The Experts below are selected from a list of 19170 Experts worldwide ranked by ideXlab platform
Weijia Jia - One of the best experts on this subject based on the ideXlab platform.
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time sync video tag extraction using Semantic Association graph
ACM Transactions on Knowledge Discovery From Data, 2019Co-Authors: Wenmian Yang, Na Ruan, Wenyuan Gao, Kun Wang, Weijia Jia, Wei Zhao, Nan Liu, Yunyong ZhangAbstract:Time-sync comments (TSCs) reveal a new way of extracting the online video tags. However, such TSCs have lots of noises due to users’ diverse comments, introducing great challenges for accurate and fast video tag extractions. In this article, we propose an unsupervised video tag extraction algorithm named Semantic Weight-Inverse Document Frequency (SW-IDF). Specifically, we first generate corresponding Semantic Association graph (SAG) using Semantic similarities and timestamps of the TSCs. Second, we propose two graph cluster algorithms, i.e., dialogue-based algorithm and topic center-based algorithm, to deal with the videos with different density of comments. Third, we design a graph iteration algorithm to assign the weight to each comment based on the degrees of the clustered subgraphs, which can differentiate the meaningful comments from the noises. Finally, we gain the weight of each word by combining Semantic Weight (SW) and Inverse Document Frequency (IDF). In this way, the video tags are extracted automatically in an unsupervised way. Extensive experiments have shown that SW-IDF (dialogue-based algorithm) achieves 0.4210 F1-score and 0.4932 MAP (Mean Average Precision) in high-density comments, 0.4267 F1-score and 0.3623 MAP in low-density comments; while SW-IDF (topic center-based algorithm) achieves 0.4444 F1-score and 0.5122 MAP in high-density comments, 0.4207 F1-score and 0.3522 MAP in low-density comments. It has a better performance than the state-of-the-art unsupervised algorithms in both F1-score and MAP.
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time sync video tag extraction using Semantic Association graph
arXiv: Information Retrieval, 2019Co-Authors: Wenmian Yang, Na Ruan, Wenyuan Gao, Kun Wang, Weijia Jia, Wei Zhao, Nan Liu, Yunyong ZhangAbstract:Time-sync comments reveal a new way of extracting the online video tags. However, such time-sync comments have lots of noises due to users' diverse comments, introducing great challenges for accurate and fast video tag extractions. In this paper, we propose an unsupervised video tag extraction algorithm named Semantic Weight-Inverse Document Frequency (SW-IDF). Specifically, we first generate corresponding Semantic Association graph (SAG) using Semantic similarities and timestamps of the time-sync comments. Second, we propose two graph cluster algorithms, i.e., dialogue-based algorithm and topic center-based algorithm, to deal with the videos with different density of comments. Third, we design a graph iteration algorithm to assign the weight to each comment based on the degrees of the clustered subgraphs, which can differentiate the meaningful comments from the noises. Finally, we gain the weight of each word by combining Semantic Weight (SW) and Inverse Document Frequency (IDF). In this way, the video tags are extracted automatically in an unsupervised way. Extensive experiments have shown that SW-IDF (dialogue-based algorithm) achieves 0.4210 F1-score and 0.4932 MAP (Mean Average Precision) in high-density comments, 0.4267 F1-score and 0.3623 MAP in low-density comments; while SW-IDF (topic center-based algorithm) achieves 0.4444 F1-score and 0.5122 MAP in high-density comments, 0.4207 F1-score and 0.3522 MAP in low-density comments. It has a better performance than the state-of-the-art unsupervised algorithms in both F1-score and MAP.
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crowdsourced time sync video tagging using Semantic Association graph
International Conference on Multimedia and Expo, 2017Co-Authors: Wenmian Yang, Na Ruan, Wenyuan Gao, Kun Wang, Wensheng Ran, Weijia JiaAbstract:Time-sync comments reveal a new way of extracting the online video tags. However, such time-sync comments have lots of noises due to users' diverse comments, introducing great challenges for accurate and fast video tag extractions. In this paper, we propose an unsupervised video tag extraction algorithm named Semantic Weight-Inverse Document Frequency (SW-IDF). SW-IDF first generates corresponding Semantic Association graph (SAG) using Semantic similarities and timestamps of the time-sync comments. Then it clusters the comments into sub-graphs of different topics and assigns weight to each comment based on SAG. This can clearly differentiate the meaningful comments with the noises. In this way, the noises can be identified, and effectively eliminated. Extensive experiments have shown that SW-IDF can achieve 0.3045 precision and 0.6530 recall in high-density comments; 0.3800 precision and 0.4460 recall in low-density comments. It is the best performance among the existing unsupervised algorithms.
