Pilot Study of Apply Education AI Virtual Assistant in Groups’Wisdom Created and Shared Ecosystem—A Case Study on Education Learning as a Service(ELaaS)with mFHC Bank.
Full Paper Download,http://bit.ly/2DExgrh
---
張大明 育睿科技 執行長
richard@abctech.pro
羅志傑 育睿科技 教務長
roger@abctech.pro
王詩帆 優睿亞科技 教學設計總監
shfan23@gmail.com
淡江大學守謙國際會議中心—2019學術研討會:AI在教育科技的應用與實踐。淡水校園。
Full paper-
https://www.researchgate.net/publication/332727759_Pilot_Study_of_Apply_Education_AI_Virtual_Assistant_in_Groups'Wisdom_Created_and_Shared_Ecosystem-A_Case_Study_on_Education_Learning_as_a_ServiceELaaSwith_mFHC_Bank
脫胎自HCI International conference 文章: Finding Suitable Candidates: The Design of a Mobile Volunteering Matching System
Originated from a conference paper : Finding Suitable Candidates: The Design of a Mobile Volunteering Matching System
Learning Relations from Social Tagging DataHang Dong
An interesting research direction is to discover structured knowledge from user generated data. Our work aims to find relations among social tags and organise them into hierarchies so as to better support discovery and search for online users. We cast relation discovery in this context to a binary classification problem in supervised learning. This approach takes as input features of two tags extracted using probabilistic topic modelling, and predicts whether a broader-narrower relation holds between them. Experiments were conducted using two large, real-world datasets, the Bibsonomy dataset which is used to extract tags and their features, and the DBpedia dataset which is used as the ground truth. Three sets of features were designed and extracted based on topic distri- butions, similarity and probabilistic associations. Evaluation results with respect to the ground truth demonstrate that our method outperforms existing ones based on various features and heuristics. Future studies are suggested to study the Knowledge Base Enrichment from folksonomies and deep neural network approaches to process tagging data.
Rules for inducing hierarchies from social tagging dataHang Dong
Automatic generation of hierarchies from social tags is a challenging task. We identified three rules, set inclusion, graph centrality and information-theoretic condition from the literature and proposed two new rules, fuzzy set inclusion and probabilistic association to induce hierarchical relations. We proposed an hierarchy generation algorithm, which can incorporate each rule with different data representations, i.e., resource and Probabilistic Topic Model based representations. The learned hierarchies were compared to some of the widely used reference concept hierarchies. We found that probabilistic association and set inclusion based rules helped produce better quality hierarchies according to the evaluation metrics.
Pilot Study of Apply Education AI Virtual Assistant in Groups’Wisdom Created and Shared Ecosystem—A Case Study on Education Learning as a Service(ELaaS)with mFHC Bank.
Full Paper Download,http://bit.ly/2DExgrh
---
張大明 育睿科技 執行長
richard@abctech.pro
羅志傑 育睿科技 教務長
roger@abctech.pro
王詩帆 優睿亞科技 教學設計總監
shfan23@gmail.com
淡江大學守謙國際會議中心—2019學術研討會:AI在教育科技的應用與實踐。淡水校園。
Full paper-
https://www.researchgate.net/publication/332727759_Pilot_Study_of_Apply_Education_AI_Virtual_Assistant_in_Groups'Wisdom_Created_and_Shared_Ecosystem-A_Case_Study_on_Education_Learning_as_a_ServiceELaaSwith_mFHC_Bank
脫胎自HCI International conference 文章: Finding Suitable Candidates: The Design of a Mobile Volunteering Matching System
Originated from a conference paper : Finding Suitable Candidates: The Design of a Mobile Volunteering Matching System
Learning Relations from Social Tagging DataHang Dong
An interesting research direction is to discover structured knowledge from user generated data. Our work aims to find relations among social tags and organise them into hierarchies so as to better support discovery and search for online users. We cast relation discovery in this context to a binary classification problem in supervised learning. This approach takes as input features of two tags extracted using probabilistic topic modelling, and predicts whether a broader-narrower relation holds between them. Experiments were conducted using two large, real-world datasets, the Bibsonomy dataset which is used to extract tags and their features, and the DBpedia dataset which is used as the ground truth. Three sets of features were designed and extracted based on topic distri- butions, similarity and probabilistic associations. Evaluation results with respect to the ground truth demonstrate that our method outperforms existing ones based on various features and heuristics. Future studies are suggested to study the Knowledge Base Enrichment from folksonomies and deep neural network approaches to process tagging data.
