Mining and Matching Relationships From Interaction Contexts in a Social Manufacturing Paradigm

There is an increasing use of social interaction contexts in the cross-enterprise manufacturing problem solving. To transform these massive and unstructured data into decision-support information for cross-enterprise manufacturing demand-capability matching, we present automated solutions to two phases: (1) extracting relationships based on a semi-supervised learning approach to derive formalized heterogeneous manufacturing network from the unstructured text-based context that contains high levels of noise and irrelevant information and (2) matching group-level relationships among the entities in the established manufacturing network. The extracting phase formulates network data using multiattributed graph that can encode various entities and relationships. The matching phase is based on probabilistic multiattributed graph matching, and implemented using distributed message passing algorithm. We developed a prototype system to verify the proposed model, which is also flexible to new domains of contexts and scale to large datasets. The ultimate goal of this paper is to facilitate knowledge transferring and sharing in the context of cross-enterprise social interaction, thereby supporting the integration of the resources and capabilities among different enterprise.

[1]  Iraklis Varlamis,et al.  A Trust-Aware System for Personalized User Recommendations in Social Networks , 2014, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[2]  Michael Winsper,et al.  Using the Max-Sum Algorithm for Supply Chain Emergence in Dynamic Multiunit Environments , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[3]  Zhu Wang,et al.  Discovering and Profiling Overlapping Communities in Location-Based Social Networks , 2014, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[4]  Amy R. Pritchett,et al.  Predicting Interactions Between Agents in Agent-Based Modeling and Simulation of Sociotechnical Systems , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[5]  Solomon Eyal Shimony,et al.  Markov Network Based Ontology Matching , 2009, IJCAI.

[6]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[7]  Manoj Kumar Tiwari,et al.  Resource Scalability in Networked Manufacturing System: Social Network Analysis Based Approach , 2014 .

[8]  Dave Reynolds,et al.  SPARQL basic graph pattern optimization using selectivity estimation , 2008, WWW.

[9]  Nir Friedman,et al.  Probabilistic Graphical Models - Principles and Techniques , 2009 .

[10]  Tina Eliassi-Rad,et al.  Visual Analysis of Large Heterogeneous Social Networks by Semantic and Structural Abstraction , 2006 .

[11]  Aldo Gangemi,et al.  Ontology Design Patterns , 2005 .

[12]  Pasquale De Meo,et al.  Analysis of a Heterogeneous Social Network of Humans and Cultural Objects , 2014, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[13]  Ronald Rousseau,et al.  Social network analysis: a powerful strategy, also for the information sciences , 2002, J. Inf. Sci..

[14]  Edmond Chow,et al.  Knowledge Representation Issues in Semantic Graphs for Relationship Detection , 2005, AAAI Spring Symposium: AI Technologies for Homeland Security.

[15]  Rik Van de Walle,et al.  Evaluating the success of vocabulary reconciliation for cultural heritage collections , 2013, J. Assoc. Inf. Sci. Technol..

[16]  Wei Cao,et al.  Cloud Machining Community for Social Manufacturing , 2012 .

[17]  Andrew McCallum,et al.  Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data , 2004, J. Mach. Learn. Res..

[18]  Pingyu Jiang,et al.  Modeling and analyzing of an enterprise collaboration network supported by service-oriented manufacturing , 2012 .

[19]  Ronen Feldman,et al.  Book Reviews: The Text Mining Handbook: Advanced Approaches to Analyzing Unstructured Data by Ronen Feldman and James Sanger , 2008, CL.

[20]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[21]  William T. Freeman,et al.  On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs , 2001, IEEE Trans. Inf. Theory.

[22]  Ying Wang,et al.  Algorithms for Large, Sparse Network Alignment Problems , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[23]  David A. Smith,et al.  Improving NLP through Marginalization of Hidden Syntactic Structure , 2012, EMNLP-CoNLL.

[24]  Pingyu Jiang,et al.  Implementing of a three-phase integrated decision support model for parts machining outsourcing , 2014 .

[25]  Reik V. Donner,et al.  Conflicting Optimization Goals in Manufacturing Networks: A Statistical Analysis Based on an Idealized Discrete-Event Model , 2012, LDIC.

[26]  Jan-Willem Marck,et al.  Reasoning About Threats: From Observables to Situation Assessment , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[27]  Jared Freeman,et al.  Learning and Detecting Patterns in Multi-Attributed Network Data , 2012, AAAI Fall Symposium: Social Networks and Social Contagion.

[28]  Manoj Kumar Tiwari,et al.  Process Plan and Scheduling Integration for Networked Manufacturing Using Mobile Agent-Based Approach , 2014 .

[29]  Kai Ding,et al.  Modeling and analyzing of an enterprise relationship network in the context of social manufacturing , 2016 .

[30]  Michael D. Gordon,et al.  Literature-based discovery by lexical statistics , 1999 .

[31]  Yi-Kuei Lin,et al.  Reliability evaluation for a manufacturing network with multiple production lines , 2012, Comput. Ind. Eng..

[32]  Kristen Grauman,et al.  Observe locally, infer globally: A space-time MRF for detecting abnormal activities with incremental updates , 2009, CVPR.

[33]  Yair Weiss,et al.  Correctness of Local Probability Propagation in Graphical Models with Loops , 2000, Neural Computation.

[34]  Klaus Pohl,et al.  Comparing and Combining Predictive Business Process Monitoring Techniques , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[35]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[36]  Peihua Gu,et al.  Social manufacturing as a sustainable paradigm for mass individualization , 2016 .

[37]  Pingyu Jiang,et al.  A deep learning approach for relationship extraction from interaction context in social manufacturing paradigm , 2016, Knowl. Based Syst..

[38]  Dazhong Wu,et al.  Cloud-based design and manufacturing systems: A social network analysis , 2013 .

[39]  Joseph Weizenbaum,et al.  and Machine , 1977 .

[40]  Kristina Georgieva,et al.  Understanding the Dynamics of Industrial Networks Using Kauffman Boolean Networks , 2008, Adv. Complex Syst..

[41]  Ellen Riloff,et al.  Automatically Generating Extraction Patterns from Untagged Text , 1996, AAAI/IAAI, Vol. 2.

[42]  Sun Jin,et al.  A Bayesian network approach for fixture fault diagnosis in launch of the assembly process , 2012 .

[43]  Rakesh Nagi,et al.  Design and implementation of a virtual information system for agile manufacturing , 1997 .