Moving vehicles generate a large amount of sensor data every second. To ensure automatic driving in a complex driving environment, it needs to fulfill a large amount of data transmission, storage, and processing in a short time. Real-time perception of traffic, target characteristics, and traffic density are important to achieve safe driving and a stable driving experience. However, it is very difficult to adjust the pricing strategy according to the actual demand of the network. In order to analyze the interaction between task vehicle and service vehicle, the Stackelberg game model is introduced. Considering the communication model, calculation model, optimization objectives, and delay constraints, this paper constructs the utility function of service vehicle and task vehicle based on the Stackelberg game model. Based on the utility function, we can obtain the optimal price strategy of service vehicles and the optimal purchase strategy of task vehicles.
{"title":"Task Offloading Strategy of Internet of Vehicles Based on Stackelberg Game","authors":"Shuo Xiao, Shengzhi Wang, Zhenzhen Huang, Tianyu Wang, Wei Chen, Guopeng Zhang","doi":"10.1145/3442442.3451139","DOIUrl":"https://doi.org/10.1145/3442442.3451139","url":null,"abstract":"Moving vehicles generate a large amount of sensor data every second. To ensure automatic driving in a complex driving environment, it needs to fulfill a large amount of data transmission, storage, and processing in a short time. Real-time perception of traffic, target characteristics, and traffic density are important to achieve safe driving and a stable driving experience. However, it is very difficult to adjust the pricing strategy according to the actual demand of the network. In order to analyze the interaction between task vehicle and service vehicle, the Stackelberg game model is introduced. Considering the communication model, calculation model, optimization objectives, and delay constraints, this paper constructs the utility function of service vehicle and task vehicle based on the Stackelberg game model. Based on the utility function, we can obtain the optimal price strategy of service vehicles and the optimal purchase strategy of task vehicles.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124378100","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}
Alyssa Lees, Luciano Barbosa, Flip Korn, L. Silva, You Wu, Cong Yu
In today’s news deluge, it can often be overwhelming to understand the significance of a news article or verify the facts within. One approach to address this challenge is to identify relevant data so that crucial statistics or facts can be highlighted for the user to easily digest, and thus improve the user’s comprehension of the news story in a larger context. In this paper, we look toward structured tables on the Web, especially the high quality data tables from Wikipedia, to assist in news understanding. Specifically, we aim to automatically find tables related to a news article. For that, we leverage the content and entities extracted from news articles and their matching tables to fine-tune a Bidirectional Transformers (BERT) model. The resulting model is, therefore, an encoder tailored for article-to-table match. To find the matching tables for a given news article, the fine-tuned BERT model encodes each table in the corpus and the news article into their respective embedding vectors. The tables with the highest cosine similarities to the news article in this new representation space are considered the possible matches. Comprehensive experimental analyses show that the new approach significantly outperforms the baselines over a large, weakly-labeled, dataset obtained from Web click logs as well as a small, crowdsourced, evaluation set. Specifically, our approach achieves near 90% accuracy@5 as opposed to baselines varying between 30% and 64%.
{"title":"Collocating News Articles with Structured Web Tables✱","authors":"Alyssa Lees, Luciano Barbosa, Flip Korn, L. Silva, You Wu, Cong Yu","doi":"10.1145/3442442.3452326","DOIUrl":"https://doi.org/10.1145/3442442.3452326","url":null,"abstract":"In today’s news deluge, it can often be overwhelming to understand the significance of a news article or verify the facts within. One approach to address this challenge is to identify relevant data so that crucial statistics or facts can be highlighted for the user to easily digest, and thus improve the user’s comprehension of the news story in a larger context. In this paper, we look toward structured tables on the Web, especially the high quality data tables from Wikipedia, to assist in news understanding. Specifically, we aim to automatically find tables related to a news article. For that, we leverage the content and entities extracted from news articles and their matching tables to fine-tune a Bidirectional Transformers (BERT) model. The resulting model is, therefore, an encoder tailored for article-to-table match. To find the matching tables for a given news article, the fine-tuned BERT model encodes each table in the corpus and the news article into their respective embedding vectors. The tables with the highest cosine similarities to the news article in this new representation space are considered the possible matches. Comprehensive experimental analyses show that the new approach significantly outperforms the baselines over a large, weakly-labeled, dataset obtained from Web click logs as well as a small, crowdsourced, evaluation set. Specifically, our approach achieves near 90% accuracy@5 as opposed to baselines varying between 30% and 64%.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120859542","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}
Xiaohua Chen, R. Zhou, Genggeng Liu, Zhen Chen, Wenzhong Guo
The construction of timing-driven Steiner minimum tree is a critical issue in VLSI routing design. Meanwhile, since the interconnection model of X-architecture can make full use of routing resources compared to the traditional Manhattan architecture, constructing a Timing-Driven X-architecture Steiner Minimum Tree (TDXSMT) is of great significance to improving routing performance. In this paper, an efficient algorithm based on Social Learning Multi-Objective Particle Swarm Optimization (SLMOPSO) is proposed to construct a TDXSMT with minimizing the maximum source-to-sink pathlength. An X-architecture Prim-Dijkstra model is presented to construct an initial Steiner tree which can optimize both the wirelength and the maximum source-to-sink pathlength. In order to find a better solution, an SLMOPSO method based on the nearest and best select strategy is presented to improve the global exploration capability of the algorithm. Besides, the mutation and crossover operators are utilized to achieve the discrete particle update process, thereby better solving the discrete TDXSMT problem. The experimental results indicate that the proposed algorithm has an excellent trade-off between the wirelength and maximum source-to-sink pathlength of the routing tree and can greatly optimize the timing delay.
