The Semantic Link Network


One of the major tasks of science is to unveil various relations.


Basic Concepts

 

Semantic links are links connecting semantic nodes with semantic relations (indicated by symbols). The following are examples of semantic links: A-isPartOf->B, A-isSouthOf->B, and A-isFriendOf->B, where A and B can be anything, and "isPartOf", "isSouthOf" and "isFriendOf" are called semantic tags, semantic indicators, or semantic factors.

The Semantic Link Network is different from the traditional semantic network in goal, architecture, technology and research method.

Semantic Link Network (SLN) consists of semantic nodes, semantic links and reasoning rules. The semantic nodes can be any resources, classes of resources, or even a semantic link network. Semantic links can be established by tools or automatic discovery approaches. The reasoning rules are for semantic reasoning. New semantic links may be derived out by rule reasoning. The semantics of a semantic link network will change if its rules are changed.

Semantic Link Network is a description of relations among objective existences rather than to represent fine human knowledge. It pursues semantic richness rather than correctness.

Due to the semantic richness of semantic nodes, automatic discovery of semantic links is feasible, especially, when relevant metadata is available. This topic is never studied in traditional semantic network.

SLN naturally supports relational reasoning, analogical reasoning, and inductive reasoning. SLN would be enriched after reasoning. SLN is statically defined but autonomously evolved, it can be localized or decentralized. SLN is not a simple extension from the hyperlink to the semantic link, it is a self-organized semantics-rich data model for managing various resources. Compared with other approaches to the Semantic Web, SLN better inherits the characteristics of the Web. SLN naturally supports relational query.

With the addition and removal of semantic links, a semantic link network may evolve into semantic communities. The approach to discovering semantic communities in a large semantic link network is different from those for general graph. The semantic communities dynamically support intelligent applications. Further more, the semantic link network has distinguished network effect, which is useful in social network study [H.Zhuge, Communities and Emerging Semantics in Semantic Link Network: Discovery and Learning, IEEE Transactions on Knowledge and Data Engineering, 21(6)(2009)785-799].

Definition. A semantic link network (SLN) is a relational network consisting of the following main parts: a set of semantic nodes, a set of semantic links between the nodes, and a semantic space. Semantic nodes can be anything. The semantic link between nodes is regulated by the attributes of nodes or generated by interactions between nodes. The semantic space includes a classification hierarchy of concepts and a set of rules for reasoning and inferring semantic links, for influence nodes and links, for networking, and for evolving the network. [1]

The following figure shows a semantic link network of family relations.



SLN 2.0: a Self-Organized Semantic Data Model for the Future Web


A semantic data model is an abstraction of the real world by defining the relations between data. Open domain applications challenge traditional semantic data models.

There are two strategies to construct an SLN as a data model:
(1) Schema-based strategy. This requires users to contribute to and share the same schema.
(2) Self-organized strategy. Users create their own schemas, and then define SLN instances. Users can only maintain their own schemas.
(3) No-Schema strategy. Users freely link nodes one another.

Detailed introduction is available in [1].

The study of the SLN also includes the P2P-SLN, the semantic link network on P2P network for supporting decentralized intelligent applications. It is also used as the semantic overlay of the Knowledge Grid [1].

SLN can support function modelling by making use of object-oriented modelling and multi-agent techniques.  

SLN 3.0: Cyber-Physical-Socio-Mental Network


Semantic link networks also help form the structure of the social space and mental space. SLN 3.0 puts SLN into a complex space consisting of cyber space, physical space, social space and mental space. Exploring the rules of networking in the social space and the mental space are critical for humans to understand the society and intelligence.

The following figure depicts the relations among the SLN, OOMD (Object-Oriented Modeling and Design), MA (Multiple Agent) and the spaces.

The following figure shows the links in SLN 3.0.



The Cyber-Physical-Socio-Mental Network is introduced in detail in the 2nd edition of the book The Knowledge Grid [1].