Rules for inducing hierarchies from social tagging dataHang Dong
Automatic generation of hierarchies from social tags is a challenging task. We identified three rules, set inclusion, graph centrality and information-theoretic condition from the literature and proposed two new rules, fuzzy set inclusion and probabilistic association to induce hierarchical relations. We proposed an hierarchy generation algorithm, which can incorporate each rule with different data representations, i.e., resource and Probabilistic Topic Model based representations. The learned hierarchies were compared to some of the widely used reference concept hierarchies. We found that probabilistic association and set inclusion based rules helped produce better quality hierarchies according to the evaluation metrics.
Enrichment of Cross-Lingual Information on Chinese Genealogical Linked DataHang Dong
With the emergence of non-English Linked Datasets, discrepancy in language has become a major obstacle for cross-lingual access of resources in the Semantic Web. To prevent non-English monolingual Linked Datasets to form “islands” in the Web of Data, it is suggested to enrich a further layer of multilingual information on the Linked Open Data cloud. In the domain of culture heritage, enriching cross-lingual information can enhance the multilingual retrieval of cultural heritage resources, and promote international communication in the field. In this article, methods to enrich cross-lingual information for Linked Data are summarized, with a review on the cultural heritage domain. The mobile App Demo, Learn Chinese Surnames, winning the Shanghai Library Open Data Application Development Contest on 2016, is then introduced as a case study, to present the practice of enriching English-described information on a Chinese genealogical Linked Dataset, through consuming multilingual sources in the Linked Open Data cloud. Further in the data validation and conclusion, the issues of data quality and experience of consuming Linked Data are summarized.
presentation at adls 2016, 第十三届数字图书馆前沿问题高级研讨班(ADLS2016), Dec 5-6 2016, http://society.library.sh.cn/adls2016
Title: Consuming Linked Data and Application Development: a case study on Shanghai Library Genealogical Open Data
Learning structured knowledge from social tagging data: a critical review of ...Hang Dong
For more than a decade, researchers have been proposing various methods and techniques to mine social tagging data and to learn structured knowledge. It is essential to conduct a comprehensive survey on the related work, which would benefit the research community by providing better understanding of the state-of-the-art and insights into the future research directions. The paper first defines the spectrum of Knowledge Organization Systems, from unstructured with less semantics to highly structured with richer semantics. It then reviews the related work by classifying the methods and techniques into two main categories, namely, learning term lists and learning relations. The method and techniques originated from natural language processing, data mining, machine learning, social network analysis, and semantic Web are discussed in detail under the two categories. We summarize the prominent issues with the current research and highlight future directions on learning constantly evolving knowledge from social media data.
Modeling health related topics in an online forum designed for the deaf & har...Hang Dong
The main objective of this presentation is to elicit some discussions about whether a computational or a semi-computational method is suitable for understanding health related topics.
The slides were presented at the 1st XJTLU Research Symposium on Healthy Ageing & Society, Xi'an Jiaotong-Liverpool University, Suzhou, on 14 Dec, 2015. For uploading online, the slides were last revised on 9th Jan 2016. All websites referenced in the slides were retrieved on 5th Jan 2016.
Identifying Evaluation Standards for Online Information Literacy Tutorials (O...Hang Dong
This document summarizes existing research on evaluating online information literacy tutorials (OILTs). It finds that the most common evaluation methods are usability testing and pre- and post-tests. It also finds that each study evaluates a specific OILT rather than using standardized evaluation criteria. The document then proposes categorizing OILTs in different ways, such as modular vs. non-modular and use of text vs. video, before developing comprehensive evaluation standards. It concludes that OILTs should be evaluated according to these categories to ensure elements like appropriate length, interactive components, and accuracy of resources.
Wuhan is a large city located in central China with over 10 million residents. Some key attractions include East Lake, known for its scenic views, and the Hubei Provincial Museum. Special foods unique to Wuhan include hot and dry noodles, bean skin, soup with lotus root and chop, and Wuchang fish, which is famous throughout China. Mao Zedong once wrote a poem praising the fish of Wuchang.
3. 知识结构: 从低语义到高语义
低语义
高语义
社会标签 / 大众分类法社会标签 / 大众分类法
术语 / 概念列表术语 / 概念列表
概念层级概念层级
分类法分类法
本体本体
图片改编自: R. R. Souza, D. Tudhope, and M. B. Almeida,
“Towards a taxonomy of KOS: Dimensions for classifying
Knowledge Organization Systems,” 2012.
4. 本体学习 Ontology learning
• 建立类似分类法的知识
结构需要大量的人力和
时间
• 从自然语言文本中自动
化或者半自动化地建立
本体
• 社交网络中产生的新语
言往往不被现有的分类
体系收入,为本体学习
提供了新的需求和素材
图片改编自 from the Figure 1 in Paul Buitelaar, Philipp Cimiano, and Bernardo Magnini:
‘Ontology Learning from Text: An Overview’, 2003
建立
关系
抽取
概念
5. 情报学中语义关系的种类
图片改编自: Stock, W. G. (2010). Concepts and semantic relations in information science. Journal of the Association for
Information Science and Technology, 61(10), 1951-1969.