{"title":"Timing-Driven X-architecture Steiner Minimum Tree Construction Based on Social Learning Multi-Objective Particle Swarm Optimization","authors":"Xiaohua Chen, R. Zhou, Genggeng Liu, Zhen Chen, Wenzhong Guo","doi":"10.1145/3442442.3451143","DOIUrl":"https://doi.org/10.1145/3442442.3451143","url":null,"abstract":"The construction of timing-driven Steiner minimum tree is a critical issue in VLSI routing design. Meanwhile, since the interconnection model of X-architecture can make full use of routing resources compared to the traditional Manhattan architecture, constructing a Timing-Driven X-architecture Steiner Minimum Tree (TDXSMT) is of great significance to improving routing performance. In this paper, an efficient algorithm based on Social Learning Multi-Objective Particle Swarm Optimization (SLMOPSO) is proposed to construct a TDXSMT with minimizing the maximum source-to-sink pathlength. An X-architecture Prim-Dijkstra model is presented to construct an initial Steiner tree which can optimize both the wirelength and the maximum source-to-sink pathlength. In order to find a better solution, an SLMOPSO method based on the nearest and best select strategy is presented to improve the global exploration capability of the algorithm. Besides, the mutation and crossover operators are utilized to achieve the discrete particle update process, thereby better solving the discrete TDXSMT problem. The experimental results indicate that the proposed algorithm has an excellent trade-off between the wirelength and maximum source-to-sink pathlength of the routing tree and can greatly optimize the timing delay.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123603378","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}
Both authors contributed equally to this research. This paper presents the method that we tackled the FinSBD-3 shared task (structure boundary detection) to extract the boundaries of sentences, lists, and items, including structure elements like footer, header, tables from noisy unstructured English and French financial texts. The deep attention model based on word embedding using data augmentation and BERT model named as hybrid deep learning model to detect the sentence, list-item, footer, header, tables boundaries in noisy English and French texts and classify the list-item sentences into list & different item types using deep attention model. The experiment is shown that the proposed method could be an effective solution to deal with the FinSBD-3 shared task. The submitted result ranks first based on the task metrics in the final leader board.
{"title":"aiai at the FinSBD-3 task: Structure Boundary Detection of Noisy Financial Texts in English and French Using Data Augmentation and Hybrid Deep Learning Model","authors":"Ke Tian, Hua Chen","doi":"10.1145/3442442.3451380","DOIUrl":"https://doi.org/10.1145/3442442.3451380","url":null,"abstract":"Both authors contributed equally to this research. This paper presents the method that we tackled the FinSBD-3 shared task (structure boundary detection) to extract the boundaries of sentences, lists, and items, including structure elements like footer, header, tables from noisy unstructured English and French financial texts. The deep attention model based on word embedding using data augmentation and BERT model named as hybrid deep learning model to detect the sentence, list-item, footer, header, tables boundaries in noisy English and French texts and classify the list-item sentences into list & different item types using deep attention model. The experiment is shown that the proposed method could be an effective solution to deal with the FinSBD-3 shared task. The submitted result ranks first based on the task metrics in the final leader board.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124608180","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}
Web tables contain a large amount of useful knowledge. Takco is a new large-scale platform designed for extracting facts from tables that can be added to Knowledge Graphs (KGs) like Wikidata. Focusing on achieving high precision, current techniques are biased towards extracting redundant facts, i.e., facts already in the KG. Takco aims to find more novel facts, still at high precision. Our demonstration has two goals. The first one is to illustrate the main features of Takco’s novel interpretation algorithm. The second goal is to show to what extent other state-of-the-art systems are biased towards the extraction of redundant facts using our platform, thus raising awareness on this important problem.
