Knowledge Driven Intelligent Survey Systems for Linguists

In this paper, we propose Knowledge Graph (KG), an articulated underlying semantic structure, to be a semantic bridge between human and systems. To illustrate our proposal, we focus on KG based intelligent survey systems. In state of the art systems, knowledge is hard-coded or implicit in these systems, making it hard for researchers to reuse, customise, link, or transmit the structured knowledge. Furthermore, such systems do not facilitate dynamic interaction based on the semantic structure. We design and implement a knowledge-driven intelligent survey system which is based on knowledge graph, a widely used technology that facilitates sharing and querying hypotheses, survey content, results, and analyses. The approach is developed, implemented, and tested in the field of Linguistics. Syntacticians and morphologists develop theories of grammar of natural languages. To evaluate theories, they seek intuitive grammaticality (well-formedness) judgments from native speakers, which either support a theory or provide counter-evidence. Our preliminary experiments show that a knowledge graph based linguistic survey can provide more nuanced results than traditional document-based grammaticality judgment surveys by allowing for tagging and manipulation of specific linguistic variables.supports very fast parallel bit computation. As a semantic compression method for structured data, besides the reduction of syntactic verbosity and data redundancy, we also invoke semantics in the RDF datasets. Experiments on various datasets show competitive results in terms of compression ratio.