Extracting Domain Knowledge Elements of Construction Safety Management: Rule-Based Approach Using Chinese Natural Language Processing

The literature and practices of construction safety management have highlighted the importance of domain knowledge. Effectively extracting the domain knowledge elements (DKEs) of construction safety management remains a challenging task. To address this problem, this paper develops a rule-based natural language processing (NLP) approach for extracting DKEs from Chinese text documents in the domain of construction safety management. First, a linguistic pattern of DKEs was constructed according to lexical analysis and syntactic dependency parsing. Then, the extraction rules and workflow paths were established and tested. The results indicated that most DKEs in the domain of construction safety management are composed of specific compound parts of speech (nouns and noun phrases), specific word dependencies (attribution, verb-object, subject-verb, preposition-object, and coordinate relationship), and words of specific lengths (two to six Chinese characters). This work is the first to reveal the Chinese linguistic patterns and linguistic features of DKEs in the domain of construction safety management. The findings of this study can facilitate the establishment and supplementation of domain lexicons and knowledge-based safety management systems and can guide safety training for construction safety management

Arabic open information extraction system using dependency parsing

Arabic is a Semitic language and one of the most natural languages distinguished by the richness in morphological enunciation and derivation. This special and complex nature makes extracting information from the Arabic language difficult and always needs improvement. Open information extraction systems (OIE) have been emerged and used in different languages, especially in English. However, it has almost not been used for the Arabic language. Accordingly, this paper aims to introduce an OIE system that extracts the relation tuple from Arabic web text, exploiting Arabic dependency parsing and thinking carefully about all possible text relations. Based on clause types' propositions as extractable relations and constituents' grammatical functions, the identities of corresponding clause types are established. The proposed system named Arabic open information extraction(AOIE) can extract highly scalable Arabic text relations while being domain independent. Implementing the proposed system handles the problem using supervised strategies while the system relies on unsupervised extraction strategies. Also, the system has been implemented in several domains to avoid information extraction in a specific field. The results prove that the system achieves high efficiency in extracting clauses from large amounts of text