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Ontology and CNN-based Inference of the Threat Relationship Between UAVs and Surrounding Objects
MyungJoong Jeon, MinHo Lee, HyunKyu Park, YoungTack Park, Hyung-Sik Yoon, Yun-Geun Kim
http://doi.org/10.5626/JOK.2020.47.4.404
The technology that identifies the relationship between surrounding objects and recognizes the situation is considered as an important and necessary technology in various areas. Numerous methodologies are being studied for this purpose. Most of the studies have solved the problem by building the domain knowledge into ontology for reasoning of situation awareness. However, based on the existing approach; it is difficult to deal with new situations in the absence of domain experts due to the dependency of experts on relevant domain knowledge. In addition, it is difficult to build the knowledge to infer situations that experts have not considered. Therefore, this study proposes a model for using ontology and CNN for reasoning of the relationships between UAVs and surrounding objects to solve the existing problems. Based on the assumption that the accuracy of ontology reasoning is insufficient, first, the reasoning was performed using the information from the detected surrounding objects. Later, the results of ontology reasoning are revised by CNN inference. Due to the limitations of actual data acquisition, data generator was built to generate data similar to real data. For evaluation of this study, two models of relationships between two objects were built and evaluated; both the models showed over 90% accuracy.
An Autonomous Threat Situation Awareness System for UAV based on Ontology
MyungJoong Jeon, HyunKyu Park, YoungTack Park, Hyung-Sik Yoon, Yun-Geun Kim
http://doi.org/10.5626/JOK.2019.46.10.1044
An autonomous threat situational awareness system is necessary for Unmanned Aerial Vehicles(UAVs) in a variety of fields. Although various of approaches to autonomous threat situational awareness have been proposed, most of them involved reasoning of the semantic information of the object. Therefore, in this paper, based on the existing semantic information of an object, we propose a method to achieve threat situational awareness for a UAV based on reasoning of the relationship between the objects. In this paper, there are three main ways that are used to recognize a threat to a UAV: First, information on the recognized objects is expressed using an LOD(Level of Detail)-based grid map. Second, the concepts of objects around the UAV are defined as ontology while the relationships and situations between objects are defined as SWRL(Semantic Web Rule Language). Third, through the ontology reasoning, the simulator visualizes the recognition of the relationships of objects and threat situations for the UAV.
Integrated Explanation System for a Scalable Data based on SPARQL Results
MyungJoong Jeon, HyunKyu Park, YoungTack Park
http://doi.org/10.5626/JOK.2018.45.10.1004
Recently, there has been an increasing demand for an explanation of query results in a variety of QA systems and expert systems. However, the systems being studied today only focus on the scalable query processing. Therefore, this paper proposes an integrated system that explains the causal relationship to the query results based on large volumes of retrievable data. The system uses a distributed rule-based SWRL engine for reasoning about large amounts of knowledge. And in this case uses evidence of reasoning as input for a distributed ATMS to express the structure of the causal relationship. Finally, after obtaining the answers using SPARQLGX, and a scalable SPARQL query processor, this system explains the evidence of answers using a reference to the previously established dependency structure. The evaluation of the proposed explanation system used the benchmark data(Lehigh University Benchmark) and used 14 test queries provided by the LUBM for evaluating the response time and explanation time in this case.
Distributed In-Memory based Large Scale RDFS Reasoning and Query Processing Engine for the Population of Temporal/Spatial Information of Media Ontology
Wan-Gon Lee, Nam-Gee Lee, MyungJoong Jeon, Young-Tack Park
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.
SPARQL Query Processing System over Scalable Triple Data using SparkSQL Framework
MyungJoong Jeon, JinYoung Hong, YoungTack Park
Every year, RDFS data tends further toward scalability; hence, the manner of SPARQL processing needs to be changed for fast query. The query processing method of SPARQL has been studied using a scalable distributed processing framework. Current studies indicate that the query engine based on the scalable distributed processing framework i.e., Hadoop(MapReduce) is not suitable for real-time processing because of the repetitive tasks; in addition, it is difficult to construct a query engine based on an In-memory Distributed Query engine, because distributed structure on the low-level is required to be considered. In this paper, we proposed a method to construct a query engine for improving the speed of the query process with the mass triple data. The query engine processes the query of SPARQL using the SparkSQL, which is an In-memory based, distributed query processing framework. SparkSQL is a high-level distributed query engine that facilitates existing SQL statement. In order to process the SPARQL query, after generating the Algebra Tree using Jena, the Algebra Tree is required to be translated to Spark Algebra Tree for application in the Spark system, and construction of the system that generated the SparkSQL query. Furthermore, we proposed the design of triple property table based on DataFrame for more efficient query processing in the Spark system. Finally, we verified the validity through comparative evaluation with the query engine, which is the existing distributed processing framework.
Scalable Ontology Reasoning Using GPU Cluster Approach
JinYung Hong, MyungJoong Jeon, YoungTack Park
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.
Scalable RDFS Reasoning Using the Graph Structure of In-Memory based Parallel Computing
MyungJoong Jeon, ChiSeoung So, Batselem Jagvaral, KangPil Kim, Jin Kim, JinYoung Hong, YoungTack Park
In recent years, there has been a growing interest in RDFS Inference to build a rich knowledge base. However, it is difficult to improve the inference performance with large data by using a single machine. Therefore, researchers are investigating the development of a RDFS inference engine for a distributed computing environment. However, the existing inference engines cannot process data in real-time, are difficult to implement, and are vulnerable to repetitive tasks. In order to overcome these problems, we propose a method to construct an in-memory distributed inference engine that uses a parallel graph structure. In general, the ontology based on a triple structure possesses a graph structure. Thus, it is intuitive to design a graph structure-based inference engine. Moreover, the RDFS inference rule can be implemented by utilizing the operator of the graph structure, and we can thus design the inference engine according to the graph structure, and not the structure of the data table. In this study, we evaluate the proposed inference engine by using the LUBM1000 and LUBM3000 data to test the speed of the inference. The results of our experiment indicate that the proposed in-memory distributed inference engine achieved a performance of about 10 times faster than an in-storage inference engine.
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