• ETRI Journal
• /
• v.44 no.5
• /
• pp.780-793
• /
• 2022

Large-scale ontology management is one of the main issues when using ontology data practically. Although many approaches have been proposed in relational database management systems (RDBMSs) or object-oriented DBMSs (OODBMSs) to develop large-scale ontology management systems, they have several limitations because ontology data structures are intrinsically different from traditional data structures in RDBMSs or OODBMSs. In addition, users have difficulty using ontology data because many terminologies (ontology nodes) in large-scale ontology data match with a given string keyword. Therefore, in this study, we propose a (graph database-based ontology management system (GOMS) to efficiently manage large-scale ontology data. GOMS uses a graph DBMS and provides new query templates to help users find key concepts or instances. Furthermore, to run queries with multiple joins and path conditions efficiently, we propose GOMS encoding as a filtering tool and develop hash-based join processing algorithms in the graph DBMS. Finally, we experimentally show that GOMS can process various types of queries efficiently.

• Journal of KIISE
• /
• v.43 no.5
• /
• pp.553-561
• /
• 2016

Several machine learning techniques are able to automatically populate ontology data from web sources. Also the interest for large scale ontology reasoning is increasing. However, there is a problem leading to the speculative result to imply uncertainties. Hence, there is a need to consider the reliability problems of various data obtained from the web. Currently, large scale ontology reasoning methods based on the trust value is required because the inference-based reliability of quantitative ontology is insufficient. In this study, we proposed a large scale OWL Horst reasoning method based on a confidence value using spark, a distributed in-memory framework. It describes a method for integrating the confidence value of duplicated data. In addition, it explains a distributed parallel heuristic algorithm to solve the problem of degrading the performance of the inference. In order to evaluate the performance of reasoning methods based on the confidence value, the experiment was conducted using LUBM3000. The experiment results showed that our approach could perform reasoning twice faster than existing reasoning systems like WebPIE.

• International conference on construction engineering and project management
• /
• 2009.05a
• /
• pp.942-948
• /
• 2009

Today's construction is a large-scale and long life span program, so called Mega-scale project, that every moment constructor faces much of hardships, It is because of a large amount of stakeholders, data and complicated relationship among workers. In order to overcome these problems, IETM(Interactive Electronic Technical Manual) has been introduced to construction industry recently, It is regarded as a useful tool for handling the data, procedures of the construction, but it is similar to existing IT-based information systems, the PMIS(Project Management Information System) and the KMS(Knowledge Management System), without characterizing. This research is intended to find out IETM's property and to present the Ontology scheme discriminating Construction IETM from existing systems..

• Journal of KIISE
• /
• v.43 no.9
• /
• pp.963-973
• /
• 2016

Providing a semantic knowledge system using media ontologies requires not only conventional axiom reasoning but also knowledge extension based on various types of reasoning. In particular, spatio-temporal information can be used in a variety of artificial intelligence applications and the importance of spatio-temporal reasoning and expression is continuously increasing. In this paper, we append the LOD data related to the public address system to large-scale media ontologies in order to utilize spatial inference in reasoning. We propose an RDFS/Spatial inference system by utilizing distributed memory-based framework for reasoning about large-scale ontologies annotated with spatial information. In addition, we describe a distributed spatio-temporal SPARQL parallel query processing method designed for large scale ontology data annotated with spatio-temporal information. In order to evaluate the performance of our system, we conducted experiments using LUBM and BSBM data sets for ontology reasoning and query processing benchmark.

