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Various aquatic diseases occur frequently in recent years, particularly with the rapid development of the aquaculture industry. Once these diseases spread very rapidly, a very serious risk can be put on aquaculture. Furthermore, the network data of aquatic diseases also presents highly dispersed, multi-source heterogeneous features with the development of Internet technology. It is a high demand to rapidly and accurately obtain the required information, due to the explosive growth of network data. However, traditional information acquisition cannot fully meet the search engines. The retrieval keywords or shallow semantic analysis can bring a large number of related web links, leading to vague and redundant answers. The intelligent Q&A system can be selected to support the users' natural language input, and then accurately capture the user intent, finally returning concise and accurate answers. Among them, the emergence and rapid development of knowledge graphs can provide a high-quality knowledge base for intelligent question-answering systems, in order to promote the application of question-answering systems in various fields. The knowledge graph construction can be divided mainly into four steps: data acquisition, ontology construction, knowledge extraction and storage. Firstly, the crawler technology is used to obtain the relevant aquatic disease data, and then data preprocessing can be performed, including data cleaning and analysis. Secondly, the aquatic diseases ontology can be constructed using the data content and representation characteristics, in order to predefine the relations and properties types between entities. As such, the boundaries of knowledge extraction are clarified during this time. Secondly, the rule logic can be used to extract the semi-structured data. The entity and relation joint extraction is then used to extract unstructured data. Finally, the extracted triple data is stored in the Neo4j graph database, in order to realize the visual management of the knowledge graph and a certain degree of knowledge reasoning. In this study, a new text annotation system of "aquatic disease + relationship +BMES" was proposed in the unstructured knowledge. The relationship extraction was also integrated into the named entity recognition task. Then, the ternary model was directly constructed to transform the entity relationship extraction into sequence annotation, in order to improve the annotation efficiency at least twice for the joint extraction of entity and relationship. At the same time, the triplet data was obtained for the triplet-building module using label matching and mapping. The overlapping relation extraction was then solved in this case. The BERT-BiLSTM+CRF end-to-end model was used to carry out the test. The test results showed that the triad extraction shared a high recall rate (89.64%), accuracy (94.04%), and F1 (91.34%), which was significantly better than CNN+BiLSTM+CRF and BiLSTM+CRF models. The extracted knowledge was stored in the Neo4j graph database, and then realized knowledge visualization management and knowledge reasoning analysis. The aquatic disease knowledge map presented high precision and fine granularity. The finding can provide a new idea for the field of intelligent Q&A. Anyway, the semi-automatic construction of a knowledge graph can also offer technical support for the recommendation system, knowledge base construction, search and application knowledge base construction. [ABSTRACT FROM AUTHOR]

Copyright of Psychological Science is the property of Psychological Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Communication & Culture is the property of Fu Jen Catholic University, Taiwan Graduate Institute of Mass Communication and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The existing spam website detection methods are mainly aimed at self-built spam websites， and not suitable for injected spam websites because of the low efficiency of link detection. This paper proposes a new detection method， in which a detection framework is based on multi-dimensional features of webpage structure and text. The framework divides the webpage into blocks. Then content features are extracted by calculating odd ratio and structural features based on tags， attribute keys and attribute values are extracted by using the one-hot rate. The detection model is generated by proper machine learning and used to detect spam links. The detection accuracy of this framework is increased by up to 13%， compared with the algorithms based on content detection and on blacklist matching. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Hebei University of Science & Technology is the property of Hebei University of Science & Technology, Journal of Hebei University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Mass Communication Research is the property of Guoli Zhengzhi Daxue Xinwenxue Yanjiusuo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to speed up the inference of link delay distribution in unicast scenario, this paper proposed an explicit computation algorithm. Because of the difference among the link delay characteristics caused by the diversity of network elements, the algorithm designated different bin sizes according to the end-to-end measurements for the links. After that, it took advantage of the two-level binary trees introduced by back-to-back probes to calculate the delay distribution of each link using explicit computations. Simulation results show that this algorithm can reduce the running time of estimating link delay distributions to a few hundreds of milliseconds and capture the link delay characteristics more accurately. This algorithm is computationally simple, as it is based on only explicit computations. Therefore, it satisfies the real-time requirement in practical scenarios. [ABSTRACT FROM AUTHOR]

To increase the precision of PageRank algorithm, we analyzed the basic principle and the disadvantage of the PageRank algorithm, and calculated the relevance between the query key words and the anchor texts of hyperlink with vector space model. A new hyperlink algorithm was put forward based on the relevance of hyperlink anchor texts. The experimental results show that this algorithm is effective, and it can raise the precision of PageRank algorithm. [ABSTRACT FROM AUTHOR]