Contemporarily, in light of the intelligent transportation systems (ITS) sector, the tendency can be observed that the solution of the multi-objective cyber-physical optimization problems with imperfect information takes an increasingly weighted role. In the present scientific work, the authors want to take these developments into account by introducing an innovative cyber-physical architectural design and corresponding the two-stage heuristic computing approach. It is utilized in synergy with the MCSA11Multi-tier Cyber-physical System Architecture and DCEx architectural principles for the workflow scheduling of Monte-Carlo simulation, which is based on the intelligent and sustainable route-order dispatching process model. Factors such as emissions, transport costs, risks, and the individual weighting of orders are reflected in the model. In particular, the authors define a stochastic ILP-based22Integer Linear Programming monte-carlo workflow model. They further propose two-stage scheduling heuristic with 6-HEFT DAG relaxation as first stage and apply state-of-the-art techniques as a part of SCIP framework to solve 2nd 1–0 ILP-based stage; evaluate the performance of the scheduling approach. The authors obtain preliminary results of the second stage behavior using a realistic heterogeneous computing scenario and corresponding constraint structures within MACS simulator engine33Modular Architecture for Complex Computing Systems Analysis. The results from the experiments illustrate moderate complexity of the approach. Scalability of the model looks promising for the applicability in various industry-related scenarios and corresponding computing environments.

Summary News image captioning aims to generate captions or descriptions for news images automatically, serving as draft captions for creating news image captions manually. News image captions are different from generic captions as news image captions contain more detailed information such as entity names and events. Therefore, both images on news and the accompanying text are the source of generating caption of news image. Pointer‐generator network is a neural method defined for text summarization. This article proposes the Multimodal pointer‐generation network by incorporating visual information into the original network for news image captioning. The multimodal attention mechanism is proposed by splitting attention into visual attention paid to the image and textual attention paid to the text. The multimodal pointer mechanism is proposed by using both textual attention and visual attention to compute pointer distributions, where visual attention is first transformed into textual attention via the word‐image relationships. The multimodal coverage mechanism is defined to reduce repetitions of attentions or repetitions of pointer distributions. Experiments on theDailyMailtest dataset and the out‐of‐domainBBCtest dataset show that the proposed model outperforms the original pointer‐generator network, the generic image captioning method, the extractive news image captioning method, and theLDA‐based method accordingBLEU,METEOR, andROUGL‐Levaluations. Experiments also show that the proposed multimodal coverage mechanisms can improve the model, and that transforming visual attention to pointer distributions can improve the model.

The key to realize advanced document summarization is semantic representation of documents. This paper investigates the role of Semantic Link Network in representing and understanding documents for multi-document summarization. It proposes a novel abstractive multi-document summarization framework by first transforming documents into a Semantic Link Network of concepts and events and then transforming the Semantic Link Network into the summary of the documents based on the selection of important concepts and events while keeping semantics coherence. Experiments on benchmark datasets show that the proposed summarization approach significantly outperforms relevant state-of-the-art baselines and the Semantic Link Network plays an important role in representing and understanding documents.

Summary Related work is a component of a scientific paper, which introduces other researchers' relevant works and makes comparisons with the current author's work. Automatically generating the related work section of a writing paper provides a tool for researchers to accomplish the related work section efficiently without missing related works. This paper proposes an approach to automatically generating a related work section by comparing the main text of the paper being written with the citations of other papers that cite the same references. Our approach first collects the papers that cite the reference papers of the paper being written and extracts the corresponding citation sentences to form a citation document. It then extracts keywords from the citation document and the paper being written and constructs a graph of the keywords. Once the keywords that discriminate the two documents are determined, the minimum Steiner tree that covers the discriminative keywords and the topic keywords is generated. The summary is generated by extracting the sentences covering the Steiner tree. According to ROUGE evaluations, the experiments show that the citations are suitable for related work generation and our approach outperforms the three baseline methods of MEAD, LexRank, and ReWoS. This work verifies the general summarization method based on connotation and extension through citation.

Summary Users of social media have different influences on the evolution of a Web event. Finding influential users could benefit such information services as recommendation and market analysis. However, most of the existing methods are only based on social networks of users or user behaviors while the role of the contents contributed by users in social media is ignored. In fact, a Web event evolves with both user behaviors and the contents. This paper proposes an approach to find influential users by extracting user behavior network and association network of words within the contents and then uses PageRank algorithm and HITS algorithm to calculate the influence of users on the integration of two networks. The proposed approach is effective on several real‐world datasets.

Summary The rapid development of data science and artificial intelligence provides new methods and tools for exploring semantics, knowledge, and grids—the evolving interconnection environment. Different streams of research including Internet of Things, cloud computing, edge‐computing, semantics modeling, and cyber‐physical society are converging to support an intelligent interconnection environment. It is an important issue to create the method for ensuring the sustainable development of this interconnection environment. Big data and machine learning provide an empirical model for resolving this issue that is different from the method based on semantics and knowledge.

Rapid growth of multi-modal documents containing images on the Internet expresses strong demand on multi-modal summarization. The challenge is to create a computing method that can uniformly process text and image. Deep learning provides basic models for meeting this challenge. This paper treats extractive multi-modal summarization as a classification problem and proposes a sentence–image classification method based on the multi-modal RNN model. Our method encodes words and sentences with the hierarchical RNN models and encodes the ordered image set with the CNN model and the RNN model, and then calculates the selection probability of sentences and the sentence–image alignment probability through a logistic classifier taking text coverage, text redundancy, image set coverage, and image set redundancy as features. Two methods are proposed to compute the image set redundancy feature by combining the important scores of sentences and the hidden sentence–image alignment. Experiments on the extended DailyMail corpora constructed by collecting images and captions from the Web show that our method outperforms 11 baseline text summarization methods and that adopting the two image-related features in the classification method can improve text summarization. Our method is able to mine the hidden sentence–image alignments and to create informative well-aligned multi-modal summaries.

