Collaborative knowledge ACM (dataset)

Внешний датасет с данными

http://www.uic.unn.ru/pustyn/data-sets/digida/CollabKnowledge.csv

Какие поля используем

• "Item Type" - например, journalArticle или book или conferencePaper
• "Publication Year" - год публикации
• "Author" - автор
• "Title" - Название работы
• "Publication Title" - название журнала
• "ISBN",
• "ISSN"
• "DOI"
• "Url" - ссылка
• "Abstract Note",

"Date Added","Date Modified","Access Date","Pages","Num Pages","Issue","Volume","Number Of Volumes","Journal Abbreviation","Short Title","Series","Series Number","Series Text","Series Title","Publisher","Place","Language","Rights","Type","Archive","Archive Location","Library Catalog","Call Number","Extra","Notes","File Attachments","Link Attachments","Manual Tags","Automatic Tags","Editor","Series Editor","Translator","Contributor","Attorney Agent","Book Author","Cast Member","Commenter","Composer","Cosponsor","Counsel","Interviewer","Producer","Recipient","Reviewed Author","Scriptwriter","Words By","Guest","Number","Edition","Running Time","Scale","Medium","Artwork Size","Filing Date","Application Number","Assignee","Issuing Authority","Country","Meeting Name","Conference Name","Court","References","Reporter","Legal Status","Priority Numbers","Programming Language","Version","System","Code","Code Number","Section","Session","Committee","History","Legislative Body"

Представляем авторов и их книги в виде таблицы

Название	Издание	Авторы	URL	Резюме
A Method of Recommending Physical Education Network Course Resources Based on Collaborative Filtering Technology	Sci. Program.	Zhang, Zhihao; Usman, Muhammad	Through the current research on e-learning, it is found that the present e-learning system applied to the recommendation activities of learning resources has only two search methods: Top-N and keywords. These search methods cannot effectively recommend learning resources to learners. Therefore, the collaborative filtering recommendation technology is applied, in this paper, to the process of personalized recommendation of learning resources. We obtain user content and functional interest and predict the comprehensive interest of web and big data through an infinite deep neural network. Based on the collaborative knowledge graph and the collaborative filtering algorithm, the semantic information of teaching network resources is extracted from the collaborative knowledge graph. According to the principles of the nearest neighbor recommendation, the course attribute value preference matrix (APM) is obtained first. Next, the course-predicted values are sorted in descending order, and the top T courses with the highest predicted values are selected as the final recommended course set for the target learners. Each course has its own online classroom; the teacher will publish online class details ahead of time, and students can purchase online access to the classroom number and password. The experimental results show that the optimal number of clusters k is 9. Furthermore, for extremely sparse matrices, the collaborative filtering technique method is more suitable for clustering in the transformed low-dimensional space. The average recommendation satisfaction degree of collaborative filtering technology method is approximately 43.6%, which demonstrates high recommendation quality.	https://doi.org/10.1155/2021/9531111
Evolutionary Game Analysis of Knowledge Sharing in Low-Carbon Innovation Network	Complex.	Zheng, Cuicui; Lv, Zhihan	Low-carbon technological innovation is the main means to develop a low-carbon economy, and network knowledge sharing and collaborative innovation is an effective model for the development of low-carbon technologies. First of all, this article adopts a decision-making experiment and evaluation laboratory method and interpretation structure model, combines the two methods, extracts the advantages of the two, and discards the shortcomings of the two, thus constructing a new optimized and upgraded interpretation structure model. We give methods to explore the main influencing factors of collaborative innovation of low-carbon technologies for online knowledge sharing. Based on the industrial network knowledge sharing and cooperation network environment, the network evolution game model of network knowledge sharing knowledge collaboration is constructed to study the rewards and punishments, the profit distribution rate, the knowledge potential difference, and the parameter pairing of the network knowledge sharing cooperation network structure in the process of network knowledge sharing and collaborative knowledge innovation. The influence of the network knowledge sharing cooperation strategy is obtained through simulation to change the size of the relevant parameters so that the network knowledge sharing cooperation agent chooses the network evolution game of the sharing strategy to realize the optimal evolutionary stable strategy. According to the simulation results, this article proposes suggestions from the following aspects, aiming to improve the overall knowledge synergy effect of the network knowledge sharing and cooperation network.	https://doi.org/10.1155/2021/9995344