Krys Kochut - One of the best experts on this subject based on the ideXlab platform.
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sparqler extended sparql for Semantic Association discovery
European Semantic Web Conference, 2007Co-Authors: Krys Kochut, Maciej JanikAbstract:Complex relationships, frequently referred to as Semantic associa-tions, are the essence of the Semantic Web. Query and retrieval of Semantic Associations has been an important task in many analytical and scientific activities, such as detecting money laundering and querying for metabolic pathways in biochemistry. We believe that support for Semantic path queries should be an integral component of RDF query languages. In this paper, we present SPARQLeR, a novel extension of the SPARQL query language which adds the support for Semantic path queries. The proposed extension fits seamlessly within the overall syntax and Semantics of SPARQL and allows easy and natural formulation of queries involving a wide variety of regular path patterns in RDF graphs. SPARQLeR's path patterns can capture many low-level details of the queried Associations. We also present an implementation of SPARQLeR and its initial performance results. Our implementation is built over BRAHMS, our own RDF storage system.
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BRAHMS: A WorkBench RDF store and high performance memory system for Semantic Association discovery
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2005Co-Authors: Maciej Janik, Krys KochutAbstract:Discovery of Semantic Associations in Semantic Web ontologies is an important task in various analytical activities. Several query languages and storage systems have been designed and implemented for storage and retrieval of information in RDF ontologies. However, they are inadequate for Semantic Association discovery. In this paper we present the design and implementation of BRAHMS, an efficient RDF storage system, specifically designed to support fast Semantic Association discovery in large RDF bases. We present memory usage and timing results of several tests performed with BRAHMS and compare them to similar tests performed using Jena, Sesame, and Redland, three of the well-known RDF storage systems. Our results show that BRAHMS handles basic Association discovery well, while the RDF query languages and even the low-level APIs in the other three tested systems are not suitable for the implementation of Semantic Association discovery algorithms.
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International Semantic Web Conference - BRAHMS: a workbench RDF store and high performance memory system for Semantic Association discovery
The Semantic Web – ISWC 2005, 2005Co-Authors: Maciej Janik, Krys KochutAbstract:Discovery of Semantic Associations in Semantic Web ontologies is an important task in various analytical activities. Several query languages and storage systems have been designed and implemented for storage and retrieval of information in RDF ontologies. However, they are inadequate for Semantic Association discovery. In this paper we present the design and implementation of BRAHMS, an efficient RDF storage system, specifically designed to support fast Semantic Association discovery in large RDF bases. We present memory usage and timing results of several tests performed with BRAHMS and compare them to similar tests performed using Jena, Sesame, and Redland, three of the well-known RDF storage systems. Our results show that BRAHMS handles basic Association discovery well, while the RDF query languages and even the low-level APIs in the other three tested systems are not suitable for the implementation of Semantic Association discovery algorithms.
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Semantic Association Identification and Knowledge Discovery for National Security Applications 1
2003Co-Authors: Amit P. Sheth, Boanerges Aleman-meza, I. Budak Arpinar, Clemens Bertram, Cartic Ramakrishnan, Chris Halaschek, Kemafor Anyanwu, David Avant, F. Sena Arpinar, Krys KochutAbstract:Enterprises have access to vast amount of internal, deep Web and open Web information. Transforming this heterogeneous and distributed information into actionable and insightful information is the key to the emerging new class of business intelligence and national security applications. This paper attempts to bring together novel academic research and commercialized Semantic Web technology to provide these new capabilities. In particular, we discuss academic research on Semantic Association identification, use of commercial Semantic Web technology for Semantic metadata extraction, and a prototypical demonstration of this research and technology through an aviation security application of significance to national security.
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ESWC - SPARQLeR: Extended Sparql for Semantic Association Discovery
Lecture Notes in Computer Science, 1Co-Authors: Krys Kochut, Maciej JanikAbstract:Complex relationships, frequently referred to as Semantic associa-tions, are the essence of the Semantic Web. Query and retrieval of Semantic Associations has been an important task in many analytical and scientific activities, such as detecting money laundering and querying for metabolic pathways in biochemistry. We believe that support for Semantic path queries should be an integral component of RDF query languages. In this paper, we present SPARQLeR, a novel extension of the SPARQL query language which adds the support for Semantic path queries. The proposed extension fits seamlessly within the overall syntax and Semantics of SPARQL and allows easy and natural formulation of queries involving a wide variety of regular path patterns in RDF graphs. SPARQLeR's path patterns can capture many low-level details of the queried Associations. We also present an implementation of SPARQLeR and its initial performance results. Our implementation is built over BRAHMS, our own RDF storage system.
