Pub Date : 2023-06-09DOI: 10.1142/s1793351x23500022
Mengchen Xiong, Xiao Xu, D. Yang, Fabián Seguel, E. Steinbach
{"title":"Hierarchical Attention-based Sensor Fusion Strategy for Depth Estimation in Diverse Weather","authors":"Mengchen Xiong, Xiao Xu, D. Yang, Fabián Seguel, E. Steinbach","doi":"10.1142/s1793351x23500022","DOIUrl":"https://doi.org/10.1142/s1793351x23500022","url":null,"abstract":"","PeriodicalId":43471,"journal":{"name":"International Journal of Semantic Computing","volume":"53 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90603470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-09DOI: 10.1142/s1793351x23300030
A. S. Novo, Fatih Gedikli
{"title":"Named entities as key features for detecting semantically similar news articles","authors":"A. S. Novo, Fatih Gedikli","doi":"10.1142/s1793351x23300030","DOIUrl":"https://doi.org/10.1142/s1793351x23300030","url":null,"abstract":"","PeriodicalId":43471,"journal":{"name":"International Journal of Semantic Computing","volume":"2 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89444408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-09DOI: 10.1142/s1793351x23640031
Leonardo Vilela Cardoso, S. Guimarães, Zenilton Kleber Goncalves do Patrocinio Junior
{"title":"Hierarchical time-aware summarization with an adaptive transformer for video captioning","authors":"Leonardo Vilela Cardoso, S. Guimarães, Zenilton Kleber Goncalves do Patrocinio Junior","doi":"10.1142/s1793351x23640031","DOIUrl":"https://doi.org/10.1142/s1793351x23640031","url":null,"abstract":"","PeriodicalId":43471,"journal":{"name":"International Journal of Semantic Computing","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89701286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-09DOI: 10.1142/s1793351x2364002x
A. Cuzzocrea
{"title":"A Reference Architecture for Supporting Multidimensional Big Data: Analytics over Big Web Knowledge Bases: Definitions, Implementation, Case Studies","authors":"A. Cuzzocrea","doi":"10.1142/s1793351x2364002x","DOIUrl":"https://doi.org/10.1142/s1793351x2364002x","url":null,"abstract":"","PeriodicalId":43471,"journal":{"name":"International Journal of Semantic Computing","volume":"57 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83452235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-09DOI: 10.1142/s1793351x23630011
Lars Quakulinski, A. Koumpis, O. Beyan
{"title":"Transparency in Medical Artificial Intelligence Systems","authors":"Lars Quakulinski, A. Koumpis, O. Beyan","doi":"10.1142/s1793351x23630011","DOIUrl":"https://doi.org/10.1142/s1793351x23630011","url":null,"abstract":"","PeriodicalId":43471,"journal":{"name":"International Journal of Semantic Computing","volume":"74 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76078039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-09DOI: 10.1142/s1793351x23300017
Apurva Kulkarni, Chandrashekar Ramanathan, V. E. Venugopal
{"title":"Semantics-aware Document Retrieval for Government Administrative Data","authors":"Apurva Kulkarni, Chandrashekar Ramanathan, V. E. Venugopal","doi":"10.1142/s1793351x23300017","DOIUrl":"https://doi.org/10.1142/s1793351x23300017","url":null,"abstract":"","PeriodicalId":43471,"journal":{"name":"International Journal of Semantic Computing","volume":"78 3","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72395915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-09DOI: 10.1142/s1793351x23620027
A. Kriegler, Csaba Beleznai, M. Gelautz, Markus Murschitz, Kai Gobel
{"title":"PrimitivePose: Generic Model and Representation for 3D Bounding Box Prediction of Unseen Objects","authors":"A. Kriegler, Csaba Beleznai, M. Gelautz, Markus Murschitz, Kai Gobel","doi":"10.1142/s1793351x23620027","DOIUrl":"https://doi.org/10.1142/s1793351x23620027","url":null,"abstract":"","PeriodicalId":43471,"journal":{"name":"International Journal of Semantic Computing","volume":"55 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89199591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-18DOI: 10.1142/s1793351x23600012
Carsten Felix Draschner, Hajira Jabeen, Jens Lehmann
In recent years, exciting sources of data have been modeled as knowledge graphs (KGs). This modeling represents both structural relationships and the entity-specific multi-modal data in KGs. In various data analytics pipelines and machine learning (ML), the task of semantic similarity estimation plays a significant role. Assigning similarity values to entity pairs is needed in recommendation systems, clustering, classification, entity matching/disambiguation and many others. Efficient and scalable frameworks are needed to handle the quadratic complexity of all-pair semantic similarity on Big Data KGs. Moreover, heterogeneous KGs demand multi-modal semantic similarity estimation to cover the versatile contents like categorical relations between classes or their attribute literals like strings, timestamps or numeric data. In this paper, we propose the SimE4KG framework as a resource providing generic open-source modules that perform semantic similarity estimation in multi-modal KGs. To justify the computational costs of similarity estimation, the SimE4KG generates reproducible, reusable and explainable results. The pipeline results are a native semantic RDF KG, including the experiment results, hyper-parameter setup and explanation of the results, like the most influential features. For fast and scalable execution in memory, we implemented the distributed approach using Apache Spark. The entire development of this framework is integrated into the holistic distributed Semantic ANalytics StAck (SANSA).
