Xiaoliang Zhang, F. Gao, Lunsheng Zhou, Shenqi Jing, Zhongmin Wang, Yongqing Wang, Shumei Miao, Xin Zhang, Jianjun Guo, Tao Shan, Yun Liu
Existing pharmaceutical information extraction research often focus on standalone entity or relationship identification tasks over drug instructions. There is a lack of a holistic solution for drug knowledge extraction. Moreover, current methods perform poorly in extracting fine-grained interaction relations from drug instructions. To solve these problems, this paper proposes an information extraction framework for drug instructions. The framework proposes deep learning models with fine-tuned pre-training models for entity recognition and relation extraction, in addition, it incorporates an novel entity pair calibration process to promote the performance for fine-grained relation extraction. The framework experiments on more than 60k Chinese drug description sentences from 4000 drug instructions. Empirical results show that the framework can successfully identify drug related entities (F1 ≥ 0.95) and their relations (F1 ≥ 0.83) from the realistic dataset, and the entity pair calibration plays an important role (~5% F1 score improvement) in extracting fine-grained relations.
{"title":"Fine-Grained Drug Interaction Extraction Based on Entity Pair Calibration and Pre-Training Model for Chinese Drug Instructions","authors":"Xiaoliang Zhang, F. Gao, Lunsheng Zhou, Shenqi Jing, Zhongmin Wang, Yongqing Wang, Shumei Miao, Xin Zhang, Jianjun Guo, Tao Shan, Yun Liu","doi":"10.4018/ijswis.307908","DOIUrl":"https://doi.org/10.4018/ijswis.307908","url":null,"abstract":"Existing pharmaceutical information extraction research often focus on standalone entity or relationship identification tasks over drug instructions. There is a lack of a holistic solution for drug knowledge extraction. Moreover, current methods perform poorly in extracting fine-grained interaction relations from drug instructions. To solve these problems, this paper proposes an information extraction framework for drug instructions. The framework proposes deep learning models with fine-tuned pre-training models for entity recognition and relation extraction, in addition, it incorporates an novel entity pair calibration process to promote the performance for fine-grained relation extraction. The framework experiments on more than 60k Chinese drug description sentences from 4000 drug instructions. Empirical results show that the framework can successfully identify drug related entities (F1 ≥ 0.95) and their relations (F1 ≥ 0.83) from the realistic dataset, and the entity pair calibration plays an important role (~5% F1 score improvement) in extracting fine-grained relations.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"33 1","pages":"1-23"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75170282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hind Alsharif, W. Alhalabi, A. Alkhateeb, S. Shihata, K. Bajunaid, Salwa Abdullah Almansouri, M. Pasovic, R. Satava, A. Sabbagh
This paper aims to assess the needs of neurosurgical training in order to strategize the future plans for simulation and rehearsal. The project main objective is to investigate the ability virtual reality to enhance the training.An online questionnaire has been conducted among surgeons practicing in different countries across the globe. The study shows significant differences in rehearsal methods and surgical teaching methods practiced by the respondents. Among respondents, 90% did believe that virtual reality technology can serve surgical training, and almost all respondents agreed that there is a gap in the existing neurosurgical training in terms of operating room ergonomics. Adequate education on surgical ergonomics might lead to an improvement in the outcomes for both surgeon and patient. The contribution of the paper is two fold. From one side investigates the new requirements for the enhancement of Neurosurgenos’ training and adoption on Virtual Reality Simulator. From the other side contributes to the body of knowledge related to the required Ergonomics skills.