LkeRec: Toward Lightweight End-to-End Joint Representation Learning for Building Accurate and Effective Recommendation	ACM Trans. Inf. Syst.	Yan, Surong; Lin, Kwei-Jay; Zheng, Xiaolin; Wang, Haosen	Explicit and implicit knowledge about users and items have been used to describe complex and heterogeneous side information for recommender systems (RSs). Many existing methods use knowledge graph embedding (KGE) to learn the representation of a user-item knowledge graph (KG) in low-dimensional space. In this article, we propose a lightweight end-to-end joint learning framework for fusing the tasks of KGE and RSs at the model level. Our method proposes a lightweight KG embedding method by using bidirectional bijection relation-type modeling to enable scalability for large graphs while using self-adaptive negative sampling to optimize negative sample generating. Our method further generates the integrated views for users and items based on relation-types to explicitly model users’ preferences and items’ features, respectively. Finally, we add virtual “recommendation” relations between the integrated views of users and items to model the preferences of users on items, seamlessly integrating RS with user-item KG over a unified graph. Experimental results on multiple datasets and benchmarks show that our method can achieve a better accuracy of recommendation compared with existing state-of-the-art methods. Complexity and runtime analysis suggests that our method can gain a lower time and space complexity than most of existing methods and improve scalability.	https://doi.org/10.1145/3486673
Entity Linking Based on Sentence Representation	Complex.	Jia, Bingjing; Wu, Zhongli; Zhou, Pengpeng; Wu, Bin; Wang, Wei	Entity linking involves mapping ambiguous mentions in documents to the correct entities in a given knowledge base. Most existing methods failed to link when a mention appears multiple times in a document, since the conflict of its contexts in different locations may lead to difficult linking. Sentence representation, which has been studied based on deep learning approaches recently, can be used to resolve the above issue. In this paper, an effective entity linking model is proposed to capture the semantic meaning of the sentences and reduce the noise introduced by different contexts of the same mention in a document. This model first uses the symmetry of the Siamese network to learn the sentence similarity. Then, the attention mechanism is added to improve the interaction between input sentences. To show the effectiveness of our sentence representation model combined with attention mechanism, named ELSR, extensive experiments are conducted on two public datasets. Results illustrate that our model outperforms the baselines and achieves the superior performance.	https://doi.org/10.1155/2021/8895742
Why or Why Not? The Effect of Justification Styles on Chatbot Recommendations	ACM Trans. Inf. Syst.	Wilkinson, Daricia; Alkan, Öznur; Liao, Q. Vera; Mattetti, Massimiliano; Vejsbjerg, Inge; Knijnenburg, Bart P.; Daly, Elizabeth	Chatbots or conversational recommenders have gained increasing popularity as a new paradigm for Recommender Systems (RS). Prior work on RS showed that providing explanations can improve transparency and trust, which are critical for the adoption of RS. Their interactive and engaging nature makes conversational recommenders a natural platform to not only provide recommendations but also justify the recommendations through explanations. The recent surge of interest inexplainable AI enables diverse styles of justification, and also invites questions on how styles of justification impact user perception. In this article, we explore the effect of “why” justifications and “why not” justifications on users’ perceptions of explainability and trust. We developed and tested a movie-recommendation chatbot that provides users with different types of justifications for the recommended items. Our online experiment (n = 310) demonstrates that the “why” justifications (but not the “why not” justifications) have a significant impact on users’ perception of the conversational recommender. Particularly, “why” justifications increase users’ perception of system transparency, which impacts perceived control, trusting beliefs and in turn influences users’ willingness to depend on the system’s advice. Finally, we discuss the design implications for decision-assisting chatbots.	https://doi.org/10.1145/3441715