Maciej Janik - One of the best experts on this subject based on the ideXlab platform.
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sparqler extended sparql for Semantic Association discovery
European Semantic Web Conference, 2007Co-Authors: Krys Kochut, Maciej JanikAbstract:Complex relationships, frequently referred to as Semantic associa-tions, are the essence of the Semantic Web. Query and retrieval of Semantic Associations has been an important task in many analytical and scientific activities, such as detecting money laundering and querying for metabolic pathways in biochemistry. We believe that support for Semantic path queries should be an integral component of RDF query languages. In this paper, we present SPARQLeR, a novel extension of the SPARQL query language which adds the support for Semantic path queries. The proposed extension fits seamlessly within the overall syntax and Semantics of SPARQL and allows easy and natural formulation of queries involving a wide variety of regular path patterns in RDF graphs. SPARQLeR's path patterns can capture many low-level details of the queried Associations. We also present an implementation of SPARQLeR and its initial performance results. Our implementation is built over BRAHMS, our own RDF storage system.
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BRAHMS: A WorkBench RDF store and high performance memory system for Semantic Association discovery
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2005Co-Authors: Maciej Janik, Krys KochutAbstract:Discovery of Semantic Associations in Semantic Web ontologies is an important task in various analytical activities. Several query languages and storage systems have been designed and implemented for storage and retrieval of information in RDF ontologies. However, they are inadequate for Semantic Association discovery. In this paper we present the design and implementation of BRAHMS, an efficient RDF storage system, specifically designed to support fast Semantic Association discovery in large RDF bases. We present memory usage and timing results of several tests performed with BRAHMS and compare them to similar tests performed using Jena, Sesame, and Redland, three of the well-known RDF storage systems. Our results show that BRAHMS handles basic Association discovery well, while the RDF query languages and even the low-level APIs in the other three tested systems are not suitable for the implementation of Semantic Association discovery algorithms.
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International Semantic Web Conference - BRAHMS: a workbench RDF store and high performance memory system for Semantic Association discovery
The Semantic Web – ISWC 2005, 2005Co-Authors: Maciej Janik, Krys KochutAbstract:Discovery of Semantic Associations in Semantic Web ontologies is an important task in various analytical activities. Several query languages and storage systems have been designed and implemented for storage and retrieval of information in RDF ontologies. However, they are inadequate for Semantic Association discovery. In this paper we present the design and implementation of BRAHMS, an efficient RDF storage system, specifically designed to support fast Semantic Association discovery in large RDF bases. We present memory usage and timing results of several tests performed with BRAHMS and compare them to similar tests performed using Jena, Sesame, and Redland, three of the well-known RDF storage systems. Our results show that BRAHMS handles basic Association discovery well, while the RDF query languages and even the low-level APIs in the other three tested systems are not suitable for the implementation of Semantic Association discovery algorithms.
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ESWC - SPARQLeR: Extended Sparql for Semantic Association Discovery
Lecture Notes in Computer Science, 1Co-Authors: Krys Kochut, Maciej JanikAbstract:Complex relationships, frequently referred to as Semantic associa-tions, are the essence of the Semantic Web. Query and retrieval of Semantic Associations has been an important task in many analytical and scientific activities, such as detecting money laundering and querying for metabolic pathways in biochemistry. We believe that support for Semantic path queries should be an integral component of RDF query languages. In this paper, we present SPARQLeR, a novel extension of the SPARQL query language which adds the support for Semantic path queries. The proposed extension fits seamlessly within the overall syntax and Semantics of SPARQL and allows easy and natural formulation of queries involving a wide variety of regular path patterns in RDF graphs. SPARQLeR's path patterns can capture many low-level details of the queried Associations. We also present an implementation of SPARQLeR and its initial performance results. Our implementation is built over BRAHMS, our own RDF storage system.
Wenmian Yang - One of the best experts on this subject based on the ideXlab platform.
