A Computational Cognitive Model for Semantic Sub-Network Extraction from Natural Language Queries

Semantic query sub-network is the representation of a natural language query as a graph of semantically connected words. Such sub-networks can be identified as sub-graphs in larger ontologies like DBpedia or Google knowledge graph, which allows for domain and concepts identification, especially in noisy queries. In this paper, we present a novel standalone NLP technique that leverages the cognitive psychology notion of semantic forms for semantic subnetwork extraction from natural language queries. Semantic forms, borrowed from cognitive psychology models, are one of the fundamental structures employed by human cognition to construct semantic information in the brain. We propose a computational cognitive model by means of conditional random fields and explore the interaction patterns among such forms. Our results suggest that the cognitive abstraction provided by semantic forms during labelling can significantly improve parsing and sub-network extraction compared to pure lexical approaches like parts of speech tagging. We conduct experiments on approximately 5000 queries from three diverse datasets to demonstrate the robustness and efficiency of the proposed approach.