{"title":"TAKCO: A Platform for Extracting Novel Facts from Tables","authors":"B. Kruit, P. Boncz, J. Urbani","doi":"10.1145/3442442.3458611","DOIUrl":"https://doi.org/10.1145/3442442.3458611","url":null,"abstract":"Web tables contain a large amount of useful knowledge. Takco is a new large-scale platform designed for extracting facts from tables that can be added to Knowledge Graphs (KGs) like Wikidata. Focusing on achieving high precision, current techniques are biased towards extracting redundant facts, i.e., facts already in the KG. Takco aims to find more novel facts, still at high precision. Our demonstration has two goals. The first one is to illustrate the main features of Takco’s novel interpretation algorithm. The second goal is to show to what extent other state-of-the-art systems are biased towards the extraction of redundant facts using our platform, thus raising awareness on this important problem.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"240 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125932354","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}
Bettina Berendt, Özgür Karadeniz, Stefan Mertens, L. d’Haenens
“Fairness” is a multi-faceted concept that is contested within and across disciplines. In machine learning, it usually denotes some form of equality of measurable outcomes of algorithmic decision making. In this paper, we start from a viewpoint of sociology and media studies, which highlights that to even claim fair treatment, individuals and groups first have to be visible. We draw on a notion and a quantitative measure of diversity that expresses this wider requirement. We used the measure to design and build the Diversity Searcher, a Web-based tool to detect and enhance the representation of socio-political actors in news media. We show how the tool's combination of natural language processing and a rich user interface can help news producers and consumers detect and understand diversity-relevant aspects of representation, which can ultimately contribute to enhancing diversity and fairness in media. We comment on our observation that, through interactions with target users during the construction of the tool, NLP results and interface questions became increasingly important, such that the formal measure of diversity has become a catalyst for functionality, but in itself less important.
{"title":"Fairness beyond “equal”: The Diversity Searcher as a Tool to Detect and Enhance the Representation of Socio-political Actors in News Media","authors":"Bettina Berendt, Özgür Karadeniz, Stefan Mertens, L. d’Haenens","doi":"10.1145/3442442.3452303","DOIUrl":"https://doi.org/10.1145/3442442.3452303","url":null,"abstract":"“Fairness” is a multi-faceted concept that is contested within and across disciplines. In machine learning, it usually denotes some form of equality of measurable outcomes of algorithmic decision making. In this paper, we start from a viewpoint of sociology and media studies, which highlights that to even claim fair treatment, individuals and groups first have to be visible. We draw on a notion and a quantitative measure of diversity that expresses this wider requirement. We used the measure to design and build the Diversity Searcher, a Web-based tool to detect and enhance the representation of socio-political actors in news media. We show how the tool's combination of natural language processing and a rich user interface can help news producers and consumers detect and understand diversity-relevant aspects of representation, which can ultimately contribute to enhancing diversity and fairness in media. We comment on our observation that, through interactions with target users during the construction of the tool, NLP results and interface questions became increasingly important, such that the formal measure of diversity has become a catalyst for functionality, but in itself less important.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114562815","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}
Wikipedia articles are known for their exhaustive knowledge and extensive collaboration. Users perform various tasks that include editing in terms of adding new facts or rectifying some mistakes, looking up new topics, or simply browsing. In this paper, we investigate the impact of gradual edits on the re-positioning and organization of the factual information in Wikipedia articles. Literature shows that in a collaborative system, a set of contributors are responsible for seeking, perceiving, and organizing the information. However, very little is known about the evolution of information organization on Wikipedia articles. Based on our analysis, we show that in a Wikipedia article, the crowd is capable of placing the factual information to its correct position, eventually reducing the knowledge gaps. We also show that the majority of information re-arrangement occurs in the initial stages of the article development and gradually decreases in the later stages. Our findings advance our understanding of the fundamentals of information organization on Wikipedia articles and can have implications for developers aiming to improve the content quality and completeness of Wikipedia articles.