Graph Neural Collaborative Topic Model for Citation Recommendation	ACM Trans. Inf. Syst.	Xie, Qianqian; Zhu, Yutao; Huang, Jimin; Du, Pan; Nie, Jian-Yun	Due to the overload of published scientific articles, citation recommendation has long been a critical research problem for automatically recommending the most relevant citations of given articles. Relational topic models (RTMs) have shown promise on citation prediction via joint modeling of document contents and citations. However, existing RTMs can only capture pairwise or direct (first-order) citation relationships among documents. The indirect (high-order) citation links have been explored in graph neural network–based methods, but these methods suffer from the well-known explainability problem. In this article, we propose a model called Graph Neural Collaborative Topic Model that takes advantage of both relational topic models and graph neural networks to capture high-order citation relationships and to have higher explainability due to the latent topic semantic structure. Experiments on three real-world citation datasets show that our model outperforms several competitive baseline methods on citation recommendation. In addition, we show that our approach can learn better topics than the existing approaches. The recommendation results can be well explained by the underlying topics.	https://doi.org/10.1145/3473973
Data-Driven Detection and Characterization of Communities of Accounts Collaborating in MOOCs	Future Gener. Comput. Syst.	Ruipérez-Valiente, José A.; Jaramillo-Morillo, Daniel; Joksimović, Srećko; Kovanović, Vitomir; Muñoz-Merino, Pedro J.; Gašević, Dragan		https://doi.org/10.1016/j.future.2021.07.003
Hierarchical Attentive Knowledge Graph Embedding for Personalized Recommendation	Electron. Commer. Rec. Appl.	Sha, Xiao; Sun, Zhu; Zhang, Jie		https://doi.org/10.1016/j.elerap.2021.101071
What You See is What It Means! Semantic Representation Learning of Code Based on Visualization and Transfer Learning	ACM Trans. Softw. Eng. Methodol.	Keller, Patrick; Kaboré, Abdoul Kader; Plein, Laura; Klein, Jacques; Le Traon, Yves; Bissyandé, Tegawendé F.	Recent successes in training word embeddings for Natural Language Processing (NLP) tasks have encouraged a wave of research on representation learning for source code, which builds on similar NLP methods. The overall objective is then to produce code embeddings that capture the maximum of program semantics. State-of-the-art approaches invariably rely on a syntactic representation (i.e., raw lexical tokens, abstract syntax trees, or intermediate representation tokens) to generate embeddings, which are criticized in the literature as non-robust or non-generalizable. In this work, we investigate a novel embedding approach based on the intuition that source code has visual patterns of semantics. We further use these patterns to address the outstanding challenge of identifying semantic code clones. We propose the WySiWiM (‘‘What You See Is What It Means”) approach where visual representations of source code are fed into powerful pre-trained image classification neural networks from the field of computer vision to benefit from the practical advantages of transfer learning. We evaluate the proposed embedding approach on the task of vulnerable code prediction in source code and on two variations of the task of semantic code clone identification: code clone detection (a binary classification problem), and code classification (a multi-classification problem). We show with experiments on the BigCloneBench (Java), Open Judge (C) that although simple, our WySiWiM  approach performs as effectively as state-of-the-art approaches such as ASTNN or TBCNN. We also showed with data from NVD and SARD that WySiWiM  representation can be used to learn a vulnerable code detector with reasonable performance (accuracy ∼90%). We further explore the influence of different steps in our approach, such as the choice of visual representations or the classification algorithm, to eventually discuss the promises and limitations of this research direction.	https://doi.org/10.1145/3485135
Beyond Entertainment: Unpacking Danmaku and Comments' Role of Information Sharing and Sentiment Expression in Online Crisis Videos	Proc. ACM Hum.-Comput. Interact.	