• Journal of KIISE
• /
• v.42 no.7
• /
• pp.852-859
• /
• 2015

As the amount of knowledge information significantly increases, a lot of progress has been made in the studies focusing on how to reason large scale ontology effectively at the level of RDFS or OWL. These reasoning methods are divided into TBox classifications and ABox realizations. A TBox classification mainly deals with integrity and dependencies in schema, whereas an ABox realization mainly handles a variety of issues in instances. Therefore, the ABox realization is very important in practical applications. In this paper, we propose a realization method for analyzing the constraint of the specified class, so that the reasoning system automatically infers the classes to which instances belong. Unlike conventional methods that take advantage of the object oriented language based distributed file system, we propose a large scale ontology reasoning method using spark, which is a functional programming-based in-memory system. To verify the effectiveness of the proposed method, we used instances created from the Wine ontology by W3C(120 to 600 million triples). The proposed system processed the largest 600 million triples and generated 951 million triples in 51 minutes (696 K triple / sec) in our largest experiment.

• Genomics & Informatics
• /
• v.17 no.2
• /
• pp.20.1-20.5
• /
• 2019

Entity normalization, or entity linking in the general domain, is an information extraction task that aims to annotate/bind multiple words/expressions in raw text with semantic references, such as concepts of an ontology. An ontology consists minimally of a formally organized vocabulary or hierarchy of terms, which captures knowledge of a domain. Presently, machine-learning methods, often coupled with distributional representations, achieve good performance. However, these require large training datasets, which are not always available, especially for tasks in specialized domains. CONTES (CONcept-TErm System) is a supervised method that addresses entity normalization with ontology concepts using small training datasets. CONTES has some limitations, such as it does not scale well with very large ontologies, it tends to overgeneralize predictions, and it lacks valid representations for the out-of-vocabulary words. Here, we propose to assess different methods to reduce the dimensionality in the representation of the ontology. We also propose to calibrate parameters in order to make the predictions more accurate, and to address the problem of out-of-vocabulary words, with a specific method.

• Journal of KIISE:Computing Practices and Letters
• /
• v.14 no.2
• /
• pp.130-142
• /
• 2008

Different from other ontology languages, TopicMap is capable of integrating numerous amount of heterogenous information resources using the locational information without any information transformation. Although many conventional editors have been developed for topic maps, they are standalone-type only for writing XTM documents. As a result, these tools request too much time for handling large-scale data and provoke practical problems to integrate with legacy systems which are mostly based on relational database. In this paper, we model a large-scale topic map structure based on XTM 1.0 into RDB structure to minimize the processing time and build up the ontology in legacy systems. We implement a topic map platform called X-TOP that can enhance the efficiency of ontology construction and provide interoperability between XTM documents and database. Moreover, we can use conventional SQL tools and other application development tools for topic map construction in X-TOP. The X-TOP is implemented to have 3-tier architecture to support flexible user interfaces and diverse DBMS. This paper shows the usability of X-TOP by means of the comparison with conventional tools and the application to healthcare cancer ontology management.

• Journal of KIISE
• /
• v.43 no.1
• /
• pp.61-70
• /
• 2016

In recent years, there has been a need for techniques for large-scale ontology inference in order to infer new knowledge from existing knowledge at a high speed, and for a diversity of semantic services. With the recent advances in distributed computing, developments of ontology inference engines have mostly been studied based on Hadoop or Spark frameworks on large clusters. Parallel programming techniques using GPGPU, which utilizes many cores when compared with CPU, is also used for ontology inference. In this paper, by combining the advantages of both techniques, we propose a new method for reasoning large RDFS ontology data using a Spark in-memory framework and inferencing distributed data at a high speed using GPGPU. Using GPGPU, ontology reasoning over high-capacity data can be performed as a low cost with higher efficiency over conventional inference methods. In addition, we show that GPGPU can reduce the data workload on each node through the Spark cluster. In order to evaluate our approach, we used LUBM ranging from 10 to 120. Our experimental results showed that our proposed reasoning engine performs 7 times faster than a conventional approach which uses a Spark in-memory inference engine.