横向组合关系 纵向聚合关系
等价关系
层级关系
关联关系
上下位关系
部分-整体关系 实例
语义关系
7. 概念抽取: 词型归一化
Dong, H., Wang, W., & Coenen, F. (2017). Deriving Dynamic Knowledge from Academic Social Tagging Data: A
Novel Research Direction. In iConference 2017 Proceedings (pp. 661-666). https://doi.org/10.9776/17313
10. 概念抽取:语义匹配
• 将标签匹配到现有的外部词表中
• 匹配到WordNet: 仅49%的标签可从语义上匹配到WordNet中 (Andrew, Pane &
Zaihrayeu, 2011)
• 匹配到Wikipedia (Joorabchi, English, Mahdi, 2015)
• 匹配到以Dbpedia为主的
Linked Open Data Cloud
(García-Silva et al., 2015)
11. 关系的形成 Relation Learning
H. Dong, W. Wang and H. N. Liang, "Learning Structured Knowledge from Social Tagging Data: A Critical Review of Methods and Techniques," 2015 IEEE
International Conference on Smart City/SocialCom/SustainCom (SmartCity), Chengdu, 2015, pp. 307-314.
12. 从标签中自动建立层级关系的主要方法
• 基于一定规则的方法
• 社会网络分析图中心性的方法 (Heymann, 2006)
• 利用标签对应资源或用户的集合的包含度的方法 (Mika, 2005)
• 基于语义匹配的方法
• 匹配到Dbpedia, WordNet, ConceptNet, Yago, ACM category, MESH…
(Strohmaier et al., 2012; García-Silva et al., 2015)
• 机器学习方法
• 无监督方法: 分层聚类 (Strohmaier et al., 2012; Zhou et al., 2007)
• 有监督方法: 提取特征进行二元分类 (Rêgo et al., 2015)
25. 参考文献
• Dong, H., Wang, W., & Liang, H. N. (2015, December). Learning Structured Knowledge from Social Tagging Data: A Critical Review of Methods and
Techniques. In Smart City/SocialCom/SustainCom (SmartCity), 2015 IEEE International Conference on (pp. 307-314). IEEE.
• Souza, R. R., Tudhope, D., & Almeida, M. B. (2012). Towards a taxonomy of KOS: Dimensions for classifying Knowledge Organization Systems. Knowledge
organization, 39(3), 179-192. Paul Buitelaar, Philipp Cimiano, and Bernardo Magnini: ‘Ontology Learning from Text: An Overview’, 2003
• Stock, W. G. (2010). Concepts and semantic relations in information science. Journal of the Association for Information Science and
Technology, 61(10), 1951-1969.
• Dong, H., Wang, W., & Coenen, F. (2017). Deriving Dynamic Knowledge from Academic Social Tagging Data: A Novel Research Direction. In iConference
2017 Proceedings (pp. 661-666). https://doi.org/10.9776/17313
• Andrews, P., Pane, J., & Zaihrayeu, I. (2011). Semantic disambiguation in folksonomy: a case study. In Advanced language technologies for digital
libraries (pp. 114-134). Springer, Berlin, Heidelberg.
• Joorabchi, A., English, M., & Mahdi, A. E. (2015). Automatic mapping of user tags to Wikipedia concepts: The case of a Q&A website – StackOverflow.
Journal of Information Science. doi:10.1177/0165551515586669
• García-Silva, A., García-Castro, L. J., García, A., & Corcho, O. (2015). Building Domain Ontologies Out of Folksonomies and Linked Data. International
Journal on Artificial Intelligence Tools, 24(2).
• Heymann, P., & Garcia-Molina, H. (2006). Collaborative Creation of Communal Hierarchical Taxonomies in Social Tagging Systems. Retrieved from
http://ilpubs.stanford.edu:8090/775/
• Strohmaier, M., Helic, D., Benz, D., K, C., #246, rner, & Kern, R. (2012). Evaluation of Folksonomy Induction Algorithms. ACM Trans. Intell. Syst. Technol., 3(4),
1-22. doi:10.1145/2337542.2337559
• Rego, A. S. C, Marinho, L. B., & Pires, C. E. S. (2015). A supervised learning approach to detect subsumption relations between tags in folksonomies. Paper
presented at the Proceedings of the 30th Annual ACM Symposium on Applied Computing, Salamanca, Spain.
• Zhou, M., Bao, S., Wu, X., & Yu, Y. (2007). An unsupervised model for exploring hierarchical semantics from social annotations: Springer.