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time sync video tag extraction using Semantic Association graph
ACM Transactions on Knowledge Discovery From Data, 2019Co-Authors: Wenmian Yang, Na Ruan, Wenyuan Gao, Kun Wang, Weijia Jia, Wei Zhao, Nan Liu, Yunyong ZhangAbstract:Time-sync comments (TSCs) reveal a new way of extracting the online video tags. However, such TSCs have lots of noises due to users’ diverse comments, introducing great challenges for accurate and fast video tag extractions. In this article, we propose an unsupervised video tag extraction algorithm named Semantic Weight-Inverse Document Frequency (SW-IDF). Specifically, we first generate corresponding Semantic Association graph (SAG) using Semantic similarities and timestamps of the TSCs. Second, we propose two graph cluster algorithms, i.e., dialogue-based algorithm and topic center-based algorithm, to deal with the videos with different density of comments. Third, we design a graph iteration algorithm to assign the weight to each comment based on the degrees of the clustered subgraphs, which can differentiate the meaningful comments from the noises. Finally, we gain the weight of each word by combining Semantic Weight (SW) and Inverse Document Frequency (IDF). In this way, the video tags are extracted automatically in an unsupervised way. Extensive experiments have shown that SW-IDF (dialogue-based algorithm) achieves 0.4210 F1-score and 0.4932 MAP (Mean Average Precision) in high-density comments, 0.4267 F1-score and 0.3623 MAP in low-density comments; while SW-IDF (topic center-based algorithm) achieves 0.4444 F1-score and 0.5122 MAP in high-density comments, 0.4207 F1-score and 0.3522 MAP in low-density comments. It has a better performance than the state-of-the-art unsupervised algorithms in both F1-score and MAP.
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time sync video tag extraction using Semantic Association graph
arXiv: Information Retrieval, 2019Co-Authors: Wenmian Yang, Na Ruan, Wenyuan Gao, Kun Wang, Weijia Jia, Wei Zhao, Nan Liu, Yunyong ZhangAbstract:Time-sync comments reveal a new way of extracting the online video tags. However, such time-sync comments have lots of noises due to users' diverse comments, introducing great challenges for accurate and fast video tag extractions. In this paper, we propose an unsupervised video tag extraction algorithm named Semantic Weight-Inverse Document Frequency (SW-IDF). Specifically, we first generate corresponding Semantic Association graph (SAG) using Semantic similarities and timestamps of the time-sync comments. Second, we propose two graph cluster algorithms, i.e., dialogue-based algorithm and topic center-based algorithm, to deal with the videos with different density of comments. Third, we design a graph iteration algorithm to assign the weight to each comment based on the degrees of the clustered subgraphs, which can differentiate the meaningful comments from the noises. Finally, we gain the weight of each word by combining Semantic Weight (SW) and Inverse Document Frequency (IDF). In this way, the video tags are extracted automatically in an unsupervised way. Extensive experiments have shown that SW-IDF (dialogue-based algorithm) achieves 0.4210 F1-score and 0.4932 MAP (Mean Average Precision) in high-density comments, 0.4267 F1-score and 0.3623 MAP in low-density comments; while SW-IDF (topic center-based algorithm) achieves 0.4444 F1-score and 0.5122 MAP in high-density comments, 0.4207 F1-score and 0.3522 MAP in low-density comments. It has a better performance than the state-of-the-art unsupervised algorithms in both F1-score and MAP.
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crowdsourced time sync video tagging using Semantic Association graph
International Conference on Multimedia and Expo, 2017Co-Authors: Wenmian Yang, Na Ruan, Wenyuan Gao, Kun Wang, Wensheng Ran, Weijia JiaAbstract:Time-sync comments reveal a new way of extracting the online video tags. However, such time-sync comments have lots of noises due to users' diverse comments, introducing great challenges for accurate and fast video tag extractions. In this paper, we propose an unsupervised video tag extraction algorithm named Semantic Weight-Inverse Document Frequency (SW-IDF). SW-IDF first generates corresponding Semantic Association graph (SAG) using Semantic similarities and timestamps of the time-sync comments. Then it clusters the comments into sub-graphs of different topics and assigns weight to each comment based on SAG. This can clearly differentiate the meaningful comments with the noises. In this way, the noises can be identified, and effectively eliminated. Extensive experiments have shown that SW-IDF can achieve 0.3045 precision and 0.6530 recall in high-density comments; 0.3800 precision and 0.4460 recall in low-density comments. It is the best performance among the existing unsupervised algorithms.
Kun Wang - One of the best experts on this subject based on the ideXlab platform.
