Local-based semantic navigation on a networked representation of information

Area of Science:

• Computer Science
• Network Analysis
• Information Retrieval

Background:

• Large networked systems pose challenges for global structural understanding and navigation.
• Current navigation methods often rely on suboptimal solutions, such as extracting simplified topological maps.

Purpose of the Study:

• To develop a Markov chain-based algorithm for tagging networked terms using topological features.
• To create a similarity-based map of networked information for improved navigation.
• To evaluate the efficiency and semantic coherence of navigation paths generated by the algorithm.

Main Methods:

• Utilizing Markov chains to assign tags to networked terms based on their topological properties.
• Computing term similarity using the generated topological tags.
• Developing a navigation strategy driven by term similarity.
• Comparing path lengths and semantic coherence against shortest paths.

Main Results:

• The algorithm successfully generates a map of networked information based on topological features.
• A simple greedy navigation strategy achieved an average success rate exceeding 80% on the Simple English Wikipedia.
• Navigation paths were found to be semantically coherent.
• Path costs were typically one to threefold the length of shortest paths.

Conclusions:

• The proposed Markov chain algorithm effectively tags networked terms based on topology, enabling similarity-based navigation.
• Topological tagging provides a viable method for creating coherent maps of complex networks.
• The algorithm offers a promising approach for improving navigation efficiency and semantic understanding in large-scale information systems.