Rapid growth of multi-modal documents on the Internet makes multi-modal summarization research necessary.Most previous research summarizes texts or images separately.Recent neural summarization research shows the strength of the Encoder-Decoder model in text summarization.This paper proposes an abstractive text-image summarization model using the attentional hierarchical Encoder-Decoder model to summarize a text document and its accompanying images simultaneously, and then to align the sentences and images in summaries.A multi-modal attentional mechanism is proposed to attend original sentences, images, and captions when decoding.The DailyMail dataset is extended by collecting images and captions from the Web.Experiments show our model outperforms the neural abstractive and extractive text summarization methods that do not consider images.In addition, our model can generate informative summaries of images.

This paper investigates differential topic models (dTM) for summarizing the differences among document groups. Starting from a simple probabilistic generative model, we propose dTM-SAGE that explicitly models the deviations on group-specific word distributions to indicate how words are used differen-tially across different document groups from a background word distribution. It is more effective to capture unique characteristics for comparing document groups. To generate dTM-based comparative summaries, we propose two sentence scoring methods for measuring the sentence discriminative capacity. Experimental results on scientific papers dataset show that our dTM-based comparative summari-zation methods significantly outperform the generic baselines and the state-of-the-art comparative summarization methods under ROUGE metrics.

This paper studies the abstractive multi-document summarization for event-oriented news texts through event information extraction and abstract representation. Fine-grained event mentions and semantic relations between them are extracted to build a unified and connected event semantic link network, an abstract representation of source texts. A network reduction algorithm is proposed to summarize the most salient and coherent event information. New sentences with good linguistic quality are automatically generated and selected through sentences over-generation and greedy-selection processes. Experimental results on DUC 2006 and DUC 2007 datasets show that our system significantly outperforms the state-of-the-art extractive and abstractive baselines under both pyramid and ROUGE evaluation metrics.

It is important to help researchers find valuable papers from a large literature collection. To this end, many graph‐based ranking algorithms have been proposed. However, most of these algorithms suffer from the problem of ranking bias. Ranking bias hurts the usefulness of a ranking algorithm because it returns a ranking list with an undesirable time distribution. This paper is a focused study on how to alleviate ranking bias by leveraging the heterogeneous network structure of the literature collection. We propose a new graph‐based ranking algorithm, MutualRank, that integrates mutual reinforcement relationships among networks of papers, researchers, and venues to achieve a more synthetic, accurate, and less‐biased ranking than previous methods. MutualRank provides a unified model that involves both intra‐ and inter‐network information for ranking papers, researchers, and venues simultaneously. We use the ACL Anthology Network as the benchmark data set and construct the gold standard from computer linguistics course websites of well‐known universities and two well‐known textbooks. The experimental results show that MutualRank greatly outperforms the state‐of‐the‐art competitors, including PageRank, HITS, CoRank, Future Rank, and P‐Rank, in ranking papers in both improving ranking effectiveness and alleviating ranking bias. Rankings of researchers and venues by MutualRank are also quite reasonable.

Summary People often read with aims and reading process significantly influences understanding. This paper defines a new measure of information in text named Aimed Information Quantity in Text by simulating human reading process with reading aims. Aimed Information Quantity in Text reflects not only human memory of words but also relevancy between words emerging in aimed reading process. We demonstrate its application in recommendation. Copyright © 2014 John Wiley & Sons, Ltd.

Summary Graph is an efficient tool for representing structured data such as proteins, molecular compounds, and social networks. Graph kernel is a technique to measure the similarity between graphs. However, existing graph kernels still have several limitations: (1) semantics on link is ignored; (2) node is associated with single label; (3) most graph kernels require more than cubic time, which is still computationally expensive; and, (4) there is seldom consideration of handling the graph comparison when the number of node types becomes huge. In this paper, we utilize semantic link network (SLN) to represent complex structured data with richer semantic information. Topic model is employed for dimension reduction and tagging each node with multiple labels. And a novel linear‐time graph kernel for SLN is designed to calculate the similarity between two SLNs. This work remedies the limitations of the conventional graph kernels. The effectiveness and efficiency of this approach are evaluated by the document classification task on public corpora. Empirical results demonstrate that the proposed method can achieve better performance than the traditional topic model‐based classification approach. Copyright © 2015 John Wiley & Sons, Ltd.

Summary As smart phones with GPS become popular, more and more textual documents with geographical locations are published on the Web. Keyword‐based location services like vehicle navigation, tour planning, nearby object querying, and location pattern discovering of spatial objects are becoming popular and important. However, processing both text and geographical locations brings more challenges to information‐retrieval techniques. In this paper, we focus on the problem of finding textual topics of clusters containing spatial objects with text descriptions. The key is how to combine clustering techniques with topic‐retrieval models to integrate both geo‐location information and text information. We investigated methods that combine clustering methods with the Latent Dirichlet Allocation model to discover topics of clusters of documents with geo‐locations. Six different methods of combination are investigated, each having outputs with different meanings, which can be further leveraged to answer different types of queries over spatial documents. Experiments are conducted on both synthetic and real data. The results show that the combination of the probabilistic topic model with clustering algorithms is an efficient and effective way to discover meaningful clusters in different facets and levels of documents with textual and geographical information. Copyright © 2015 John Wiley & Sons, Ltd.