Relevant research topics

Semantic data model
Semantic link network of things (humans, events, etc)
Discovery of implicit semantic links
Discovery of semantic community
Emerging semantics in semantic link network
Search and Q/A over semantic link network
Complex reasoning
Semantic social network including measures
Principles of influence and evolution
Intelligent applications
Modelling mind

Chapter 2 of The Knowledge Grid -- Toward Cyber-Physical Society

2. The Semantic Link Network
2.1 The Idea of Mapping
2.2 Basic Concepts and Characteristics
2.3 Relational Reasoning and the Semantic Space
2.4 An Algebraic Model of the SLN
2.5 SLN Normalization
2.5.1 The normal forms of an SLN
2.5.2 Operations on SLNs
2.6 Constraints and Browsing
2.7 SLN Ranking
2.7.1 Hyperlink network ranking
2.7.2 SLN ranking
2.7.3 A ranking algorithm
2.8 Implementation of SLN Operations
2.8.1 Matching between SLNs
2.8.2 The union operation
2.8.3 SLN-level reasoning
2.9 SLN Analogical Reasoning
2.9.1 Analogical reasoning modes
2.9.2 Process and algorithm
2.10 Dynamic SLNs
2.11 SLN Abstraction
2.12 Application: SLN-based Image Retrieval
2.13 Application: Active Document Framework (ADF)
2.14 Application: e-Learning
2.15 Potential Applications, Relevant Work and Q&A
2.16 SLN 2.0: Autonomous Semantic Data Model
2.17 Uncertain Semantic Link Network
2.18 Discovering Semantic Link Network
2.18.1 General process
2.18.2 Discover semantic links by content analysis
2.18.3 Enrich semantic links through evolution
2.19 SLN 3.0: Cyber-Physical-Socio-Mental Network
2.19.1 Origin of semantics
2.19.2 Characteristics
2.19.3 Intention and extension
2.19.4 Semantic Link Network of events SLN-E
2.19.5 Through minds via words
2.19.6 Through society, culture and thought
2.19.7 Principles of emerging semantics
2.19.8 Discovering semantic communities ? semantic localization
2.19.9 SLN-based relation and knowledge evolution
2.19.10 Building and performing semantic images
2.19.11 Structure and networking rules of social space
2.19.12 Communication rules and principles
2.19.13 Influence between mental space and social space
2.19.14 Application in cognitive-behavioral therapy
2.20 Principles of Mental Concepts
2.21 Discussion: Philosophy, language, and semantics

References

[1] H.Zhuge, The Knowledge Grid -- Toward Cyber-Physical Society, World Scientific, 2012. (Chapter 2 The Semantic Link Network)

 

[2] H.Zhuge, Semantic linking through spaces for cyber-physical-socio intelligence: A methodology, Artificial Intelligence, 175(2011)988-1019.

 

[3] H. Zhuge and B. Xu: Basic operations, completeness and dynamicity of cyber physical socio semantic link network CPSocio-SLN. Concurrency and Computation: Practice and Experience 23(9)(2011)924-939.

 

[4] H. Zhuge and J. Zhang, Automatically constructing semantic link network on documents. Concurrency and Computation: Practice and Experience 23(9) (2011)956-971.

 

[5] H.Zhuge, Interactive Semantics, Artificial Intelligence, 174(2010)190-204.

 

[6] H.Zhuge and J.Zhang, Topological Centrality and Its Applications, Journal of the American Society for Information Science and Technology, 61(9)(2010)1824-1841.

 

[7] H. Zhuge and Y. Sun, The schema theory for semantic link network. Future Generation Computer Systems. 26(3)(2010)408-420.

 

[8] H. Zhuge, Special section: Semantic Link Network. Future Generation Computer Systems. 26(3)(2010)359-360.