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time sync video tag extraction using Semantic Association graph
ACM Transactions on Knowledge Discovery From Data, 2019Co-Authors: Wenmian Yang, Na Ruan, Wenyuan Gao, Kun Wang, Weijia Jia, Wei Zhao, Nan Liu, Yunyong ZhangAbstract:Time-sync comments (TSCs) reveal a new way of extracting the online video tags. However, such TSCs have lots of noises due to users’ diverse comments, introducing great challenges for accurate and fast video tag extractions. In this article, we propose an unsupervised video tag extraction algorithm named Semantic Weight-Inverse Document Frequency (SW-IDF). Specifically, we first generate corresponding Semantic Association graph (SAG) using Semantic similarities and timestamps of the TSCs. Second, we propose two graph cluster algorithms, i.e., dialogue-based algorithm and topic center-based algorithm, to deal with the videos with different density of comments. Third, we design a graph iteration algorithm to assign the weight to each comment based on the degrees of the clustered subgraphs, which can differentiate the meaningful comments from the noises. Finally, we gain the weight of each word by combining Semantic Weight (SW) and Inverse Document Frequency (IDF). In this way, the video tags are extracted automatically in an unsupervised way. Extensive experiments have shown that SW-IDF (dialogue-based algorithm) achieves 0.4210 F1-score and 0.4932 MAP (Mean Average Precision) in high-density comments, 0.4267 F1-score and 0.3623 MAP in low-density comments; while SW-IDF (topic center-based algorithm) achieves 0.4444 F1-score and 0.5122 MAP in high-density comments, 0.4207 F1-score and 0.3522 MAP in low-density comments. It has a better performance than the state-of-the-art unsupervised algorithms in both F1-score and MAP.
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time sync video tag extraction using Semantic Association graph
arXiv: Information Retrieval, 2019Co-Authors: Wenmian Yang, Na Ruan, Wenyuan Gao, Kun Wang, Weijia Jia, Wei Zhao, Nan Liu, Yunyong ZhangAbstract:Time-sync comments reveal a new way of extracting the online video tags. However, such time-sync comments have lots of noises due to users' diverse comments, introducing great challenges for accurate and fast video tag extractions. In this paper, we propose an unsupervised video tag extraction algorithm named Semantic Weight-Inverse Document Frequency (SW-IDF). Specifically, we first generate corresponding Semantic Association graph (SAG) using Semantic similarities and timestamps of the time-sync comments. Second, we propose two graph cluster algorithms, i.e., dialogue-based algorithm and topic center-based algorithm, to deal with the videos with different density of comments. Third, we design a graph iteration algorithm to assign the weight to each comment based on the degrees of the clustered subgraphs, which can differentiate the meaningful comments from the noises. Finally, we gain the weight of each word by combining Semantic Weight (SW) and Inverse Document Frequency (IDF). In this way, the video tags are extracted automatically in an unsupervised way. Extensive experiments have shown that SW-IDF (dialogue-based algorithm) achieves 0.4210 F1-score and 0.4932 MAP (Mean Average Precision) in high-density comments, 0.4267 F1-score and 0.3623 MAP in low-density comments; while SW-IDF (topic center-based algorithm) achieves 0.4444 F1-score and 0.5122 MAP in high-density comments, 0.4207 F1-score and 0.3522 MAP in low-density comments. It has a better performance than the state-of-the-art unsupervised algorithms in both F1-score and MAP.
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crowdsourced time sync video tagging using Semantic Association graph
International Conference on Multimedia and Expo, 2017Co-Authors: Wenmian Yang, Na Ruan, Wenyuan Gao, Kun Wang, Wensheng Ran, Weijia JiaAbstract:Time-sync comments reveal a new way of extracting the online video tags. However, such time-sync comments have lots of noises due to users' diverse comments, introducing great challenges for accurate and fast video tag extractions. In this paper, we propose an unsupervised video tag extraction algorithm named Semantic Weight-Inverse Document Frequency (SW-IDF). SW-IDF first generates corresponding Semantic Association graph (SAG) using Semantic similarities and timestamps of the time-sync comments. Then it clusters the comments into sub-graphs of different topics and assigns weight to each comment based on SAG. This can clearly differentiate the meaningful comments with the noises. In this way, the noises can be identified, and effectively eliminated. Extensive experiments have shown that SW-IDF can achieve 0.3045 precision and 0.6530 recall in high-density comments; 0.3800 precision and 0.4460 recall in low-density comments. It is the best performance among the existing unsupervised algorithms.
