Thesaurus construction through knowledge representation

Developing A Case-Based Reasoning Model for Safety Accident Pre-Control and Decision Making in the Construction Industry

Case-based reasoning (CBR) has been extensively employed in various construction management areas, involving construction cost prediction, duration estimation, risk management, tendering, bidding and procurement. However, there has been a dearth of research integrating CBR with construction safety management for preventing safety accidents. This paper proposes a CBR model which focuses on case retrieval and reuse to provide safety solutions for new problems. It begins with the identification of case problem attribute and solution attribute, the state of hazard is used to describe the problem attribute based on principles of people's unsafe behavior and objective's unsafe state. Frame-based knowledge representation method is adopted to establish the case database from dimensions of slot, facet and facet's value. Besides, cloud graph method is introduced to determine the attribute weight through analyzing the numerical characteristics of expectation value, entropy value and hyper entropy value. Next, thesaurus method is employed to calculate the similarity between cases including word level similarity and sentence level similarity. Principles and procedures have been provided on case revise and case retain. Finally, a real-world case is conducted to illustrate the applicability and effectiveness of the proposed model. Considering the high potential for pre-control and decision-making of construction safety accident, the proposed model is expected to contribute safety managers to take decisions on prevention measures more efficiently.

Exploration and study of multilingual thesauri automation construction for digital libraries in China

Purpose

The paper aims to explore multilingual thesauri automation construction based on the freely available digital library resources. The key methods and study results are presented in the paper. It also proposes a way that terms are automatically extracted from multilingual parallel corpus.

Design/methodology/approach

The study adopted the technology of natural language processing to analyze the linguistics characteristics of terms, and combined this with statistical analyses to extract the terms from technological documents. The methods consist of automatically extracting and filtering terms, judging and building relationship among terms, building the multilingual parallel corpus, and extracting term pairs between Chinese and foreign languages through calculating their associated probability. The experiments run on the Java test platform.

Findings

The study obtains the following conclusions: finding the similarities and differences between the Chinese thesaurus standard and international thesaurus standard. The methods for automatically extracting terms and building relationships among them are presented. Eventually the multilingual terms' translation sets are generated based on real corpora. The results of the study show that the proposed methods can obtain better performance. The effect of automatic terms' translation alignment method is better than that of traditional IBM model method.

Practical implications

The study results can provide references for further study and application of multilingual thesauri automation construction using Chinese as a pivot.

Originality/value

The paper proposes new ideas on thesaurus automation construction in the digital age. The presented method based on linguistics and statistics is a new attempt. According to the experimental results, this exploration and study is innovative and valuable. In addition, these ideas and methods give a good start for improving information services of the PRC's National Science and Technology Digital Library.