{"title":"SimE4KG: Distributed and Explainable Multi-Modal Semantic Similarity Estimation for Knowledge Graphs","authors":"Carsten Felix Draschner, Hajira Jabeen, Jens Lehmann","doi":"10.1142/s1793351x23600012","DOIUrl":"https://doi.org/10.1142/s1793351x23600012","url":null,"abstract":"In recent years, exciting sources of data have been modeled as knowledge graphs (KGs). This modeling represents both structural relationships and the entity-specific multi-modal data in KGs. In various data analytics pipelines and machine learning (ML), the task of semantic similarity estimation plays a significant role. Assigning similarity values to entity pairs is needed in recommendation systems, clustering, classification, entity matching/disambiguation and many others. Efficient and scalable frameworks are needed to handle the quadratic complexity of all-pair semantic similarity on Big Data KGs. Moreover, heterogeneous KGs demand multi-modal semantic similarity estimation to cover the versatile contents like categorical relations between classes or their attribute literals like strings, timestamps or numeric data. In this paper, we propose the SimE4KG framework as a resource providing generic open-source modules that perform semantic similarity estimation in multi-modal KGs. To justify the computational costs of similarity estimation, the SimE4KG generates reproducible, reusable and explainable results. The pipeline results are a native semantic RDF KG, including the experiment results, hyper-parameter setup and explanation of the results, like the most influential features. For fast and scalable execution in memory, we implemented the distributed approach using Apache Spark. The entire development of this framework is integrated into the holistic distributed Semantic ANalytics StAck (SANSA).","PeriodicalId":43471,"journal":{"name":"International Journal of Semantic Computing","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135861826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-29DOI: 10.1142/s1793351x23610019
Kevin Cedric Guyard, Michel Deriaz
Recommendation systems are becoming more and more present in our daily lives, whether it is for suggesting items to buy, movies to watch or music to listen. They can be used in a large number of contexts. In this paper, we propose the use of a recommendation system in the context of a recruitment platform. The use of the recommendation system allows to obtain precise profile recommendations based on the competences of a candidate to meet the stated requirements and to avoid companies to have to perform a very time-consuming manual sorting of the candidates. Thus, this paper presents the context in which we propose this recommendation system, the data preprocessing, the general approach based on a hybrid content-based filtering (CBF) and similarity index (SI) system, as well as the means implemented to reduce the computational cost of such a system with the increasing evolution of the platform.
{"title":"A Scalable Recommendation System Approach for a Companies — Seniors Matching","authors":"Kevin Cedric Guyard, Michel Deriaz","doi":"10.1142/s1793351x23610019","DOIUrl":"https://doi.org/10.1142/s1793351x23610019","url":null,"abstract":"Recommendation systems are becoming more and more present in our daily lives, whether it is for suggesting items to buy, movies to watch or music to listen. They can be used in a large number of contexts. In this paper, we propose the use of a recommendation system in the context of a recruitment platform. The use of the recommendation system allows to obtain precise profile recommendations based on the competences of a candidate to meet the stated requirements and to avoid companies to have to perform a very time-consuming manual sorting of the candidates. Thus, this paper presents the context in which we propose this recommendation system, the data preprocessing, the general approach based on a hybrid content-based filtering (CBF) and similarity index (SI) system, as well as the means implemented to reduce the computational cost of such a system with the increasing evolution of the platform.","PeriodicalId":43471,"journal":{"name":"International Journal of Semantic Computing","volume":"363 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135468878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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