Semantics, knowledge and grids represent three streams of studying humans and cyberspace that humans create and interact. Relevant research concerns all areas of computing, especially web semantics, artificial intelligence, and advanced computing infrastructures. This special issue selected eight papers reporting relevant research and applications from the 9th International Conference on Semantics, Knowledge and Grids. Semantics is a fundamental issue of computing. Various symbol systems have been developed to represent semantics. An important idea is to distinguish social semantics from natural semantics like mathematics and to build social semantics through interaction processes 1. An interactive system and its semantic image co-evolve to render interactive semantics. The process of building and explaining semantic image can be based on an evolving structure incorporating multidimensional classification space and self-organized semantic link network 2. Knowledge is a fundamental issue of philosophy and artificial intelligence. Human can map the representation of the external world into the representation in mind, and map the internal representation into the knowledge space through generalization. Computers are limited in ability to realize this mapping because of the fundamental differences in structure, function and social characteristics. It is necessary to study an indirect mapping from the external representation into a cyber knowledge space by modelling human cognition 3. The cyber-infrastructure, along with its resources (including users and various connected devices), constitutes a vast artificial environment. Harmonious development of its cyber-infrastructure not only enables people to conveniently access, share and process big data but also ensures sustainable development of society 4. Research on grids refers to advanced computing infrastructures, including grid computing, cloud computing, Internet of things, and cyber-physical system. Many new challenges and technologies emerge in recent years, especially the big data generated by various devices. Mapping big data into knowledge space is a challenge. It is a challenge to develop an infrastructure to support not only big data management and analysis but also knowledge discovery, sharing and management. The International Conference on Semantics, Knowledge and Grids is a cross-area international forum on semantic computing, knowledge networking, and advanced computing architectures (including grid, cloud, Web X.0, CPS, Internet of things, social network and big data.). Its mission is to promote cross-area research and accelerate the development of relevant areas. SKG has built its reputation through 10 years' professional organization. The conference invites well-known experts in diverse areas to give keynotes every year. Submissions come from all over the world. The conference proceedings is published by IEEE. This special issue consists of eight papers that discuss different aspects of the theme of the conference. The first paper is at the application level of the theme. It surveys recommendation techniques based on offline data processing, with emphasis on new techniques and evaluation measurements, benchmark data sets and available open source tools. Some challenges for future research are outlined 5. The second paper introduces an approach to deploy service-based applications in the cloud 6, which is regarded as a heterogeneous ecosystem and a new economic model. Techniques on service-based application deployment in Cloud platforms are introduced. The third paper introduces an approach to predict bus routes or the arrival time of buses in developing countries by integrating sensing ability and social networks to understand and measure the influence between social events and vehicle velocity 7. Different from previous ranking approaches, the fourth paper introduces an approach to measure information quantity in text with reading aim 8. It can be used to select appropriate texts for recommendation and summarization. The fifth paper introduces an approach to discover online hot events through association link network 9, which is a special type of semantic link network. This approach consists of three stages: extract significant features to represent the content of each document from the online document stream, classify the online document stream into topically related events considering event evolution, and create an event detection algorithm based on the association link network. The sixth paper introduces an approach to discover topic clusters in documents with geographical location 10. This work is significant as more and more textual documents with geographical locations are published on the Web with the popularity of smartphones with GPS and location-based services like routing, planning, querying, finding and discovering spatial objects are becoming popular. The combination of the probabilistic topic model with clustering algorithms could be an effective way to discover meaningful clusters in different facets and levels of documents with textual and geographical information. The seventh paper tries to break the limitations of the conventional graph kernels to realize a semantic link network. The effectiveness and efficiency are evaluated by the document classification on public corpora. Empirical results demonstrate that the proposed method can achieve better performance than the traditional topic model-based approaches 11. The eighth paper reviews the field of semantic web services, compares web ontology language for services, web service modelling ontology and semantic annotations for the web services description language from the views of the service requester, provider and the broker. The comparison helps users to better understand the strengths and limitations of these approaches to formalizing semantic web services and to choose the most suitable solution for an application 12. The future world will be built on an advanced cyber-infrastructure, which consists of various networks that pervade the nature, society and virtual worlds 4. The cyberspace will be able to sense human behaviours and provide appropriate services for humans. Discovering the rules that are suitable for cyberspace, social space and physical space is a challenge. Collaborative research on semantics, knowledge and grids is an important way to realize a cyber-physical society where human can work and live with machines harmoniously.

Graphs are becoming increasingly dominant in modeling real-life networked data including social and biological networks, the WWW and the Semantic Web, etc. Graph pattern queries are useful for gathering information with expressive semantics from these graph-structured data. Current methods for graph pattern query processing have performance deficiency caused by inefficiencies of the underlying reachability index and costly merge-join operations on huge amounts of tuple-formatted intermediate results. To overcome the above problems, this paper contributes in the following aspects to boost graph pattern query evaluation. First, we propose an improved hop-based reachability indexing scheme 3-Hop which gains faster reachability query evaluation, less indexing costs and better scalabilities than state-of-the-art hop-based methods. Second, we propose a two-stage node filtering algorithm based on 3-Hop to answer tree pattern queries more efficiently. Tree pattern queries serve as the underlying facility for graph pattern query evaluation. Furthermore, we use a graph representation of the intermediate results during node filtering and final results enumeration. Experiments on real-life and synthetic datasets demonstrate the effectiveness of the proposed methods.

