In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shippi...In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.展开更多
There is a growing amount of data uploaded to the internet every day and it is important to understand the volume of those data to find a better scheme to process them.However,the volume of internet data is beyond the...There is a growing amount of data uploaded to the internet every day and it is important to understand the volume of those data to find a better scheme to process them.However,the volume of internet data is beyond the processing capabilities of the current internet infrastructure.Therefore,engineering works using technology to organize and analyze information and extract useful information are interesting in both industry and academia.The goal of this paper is to explore the entity relationship based on deep learning,introduce semantic knowledge by using the prepared language model,develop an advanced entity relationship information extraction method by combining Robustly Optimized BERT Approach(RoBERTa)and multi-task learning,and combine the intelligent characters in the field of linguistic,called Robustly Optimized BERT Approach+Multi-Task Learning(RoBERTa+MTL).To improve the effectiveness of model interaction,multi-task teaching is used to implement the observation information of auxiliary tasks.Experimental results show that our method has achieved an accuracy of 88.95 entity relationship extraction,and a further it has achieved 86.35%of accuracy after being combined with multi-task learning.展开更多
Knowledge graphs are employed in several tasks,such as question answering and recommendation systems,due to their ability to represent relationships between concepts.Automatically constructing such a graphs,however,re...Knowledge graphs are employed in several tasks,such as question answering and recommendation systems,due to their ability to represent relationships between concepts.Automatically constructing such a graphs,however,remains an unresolved challenge within knowledge representation.To tackle this challenge,we propose CtxKG,a method specifically aimed at extracting knowledge graphs in a context of limited resources in which the only input is a set of unstructured text documents.CtxKG is based on OpenIE(a relationship triple extraction method)and BERT(a language model)and contains four stages:the extraction of relationship triples directly from text;the identification of synonyms across triples;the merging of similar entities;and the building of bridges between knowledge graphs of different documents.Our method distinguishes itself from those in the current literature(i)through its use of the parse tree to avoid the overlapping entities produced by base implementations of OpenIE;and(ii)through its bridges,which create a connected network of graphs,overcoming a limitation similar methods have of one isolated graph per document.We compare our method to two others by generating graphs for movie articles from Wikipedia and contrasting them with benchmark graphs built from the OMDb movie database.Our results suggest that our method is able to improve multiple aspects of knowledge graph construction.They also highlight the critical role that triple identification and named-entity recognition have in improving the quality of automatically generated graphs,suggesting future paths for investigation.Finally,we apply CtxKG to build BlabKG,a knowledge graph for the Blue Amazon,and discuss possible improvements.展开更多
文摘In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.
文摘There is a growing amount of data uploaded to the internet every day and it is important to understand the volume of those data to find a better scheme to process them.However,the volume of internet data is beyond the processing capabilities of the current internet infrastructure.Therefore,engineering works using technology to organize and analyze information and extract useful information are interesting in both industry and academia.The goal of this paper is to explore the entity relationship based on deep learning,introduce semantic knowledge by using the prepared language model,develop an advanced entity relationship information extraction method by combining Robustly Optimized BERT Approach(RoBERTa)and multi-task learning,and combine the intelligent characters in the field of linguistic,called Robustly Optimized BERT Approach+Multi-Task Learning(RoBERTa+MTL).To improve the effectiveness of model interaction,multi-task teaching is used to implement the observation information of auxiliary tasks.Experimental results show that our method has achieved an accuracy of 88.95 entity relationship extraction,and a further it has achieved 86.35%of accuracy after being combined with multi-task learning.
基金The authors of this work would like to thank the Center for Artificial Intelligence(C4AI-USP)and the support from the São Paulo Research Foundation(FAPESP grant#2019/07665-4)and from the IBM CorporationFabio G.Cozman acknowledges partial support by CNPq grant Pq 305753/2022-3This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brazil(CAPES)-Finance Code 001。
文摘Knowledge graphs are employed in several tasks,such as question answering and recommendation systems,due to their ability to represent relationships between concepts.Automatically constructing such a graphs,however,remains an unresolved challenge within knowledge representation.To tackle this challenge,we propose CtxKG,a method specifically aimed at extracting knowledge graphs in a context of limited resources in which the only input is a set of unstructured text documents.CtxKG is based on OpenIE(a relationship triple extraction method)and BERT(a language model)and contains four stages:the extraction of relationship triples directly from text;the identification of synonyms across triples;the merging of similar entities;and the building of bridges between knowledge graphs of different documents.Our method distinguishes itself from those in the current literature(i)through its use of the parse tree to avoid the overlapping entities produced by base implementations of OpenIE;and(ii)through its bridges,which create a connected network of graphs,overcoming a limitation similar methods have of one isolated graph per document.We compare our method to two others by generating graphs for movie articles from Wikipedia and contrasting them with benchmark graphs built from the OMDb movie database.Our results suggest that our method is able to improve multiple aspects of knowledge graph construction.They also highlight the critical role that triple identification and named-entity recognition have in improving the quality of automatically generated graphs,suggesting future paths for investigation.Finally,we apply CtxKG to build BlabKG,a knowledge graph for the Blue Amazon,and discuss possible improvements.