• Journal of Intelligence and Information Systems
• /
• v.25 no.2
• /
• pp.77-97
• /
• 2019

The large amount of data that emerges from the initial connection environment of the Fourth Industrial Revolution is a major factor that distinguishes the Fourth Industrial Revolution from the existing production environment. This environment has two-sided features that allow it to produce data while using it. And the data produced so produces another value. Due to the massive scale of data, future information systems need to process more data in terms of quantities than existing information systems. In addition, in terms of quality, only a large amount of data, Ability is required. In a small-scale information system, it is possible for a person to accurately understand the system and obtain the necessary information, but in a variety of complex systems where it is difficult to understand the system accurately, it becomes increasingly difficult to acquire the desired information. In other words, more accurate processing of large amounts of data has become a basic condition for future information systems. This problem related to the efficient performance of the information system can be solved by building a semantic web which enables various information processing by expressing the collected data as an ontology that can be understood by not only people but also computers. For example, as in most other organizations, IT has been introduced in the military, and most of the work has been done through information systems. Currently, most of the work is done through information systems. As existing systems contain increasingly large amounts of data, efforts are needed to make the system easier to use through its data utilization. An ontology-based system has a large data semantic network through connection with other systems, and has a wide range of databases that can be utilized, and has the advantage of searching more precisely and quickly through relationships between predefined concepts. In this paper, we propose a defense ontology as a method for effective data management and decision support. In order to judge the applicability and effectiveness of the actual system, we reconstructed the existing air force munitions situation management system as an ontology based system. It is a system constructed to strengthen management and control of logistics situation of commanders and practitioners by providing real - time information on maintenance and distribution situation as it becomes difficult to use complicated logistics information system with large amount of data. Although it is a method to take pre-specified necessary information from the existing logistics system and display it as a web page, it is also difficult to confirm this system except for a few specified items in advance, and it is also time-consuming to extend the additional function if necessary And it is a system composed of category type without search function. Therefore, it has a disadvantage that it can be easily utilized only when the system is well known as in the existing system. The ontology-based logistics situation management system is designed to provide the intuitive visualization of the complex information of the existing logistics information system through the ontology. In order to construct the logistics situation management system through the ontology, And the useful functions such as performance - based logistics support contract management and component dictionary are further identified and included in the ontology. In order to confirm whether the constructed ontology can be used for decision support, it is necessary to implement a meaningful analysis function such as calculation of the utilization rate of the aircraft, inquiry about performance-based military contract. Especially, in contrast to building ontology database in ontology study in the past, in this study, time series data which change value according to time such as the state of aircraft by date are constructed by ontology, and through the constructed ontology, It is confirmed that it is possible to calculate the utilization rate based on various criteria as well as the computable utilization rate. In addition, the data related to performance-based logistics contracts introduced as a new maintenance method of aircraft and other munitions can be inquired into various contents, and it is easy to calculate performance indexes used in performance-based logistics contract through reasoning and functions. Of course, we propose a new performance index that complements the limitations of the currently applied performance indicators, and calculate it through the ontology, confirming the possibility of using the constructed ontology. Finally, it is possible to calculate the failure rate or reliability of each component, including MTBF data of the selected fault-tolerant item based on the actual part consumption performance. The reliability of the mission and the reliability of the system are calculated. In order to confirm the usability of the constructed ontology-based logistics situation management system, the proposed system through the Technology Acceptance Model (TAM), which is a representative model for measuring the acceptability of the technology, is more useful and convenient than the existing system.

• Journal of KIISE:Software and Applications
• /
• v.34 no.12
• /
• pp.1021-1036
• /
• 2007

As the requirement of Semantic Web and knowledge management has been rising, ontology developments have been carried out actively. Ontology is now at the point that systematic developing standardization should be made up like the developing a large scale software. Yet, It has not been made to optimize and standardize ontology development methodologies. In this study, to compare ontology development methodologies, METHONTOLOGY, CommonKADS, OTK in foreign countries and EOE in Korea are selected. The evaluating method is to introduce CMM-I version 1.1 framework. Ontology development methodologies have been evaluated in process areas that introduce for CMM-I maturity level 2 and 3. The purpose of this study is to find matured process and weak process in ontology development methodology on the view of process maturity, and suggests the areas to be Improved in it. The result of this study can be applied as basic data to establish and improve ontology development methodology.