendation process can improve the accuracy and quality of recommendations and at the same time help to overcome the weaknesses associated with conventional recommendations.

ONTOLOGY-BASED GENERATION OF MULTILINGUAL QUESTIONS FOR ASSESSMENT IN MEDICAL EDUCATION

The requirements of state-of-the-art curricula and teaching processes in medical education have brought both new and improved the existing assessment methods. Recently, several promising methods have emerged, among them the Comprehensive Integrative Puzzle (CIP), which shows great potential. However, the construction of such questions requires high efforts of a team of experts and is time-consuming. Furthermore, despite the fact that English language is accepted as an international language, for educational purposes there is also a need for representing data and knowledge in native language. In this paper, we present an approach for automatic generation of CIP assessment questions based on using ontologies for knowledge representation. In this way, it is possible to provide multilingual support in the teaching and learning process because the same ontological concept can be applied to corresponding language expressions in different languages. The proposed approach shows promising results indicated by dramatic speeding up of construction of CIP questions compared to manual methods. The presented results represent a strong indication that adoption of ontologies for knowledge representation may enable scalability in multilingual domain-specific education regardless of the language used. High level of automation in the assessment process proven on the CIP method in medical education as one of the most challenging domains, promises high potential for new innovative teaching methodologies in other educational domains as well.

A Review on Ontology Development Methodologies for Developing Ontological Knowledge Representation Systems for various Domains

A Review on Ontology Development Methodologies for Developing Ontological Knowledge Representation Systems for various Domains

Construction and resource short-term planning using a BIM-based ontological decision support system

The dynamic nature and increasing complexity of the construction projects impose many challenges for project planning and control. As a project progresses, more information becomes available and the level of uncertainty decreases. It can be used to proactively check the validity of the previous decisions and develop revised and more detailed plans for the upcoming activities in construction planning meetings. For this purpose, this study implements ontological knowledge representation and semantic reasoning techniques to propose an intelligent information collection and decision support system framework for short-term collaborative construction and resource planning. Moreover, a new approach is suggested that allows for incorporating resource specifications and limitations, and complex multi-factor constraints in the ontological planning process. The framework was tested based on a real-world construction project and different application cases were discussed. The framework showed a promising performance for analyzing different scenarios and help the planners making informative decisions.