It is important to help researchers find valuable scientific papers from a large literature collection containing information of authors, papers and venues. Graph-based algorithms have been proposed to rank papers based on networks formed by citation and co-author relationships. This paper proposes a new graph-based ranking framework MutualRank that integrates mutual reinforcement relationships among networks of papers, researchers and venues to achieve a more synthetic, accurate and fair ranking result than previous graph-based methods. MutualRank leverages the network structure information among papers, authors, and their venues available from a literature collection dataset and sets up a unified mutual reinforcement model that involves both intra- and inter-network information for ranking papers, authors and venues simultaneously. To evaluate, we collect a set of recommended papers from websites of graduate-level computational linguistics courses of 15 top universities as the benchmark and apply different methods to estimate paper importance. The results show that MutualRank greatly outperforms the competitors including Pag-eRank, HITS and CoRank in ranking papers as well as researchers. The experimental results also demonstrate that venues ranked by MutualRank are reasonable.

The paper "Stack-based Algorithms for Pattern Matching on DAGs" generalizes the classical holistic twig join algorithms and proposes PathStackD, TwigStackD and DagStackD to respectively evaluate path, twig and DAG pattern queries on directed acyclic graphs. In this paper, we investigate the major results of that paper, pointing out several discrepancies and proposing solutions to resolving them. We show that the original algorithms do not find particular types of query solutions that are common in practice. We also analyze the effect of an underlying assumption on the correctness of the algorithms and discuss the pre-filtering process that the original work proposes to prune redundant nodes. Our experimental study on both real and synthetic data substantiates our conclusions.

As data are increasingly modeled as graphs for expressing complex relationships, the tree pattern query on graph-structured data becomes an important type of queries in real-world applications. Most practical query languages, such as XQuery and SPARQL, support logical expressions using logical-AND/OR/NOT operators to define structural constraints of tree patterns. In this paper, (1) we propose generalized tree pattern queries (GTPQs) over graph-structured data, which fully support propositional logic of structural constraints. (2) We make a thorough study of fundamental problems including satisfiability, containment and minimization, and analyze the computational complexity and the decision procedures of these problems. (3) We propose a compact graph representation of intermediate results and a pruning approach to reduce the size of intermediate results and the number of join operations -- two factors that often impair the efficiency of traditional algorithms for evaluating tree pattern queries. (4) We present an efficient algorithm for evaluating GTPQs using 3-hop as the underlying reachability index. (5) Experiments on both real-life and synthetic data sets demonstrate the effectiveness and efficiency of our algorithm, from several times to orders of magnitude faster than state-of-the-art algorithms in terms of evaluation time, even for traditional tree pattern queries with only conjunctive operations.

Classification is the most basic method for organizing resources in the physical space, cyber space, socio space, and mental space. To create a unified model that can effectively manage resources in different spaces is a challenge. The Resource Space Model RSM is to manage versatile resources with a multidimensional classification space. It supports generalization and specialization on multidimensional classifications. This paper introduces the basic concepts of RSM, and proposes the Probabilistic Resource Space Model, P-RSM, to deal with uncertainty in managing various resources in different spaces of the cyber-physical society. P-RSM's normal forms, operations, and integrity constraints are developed to support effective management of the resource space. Characteristics of the P-RSM are analyzed through experiments. This model also enables various services to be described, discovered, and composed from multiple dimensions and abstraction levels with normal form and integrity guarantees. Some extensions and applications of the P-RSM are introduced.

In recent years, we witness the increasing trend that more applications are developed by composing Web services. Services interact with each other in ways not necessarily foreseen during their development phase. In this setting, mismatches usually exist between services, and adapters are typically synthesized to reconcile mismatches occurring in certain interactions. The technique that identifies the most suitable provider service from a set of functionally equivalent candidates with respect to certain requirements specified by the requester is essential. To address this challenge, we propose a technique called adaptability assessment , which 1) provides a set of conditions that determines when service interactions can be conducted and 2) computes an adaptation degree that specifies to what extent a service protocol is adaptable to another service protocol. Adaptability assessment complements the techniques that synthesize adapters. Specifically, when adaptability assessment suggests that two service protocols can conduct some interactions according to the adaptation mechanisms of a certain adapter and these interactions can fulfill the requester's requirements, then the effort of synthesizing an adapter is beneficial to potential service interactions. As such, the requester can acknowledge whether his/her expected interactions can be conducted or not and under which conditions. This is important before conducting an interaction, particularly when this interaction is critical, long running, and nonrepeatable.