He, Changyang; He, Lu; Lu, Tun; Li, Bo	Online videos are playing an increasingly important role in timely information dissemination especially during public crises. Video commentary, synchronous or asynchronous, is indispensable in viewers' engagement and participation, and may in turn contribute to video with additional information and emotions. Yet, the roles of video commentary in crisis communications are largely unexplored, which we believe that an investigation not only provides timely feedback but also offers concrete guidelines for better information dissemination. In this work, we study two distinct commentary features of online videos: traditional asynchronous comments and emerging synchronous danmaku. We investigate how users utilize these two features to express their emotions and share information during a public health crisis. Through qualitative analysis and applying machine learning techniques on a large-scale danmaku and comment dataset of Chinese COVID-19-related videos, we uncover the distinctive roles of danmaku and comments in crisis communication, and propose comprehensive taxonomies for information themes and emotion categories of commentary. We also discover the unique patterns of crisis communications presented by danmaku, such as collective emotional resonance and style-based highlighting for emphasizing critical information. Our study captures the unique values and salient features of the emerging commentary interfaces, in particular danmaku, in the context of crisis videos, and further provides several design implications to enable more effective communications through online videos to engage and empower users during crises.	https://doi.org/10.1145/3479555
Quantum Machine Learning Algorithm for Knowledge Graphs	ACM Transactions on Quantum Computing	Ma, Yunpu; Tresp, Volker	Semantic knowledge graphs are large-scale triple-oriented databases for knowledge representation and reasoning. Implicit knowledge can be inferred by modeling the tensor representations generated from knowledge graphs. However, as the sizes of knowledge graphs continue to grow, classical modeling becomes increasingly computationally resource intensive. This article investigates how to capitalize on quantum resources to accelerate the modeling of knowledge graphs. In particular, we propose the first quantum machine learning algorithm for inference on tensorized data, i.e., on knowledge graphs. Since most tensor problems are NP-hard [18], it is challenging to devise quantum algorithms to support the inference task. We simplify the modeling task by making the plausible assumption that the tensor representation of a knowledge graph can be approximated by its low-rank tensor singular value decomposition, which is verified by our experiments. The proposed sampling-based quantum algorithm achieves speedup with a polylogarithmic runtime in the dimension of knowledge graph tensor.	https://doi.org/10.1145/3467982
Assessing the Quality of Sources in Wikidata Across Languages: A Hybrid Approach	J. Data and Information Quality	Amaral, Gabriel; Piscopo, Alessandro; Kaffee, Lucie-aimée; Rodrigues, Odinaldo; Simperl, Elena	Wikidata is one of the most important sources of structured data on the web, built by a worldwide community of volunteers. As a secondary source, its contents must be backed by credible references; this is particularly important, as Wikidata explicitly encourages editors to add claims for which there is no broad consensus, as long as they are corroborated by references. Nevertheless, despite this essential link between content and references, Wikidata's ability to systematically assess and assure the quality of its references remains limited. To this end, we carry out a mixed-methods study to determine the relevance, ease of access, and authoritativeness of Wikidata references, at scale and in different languages, using online crowdsourcing, descriptive statistics, and machine learning. Building on previous work of ours, we run a series of microtasks experiments to evaluate a large corpus of references, sampled from Wikidata triples with labels in several languages. We use a consolidated, curated version of the crowdsourced assessments to train several machine learning models to scale up the analysis to the whole of Wikidata. The findings help us ascertain the quality of references in Wikidata and identify common challenges in defining and capturing the quality of user-generated multilingual structured data on the web. We also discuss ongoing editorial practices, which could encourage the use of higher-quality references in a more immediate way. All data and code used in the study are available on GitHub for feedback and further improvement and deployment by the research community.	https://doi.org/10.1145/3484828
