Xiangfeng Luo

Building Association Link Network for Semantic Link on Web Resources

Association Link Network (ALN) aims to establish associated relations among various resources. By extending the hyperlink network World Wide Web to an association-rich network, ALN is able to effectively support Web intelligence activities such as Web browsing, Web knowledge discovery, and publishing, etc. Since existing methods for building semantic link on Web resources cannot effectively and automatically organize loose Web resources, effective Web intelligence activities are still challenging. In this paper, a discovery algorithm of associated resources is first proposed to build original ALN for organizing loose Web resources. Second, three schemas for constructing kernel ALN and connection-rich ALN (C-ALN) are developed gradually to optimize the organizing of Web resources. After that, properties of different types of ALN are discussed, which show that C-ALN has good performances to support Web intelligence activities. Moreover, an evaluation method is presented to verify the correctness of C-ALN for semantic link on documents. Finally, an application using C-ALN to organize Web services is presented, which shows that C-ALN is an effective and efficient tool for building semantic link on the resources of Web services.

Semantic Link Network-Based Model for Organizing Multimedia Big Data

Recent research shows that multimedia resources in the wild are growing at a staggering rate. The rapid increase number of multimedia resources has brought an urgent need to develop intelligent methods to organize and process them. In this paper, the semantic link network model is used for organizing multimedia resources. A whole model for generating the association relation between multimedia resources using semantic link network model is proposed. The definitions, modules, and mechanisms of the semantic link network are used in the proposed method. The integration between the semantic link network and multimedia resources provides a new prospect for organizing them with their semantics. The tags and the surrounding texts of multimedia resources are used to measure their semantic association. The hierarchical semantic of multimedia resources is defined by their annotated tags and surrounding texts. The semantics of tags and surrounding texts are different in the proposed framework. The modules of semantic link network model are implemented to measure association relations. A real data set including 100 thousand images with social tags from Flickr is used in our experiments. Two evaluation methods, including clustering and retrieval, are performed, which shows the proposed method can measure the semantic relatedness between Flickr images accurately and robustly.

Knowle: A semantic link network based system for organizing large scale online news events

Abstract An explosive growth in the volume, velocity, and variety of the data available on the Internet has been witnessed recently. The data originated from multiple types of sources including mobile devices, sensors, individual archives, social networks, Internet of Things, enterprises, cameras, software logs, health data has led to one of the most challenging research issues of the big data era. In this paper, Knowle—an online news management system upon semantic link network model is introduced. Knowle is a news event centrality data management system. The core elements of Knowle are news events on the Web, which are linked by their semantic relations. Knowle is a hierarchical data system, which has three different layers including the bottom layer (concepts), the middle layer (resources), and the top layer (events). The basic blocks of the Knowle system—news collection, resources representation, semantic relations mining, semantic linking news events are given. Knowle does not require data providers to follow semantic standards such as RDF or OWL, which is a semantics-rich self-organized network. It reflects various semantic relations of concepts, news, and events. Moreover, in the case study, Knowle is used for organizing and mining health news, which shows the potential on forming the basis of designing and developing big data analytics based innovation framework in the health domain.

Crowdsourcing based Description of Urban Emergency Events using Social Media Big Data

Crowdsourcing is a process of acquisition, integration, and analysis of big and heterogeneous data generated by a diversity of sources in urban spaces, such as sensors, devices, vehicles, buildings, and human. Especially, nowadays, no countries, no communities, and no person are immune to urban emergency events. Detection about urban emergency events, e.g., fires, storms, traffic jams is of great importance to protect the security of humans. Recently, social media feeds are rapidly emerging as a novel platform for providing and dissemination of information that is often geographic. The content from social media usually includes references to urban emergency events occurring at, or affecting specific locations. In this paper, in order to detect and describe the real time urban emergency event, the 5W (What, Where, When, Who, and Why) model is proposed. Firstly, users of social media are set as the target of crowd sourcing. Secondly, the spatial and temporal information from the social media are extracted to detect the real time event. Thirdly, a GIS based annotation of the detected urban emergency event is shown. The proposed method is evaluated with extensive case studies based on real urban emergency events. The results show the accuracy and efficiency of the proposed method.

Mining temporal explicit and implicit semantic relations between entities using web search engines

Abstract In this paper, we study the problem of mining temporal semantic relations between entities. The goal of the studied problem is to mine and annotate a semantic relation with temporal, concise, and structured information, which can release the explicit, implicit, and diversity semantic relations between entities. The temporal semantic annotations can help users to learn and understand the unfamiliar or new emerged semantic relations between entities. The proposed temporal semantic annotation structure integrates the features from IEEE and Renlifang. We propose a general method to generate temporal semantic annotation of a semantic relation between entities by constructing its connection entities, lexical syntactic patterns, context sentences, context graph, and context communities. Empirical experiments on two different datasets including a LinkedIn dataset and movie star dataset show that the proposed method is effective and accurate. Different from the manually generated annotation repository such as Wikipedia and LinkedIn, the proposed method can automatically mine the semantic relation between entities and does not need any prior knowledge such as ontology or the hierarchical knowledge base. The proposed method can be used on some applications, which proves the effectiveness of the proposed temporal semantic relations on many web mining tasks.

