Spatial Relation Extraction from Chinese Characterized Documents Based on Semantic Knowledge
Extracting spatial relation from text documents in natural languages(news, journal, blog, social network etc.) is an important method of obtaining spatial information in the era of big data. Former methods of extracting spatial relation from Chinese characterized text only focused on the features of Chinese characters and phrases, which easily cause ambiguous matching. This paper presented a new rule-based method that integrates lexical, syntactic and semantic knowledge. The extracting rule in this method was composed of spatial words and syntactic dependences between these words, which jointly formed a tree structure. The tree nodes represent the spatial words and they were connected by syntactic dependences. Spatial words were the words that can be used to express spatial relations, which were subsequently classified into 6 categories: geographical entities, preposition, locative nouns, spatial predicate, metaphorical spatial nouns and assistant words. In the process of rule matching, finite automata was used to identify new spatial relation instances that satisfy the following two conditions:(1) same syntactic dependence structure with regard to the extracting rules;(2) similarity of the spatial words. The part-of-speech, semantic similarity were used to measure the consistency between spatial words. The experiment of extracting the direction relations from Encyclopedia of China shows that the accuracy and the recall rate of this method achieve 86.67% and 63.11% respectively, which is better than the former methods. Comparing with the former methods, the improvements of this method include:(1) the process of extracting rule generation does not require human intervention;(2) the ambiguous matching can be diminished by integrating syntactic dependence knowledge, which evidently promoted the performance of spatial relation identification.