Understanding Wikipedia Practices Through Hindi, Urdu, and English Takes on an Evolving Regional Conflict	Proc. ACM Hum.-Comput. Interact.	Hickman, Molly G.; Pasad, Viral; Sanghavi, Harsh Kamalesh; Thebault-Spieker, Jacob; Lee, Sang Won	Wikipedia is the product of thousands of editors working collaboratively to provide free and up-to-date encyclopedic information to the project's users. This article asks to what degree Wikipedia articles in three languages - Hindi, Urdu, and English - achieve Wikipedia's mission of making neutrally-presented, reliable information on a polarizing, controversial topic available to people around the globe. We chose the topic of the recent revocation of Article 370 of the Constitution of India, which, along with other recent events in and concerning the region of Jammu and Kashmir, has drawn attention to related articles on Wikipedia. This work focuses on the English Wikipedia, being the preeminent language edition of the project, as well as the Hindi and Urdu editions. Hindi and Urdu are the two standardized varieties of Hindustani, a lingua franca of Jammu and Kashmir. We analyzed page view and revision data for three Wikipedia articles to gauge popularity of the pages in our corpus, and responsiveness of editors to breaking news events and problematic edits. Additionally, we interviewed editors from all three language editions to learn about differences in editing processes and motivations, and we compared the text of the articles across languages as they appeared shortly after the revocation of Article 370. Across languages, we saw discrepancies in article tone, organization, and the information presented, as well as differences in how editors collaborate and communicate with one another. Nevertheless, in Hindi and Urdu, as well as English, editors predominantly try to adhere to the principle of neutral point of view (NPOV), and for the most part, the editors quash attempts by other editors to push political agendas.	https://doi.org/10.1145/3449108
To Reuse or Not To Reuse? A Framework and System for Evaluating Summarized Knowledge	Proc. ACM Hum.-Comput. Interact.	Liu, Michael Xieyang; Kittur, Aniket; Myers, Brad A.	As the amount of information online continues to grow, a correspondingly important opportunity is for individuals to reuse knowledge which has been summarized by others rather than starting from scratch. However, appropriate reuse requires judging the relevance, trustworthiness, and thoroughness of others' knowledge in relation to an individual's goals and context. In this work, we explore augmenting judgements of the appropriateness of reusing knowledge in the domain of programming, specifically of reusing artifacts that result from other developers' searching and decision making. Through an analysis of prior research on sensemaking and trust, along with new interviews with developers, we synthesized a framework for reuse judgements. The interviews also validated that developers express a desire for help with judging whether to reuse an existing decision. From this framework, we developed a set of techniques for capturing the initial decision maker's behavior and visualizing signals calculated based on the behavior, to facilitate subsequent consumers' reuse decisions, instantiated in a prototype system called Strata. Results of a user study suggest that the system significantly improves the accuracy, depth, and speed of reusing decisions. These results have implications for systems involving user-generated content in which other users need to evaluate the relevance and trustworthiness of that content.	https://doi.org/10.1145/3449240
Student Leadership, Systems Change: Opportunities and Tensions for Youth Impact on District-Wide Computer Science Initiatives	ACM Trans. Comput. Educ.	Phelps, David; Santo, Rafi	Computer Science education (CSed) often aims to position youth as designers, creators, and those with a voice in their world. But do youth have opportunities to design, create, and have voice around the shape of their CSed learning experiences? In this study, we explore ways that school districts engage youth to contribute to the shaping and enactment of their CS instructional systems, efforts districts make to have these leadership roles create impact within these systems, and the tensions associated with these processes. Through in depth analysis of five district case studies, our findings highlight variance around the nature of leadership roles, how access to leadership roles is structured, student autonomy within and ownership over leadership roles, how roles reach into and index differential power over instructional systems, and how district processes of scaffolding and infrastructuring mediate the ultimate impact that students in these roles are able to have on CS instructional systems. Findings also surfaced ways that district actors dealt with a number of tensions associated with student leadership within CS instructional systems. This study provides educators, administrators, and researchers with an expansive view of the potential for students to play legitimate roles within the context of system-wide instructional efforts around CS, and aims to expand conceptions of ‘equitable computer science’—up to this point largely conceived of through the lenses of access to, participation in, and experiences of CS learning—to focus on equity as also being about who has ‘a seat at the table’ when it comes to CS.	https://doi.org/10.1145/3461716