A knowledge graph method for hazardous chemical management: Ontology design and entity identification

CPBA-CLIM: An entity-relation extraction model for ontology-based knowledge graph construction in hazardous chemical incident management

In recent years, hazardous chemical incidents have occurred frequently, resulting in significant human casualties, property damage, and environmental pollution due to human or natural factors. Accurately mining the lessons learned from accumulating incident reports and constructing the knowledge graph for hazardous chemical incident management can assist managers in identifying patterns and analyzing common attributes, thereby preventing the recurrence of similar incidents. This article addresses the challenges of dispersed textual information, specialized vocabulary, and data formats in hazardous chemical incidents. We propose a novel entity-relation extraction model called CPBA-CLIM (content-position-based attention-cross-label intersect matching) to provide an accurate data foundation for constructing the hazardous chemical incident knowledge graph. The content-position-based attention module, based on content-position attention, incorporates contextual semantic information into the combined encoding of bidirectional encoder representations from the transformer's content and position to obtain dynamic word vectors that align with the thematic context of the text. Additionally, the cross-label intersect matching strategy evaluates the rationality of entity-relation interactions in sets containing potential overlaps, reducing the impact of entity-relation overlap on triplet extraction accuracy. Comparative experimental results on public datasets demonstrate the model's outstanding performance in overlapping triplets. Qualitative experiments on a self-constructed dataset integrate our model with ontology construction techniques, successfully establishing a knowledge graph for managing hazardous chemical incidents. This research effectively enhances the degree of automation and efficiency in knowledge graph construction, thus offering support and decision-making foundations for hazardous chemical safety management.

The potential of a data centred approach & knowledge graph data representation in chemical safety and drug design

Big Data pervades nearly all areas of life sciences, yet the analysis of large integrated data sets remains a major challenge. Moreover, the field of life sciences is highly fragmented and, consequently, so is its data, knowledge, and standards. This, in turn, makes integrated data analysis and knowledge gathering across sub-fields a demanding task. At the same time, the integration of various research angles and data types is crucial for modelling the complexity of organisms and biological processes in a holistic manner. This is especially valid in the context of drug development and chemical safety assessment where computational methods can provide solutions for the urgent need of fast, effective, and sustainable approaches. At the same time, such computational methods require the development of methodologies suitable for an integrated and data centred Big Data view. Here we discuss Knowledge Graphs (KG) as a solution to a data centred analysis approach for drug and chemical development and safety assessment. KGs are knowledge bases, data analysis engines, and knowledge discovery systems all in one, allowing them to be used from simple data retrieval, over meta-analysis to complex predictive and knowledge discovery systems. Therefore, KGs have immense potential to advance the data centred approach, the re-usability, and informativity of data. Furthermore, they can improve the power of analysis, and the complexity of modelled processes, all while providing knowledge in a natively human understandable network data model.

Identifying the Weaker Function Links in the Hazardous Chemicals Road Transportation System in China

Safety of the hazardous chemicals road transportation system (HCRTS) is an important, complex, social, and environmental sensitive problem. The complexity, dynamics, and multi-link features of HCRTS have made it necessary to think beyond traditional risk analysis methods. Based on the relevant literature, Functional Resonance Analysis Method (FRAM) is a relatively new systemic method for modeling and analyzing complex socio-technical systems. In this study, a methodology that integrates FRAM, fuzzy sets, and risk matrix is presented to quantitatively assess the risks factors representing failure function links in HCRTS. As the strength of function links can be illustrated by the RI (risk index) of risk factors identified in failure function links, 32 risk factors representing 12 failure function links were first identified by accident causes analysis and the framework of FRAM. Fuzzy sets were then utilized to calculate the weight of the likelihood and consequence of the risk factors. Finally, according to the assessment results of the identified risk factors by a two-dimensional risk matrix, the weaker function links in the whole HCRTS chain were identified. HCs road companies, regulatory authorities, relevant practitioners, and other stakeholders should pay more attention to these links.