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Interpreting natural language is an increasingly important task in computeralgorithms due to the growing availability of unstructured textual data.Natural Language Processing (NLP) applications rely on semantic networks forstructured knowledge representation. The fundamental properties of semanticnetworks must be taken into account when designing NLP algorithms, yet theyremain to be structurally investigated. We study the properties of semanticnetworks from ConceptNet, defined by 7 semantic relations from 11 differentlanguages. We find that semantic networks have universal basic properties: theyare sparse, highly clustered, and many exhibit power-law degree distributions.Our findings show that the majority of the considered networks are scale-free.Some networks exhibit language-specific properties determined by grammaticalrules, for example networks from highly inflected languages, such as e.g.Latin, German, French and Spanish, show peaks in the degree distribution thatdeviate from a power law. We find that depending on the semantic relation typeand the language, the link formation in semantic networks is guided bydifferent principles. In some networks the connections are similarity-based,while in others the connections are more complementarity-based. Finally, wedemonstrate how knowledge of similarity and complementarity in semanticnetworks can improve NLP algorithms in missing link inference.

Topological properties and organizing principles of semantic networks