On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
IEEE Transactions on Knowledge and Data Engineering
Discovering semantic biomedical relations utilizing the Web
ACM Transactions on Knowledge Discovery from Data (TKDD)
Learning non-taxonomic relationships from web documents for domain ontology construction
Data & Knowledge Engineering
Linking FrameNet to the Suggested Upper Merged Ontology
Proceedings of the 2006 conference on Formal Ontology in Information Systems: Proceedings of the Fourth International Conference (FOIS 2006)
Knowledge map creation and maintenance for virtual communities of practice
Information Processing and Management: an International Journal
Grammar-based geodesics in semantic networks
Knowledge-Based Systems
Mining learning-dependency between knowledge units from text
The VLDB Journal — The International Journal on Very Large Data Bases
Semantic network analysis of ontologies
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
The SWRC ontology – semantic web for research communities
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
Community detection based on a semantic network
Knowledge-Based Systems
A cloud of FAQ: A highly-precise FAQ retrieval system for the Web 2.0
Knowledge-Based Systems
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A knowledge map can be viewed as a directed graph, in which each node is a knowledge unit (KU), and each edge is a learning-dependency between two KUs. Understanding the topological properties of knowledge map can help us gain better insights into human cognition structure and its mechanism, design better knowledge map construction algorithms, and guide learners' navigational learning through knowledge map. In this paper, we perform topological analysis on 12 knowledge maps from computer science, mathematics, and physics. We discover that they exhibit small-world and scale-free properties like many other networks. Specifically, we show the locality of learning-dependency and hierarchical modular structure in the 12 knowledge maps. In addition, we study how KUs affect the network efficiency by removing KUs based on different centrality measures. We find that the importance of KUs varies greatly.