Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Mobile Networks and Applications
Published in: Mobile Networks and Applications 1/2023

07-03-2023

Intelligent Semantic Annotation for Mobile Services for IoT Computing from Heterogeneous Data

Authors: Yueshen Xu, Xinyu Zhao, Zhiping Jiang, Zhibo Qiu, Lei Hei, Rui Li

Published in: Mobile Networks and Applications | Issue 1/2023

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The rapid development of Internet-of-Things (IoT) computing leads to many problems, among which the management of massive mobile services has attracted much attention. When developers are looking for a service for IoT computing from mobile services, they typically try to discover the services according to annotations. If the mobile services are not assigned by proper annotations, it will be difficult for developers to find the suitable service. For providers, if the services that they provide cannot be used by developers, there will be no revenue. Therefore, it is a critical to assign proper semantic annotations to the mobile services. Existing approaches usually use the call records between services and developers to construct a score matrix, and compute the similarity between services and semantic annotations. However, these approaches do not leverage the natural association between services, providers and users. To make full use of the information inherent in services, we construct a heterogeneous information network (HIN) for service data, and propose a new model named GoT, which fully utilizes the structural and semantic information. GoT contains four components, which are the metapath construction, the intra-metapath fusion, the inter-metapath fusion, and the semantic annotation recommendation. We collected a real-world Web API dataset and performed adequate experiments. The experimental results show that our model produces superior recommendation accuracy and alleviates the cold-start problem.
previous article Editorial: AI-based Data Intelligent for IoT Computing
next article Dense Attention Fusion Network for Object Counting in IoT System

