Recently, a pragmatic approach toward achieving semantic search has made significant progress with knowledge graph embedding (KGE). Although many standards, methods, and technologies are applicable to the linked open data (LOD) cloud, there are still several ongoing problems in this area. As LOD are modeled as resource description framework (RDF) graphs, we cannot directly adopt existing solutions from database management or information retrieval systems. This study addresses the issue of efficient LOD annotation organization, retrieval, and evaluation. We propose a hybrid strategy between the index and distributed approaches based on KGE to increase join query performance. Using a learned semantic index structure for semantic search, we can efficiently discover interlinked data distributed across multiple resources. Because this approach rapidly prunes numerous false hits, the performance of join query processing is remarkably improved. The performance of the proposed index structure is compared with some existing methods on real RDF datasets. As a result, the proposed indexing method outperforms existing methods due to its ability to prune a lot of unnecessary data scanned during semantic searching.

This paper presents the structure of an electronic attendance system composed of OAA (One-stop Attendance and Absence)-server and client. Wecomparesimultaneous check, fraudulent attendance, prevention of leaving the classroom, and extra equipment according to check methods, and also present a method of app and web-based attendance management. NFC and BLE-based app and location-based web for attendance management were applied to classrooms. Issues with electronic attendance management and resolution methods were also presented. RESTful web service was defined for attendance management and performance analysis was conducted according to thread numbers of OAA-serverand parser types of XML and JSON.

최근 소셜 미디어와 같은 빅데이터에 지식 그래프 임베딩 및 그래프 합성곱 신경망을 활용한 연구가 활발이 진행되고 있다. 그러나 현재까지 대규모 지식 그래프에 대한 엔티티 매칭 연구는 상대적으로 거의 연구가 되지 않고 있다. 대규모 지식 그래프는 아직 해결해야 될 이슈가 많지만, 그들 중 하나는 서로 다른 온톨로지를 사용하는 어휘 이질성 문제이다. 본 논문에서는 이 문제를 해결하기 위해 그래프 합성곱 신경망에 임베딩기법을 함께 적용한 하이브리드 엔티티 매칭 방법을 제안한다. 우리는 실제 DBP15K 데이터셋을 사용하여 제안 방법을 기존 최신의 엔티티 매칭 방법들과 성능을 비교 · 분석하였다. 실험 결과 Hit@1에서 10.7%, Hit@10에서 5.9%, 그리고 Hit@50과 Hit@100에서 약간의 성능 향상을 보였다.

The road network graph is a critical component for downstream tasks in autonomous driving, such as global route planning and navigation. In the past years, road network graphs are usually annotated by human experts manually, which is time-consuming and labor-intensive. To annotate road network graphs effectively and efficiently, automatic algorithms for road network graph detection are demanded. Most existing methods either adopt a post-processing step on semantic segmentation maps to produce road network graphs, or propose graph-based algorithms to directly predict the graphs. However, these works suffer from hard-coded algorithms and inferior performance. To enhance the previous state-of-the-art (SOTA) method RNGDet, we add an instance segmentation head to better supervise the training, and enable the network to leverage multi-scale features of the backbone. Since the new proposed approach is improved from RNGDet, we name it RNGDet++. Experimental results show that our RNGDet++ outperforms baseline methods in terms of almost all evaluation metrics on two large-scale public datasets. Our code and supplementary materials are available at \url{https://tonyxuqaq.github.io/projects/RNGDetPlusPlus/}.

Although a pragmatic approach towards achieving Semantic Web has gained some traction with Linked Data, there are still a lot of open problems in the area of Linked Data. Because Linked Data are modeled as RDF graphs, we cannot directly adopt existing solutions from database systems or Web technologies. This paper presents a hybrid method between the centralized approach and the distributed approach based on query processing to increase the join query performance. Using auxiliary indexes we can retrieve distributed data resources participating on a query result, rapidly reducing the amount of data that are really needed to be accessed on-demand. The performance of the proposed index structure is compared with some existing methods on a real RDF dataset. Our method outperforms the existing methods due to its ability to reduce a large amount of irrelevant resources.