The Semantic Link Network

Self-Organized Semantic Networking for Rendering Knowledge


Semantic links are links with semantic properties (factors or tags), closely related to the links in the semantic network. The following are examples of semantic links: A-isPartOf->B, A-isSouthOf->B, and A-isFriendOf->B. But, the Semantic Link Network is different from the 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 [9].

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 [2]. The implicit semantic links are discussed in the 2nd edition of the book The Knowledge Grid (2009).

Relevant research topics: Semantic Data Model, Construction and Maintenance, Discovery of Semantic Community, Search and reasoning, Intelligent applications, Social network.



Semantic Link Network 2.0 (SLN 2.0): Autonomous Semantic Data Model

An autonomous semantic data model, a Semantic Link Network is dynamic, open and self-organised, and it has the following characteristics.

(1) Semantic measure.
(2) Extensible concept hierarchy.
(3) Probabilistic Semantic Link Network.
(4) Semantic link discovery Mechanism.
(5) Enrich semantics through evolution.



Semantic Link Network 3.0 (SLN 3.0): Cyber-Physical-Socio-Mental Network

Semantic Link Network 3.0 will pass through not only cyberspace but also the physical space, socioeconomic space and mental space. It has the following characteristics:

(1) Autonomous.
(2) Self-evolution.
(3) Semantic community discovery.
(4) Reflecting uncertainty.
(5) Complex and temporal link.
(6) Relation prediction.

Research on SLN 3.0 concerns the following issues:

(1) The method for discovering, predicting, establishing, and maintaining complex semantic link networks.
(2) The intrinsic characteristics and rules of semantic link network motion.
(3) The emerging semantics with the evolution of the network.
(4) Diverse models for reasoning (e.g., relational reasoning, analogical reasoning, inductive reasoning, and complex reasoning), prediction and influence.
(5) Various influences in the semantic link network, and the method of making use of influence.
(6) Various flows through semantic link networks and relevant rules.

(from H. Zhuge, "The Knowledge Grid: Toward Cyber-Physical Society", 2012 [2])



A Brief History

Before the Linked Data (proposed by Tim Berners-Lee in 2006) and the Knowledge Graph (proposed by Google in 2012), the Semantic Link Network (SLN in short) had been systematically studied for creating a self-organised semantic networking method to render basic knowledge. The systematic theory and method was published in 2004 [23], and then updated in 2012 [2]. SLN 2.0 is an autonomous semantic data model. SLN 3.0 is a Cyber-Physical-Socio-Mental Network.

This research direction can trace to the definition of the rules for inheritance object-oriented environment in 1998 [28] and the Active Document Framework ADF in 2003 [27]. Since 2003, it has been developed toward a self-organised semantic networking method [24][26]. The initial work on "An Automatic Semantic Relationships Discovery Approach" [25] was cited by the book "A Framework of Web Science" (Tim Berners-Lee, et al. 2006).

The Semantic Link Network is a systematic innovation at the age of the World Wide Web and global social networking. Its characteristics, purpose, main focus, and environment are different from that of the Semantic Net. To respect the work of the Semantic Net, it was named after Semantic Net but emphasises "link" to represent the new research and application environment of the Web and global networking [2]. Interaction, reasoning, community, and automatic discovery of implicit links play an important role in the Semantic Link Network [7][9].

In recent years, the Semantic Link Network has been developed to support Cyber-Physical-Social Intelligence [3]. It has been used in many applications, including the general summarisation method [1].




References

[1] H. Zhuge, Multi-Dimensional Summarization in Cyber-Physical Society, Morgan Kaufmann, 2016.
Chapter 2 The emerging structures
    A complex system often consists of components that weakly interact with each other but are not negligible. Simon named such a system near decomposable system and proposed the propositions of near decomposability, which interprets the behaviors of many complex systems. The underlying assumption is that all observers share a unified cognition paradigm: bottom-up aggregation. This chapter extends the research object to a dual system consisting of the observed complex system and the memory of the system, proposes new propositions considering the integrity of representation and a semantic emerging structure, verifies the propositions and the emerging rules through text summarization, and proposes new principles regarding text as a near decomposable system. The structure of text emerges with representing components independently and linking components with rules. The study of emerging near decomposable structure provides a new way to understand text, summarization and the near decomposability.

[2] H.Zhuge, The Semantic Link Network, in The Knowledge Grid: Toward Cyber-Physical Society, World Scientific Publishing Co, 2012.

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

[4] 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.

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

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

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

[8] H.Zhuge, Y.Sun and J.Zhang, Schema Theory for Semantic Link Network, 4th International Conference on Semantics, Knowledge and Grid, Dec. 4-6, Beijing, China.

[9] 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.
    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.

[10] 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.

[11] 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)

[12] 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.

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

[14] 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.

[15] 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, Published Online: 19 Dec 2006, DOI: 10.1002/cpe.1145.

[16] H.Zhuge and X.Luo, Automatic Generation of Semantics for Documents in the Knowledge Grid, Journal of Systems and Software, 79(2006)969-983.

[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, 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.)

[19] 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.)

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

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

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

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

[24] H. Zhuge and J. Liu, A novel heterogeneous data integration approach for p2p semantic link network, World Wide Web Conference, 2004:334-335.

[25] H. Zhuge, L. Zheng, N. Zhang, and X. Li, An Automatic Semantic Relationships Discovery Approach, World Wide Web Conference, 2004:278-279.

[26] H.Zhuge and L.Zheng, Ranking Semantic-linked Network, World Wide Web Conference (Poster) 2003.

[27] H.Zhuge, Active e-Document Framework ADF: Model and Platform, Information and Management, 41(1)(2003)87-97.

[28] H. Zhuge, Inheritance rules for flexible model retrieval, Decision Support Systems, 22(4)(1998)379-390.