{"title":"Virtual Reality Simulator Enhances Ergonomics Skills for Neurosurgeons","authors":"Hind Alsharif, W. Alhalabi, A. Alkhateeb, S. Shihata, K. Bajunaid, Salwa Abdullah Almansouri, M. Pasovic, R. Satava, A. Sabbagh","doi":"10.4018/ijswis.297041","DOIUrl":"https://doi.org/10.4018/ijswis.297041","url":null,"abstract":"This paper aims to assess the needs of neurosurgical training in order to strategize the future plans for simulation and rehearsal. The project main objective is to investigate the ability virtual reality to enhance the training.An online questionnaire has been conducted among surgeons practicing in different countries across the globe. The study shows significant differences in rehearsal methods and surgical teaching methods practiced by the respondents. Among respondents, 90% did believe that virtual reality technology can serve surgical training, and almost all respondents agreed that there is a gap in the existing neurosurgical training in terms of operating room ergonomics. Adequate education on surgical ergonomics might lead to an improvement in the outcomes for both surgeon and patient. The contribution of the paper is two fold. From one side investigates the new requirements for the enhancement of Neurosurgenos’ training and adoption on Virtual Reality Simulator. From the other side contributes to the body of knowledge related to the required Ergonomics skills.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"87 1","pages":"1-20"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81131372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Health information becomes importantly valuable for protecting public health in the current coronavirus situation. Knowledge-based information systems can play a crucial role in helping individuals to practice risk assessment and remote diagnosis. We introduce a novel approach that will develop causality-focused knowledge learning in a robust and transparent manner. Then, the machine gains the causality and probability knowledge for inference (thinking) and accurate prediction later. Besides, the hidden knowledge can be discovered beyond the existing understanding of the diseases. The whole approach is built on a Causal Probability Description Logic Framework that combines Natural Language Processing (NLP), Causality Analysis and extended Knowledge Graph (KG) technologies together. The experimental work has processed 801 diseases in total (from the UK NHS website linking with DBpedia datasets). As a result, the machine learnt comprehensive health causal knowledge and relations among the diseases, symptoms, and other facts efficiently.
{"title":"Learning Disease Causality Knowledge From the Web of Health Data","authors":"H. Q. Yu, S. Reiff-Marganiec","doi":"10.4018/ijswis.297145","DOIUrl":"https://doi.org/10.4018/ijswis.297145","url":null,"abstract":"Health information becomes importantly valuable for protecting public health in the current coronavirus situation. Knowledge-based information systems can play a crucial role in helping individuals to practice risk assessment and remote diagnosis. We introduce a novel approach that will develop causality-focused knowledge learning in a robust and transparent manner. Then, the machine gains the causality and probability knowledge for inference (thinking) and accurate prediction later. Besides, the hidden knowledge can be discovered beyond the existing understanding of the diseases. The whole approach is built on a Causal Probability Description Logic Framework that combines Natural Language Processing (NLP), Causality Analysis and extended Knowledge Graph (KG) technologies together. The experimental work has processed 801 diseases in total (from the UK NHS website linking with DBpedia datasets). As a result, the machine learnt comprehensive health causal knowledge and relations among the diseases, symptoms, and other facts efficiently.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"18 1","pages":"1-19"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84977468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The demand for Internet security has escalated in the last two decades because the rapid proliferation in the number of Internet users has presented attackers with new detrimental opportunities. One of the simple yet powerful attack, lurking around the Internet today, is the Distributed Denial-of-Service (DDoS) attack. The expeditious surge in the collaborative environments, like IoT, cloud computing and SDN, have provided attackers with countless new avenues to benefit from the distributed nature of DDoS attacks. The attackers protect their anonymity by infecting distributed devices and utilizing them to create a bot army to constitute a large-scale attack. Thus, the development of an effective as well as efficient DDoS defense mechanism becomes an immediate goal. In this exposition, we present a DDoS threat analysis along with a few novel ground-breaking defense mechanisms proposed by various researchers for numerous domains. Further, we talk about popular performance metrics that evaluate the defense schemes. In the end, we list prevalent DDoS attack tools and open challenges.