Web Information Systems and Mining

Web Information Retrieval.- The Research on Chinese Coreference Resolution Based on Maximum Entropy Model and Rules.- Performance Improvement in Automatic Question Answering System Based on Dependency Term.- An Expert System Based Approach to Modeling and Selecting Requirement Engineering Techniques.- Web Access Latency Reduction Using CRF-Based Predictive Caching.- Web Information Extraction.- A Property Restriction Based Knowledge Merging Method.- A Multi-view Approach for Relation Extraction.- Self-similarity Clustering Event Detection Based on Triggers Guidance.- Analysis and Interpretation of Semantic HTML Tables.- Web Information Classification.- An Improved Feature Selection for Categorization Based on Mutual Information.- A Feature Selection Method Based on Fishers Discriminant Ratio for Text Sentiment Classification.- Web Mining.- Mining Preferred Traversal Paths with HITS.- Link Analysis on Government Website Based-on Factor.- WAPS: An Audio Program Surveillance System for Large Scale Web Data Stream.- Hot Topic Detection on BBS Using Aging Theory.- Semantic Web and Ontologies.- A Heuristic Algorithm Based on Iteration for Semantic Telecommunications Service Discovery.- An Improved Storage and Inference Method for Ontology Based Remote Sensing Interpretation System.- SASL: A Semantic Annotation System for Literature.- Applications.- Multi-issue Agent Negotiation Based on Fairness.- A Hemodynamic Predict of an Intra-Aorta Pump Application in Vitro Using Numerical Analysis.- The Sampling Synchronization of the Chaotic System with Different Structures Based on T-S Model.- Web Information Systems for Monitoring and Control of Indoor Air Quality at Subway Stations.- A Layered Overlay Multicast Algorithm with PSO for Routing Web Streams.- Solving University Course Timetabling Problems by a Novel Genetic Algorithm Based on Flow.- XML and Semi-structured Data.- Indexing Temporal XML Using FIX.- Semantic Structural Similarity Measure for Clustering XML Documents.- A Bloom Filter Based Approach for Evaluating Structural Similarity of XML Documents.- Web Services.- Similarity Based Semantic Web Service Match.- Clustering-Based Semantic Web Service Matchmaking with Automated Knowledge Acquisition.- An Efficient Approach to Web Service Selection.- Constraint Web Service Composition Based on Discrete Particle Swarm Optimization.- A QoS-Aware Service Selection Approach on P2P Network for Dynamic Cross-Organizational Workflow Development.- Semantic Model Driven Architecture Based Method for Enterprise Application Development.- Modeling and Analyzing Web Service Behavior with Regular Flow Nets.- Research on Passive Optical Network Based on Ant Colony Algorithms for Bandwidth Distribution in Uplink Direction.- Multifactor-Driven Hierarchical Routing on Enterprise Service Bus.- A Mechanism of Modeling and Verification for SaaS Customization Based on TLA.- EPN-Based Web Service Composition Approach.- Intelligent Networked Systems.- Quantum CSMA/CD Synchronous Communication Protocol with Entanglement.- Development of EPA Protocol Information Enquiry Service System Based on Embedded ARM Linux.- EasyKSORD: A Platform of Keyword Search Over Relational Databases.- Information Security.- Real-Time and Self-adaptive Method for Abnormal Traffic Detection Based on Self-similarity.- Trust-Based Fuzzy Access Control Model Research.- An Evaluation Model of CNO Intelligence Information Confidence.- A Verified Group Key Agreement Protocol for Resource-Constrained Sensor Networks.- A Bulk Email Oriented Multi-party Non-repudiation Exchange Protocol.- Software Fault Feature Clustering Algorithm Based on Sequence Pattern.- Identification of Malicious Web Pages by Inductive Learning.- The Handicap Principle for Trust in Computer Security, the Semantic Web and Social Networking.- Formal Analysis of Fairness in E-Payment Protocol Based on Strand Space.- E-Learning.- Research for Data Mining Applying in the Architecture of Web-Learning.- Design E-learning Recommendation System Using PIRT and VPRS Model.- Dynamic Content Manager - A New Conceptual Model for E-Learning.- A User Behavior Perception Model Based on Markov Process.- E-commerce.- The Knowledge Sharing Based on PLIB Ontology and XML for Collaborative Product Commerce.- Reinforcement Learning Based Web Service Compositions for Mobile Business.- Will Commodity Properties Affect Sellers Creditworthy: Evidence in C2C E-commerce Market in China.- Trust Model Based on M-CRGs in Emergency Response.- Distributed Systems.- Research on a Queue Scheduling Algorithm in Wireless Communications Network.- Mixed H 2/H ??? Control for Networked Control Systems (NCSs) with Markovian Packet-Loss.- Research on the Trust-Adaptive Scheduling for Data-Intensive Applications on Data Grids.- Robust Stability of Multi-variable Networked Control Systems with Random Time Delay.