Abstract The recent development of Web‐of‐Things (WoT) and Cyber–Physical Systems (CPS) raises a new requirement of connecting abstract computational artifacts with the physical world. This requires both new theories and engineering practices that model cyber and physical resources in a unified framework, a challenge that few current approaches are able to tackle. The solution must break the boundary between the cyber world and the physical world by providing a unified infrastructure that permits integrated models addressing issues from both worlds simultaneously. This paper proposes a framework to integrate WoT and CPS. A case study is presented to demonstrate the advantage of the framework. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract Exploring the laws of the nature and the rules of human society is the grand challenge of sciences. The Internet, Web, various communication networks and digital devices are connecting each other to form an enormous cyber space. The cyber space gradually connects the natural physical space via various cyber‐physical interfaces to form Cyber Physical Systems. Interacting with humans, Cyber Physical Systems will connect human society to form a new world—Cyber Physical Society. Humans' daily life and work will become increasingly relying on the new world. Billions of years' evolution creates the most elegant and efficient natural ecosystem. It will be ideal if the Cyber Physical Society can be an ecosystem that harmoniously evolves with society, economy, culture, sciences and technologies. The natural ecosystem gives us inspiration to ensure the harmony of the Cyber Physical Society. From ecosystem point of view, this paper proposes the notion, method, scientific issues, general architecture and resource management of Cyber Physical Society. The study of the Cyber Physical Socio Ecology (CPSE) could lead to a new branch of sciences. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract Given the inherent autonomy, heterogeneity, and continuous evolution of Web services, mismatches usually exist between service protocols. Adapters are typically used to reconcile these mismatches. Before synthesizing an adapter, a service requestor is often willing to assess whether her expected interactions can be conducted or not. The effort of synthesizing an adapter is beneficial only if the result of this assessment is positive. Previous effort analyzing service interactions focused on either (i) compatibility analysis for checking whether interactions can be conducted in a direct manner or (ii) adapter synthesization for reconciling mismatches. In this paper we propose a new kind of adaptability assessment that (i) determines whether two service protocols of a requestor and a provider are adaptable, (ii) computes an adaptation degree, and (iii) identifies conditions that determine when these two service protocols can be adapted. This adaptability assessment provides complementary criteria to the service requestor for selecting a suitable service protocol from a set of functionally equivalent candidates according to her requirements. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract Cyber Physical Socio Semantic Link network (CPSocio‐SLN) is a model and method for self‐organizing cyber physical socio resources in Cyber Physical Society (CP‐Society). This paper views CPSocio‐SLN as an evolution process through a series of operations, and investigates its basic operations, completeness, and dynamicity. A strategy for efficiently storing and managing CPSocio‐SLN based on the basic operation set is proposed to support efficient query and maintenance. An approach is suggested for simplifying CPSocio‐SLN reasoning by estimating the importance of reasoning at multiple levels. The study of the dynamicity can help understand its basic characteristics. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract Knowing semantic links among resources is the basis of realizing machine intelligence over large‐scale resources. Discovering semantic links among resources with limited human interference is a challenge issue. This paper proposes an approach to automatically discovering and predicting semantic links in a document set based on a model of document semantic link network (SLN). The approach has the following advantages: it supports probabilistic relational reasoning; SLNs and the relevant rules automatically evolve; and, it can adapt to the update of the adopted techniques. The approach can support cyber space applications, such as documentation recommendation and relational queries, on large documents. Copyright © 2010 John Wiley & Sons, Ltd.

Humans have been recognizing, establishing and making use of various relations consciously and unconsciously since the formation of human society. Waving and maintaining various relations accompany everyone's life. Humans' social behaviors create or emphasize relations. Various networks evolve with the development of society. Some relational networks gradually become independent of individuals' life during evolution so that they become a part of knowledge and culture sharable cross generations. The study of social relations trace back to the age of Laozi (576-BC), Confucius (551-479 BC), Thales (624 BC-546 BC) and Socrates (469 BC-399 BC). The formal definition of general relation traces to the invention of set theory in 1874. Humans have intelligence to observe and participate in social processes, to think, and to know the effect of establishing a relation. Humans can also actively select appropriate relations and persons according to requirement, situation and social rules. Machines are obviously limited in these abilities. Various graph-based models have been used to connect resources in the cyber space. Two issues are fundamental: (1) machines know little relation in human society and the nature, data structures in machines are for machines to process not for humans to read, so it is not realistic to expect machines to discover social and natural laws and resolve relevant issues without human instruction; and, (2) machines are hard to know the effect of establishing and making use of relations, and to explain computing result according to society and nature. The cause is that machines do not have any worldview. Connecting various networks and machines with nature, society, and even human minds can create a new world where individuals have semantic images that can enhance mutual understanding. The semantic images can be constructed by a Semantic Link Network SLN consisting of nodes with rich semantics, semantic links between nodes, and rules for reasoning, influencing and evolving the network. Waving semantic link networks in the cyber physical society and exploring the laws of semantic networking are challenge issues.

Abstract Network structure analysis plays an important role in characterizing complex systems. Different from previous network centrality measures, this article proposes the topological centrality measure reflecting the topological positions of nodes and edges as well as influence between nodes and edges in general network. Experiments on different networks show distinguished features of the topological centrality by comparing with the degree centrality, closeness centrality, betweenness centrality, information centrality, and PageRank. The topological centrality measure is then applied to discover communities and to construct the backbone network. Its characteristics and significance is further shown in e‐Science applications.

Abstract This paper proposes a framework consisting of a semantic object model and a scalable platform for supporting effective publishing and sharing of massive knowledge on Web 2.0. The platform integrates the P2P computing techniques to provide users with flexible tools to manage personalized resources and services. The semantic object model provides an object‐oriented semantic description language and a semantic object query language for publishing and querying resources. Semantic indexing mechanisms are used to support efficient semantic query processing. The semantic object model enables users to define and manipulate semantics with more explicit object semantics and can be well adapted to the P2P platform to support scalable and extensible management on semantics‐rich Web resources. Copyright © 2008 John Wiley & Sons, Ltd.