{"title":"Distributed Denial-of-Service (DDoS) Attacks and Defense Mechanisms in Various Web-Enabled Computing Platforms: Issues, Challenges, and Future Research Directions","authors":"Anshuman Singh, Brij B. Gupta","doi":"10.4018/ijswis.297143","DOIUrl":"https://doi.org/10.4018/ijswis.297143","url":null,"abstract":"The demand for Internet security has escalated in the last two decades because the rapid proliferation in the number of Internet users has presented attackers with new detrimental opportunities. One of the simple yet powerful attack, lurking around the Internet today, is the Distributed Denial-of-Service (DDoS) attack. The expeditious surge in the collaborative environments, like IoT, cloud computing and SDN, have provided attackers with countless new avenues to benefit from the distributed nature of DDoS attacks. The attackers protect their anonymity by infecting distributed devices and utilizing them to create a bot army to constitute a large-scale attack. Thus, the development of an effective as well as efficient DDoS defense mechanism becomes an immediate goal. In this exposition, we present a DDoS threat analysis along with a few novel ground-breaking defense mechanisms proposed by various researchers for numerous domains. Further, we talk about popular performance metrics that evaluate the defense schemes. In the end, we list prevalent DDoS attack tools and open challenges.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"65 1","pages":"1-43"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82788365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shimaa Ismail, Tarek El-Shishtawy, Abdelwahab K. Alsammak
This work presents a new alignment word-space approach for measuring the similarity between two snipped texts. The approach combines two similarity measurement methods: alignment-based and vector space-based. The vector space-based method depends on a semantic net that represents the meaning of words as vectors. These vectors are lemmatized to enrich the search space. The alignment-based method generates an alignment word space matrix (AWSM) for the snipped texts according to the generated semantic word spaces. Finally, the degree of sentence semantic similarity is measured using some proposed alignment rules. Four experiments were carried out to evaluate the performance of the proposed approach, using two different datasets. The experimental results proved that applying the lemmatization process for the input text and the vector model has a better effect. The degree of correctness of the results reaches 0.7212 which is considered one of the best two results of the published Arabic semantic similarities.
{"title":"A New Alignment Word-Space Approach for Measuring Semantic Similarity for Arabic Text","authors":"Shimaa Ismail, Tarek El-Shishtawy, Abdelwahab K. Alsammak","doi":"10.4018/ijswis.297036","DOIUrl":"https://doi.org/10.4018/ijswis.297036","url":null,"abstract":"This work presents a new alignment word-space approach for measuring the similarity between two snipped texts. The approach combines two similarity measurement methods: alignment-based and vector space-based. The vector space-based method depends on a semantic net that represents the meaning of words as vectors. These vectors are lemmatized to enrich the search space. The alignment-based method generates an alignment word space matrix (AWSM) for the snipped texts according to the generated semantic word spaces. Finally, the degree of sentence semantic similarity is measured using some proposed alignment rules. Four experiments were carried out to evaluate the performance of the proposed approach, using two different datasets. The experimental results proved that applying the lemmatization process for the input text and the vector model has a better effect. The degree of correctness of the results reaches 0.7212 which is considered one of the best two results of the published Arabic semantic similarities.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"3 1","pages":"1-18"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88087232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Samara M. Ahmed, Adil E. Rajput, A. Sarirete, Tauseef J. Chowdhry
Social media gives researchers an invaluable opportunity to gain insight into different facets of human life. Researchers put a great emphasis on categorizing the socioeconomic status (SES) of individuals to help predict various findings of interest. Forum uses, hashtags and chatrooms are common tools of conversations grouping. Crowdsourcing involves gathering intelligence to group online user community based on common interest. This paper provides a mechanism to look at writings on social media and group them based on their academic background. We analyzed online forum posts from various geographical regions in the US and characterized the readability scores of users. Specifically, we collected 10,000 tweets from the members of US Senate and computed the Flesch-Kincaid readability score. Comparing the Senators’ tweets to the ones from average internet users, we note 1) US Senators’ readability based on their tweets rate is much higher, and 2) immense difference among average citizen’s score compared to those of US Senators is attributed to the wide spectrum of academic attainment.