 

[9] H. Zhuge, Socio-Natural Thought Semantic Link Network: A Method of Semantic Networking in the Cyber Physical Society. AINA 2010: 19-26

 

[10] H.Zhuge, Communities and Emerging Semantics in Semantic Link Network: Discovery and Learning, IEEE Transactions on Knowledge and Data Engineering, 21(6)(2009)785-799. http://doi.ieeecomputersociety.org/10.1109/TKDE.2008.141. (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 dynamic complex network of learning resources. Previous graph-based community discovery approaches are limited in ability to discover semantic communities. This paper firstly 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 SLN are studied. An e-learning environment incorporating the proposed approaches, principles and strategies to support effective discovery and learning is suggested.)

 

[11]  H.Zhuge and L.Feng, Distributed Suffix Tree Overlay for Peer-to-Peer Search, IEEE Transactions on Knowledge and Data Engineering, 20, 2(2008)276-285.

 

[12] H.Zhuge, Autonomous semantic link networking model for the Knowledge Grid, Concurrency and Computation: Practice and Experience, 7(19)(2007)1065-1085. (This paper upgrades semantic link network to autonomous semantic link network. 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 Semantic Link Network formalism to support intelligent applications on large-scale network. 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 operation and multiplication operation 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 provides powerful semantic ability for the semantic overlay. Self-organizing diverse scales of semantic link network supports intelligent applications of the Knowledge Grid.) (PDF)

[13] H.Zhuge, K. Yuan, J. Liu, J. Zhang and X. Wang, Modeling Language and Tools for the Semantic Link Network, Concurrency and Computation: Practice and Experience, 20(7)(2008) 885-902.

[14] H.Zhuge and X.Li, Peer-to-Peer in Metric Space and Semantic Space, IEEE Transactions on Knowledge and Data Engineering, 6(19)(2007).

[15] H.Zhuge, L.Ding, X.Li, Networking scientific resources in the Knowledge Grid environment, Concurrency and Computation: Practice and Experience, 7(19)(2007)1087-1113. (PDF)

[16] J.Liu, L.Feng and H.Zhuge, Using semantic links to support top-K join queries in peer-to-peer networks, Concurrency and Computation: Practice and Experience, 19(15)(2007)2031-2046.

[17] H.Zhuge, Semantic Component Networking: Toward the Synergy of Static Reuse and Dynamic Clustering of Resources in the Knowledge Grid, Journal of Systems and Software, 79 (2006) 1469-1482.

[18] H.Zhuge and X.Luo, Automatic Generation of Semantics for Documents in the Knowledge Grid, Journal of Systems and Software, 79 (2006) 969C983.

[19] H.Zhuge and X.Luo, The Knowledge Map: Mathematical Model and Dynamic Behaviors, Journal of Computer Science and Technology, 20(3)(2005) 289-295.

[20] H.Zhuge, X.Sun, et al, A Scalable P2P Platform for the Knowledge Grid, IEEE Transactions on Knowledge and Data Engineering, 17(12)(2005)1721-1736. (This work concerns a semantic overlay by publishing semantic objects on P2P network.)

[21] H.Zhuge, J. Liu, L. Feng, X. Sun and C. He. Query Routing in a Peer-to-Peer Semantic Link Network. Computational Intelligence,21(2)(2005)197-216. (This work uses the semantic link network model to establish a semantic overlay for effective P2P data management.)

[22] H.Zhuge, Yunchuan Sun, et al, Algebra Model and Experiment for Semantic Link Network, High Performance Computing and Networking, 3(4)(2005) 227-237.

[23] H.Zhuge and R.Jia, et. al., Semantic Link Network Builder and Intelligent BrowserConcurrency and Computation: Practice and Experience, 16 (14) (2004) 1453 -1476.

[24] H.Zhuge, The Knowledge Grid, World Scientific, Singapore, 2004.  (Chapter 2 discusses the semantic link network model SLN.)

[25] H.Zhuge, Active e-Document Framework ADF: Model and Platform, Information and Management, 41(1)(2003)87-97. (This paper proposes an ideal active document framework for intelligent information services in the future Web.)

[26] H.Zhuge and L.Zheng, Ranking Semantic-linked Network, WWW 2003. (This paper early proposes the Semantic Link Network as the future Web.)