  Interpreting natural language is an increasingly important task in computer
algorithms due to the growing availability of unstructured textual data.
Natural Language Processing (NLP) applications rely on semantic networks for
structured knowledge representation. The fundamental properties of semantic
networks must be taken into account when designing NLP algorithms, yet they
remain to be structurally investigated. We study the properties of semantic
networks from ConceptNet, defined by 7 semantic relations from 11 different
languages. We find that semantic networks have universal basic properties: they
are 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 grammatical
rules, for example networks from highly inflected languages, such as e.g.
Latin, German, French and Spanish, show peaks in the degree distribution that
deviate from a power law. We find that depending on the semantic relation type
and the language, the link formation in semantic networks is guided by
different principles. In some networks the connections are similarity-based,
while in others the connections are more complementarity-based. Finally, we
demonstrate how knowledge of similarity and complementarity in semantic
networks can improve NLP algorithms in missing link inference.