{"title":"Flesch-Kincaid Measure as Proxy of Socio-Economic Status on Twitter: Comparing US Senator Writing to Internet Users","authors":"Samara M. Ahmed, Adil E. Rajput, A. Sarirete, Tauseef J. Chowdhry","doi":"10.4018/ijswis.297037","DOIUrl":"https://doi.org/10.4018/ijswis.297037","url":null,"abstract":"Social media gives researchers an invaluable opportunity to gain insight into different facets of human life. Researchers put a great emphasis on categorizing the socioeconomic status (SES) of individuals to help predict various findings of interest. Forum uses, hashtags and chatrooms are common tools of conversations grouping. Crowdsourcing involves gathering intelligence to group online user community based on common interest. This paper provides a mechanism to look at writings on social media and group them based on their academic background. We analyzed online forum posts from various geographical regions in the US and characterized the readability scores of users. Specifically, we collected 10,000 tweets from the members of US Senate and computed the Flesch-Kincaid readability score. Comparing the Senators’ tweets to the ones from average internet users, we note 1) US Senators’ readability based on their tweets rate is much higher, and 2) immense difference among average citizen’s score compared to those of US Senators is attributed to the wide spectrum of academic attainment.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"28 1","pages":"1-19"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82621972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cecilia Ávila-Garzón, M. Balaguera, Valentina Tabares-Morales
The development of citizenship competences plays an important role in a complex system like society. Thus, to analyze how such competences impact other contexts is a great challenge because this kind of study involves the work with people and the use of variables that depend on human behaviors. In this sense, many studies have highlighted the advantage of using simulation systems and tools. In particular, the agent-based social simulation field relies upon the Semantic Web to manage knowledge representation in social scenarios. This study focuses on how citizenship competences impact conflict resolution. Moreover, a simulation model in which citizens interact to resolve conflicts by considering citizenship competences and conflict resolution styles is also introduced. It was developed in NetLogo together with an extension that connects it with the ontology of competences. Results show that the higher interactions of citizens-conflicts, the higher level of citizenship competences, and the number of conflicts solved is higher when using citizenship competences.
{"title":"An Agent-Based Social Simulation for Citizenship Competences and Conflict Resolution Styles","authors":"Cecilia Ávila-Garzón, M. Balaguera, Valentina Tabares-Morales","doi":"10.4018/ijswis.306749","DOIUrl":"https://doi.org/10.4018/ijswis.306749","url":null,"abstract":"The development of citizenship competences plays an important role in a complex system like society. Thus, to analyze how such competences impact other contexts is a great challenge because this kind of study involves the work with people and the use of variables that depend on human behaviors. In this sense, many studies have highlighted the advantage of using simulation systems and tools. In particular, the agent-based social simulation field relies upon the Semantic Web to manage knowledge representation in social scenarios. This study focuses on how citizenship competences impact conflict resolution. Moreover, a simulation model in which citizens interact to resolve conflicts by considering citizenship competences and conflict resolution styles is also introduced. It was developed in NetLogo together with an extension that connects it with the ontology of competences. Results show that the higher interactions of citizens-conflicts, the higher level of citizenship competences, and the number of conflicts solved is higher when using citizenship competences.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"81 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85790000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The current XAI techniques present explanations mainly as visuals and structured data. However, these explanations are difficult to be interpreted by a non-expert user. Here, the use of Natural Language Generation (NLG) based techniques can help to represent explanations in human-understandable format. The paper addresses the issue of automatic generation of narratives using a modified transformer approach. Further, due to unavailability of a relevant annotated dataset for development and testing, we also propose a verbalization template approach to generate the same. The input of the transformer is linearized to convert the data-to-text task into text-to-text task. The proposed work is evaluated on a verbalized explained PIMA Indians diabetes dataset and exhibits significant improvement as compared to existing baselines for both, manual and automatic evaluation. Also, the narratives provide better comprehensibility to be trusted by human evaluators than the non-NLG counterparts. Lastly, an ablation study is performed in order to understand the contribution of each component.
