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.

Building Association Link Network for Semantic Link on Web Resources

The schema theory for semantic link network

Semantic link network (SLN) is a loosely coupled semantic data model for managing Web resources. Its nodes can be any types of resources. Its edges can be any semantic relations. Potential semantic links can be derived out according to reasoning rules on semantic relations. This paper proposes the schema theory for SLN including the concepts, rule-constraint normal forms and relevant algorithms. The theory provides the basis for normalized management of SLN and its applications. A case study demonstrates the proposed theory.

http://www.knowledgegrid.net/TR/KGRC-2008-1.pdf

Schema Theory for Semantic Link Network

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.

http://www.knowledgegrid.net/TR/KGRC-2007-3.pdf

Modeling language and tools for the semantic link network

Graph mining has been a popular research area because of its numerous application scenarios. Many unstructured and structured data can be represented as graphs, such as, documents, chemical molecular structures, and images. However, an issue in relation to current research on graphs is that they cannot adequately discover the topics hidden in graph-structured data which can be beneficial for both the unsupervised learning and supervised learning of the graphs. Although topic models have proved to be very successful in discovering latent topics, the standard topic models cannot be directly applied to graph-structured data due to the “bag-of-word” assumption. In this paper, an innovative graph topic model (GTM) is proposed to address this issue, which uses Bernoulli distributions to model the edges between nodes in a graph. It can, therefore, make the edges in a graph contribute to latent topic discovery and further improve the accuracy of the supervised and unsupervised learning of graphs. The experimental results on two different types of graph datasets show that the proposed GTM outperforms the latent Dirichlet allocation on classification by using the unveiled topics of these two models to represent graphs.

/pdf/topic-model-for-graph-mining-3kakxeqjf8.pdf

Topic Model for Graph Mining

The resource space model is a new semantic data model for managing various resources. On the basis of the model, this paper proposes a view mechanism for finding and reusing legacy resource spaces according to users' idiosyncratic interests. It establishes a flexible reorganizing mechanism of legacy resource spaces to reduce redundant work. As a viewpoint organization strategy, the resource subject tree is proposed as an effective way of discovering desirable resource space views. A case study is presented to demonstrate the proposed approach. Copyright © 2007 John Wiley & Sons, Ltd.

http://www.knowledgegrid.net/tr/KGRC-2007-4.pdf

Resource space view tour mechanism

Artificial Intelligence development is stepping into a new era due to the recent exciting achievements from neural network and statistical machine learning research communities. Statistic neural-computing based machine learning has been deemed as one of promising roads towards realizing the ideal of Artificial Intelligence promoted since last century. Learning is the key in making progress. Statistic machine learning is to obtain a probability distribution or a function from a set of training samples according to a certain optimization target over the training cost based on a predefined model. While there are many significant improvements in image, sound and text recognition and analyzing using neural network based learning strategies, a new open question emerges, that is, what is the next? To ask this question is to mean that the neural network solution is not an ultimate solution, and there will be more challenges to meet in coming future. We discussed these aspects of deep neural network research work in this paper and focused on semantics in deep neural-network computing models. We try to browse how semantics or knowledge are to be involved in deep neural network models and how the semantics and knowledge will be a key factor towards making more intelligent machines. We argue that priori semantics and knowledge in modelling a neural network is important, which could be the key for researchers to design intelligent machine models to perform complex tasks.

Semantics in Deep Neural-Network Computing

This paper extends resource using mechanism (RUM) for the resource space model (RSM) based application. Resource operation and query language has been implemented in Dunhuang culture application. Besides, operation analyst, resources auto-classifier, and RSM linker are designed and implemented.

Extended Resource Using Mechanism for Resource Space Model

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Modeling language and tools for the semantic link network

Resource space view tour mechanism

Semantics in Deep Neural-Network Computing

Extended Resource Using Mechanism for Resource Space Model