Online Comment-Based Hotel Quality Automatic Assessment Using Improved Fuzzy Comprehensive Evaluation and Fuzzy Cognitive Map

Online comment has become a popular and efficient way for sellers to acquire feedback from customers and improve their service quality. However, some key issues need to be solved about evaluating and improving the hotel service quality based on online comments automatically, such as how to use the less trustworthy online comments, how to discover the quality defects from online comments, and how to recommend more feasible or economical evaluation indexes to improve the service quality based on online comments. To solve the above problems, this paper first improves fuzzy comprehensive evaluation (FCE) by importing trustworthy degree to it and proposes an automatic hotel service quality assessment method using the improved FCE, which can automatically get more trustworthy evaluation from a large amount of less trustworthy online comments. Then, the causal relations among evaluation indexes are mined from online comments to build the fuzzy cognitive map for the hotel service quality, which is useful to unfold the problematic areas of hotel service quality, and recommend more economical solutions to improving the service quality. Finally, both case studies and experiments are conducted to demonstrate that the proposed methods are effective in evaluating and improving the hotel service quality using online comments.

Uncertainty Analysis for the Keyword System of Web Events

Webpage recommendations for hot Web events can assist people to easily follow the evolution of these Web events. At the same time, there are different levels of semantic uncertainty underlying the amount of Webpages for a Web event, such as recapitulative information and detailed information. Apparently, the grasp of the semantic uncertainty of Web events could improve the satisfactoriness of Webpage recommendations. However, traditional hit-rate-based or clustering-based Webpage recommendation methods have overlooked these different levels of semantic uncertainty. In this paper, we propose a framework to identify the different underlying levels of semantic uncertainty in terms of Web events, and then utilize these for Webpage recommendations. Our idea is to consider a Web event as a system composed of different keywords, and the uncertainty of this keyword system is related to the uncertainty of the particular Web event. Based on keyword association linked network Web event representation and Shannon entropy, we identify the different levels of semantic uncertainty, and construct a semantic pyramid (SP) to express the uncertainty hierarchy of a Web event. Finally, an SP-based Webpage recommendation system is developed. Experiments show that the proposed algorithm can significantly capture the different levels of the semantic uncertainties of Web events and it can be applied to Webpage recommendations.

Measuring semantic similarity between words by removing noise and redundancy in web snippets

Semantic similarity measures play important roles in many Web‐related tasks such as Web browsing and query suggestion. Because taxonomy‐based methods can not deal with continually emerging words, recently Web‐based methods have been proposed to solve this problem. Because of the noise and redundancy hidden in the Web data, robustness and accuracy are still challenges. In this paper, we propose a method integrating page counts and snippets returned by Web search engines. Then, the semantic snippets and the number of search results are used to remove noise and redundancy in the Web snippets (‘Web‐snippet’ includes the title, summary, and URL of a Web page returned by a search engine). After that, a method integrating page counts, semantics snippets, and the number of already displayed search results are proposed. The proposed method does not need any human annotated knowledge (e.g., ontologies), and can be applied Web‐related tasks (e.g., query suggestion) easily. A correlation coefficient of 0.851 against Rubenstein–Goodenough benchmark dataset shows that the proposed method outperforms the existing Web‐based methods by a wide margin. Moreover, the proposed semantic similarity measure significantly improves the quality of query suggestion against some page counts based methods. Copyright

Power Series Representation Model of Text Knowledge Based on Human Concept Learning

How to build a text knowledge representation model, which carries rich knowledge and has a flexible reasoning ability as well as can be automatically constructed with a low computational complexity, is a fundamental challenge for reasoning-based knowledge services, especially with the rapid growth of web resources. However, current text knowledge representation models either lose much knowledge [e.g., vector space model (VSM)] or have a high complex computation [e.g., latent Dirichlet allocation (LDA)]; even some of them cannot be constructed automatically [e.g., web ontology language, (OWL)]. In this paper, a novel text knowledge representation model, power series representation (PSR) model, which has a low complex computation in text knowledge constructing process, is proposed to leverage the contradiction between carrying rich knowledge and automatic construction. First, concept algebra of human concept learning is developed to represent text knowledge as the form of power series. Then, degree-2 power series hypothesis is introduced to simplify the proposed PSR model, which can be automatically constructed with a lower complex computation and has more knowledge than the VSM and LDA. After that, degree-2 power series hypothesis-based reasoning operations are developed, which provide a more flexible reasoning ability than OWL and LDA. Furthermore, experiments and comparisons with current knowledge representation models show that our model has better characteristics than others when representing text knowledge. Finally, a demo is given to indicate that PSR model has a good prospect over the area of web semantic search.