{"title":"Modified Transformer Architecture to Explain Black Box Models in Narrative Form","authors":"Diksha Malhotra, P. Saini, Awadhesh Kumar Singh","doi":"10.4018/ijswis.297040","DOIUrl":"https://doi.org/10.4018/ijswis.297040","url":null,"abstract":"The current XAI techniques present explanations mainly as visuals and structured data. However, these explanations are difficult to be interpreted by a non-expert user. Here, the use of Natural Language Generation (NLG) based techniques can help to represent explanations in human-understandable format. The paper addresses the issue of automatic generation of narratives using a modified transformer approach. Further, due to unavailability of a relevant annotated dataset for development and testing, we also propose a verbalization template approach to generate the same. The input of the transformer is linearized to convert the data-to-text task into text-to-text task. The proposed work is evaluated on a verbalized explained PIMA Indians diabetes dataset and exhibits significant improvement as compared to existing baselines for both, manual and automatic evaluation. Also, the narratives provide better comprehensibility to be trusted by human evaluators than the non-NLG counterparts. Lastly, an ablation study is performed in order to understand the contribution of each component.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"2 1","pages":"1-19"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89938541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work introduces a parallel clustering algorithm by modifying the existing Fractional Lion Algorithm (FLA). The proposed work replaces the conventional Euclidean distance measure with the Bhattacharya distance measure to newly propose the improved FLA (IMR-FLA). The proposed IMR-FLA is implemented in both the mapper and the reducer in the MapReduce framework to achieve the parallel clustering. The experimentation of the proposed IMR-FLA is done by using six standard databases, namely Pima Indian diabetes dataset, Heart disease dataset, Hepatitis dataset, localization dataset, breast cancer dataset, and skin segmentation dataset, from the UCI repository. The proposed IMR-FLA has the overall improved Jaccard coefficient value of 0.9357, 0.6572, 0.7462, 0.5944, 0.9418, and 0.8680, for each dataset. Similarly, the proposed IMR-FLA algorithm has outclassed other classifiers' performance with the clustering accuracy value of 0.9674, 0.9471, 0.9677, 0.777, 0.9023, and 0.9585, respectively, for the experimental databases.
{"title":"A Parallel Fractional Lion Algorithm for Data Clustering Based on MapReduce Cluster Framework","authors":"S. Chander, P. Vijaya, P. Dhyani","doi":"10.4018/ijswis.297034","DOIUrl":"https://doi.org/10.4018/ijswis.297034","url":null,"abstract":"This work introduces a parallel clustering algorithm by modifying the existing Fractional Lion Algorithm (FLA). The proposed work replaces the conventional Euclidean distance measure with the Bhattacharya distance measure to newly propose the improved FLA (IMR-FLA). The proposed IMR-FLA is implemented in both the mapper and the reducer in the MapReduce framework to achieve the parallel clustering. The experimentation of the proposed IMR-FLA is done by using six standard databases, namely Pima Indian diabetes dataset, Heart disease dataset, Hepatitis dataset, localization dataset, breast cancer dataset, and skin segmentation dataset, from the UCI repository. The proposed IMR-FLA has the overall improved Jaccard coefficient value of 0.9357, 0.6572, 0.7462, 0.5944, 0.9418, and 0.8680, for each dataset. Similarly, the proposed IMR-FLA algorithm has outclassed other classifiers' performance with the clustering accuracy value of 0.9674, 0.9471, 0.9677, 0.777, 0.9023, and 0.9585, respectively, for the experimental databases.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"8 1","pages":"1-25"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88448008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the progress of globalization, the transnational human traffic is spreading globally. It damages national economy and social order as well as infringes on the basic human rights of the victims, which has aroused general concern all over the world, becoming global issues. One of the important features in human being traffic is the factor of globalization. A destination-source model works as a deterrent which is applied in the identification of smuggling and trafficking of illegal immigrants. The related results show that the employer penalty and market wage will influence the amount of smuggling and trafficking immigrants. Tax offered by legal unskilled workers at destination countries provides financial support for the inland monitoring of illegal immigrants. The improved SVM (supported vector machine) is proposed to study online textual data used for advertisement classification, with the purpose of discerning underlying human trafficking patterns on the network and recognizing suspicious advertisements, a concern of law-enforcement agencies.
{"title":"A Study on Human Transiting Based on Big Data and Web Semantics","authors":"Qiang Zhou","doi":"10.4018/ijswis.310055","DOIUrl":"https://doi.org/10.4018/ijswis.310055","url":null,"abstract":"In the progress of globalization, the transnational human traffic is spreading globally. It damages national economy and social order as well as infringes on the basic human rights of the victims, which has aroused general concern all over the world, becoming global issues. One of the important features in human being traffic is the factor of globalization. A destination-source model works as a deterrent which is applied in the identification of smuggling and trafficking of illegal immigrants. The related results show that the employer penalty and market wage will influence the amount of smuggling and trafficking immigrants. Tax offered by legal unskilled workers at destination countries provides financial support for the inland monitoring of illegal immigrants. The improved SVM (supported vector machine) is proposed to study online textual data used for advertisement classification, with the purpose of discerning underlying human trafficking patterns on the network and recognizing suspicious advertisements, a concern of law-enforcement agencies.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"2017 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73931250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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