{"title":"Tracing the Factoids: the Anatomy of Information Re-organization in Wikipedia Articles","authors":"Amit Verma, S. Iyengar","doi":"10.1145/3442442.3452342","DOIUrl":"https://doi.org/10.1145/3442442.3452342","url":null,"abstract":"Wikipedia articles are known for their exhaustive knowledge and extensive collaboration. Users perform various tasks that include editing in terms of adding new facts or rectifying some mistakes, looking up new topics, or simply browsing. In this paper, we investigate the impact of gradual edits on the re-positioning and organization of the factual information in Wikipedia articles. Literature shows that in a collaborative system, a set of contributors are responsible for seeking, perceiving, and organizing the information. However, very little is known about the evolution of information organization on Wikipedia articles. Based on our analysis, we show that in a Wikipedia article, the crowd is capable of placing the factual information to its correct position, eventually reducing the knowledge gaps. We also show that the majority of information re-arrangement occurs in the initial stages of the article development and gradually decreases in the later stages. Our findings advance our understanding of the fundamentals of information organization on Wikipedia articles and can have implications for developers aiming to improve the content quality and completeness of Wikipedia articles.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114365933","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}
With the rapid development of the Web of Things, there have been a lot of sensors deployed. Advanced knowledge can be achieved by deep learning method and easier integration with open Web standards. A large number of the data generated by sensors required extra processing resources due to the limited resources of the sensors. Due to the limitation of bandwidth or requirement of low latency, it is impossible to transfer such large amounts of data to cloud servers for processing. Thus, the concept of distributed fog computing has been proposed to process such big data into knowledge in real-time. Large scale fog computing system is built using cheap devices, denotes as fog nodes. Therefore, the resiliency to fog node failures should be considered in design of distributed fog computing. LT codes (LTC) have important applications in the design of modern distributed computing, which can reduce the latency of the computing tasks, such as matrix multiplication in deep learning methods. In this paper, we consider that fog nodes may be failure, and an improved LT codes are applied to matrix multiplication of distributed fog computing process to reduce latency. Numerical results show that the improved LTC based scheme can reduce average overhead and degree simultaneously, which reduce the latency and computation complexity of distributed fog computing.
{"title":"Distributed Fog Computing Based on Improved LT codes for Deep Learning in Web of Things","authors":"Lei Zhang, Jie Liu, Fuquan Zhang, Yunlong Mao","doi":"10.1145/3442442.3451140","DOIUrl":"https://doi.org/10.1145/3442442.3451140","url":null,"abstract":"With the rapid development of the Web of Things, there have been a lot of sensors deployed. Advanced knowledge can be achieved by deep learning method and easier integration with open Web standards. A large number of the data generated by sensors required extra processing resources due to the limited resources of the sensors. Due to the limitation of bandwidth or requirement of low latency, it is impossible to transfer such large amounts of data to cloud servers for processing. Thus, the concept of distributed fog computing has been proposed to process such big data into knowledge in real-time. Large scale fog computing system is built using cheap devices, denotes as fog nodes. Therefore, the resiliency to fog node failures should be considered in design of distributed fog computing. LT codes (LTC) have important applications in the design of modern distributed computing, which can reduce the latency of the computing tasks, such as matrix multiplication in deep learning methods. In this paper, we consider that fog nodes may be failure, and an improved LT codes are applied to matrix multiplication of distributed fog computing process to reduce latency. Numerical results show that the improved LTC based scheme can reduce average overhead and degree simultaneously, which reduce the latency and computation complexity of distributed fog computing.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124455306","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}
V. Janev, Maria-Esther Vidal, Kemele M. Endris, Dea Pujić
Realizing smart factories according to the Industry 4.0 vision requires intelligent human-to-machine and machine-to-machine communication. To achieve this goal, components such as actuators, sensors, and cyber-physical systems along with their data, need to be described; moreover, interoperability conflicts arisen from various semantic representations of these components demand also solutions. To empowering communication in smart factories, a variety of standards and standardization frameworks have been proposed. These standards enable the description of the main properties of components, systems, and processes, as well as interactions between them. Standardization frameworks classify, align, and integrate industrial standards according to their purposes and features. Various standardization frameworks have been proposed all over the world by industrial communities, e.g., RAMI4.0 or IICF. While being expressive to categorize existing standards, standardization frameworks may present divergent classifications of the same standard. Mismatches between standard classifications generate semantic interoperability conflicts that negatively impact the effectiveness of communication in smart factories. In this article, we tackle the problem of standard interoperability across different standardization frameworks, and devise a knowledge-driven approach that allows for the description of standards and standardization frameworks into an Industry 4.0 knowledge graph (I40KG). The STO ontology represents properties of standards and standardization frameworks, as well as relationships among them. The I40KG integrates more than 200 standards and four standardization frameworks. To populate the I40KG, the landscape of standards has been analyzed from a semantic perspective and the resulting I40KG represents knowledge expressed in more than 200 industrial related documents including technical reports, research articles, and white papers. Additionally, the I40KG has been linked to existing knowledge graphs and an automated reasoning has been implemented to reveal implicit relations between standards as well as mappings across standardization frameworks. We analyze both the number of discovered relations between standards and the accuracy of these relations. Observed results indicate that both reasoning and linking processes enable for increasing the connectivity in the knowledge graph by up to 80%, whilst up to 96% of the relations can be validated. These outcomes suggest that integrating standards and standardization frameworks into the I40KG enables the resolution of semantic interoperability conflicts, empowering the communication in smart factories.