Semantics, Knowledge and GridWith the massive participations of worldwide developers and users as well as enabling collaboration and social networks, Web 2.0 is significantly influencing the development of the Web, Semantic Web and Grid by aiming at a powerful and harmonious interconnection environment.The study of the future Web concerns the exploration of the laws among large-scale and expanding resources such as the formation of community and effective knowledge sharing, the semantics facilitating various interactions (between human, between machines and between human and machine) the scalable infrastructure and applications.This special issue is to reflect the up-to-date progresses of research on the future Web.It contains eight papers selected mainly from the third International Conference on Semantics, Knowledge and Grid [1], which emphasizes on promoting cross-area research.Web mining and community discovery play an important role in supporting intelligent Web applications.Zhang and Xu [2] introduce a set of Web mining methods based on various available information on the Web, including Web document contents, hyperlink analysis, user access logs and semantic analysis.Several key Web clustering algorithms are studied, including a latent linkage information algorithm for finding relevant pages and a Web page clustering algorithm based on the defined concept of Web page correlation.Pierce et al. [3] introduce the application of Web 2.0 in the following scenarios: client-side JavaScript libraries for building and composing Grid services, integrating server-side portlets with 'rich client' AJAX tools and Web services for analyzing a global positioning system's data, building and analyzing folksonomies of scientific user communities through social bookmarking, and applying microformats and GeoRSS to problems in scientific metadata description and delivery.The future Web and Grid need to combine different models and techniques to provide users with more accessible services and resources.Dillon et al. [4] discuss three important computing paradigms including service-oriented computing, Grid computing and Web 2.0.An abstracted distributed computing model is introduced and a Grid architecture GRIDspace is discussed.Semantic Web plays an important role in making the description of Web resources more machineunderstandable, which can be used as the basic data model and query model to support heterogeneous resource management on Semantic Grid.Babik and Hluchy present an architecture to support efficient resource description, query processing and logic reasoning based on a subset of Semantic Web language [5].The system combines a tableau algorithm with a resolution-based reasoning

The World Wide Web provides plentiful contents for Web-based learning, but its hyperlink-based architecture connects Web resources for browsing freely rather than for effective learning. To support effective learning, an e-learning system should be able to discover and make use of the semantic communities and the emerging semantic relations in a dynamic complex network of learning resources. Previous graph-based community discovery approaches are limited in ability to discover semantic communities. This paper first suggests the semantic link network (SLN), a loosely coupled semantic data model that can semantically link resources and derive out implicit semantic links according to a set of relational reasoning rules. By studying the intrinsic relationship between semantic communities and the semantic space of SLN, approaches to discovering reasoning-constraint, rule-constraint, and classification-constraint semantic communities are proposed. Further, the approaches, principles, and strategies for discovering emerging semantics in dynamic SLNs are studied. The basic laws of the semantic link network motion are revealed for the first time. An e-learning environment incorporating the proposed approaches, principles, and strategies to support effective discovery and learning is suggested.

The International Conference on Semantics, Knowledge and Grid (SKG) is a cross-area international forum on semantic computing, knowledge networking, and grid computing.SKG promotes crossarea research and prods the development of relevant areas.Themes include the following aspects:• Semantics and Semantic Grid

Abstract The semantic link network (SLN) is the extension of the hyperlink Web by attaching semantics to hyperlinks. It is an approach to construct a semantic overlay on Web resources. The specification of the semantics of the SLN model is an essential issue of SLN application. This paper proposes a modeling language for SLN consisting of an algebraic definition for SLN, a SLN metamodel and a Unified Modeling Language (UML) profile for SLN. The SLN metamodel specifies the primitives of the modeling language. The UML profile for SLN defines the specific syntax on SLN to make the modeling language understandable and usable. The development of the SLN builder implementing this language and the graphical SLN browser is introduced. This work is a part of the SLN model. Copyright © 2007 John Wiley & Sons, Ltd.

Establishing an appropriate semantic overlay on peer-to-peer (P2P) networks to obtain both semantic ability and scalability is a challenge. Current DHT-based P2P networks are limited in their ability to support a semantic search. This paper proposes the distributed suffix tree (DST) overlay as the intermediate layer between the DHT overlay and the semantic overlay to support the search of a keyword sequence. Its time cost is sublinear with the length of the keyword sequence. Analysis and experiments show that the DST-based search is fast, load-balanced, and useful in realizing an accurate content search on P2P networks.

This paper presents a general virtual ring method to design and analyze small-world structured P2P networks on the base topologies embedded in ID spaces with distance metric. Its basic idea is to abstract a virtual ring from the base topology according to the distance metric, then build small-world long links in the virtual ring and map the links back onto the real network to construct the small-world routing tables for achieving logarithmic greedy routing efficiency. Four properties are proposed to characterize the base topologies that can be turned into small-world by the virtual ring method. The virtual ring method is applied to the base topologies of d-torus with Manhattan distance, high dimensional d-torus base topologies, and other base topologies including the unbalanced d-torus and the ring topology with tree distance. Theoretical analysis and simulation experiments demonstrate the efficiency and the resilience of the proposed overlays.

Semantics exhibits diversity in the real world, mental abstraction world, document world, and machine world. Studying mappings between different forms of semantics helps unveil the uniformity in the diversity. This article investigates the mappings between three typical semantic models: the Web ontology language (OWL), relational database model, and resource space model (a classification-based semantic model). By establishing mappings between the semantic primitives of the three models, we study the mapping from OWL description onto resource space and analyze the normal forms of the generated resource space. Mapping back from resource space onto OWL description is then discussed. Further, we investigate the mapping between OWL description and relational database, as well as the mapping between relational database and resource space. Normal forms of the generated relational tables are analyzed. To support advanced applications on the future Web, we suggest integrating the resource space, OWL, and databases to form a powerful semantic platform that enables different semantic models to enhance each other.

This paper presents Harmonic Ring (HRing), a structured peer-to-peer (P2P) overlay where long links are built along the ring with decreasing probabilities coinciding with the Harmonic Series. HRing constructs routing tables based on the distance between node positions instead of node IDs in order to eliminate the effect of node ID distribution on the long link distribution and load balance. It supports leave-and-rejoin load balance without incurring uneven long link distribution. In addition, node IDs can be any form, like number, string, address, and date, without the prerequisite of uniform distribution, so they can preserve the semantics and range locality of data objects. HRing supports multidimensional range queries. Each node is expected to have O(ln(n)) long links. The construction of O(ln(n)) long links for a node costs (O(ln(n)) messages. Routing queries achieve O(ln(n)) hops. Analyses and simulations demonstrate the efficiency of query routing and the effectiveness of the long link construction method.