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Show more products
Footnotes
Literature
1.
Belém FM, Almeida JM, Gonçalves MA (2017) A survey on tag recommendation methods. J Assoc Inf Sci Technol 68(4):830–844CrossRef
2.
Vairavasundaram S, Varadharajan V, Vairavasundaram I, Ravi L (2015) Data mining-based tag recommendation system: an overview. Data Min Knowl Disc 5(3):87–112CrossRef
3.
Xu Y, Wu Y, Gao H, Song S, Yin Y, Xiao X (2021) Collaborative apis recommendation for artificial intelligence of things with information fusion. Future Gen Comput Syst 125:471–479CrossRef
4.
Yin Y, Huang Q, Gao H, Xu Y (2021) Personalized apis recommen dation with cognitive knowledge mining for industrial systems. IEEE Trans Ind Inf 17(9):6153–6161CrossRef
5.
Gao H, Huang W, Liu T, Yin Y, Li Y (2022) Ppo2: Location privacy-oriented task offloading to edge computing using reinforcement learning for intelligent autonomous transport systems. IEEE Trans Intell Transp Syst 1–14
6.
Kuang L, Hua C, Wu J, Yin Y, Gao H (2020) Traffic volume prediction based on multi-sources gps trajectory data by temporal convolutional network. Mobile Netw Appl 25(4):1405–1417CrossRef
7.
Sun J, Zhu M, Jiang Y, Liu Y, Wu L (2021) Hierarchical attention model for personalized tag recommendation. J Am Soc Inf Sci 72(2):173–189
8.
Chen X, Yu Y, Jiang F, Zhang L, Gao R, Gao H (2020) Graph neural networks boosted personalized tag recommendation algorithm. In: Proceedings of 2020 International Joint Conference on Neural Networks (IJCNN), pp 1–8
9.
Gao H, Xiao J, Yin Y, Liu T, Shi J (2022) A mutually supervised graph attention network for few-shot segmentation: The perspective of fully utilizing limited samples. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), pp 1–13
10.
Rendle S, Schmidt-Thieme L (2010) Pairwise interaction tensor factorization for personalized tag recommendation. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining (WSDM), pp 81–90
11.
Nguyen HT, Wistuba M, Grabocka J, Drumond LR, Schmidt-Thieme L (2017) Personalized deep learning for tag recommendation. In: Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD). Springer, pp 186–197
12.
Belém F, Santos R, Almeida J, Goncalves M (2013) Topic diversity in tag recommendation. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp 141–148
13.
Belém F, Martins E, Pontes T, Almeida J, Gonçalves M (2011) Associative tag recommendation exploiting multiple textual features. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pp 1033–1042
14.
Lipczak M, Milios E (2011) Efficient tag recommendation for real-life data. ACM Trans Intell Syst Technol 3(1):1–21CrossRef
15.
Markines B, Cattuto C, Menczer F, Benz D, Hotho A, Stumme G (2009) Evaluating similarity measures for emergent semantics of social tagging. In: Proceedings of the 18th International Conference on World Wide Web (WWW), pp 641–650
16.
Yang X, Zhou S, Cao M (2020) An approach to alleviate the sparsity problem of hybrid collaborative filtering based recommendations: The product-attribute perspective from user reviews. Mobile Netw Appl 25(2):376–390CrossRef
17.
Sigurbj¨ornsson B, Van Zwol R (2008) Flickr tag recommendation based on collective knowledge. In: Proceedings of the 17th International Conference on World Wide Web (WWW), pp 327–336
18.
Song Y, Zhang L, Giles CL (2011) Automatic tag recommendation algorithms for social recommender systems. ACM Trans Web 5(1):1–31CrossRef
19.
Xia X, Lo D, Wang X, Zhou B (2013) Tag recommendation in software information sites. In: Proceedings of 2013 10th Working Conference on Mining Software Repositories (MSR), pp 287–296
20.
Wang S, Lo D, Vasilescu B, Serebrenik A (2018) Entagrec++: An enhanced tag recommendation system for software information sites. Empir Softw Eng 23(2):800–832CrossRef
21.
Quintanilla E, Rawat Y, Sakryukin A, Shah M, Kankanhalli M (2020) Adversarial learning for personalized tag recommendation. IEEE Trans Multimedia 23:1083–1094CrossRef
22.
Han J (2009) Mining heterogeneous information networks by exploring the power of links. In: Proceedings of International Conference on Discovery Science, pp 13–30
23.
Fu X, Zhang J, Meng Z, King I (2020) Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding. In: Proceedings of The Web Conference, pp 2331–2341
24.
Zhang J, Yu PS (2015) Integrated anchor and social link predictions across social networks. In: Proceedings of Twenty-fourth International Joint Conference on Artificial Intelligence (IJCAI)
25.
Shi C, Hu B, Zhao WX, Yu PS (2018) Heterogeneous information network embedding for recommendation. IEEE Trans Knowl Data Eng 31(2):357–370CrossRef
26.
Yang Y, Chawla N, Sun Y, Han J (2012) Predicting links in multi-relational and heterogeneous networks. In: Proceedings of IEEE 12th International Conference on Data Mining (ICDM), pp 755–764
27.
Xie F, Chen L, Ye Y, Zheng Z, Lin X (2018) Factorization machine based service recommendation on heterogeneous information networks. In: Proceedings of 2018 IEEE International Conference on Web Services (ICWS), pp 115–122
28.
Zhao W, Guan Z, Liu Z (2015) Ranking on heterogeneous manifolds for tag recommendation in social tagging services. Neurocomputing 148:521–534CrossRef
29.
Yu X, Ren X, Sun Y, Gu Q, Sturt B, Khandelwal U, Norick B, Han J (2014) Personalized entity recommendation: A heterogeneous information network approach. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining (WSDM), pp 283–292
30.
Goyal P, Ferrara E (2018) Graph embedding techniques, applications, and performance: A survey. Knowl-Based Syst 151:78–94CrossRef
31.
Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), pp 701–710
32.
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L- , Polosukhin I (2017) Attention is all you need. In: Proceedings of Conference on Advances in Neural Information Processing Systems, pp 6000–6010
Metadata
Title
Intelligent Semantic Annotation for Mobile Services for IoT Computing from Heterogeneous Data
Authors
Yueshen Xu
Xinyu Zhao
Zhiping Jiang
Zhibo Qiu
Lei Hei
Rui Li
Publication date
07-03-2023
Publisher
Springer US
Published in
Mobile Networks and Applications / Issue 1/2023
Print ISSN: 1383-469X
Electronic ISSN: 1572-8153
DOI
https://doi.org/10.1007/s11036-023-02091-0

Other articles of this Issue 1/2023

Mobile Networks and Applications 1/2023 Go to the issue

OriginalPaper

Knowledge and Skills of Industrial Employees and Managerial Staff for the Industry 4.0 Implementation

OriginalPaper

Applying Probabilistic Model Checking to Path Planning for a Smart Multimodal Transportation System Using IoT Sensor Data

Retraction Note

Retraction Note: Complex IoT Control System Modeling from Perspectives of Environment Perception and Information Security

Correction

Correction to: Methodology for Detecting Cyber Intrusions in e-Learning Systems during COVID-19 Pandemic

OriginalPaper

Prediction of user throughput from Network Parameters of LTE Network using machine learning

OriginalPaper

Energy-aware Randomized Neighbor Discovery Protocol based on Collision Detection in Wireless Ad Hoc Networks