{"title":"Analyzing a Knowledge Graph of Industry 4.0 Standards","authors":"V. Janev, Maria-Esther Vidal, Kemele M. Endris, Dea Pujić","doi":"10.1145/3442442.3453542","DOIUrl":"https://doi.org/10.1145/3442442.3453542","url":null,"abstract":"Realizing smart factories according to the Industry 4.0 vision requires intelligent human-to-machine and machine-to-machine communication. To achieve this goal, components such as actuators, sensors, and cyber-physical systems along with their data, need to be described; moreover, interoperability conflicts arisen from various semantic representations of these components demand also solutions. To empowering communication in smart factories, a variety of standards and standardization frameworks have been proposed. These standards enable the description of the main properties of components, systems, and processes, as well as interactions between them. Standardization frameworks classify, align, and integrate industrial standards according to their purposes and features. Various standardization frameworks have been proposed all over the world by industrial communities, e.g., RAMI4.0 or IICF. While being expressive to categorize existing standards, standardization frameworks may present divergent classifications of the same standard. Mismatches between standard classifications generate semantic interoperability conflicts that negatively impact the effectiveness of communication in smart factories. In this article, we tackle the problem of standard interoperability across different standardization frameworks, and devise a knowledge-driven approach that allows for the description of standards and standardization frameworks into an Industry 4.0 knowledge graph (I40KG). The STO ontology represents properties of standards and standardization frameworks, as well as relationships among them. The I40KG integrates more than 200 standards and four standardization frameworks. To populate the I40KG, the landscape of standards has been analyzed from a semantic perspective and the resulting I40KG represents knowledge expressed in more than 200 industrial related documents including technical reports, research articles, and white papers. Additionally, the I40KG has been linked to existing knowledge graphs and an automated reasoning has been implemented to reveal implicit relations between standards as well as mappings across standardization frameworks. We analyze both the number of discovered relations between standards and the accuracy of these relations. Observed results indicate that both reasoning and linking processes enable for increasing the connectivity in the knowledge graph by up to 80%, whilst up to 96% of the relations can be validated. These outcomes suggest that integrating standards and standardization frameworks into the I40KG enables the resolution of semantic interoperability conflicts, empowering the communication in smart factories.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127602726","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}
Jigsaw’s Perspective API aims to protect voices in online conversation by developing and serving machine learning models that identify toxicity text. This talk will share how the team behind Perspective thinks about the issues of Fairness, Accountability, Transparency, Ethics and Society through the lens of Google’s AI Principles. For the Perspective team, building technology that is fair and ethical is a continuous, ongoing effort. The talk will cover concrete strategies the Perspective team has already used to mitigate bias in ML models as well as new strategies currently being explored. Finally, with examples of how Perspective is being used in the real world, the talk will show how machine learning, combined with thoughtful human moderation and participation, can help improve online conversations.
{"title":"AI Principles in Identifying Toxicity in Online Conversation: Keynote at the Third Workshop on Fairness, Accountability, Transparency, Ethics and Society on the Web","authors":"Lucy Vasserman","doi":"10.1145/3442442.3452307","DOIUrl":"https://doi.org/10.1145/3442442.3452307","url":null,"abstract":"Jigsaw’s Perspective API aims to protect voices in online conversation by developing and serving machine learning models that identify toxicity text. This talk will share how the team behind Perspective thinks about the issues of Fairness, Accountability, Transparency, Ethics and Society through the lens of Google’s AI Principles. For the Perspective team, building technology that is fair and ethical is a continuous, ongoing effort. The talk will cover concrete strategies the Perspective team has already used to mitigate bias in ML models as well as new strategies currently being explored. Finally, with examples of how Perspective is being used in the real world, the talk will show how machine learning, combined with thoughtful human moderation and participation, can help improve online conversations.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114378780","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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