This special issue selects high-quality papers from the 4th International Conference on Grid and Cooperative Computing (

Abstract An important issue raised in peer‐to‐peer (P2P) applications is how to accurately and efficiently retrieve a set of K best matching data objects from different sources while minimizing the number of objects that have to be accessed. The proposed solution is to organize peers by a semantic link network representing the semantic relationships between peers' data schemas. Queries are only routed to semantically relevant peers. A pruning‐based local top‐K ranking approach is proposed to reduce the transmitted data by pruning tuples that cannot produce the desired join results with a rank value at least equal to the lowest rank value generated. Experiments evaluate its performance in terms of the number of transmitted tuples and the miss rate. Comparison with the traditional threshold algorithm for centralized systems and other top‐K ranking algorithms for P2P networks shows the features of the proposed approach. Copyright © 2006 John Wiley & Sons, Ltd.

Abstract The Knowledge Grid built on top of the peer‐to‐peer (P2P) network has been studied to implement scalable, available and sematic‐based querying. In order to improve the efficiency and scalability of querying, this paper studies the problem of multicasting queries in the Knowledge Grid. An m‐dimensional irregular mesh is a popular overlay topology of P2P networks. We present a set of novel distributed algorithms on top of an m‐dimensional irregular mesh overlay for the short delay and low network resource consumption end‐host multicast services. Our end‐host multicast fully utilizes the advantages of an m‐dimensional mesh to construct a two‐layer architecture. Compared to previous approaches, the novelty and contribution here are: (1) cluster formation that partitions the group members into clusters in the lower layer where cluster consists of a small number of members; (2) cluster core selection that searches a core with the minimum sum of overlay hops to all other cluster members for each cluster; (3) weighted shortest path tree construction that guarantees the minimum number of shortest paths to be occupied by the multicast traffic; (4) distributed multicast routing that directs the multicast messages to be efficiently distributed along the two‐layer multicast architecture in parallel, without a global control; the routing scheme enables the packets to be transmitted to the remote end hosts within short delays through some common shortest paths; and (5) multicast path maintenance that restores the normal communication once the membership alteration appears. Simulation results show that our end‐host multicast can distributively achieve a shorter delay and lower network resource consumption multicast services as compared with some well‐known end‐host multicast systems. Copyright © 2006 John Wiley & Sons, Ltd.

Abstract A semantic link network (SLN) consists of nodes (entities, features, concepts, schemas or communities) and semantic links between nodes. This paper proposes an autonomous SLN formalism to support intelligent applications on large‐scale networks. The formalism integrates the SLN logical reasoning with the SLN analogical reasoning and the SLN inductive reasoning, as well as existing techniques to form an autonomous semantic overly. The SLN logical reasoning mechanism derives implicit semantic relations by a semantic matrix and relevant addition and multiplication operations based on semantic link rules. The SLN analogical reasoning mechanism proposes conjectures on semantic relations based on structural mapping between nodes. The SLN inductive reasoning mechanism derives general semantics from special semantics. The cooperation of diverse reasoning mechanisms enhances the reasoning ability of each, therefore providing a powerful semantic ability for the semantic overlay. The self‐organizing diverse scales of the SLN support the intelligent applications of the Knowledge Grid. Copyright © 2006 John Wiley & Sons, Ltd.

Abstract Scientific documents, research instruments, researchers' abilities and co‐operation among researchers are fundamental resources of scientific research. This paper proposes an approach for effectively networking scientific resources by detecting community structures in self‐organized social network, discovering interest in information flow, capturing changes of interests over time and analyzing the relationship between peers. Copyright © 2006 John Wiley & Sons, Ltd.

Abstract A knowledge flow is invisible but it plays an important role in ordering knowledge exchange when working in a team. It can help achieve effective team knowledge management by modeling, optimizing, monitoring, and controlling the operation of knowledge flow processes. This paper proposes the notion of knowledge energy as the driving force behind the formation of an autonomous knowledge flow network, and explores the underlying principles. Knowing these principles helps teams and the support systems improve cooperation by monitoring the knowledge energy of nodes, by evaluating and adjusting knowledge flows, and by adopting appropriate strategies. A knowledge flow network management mechanism can help improve the efficiency of knowledge‐intensive distributed teamwork. Copyright © 2006 John Wiley & Sons, Ltd.

This paper first proposes three improved gossip mechanisms by mapping links into metric space and dynamically adapting the number of selected neighbors to disseminate messages. Experiments and comparisons show that these mechanisms can improve the performance of gossip in peer-to-peer (P2P) networks. This is the effect of mapping a network into a metric space that differentiates nodes and links according to linking characteristics and controlling local information flow with knowing such differences. A further study about query routing on P2P semantic link network shows that mapping a network into a semantic space can also improve the performance. An intrinsic rule is found by experimental comparisons and analysis: the performance of a P2P network can be improved by designing an appropriate mapping from the network into metric space or semantic space. A general framework for networking with metric space and semantic space is suggested

Semantics is the meaning expressed by various languages and the study of meaning. Human society co-evolves with diverse semantic spaces holding commonsense, culture, and knowledge of sciences and technologies. By surveying the origin and development of general semantics, this paper studies the semantics of future interconnection environment, proposes a new notion of open and autonomous interconnection semantics, explores relevant principles and rules, and demonstrates a cultural application.

Abstract The fundamental challenge confronting peer‐to‐peer (P2P) resource‐sharing networks is to provide efficient and scalable search services in a large‐scale, open and dynamic environment. However, current search mechanisms are not efficient or scalable enough with the expansion of the systems. To improve search efficiency, this paper proposes the trust‐based probabilistic search mechanism, called the Preferential Walk (P‐Walk). Every peer adjusts its neighbors' rank continuously according to their performance in previous searches. The basic principle of the P‐Walk is that neighbors with higher ranks in the question domain have higher probabilities of being queried. Extensive simulation results show that the P‐Walk can significantly improve the success rate of the query and the response time whilst also effectively reducing traffic volume and controlling malicious behavior. Furthermore, we measure the rank distributions of the peers and find their power‐law‐like trends based on a Gnutella‐like network. Implications are drawn from the analysis of the rank distributions. The P‐Walk mechanism works effectively in a P2P view model that provides appropriate views of the P2P network for queries of different types. Copyright © 2006 John Wiley & Sons, Ltd.

Abstract An active e‐course is an open, self‐representable and self‐organizable media mechanism. Its kernel idea is to organize learning materials in a concept space rather than in a page space. The tailored content and flexible structure of the e‐courses can be dynamically formed to cater for different learners with different backgrounds, capabilities and expectations, at different times and venues. The active e‐course can also assess learners' learning performances and give appropriate suggestions to guide them in further learning. An authoring tool for constructing course ontology and a system prototype have been developed to support an active e‐course, enabling a learner‐centred, highly interactive and adaptive learning approach. The results of an empirical study show that the system can help enhance the effectiveness and efficiency of learning. Copyright © 2005 John Wiley & Sons, Ltd.

A semantic link peer‐to‐peer (P2P) network specifies and manages semantic relationships between peers' data schemas and can be used as the semantic layer of a scalable Knowledge Grid. The proposed approach consists of an automatic semantic link discovery method, a tool for building and maintaining P2P semantic link networks (P2PSLNs), a semantic‐based peer similarity measurement for efficient query routing, and the schema mapping algorithms for query reformulation and heterogeneous data integration. The proposed approach has three important aspects. First, it uses semantic links to enrich the relationships between peers' data schemas. Second, it considers not only nodes but also the XML structure in measuring the similarity between schemas to efficiently and accurately forward queries to relevant peers. Third, it copes with semantic and structural heterogeneity and data inconsistency so that peers can exchange and translate heterogeneous information within a uniform view.

The knowledge grid needs to operate with a scalable platform to provide large-scale intelligent services. A key function of such a platform is to efficiently support various complex queries in a dynamic large-scale network environment. This paper proposes a platform to support index-based path queries by incorporating a semantic overlay with an underlying structured P2P network that provides object location and management services. Various distributed indexing structures can be dynamically formed by publishing, semantic objects as indexing nodes. Queries are forwarded along the chains of semantic object pointers to search for objects. We investigate the deployment of a scalable distributed trie index for broadcast queries on key strings, propose a decentralized load balancing method for solving the problem of uneven load distribution incurred by heterogeneity of loads and node capacities and by the distributed trie index, and give an approach for improving the availability of the semantic overlay and its trie index. Experiments demonstrate the scalability of the proposed platform.

Abstract A Resource Space Grid is a virtual Grid that aims at effectively sharing, using and managing versatile resources across the Internet. The kernel of the Resource Space Grid includes a Resource Space Model (RSM) and a uniform Resource Using Mechanism (RUM). This paper presents the Resource Space Grid's core scientific issues and methodology, architecture, model and theory, design criteria and method, and practice. A normal form theory is proposed to normalize the resource space—a coordinate system for uniformly specifying and organizing resources. The RUM provides not only the end‐users with an operable resource browser to operate resources using the built‐in Resource Operation Language (ROL), but also the application developers with the ROL‐based programming environment. The prototype platform based on the proposed model and method has been implemented and used for sharing and managing resources in distributed research teams. Operations on Resource Spaces can constitute the virtual communities of Resource Space Grids—a platform independent resource sharing environment. Copyright © 2004 John Wiley & Sons, Ltd.

Abstract Semantic Link Network (SLN) is a semantic Web model using semantic links—the natural extension of the hyperlink‐based Web. The SLN‐Builder is a software tool that enables definition, modification and verification of, as well as access to the SLN. The SLN‐Builder can convert a SLN definition into XML descriptions for cross‐platform information exchange. The Intelligent Semantic Browser is used to visualize the SLN and carry out two types of reasoning in browsing time: small granularity reasoning by chaining semantic links and large granularity reasoning by matching semantic views of SLN. With the help of the reasoning mechanism, the browser can recommend the content that is semantically relevant to the current browsing content, and it enables users to foresee the end‐side content of a semantic link chain. This paper presents the design and implementation of the SLN‐Builder and the intelligent semantic browser as well as key algorithms. Copyright © 2004 John Wiley & Sons, Ltd.

This paper proposes a timed workflow model and a temporal consistency checking approach. The proposed model incorporates the duration of task, the duration of flow, and the concept of multiple time axes task execution into the conventional workflow model. It enables the modeling of global business processes.

Conflict decision is a continuous decision process in a dynamic conflict environment. The evaluation of the dynamic conflict situation is the basis of making proper decisions at different stages of the conflict process. Traditional evaluation approaches are static and do not consider enough impact factors in real conflict environment, so cannot reflect the real conflict situation and its evolution. This paper presents a dynamic evaluation framework for supporting continuous decisions in dynamic conflict environments. The framework includes the timed evaluation models for conflict force, conflict situation, and psychological pressure, as well as a graphical trend analysis approach. The framework has been applied to a virtual military conflict decision training system. A set of dynamic curves is used to intuitively reflect the dynamic evaluation result. Decision makers can know the current conflict situation and its evolution process through these dynamic curves at any stage of the conflict process so can make more evidential decisions.