Ontology and NLP support for building disaster knowledge base

Abburu, Sunitha; Golla, Suresh Babu

doi:10.1109/cesys.2017.8321236

Cited by 10 publications

(3 citation statements)

References 12 publications

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“…The internal aspect of governance, which is related to company self-assessment [69], focuses on the efficiency of its policy and bureaucracy. Once again, the most valuable tools here are based on NLP technology, which automates routine tasks such as checking document consistency [70], generating reports according to preset templates [71], implementing search engines [72], establishing knowledge bases [73], and supporting chatbots to assist employees [74]. However, NLP methods should be distinguished from standard robotic process automation (RPA) [75], which also simplifies everyday tasks.…”

Section: Governancementioning

confidence: 99%

Practical AI Cases for Solving ESG Challenges

Burnaev,

Mironov,

Shpilman

et al. 2023

Sustainability

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Artificial intelligence (AI) is a rapidly advancing area of research that encompasses numerical methods to solve various prediction, optimization, and classification/clustering problems. Recently, AI tools were proposed to address the environmental, social, and governance (ESG) challenges associated with sustainable business development. While many publications discuss the potential of AI, few focus on practical cases in the three ESG domains altogether, and even fewer highlight the challenges that AI may pose in terms of ESG. The current paper fills this gap by reviewing practical AI applications with a main focus on IT and engineering implementations. The considered cases are based on almost one hundred publicly available research manuscripts and reports obtained via online search engines. This review involves the study of typical business and production problems associated with each ESG domain, gives background details on several selected cases (such as carbon neutrality, land management, and ESG scoring), and lists challenges that the smart algorithms can pose (such as fake news generation and increased electricity consumption). Overall, it is concluded that, while many practical cases already exist, AI in ESG is still very far away from reaching its full potential; however, one should always remember that AI itself can lead to some ESG risks.

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Section: Governancementioning

confidence: 99%

Practical AI Cases for Solving ESG Challenges

Burnaev,

Mironov,

Shpilman

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…Parsing analysis, semantic interpretation, and knowledge representation are the phases in the methodology. Abburn [28] suggested the use of natural language processing (NLP) to extract relevant information from semi-structured and structured heterogeneous documents, then used RDF to represent the extracted information in a homogeneous and machine-understandable format, and then mapped the RDF triples to the appropriate concepts in disaster management domain ontologies.…”

Section: Ontology In Knowledge Representationmentioning

confidence: 99%

Ontology-Based Methodology for Knowledge Acquisition from Groupware

et al. 2022

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Groupware exist, and they contain expertise knowledge (explicit and tacit) that is primarily for solving problems, and it is collected on-the-job through virtual teams; such knowledge should be harvested. A system to acquire on-the-job knowledge of experts from groupware in view of the enrichment of intelligent agents has become one of the important technologies that is very much in demand in the field of knowledge technology, especially in this era of textual data explosion including due to the ever-increasing remote work culture. Before acquiring new knowledge from sentences in groupware into an existing ontology, it is vital to process the groupware discussions to recognise concepts (especially new ones), as well as to find the appropriate mappings between the said concepts and the destination ontology. There are several mapping procedures in the literature, but these have been formulated on the basis of mapping two or more independent ontologies using concept-similarities and it requires a significant amount of computation. With the goal of lowering computational complexities, identification difficulties, and complications of insertion (hooking) of a concept into an existing ontology, this paper proposes: (1) an ontology-based framework with changeable modules to harvest knowledge from groupware discussions; and (2) a facts enrichment approach (FEA) for the identification of new concepts and the insertion/hooking of new concepts from sentences into an existing ontology. This takes into consideration the notions of equality, similarity, and equivalence of concepts. This unique approach can be implemented on any platform of choice using current or newly constructed modules that can be constantly revised with enhanced sophistication or extensions. In general, textual data is taken and analysed in view of the creation of an ontology that can be utilised to power intelligent agents. The complete architecture of the framework is provided and the evaluation of the results reveal that the proposed methodology performs significantly better compared to the universally recommended thresholds as well as the existing works. Our technique shows a notable high improvement on the F1 score that measures precision and recall. In terms of future work, the study recommends the development of algorithms to fully automate the framework as well as for harvesting tacit knowledge from groupware.

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“…Zheng et al explored the disaster literature using a data mining tool to find the relationships among typhoon disasters and build a disaster network [16]. Abburu et al pointed out the problems of heterogeneous structure, massive documents, semantic gaps, and lack of domain knowledge regarding disasters, and proposed a method of extracting and integrating information from semi-structured texts related to disasters by using natural language processing [17]. Most of the research on natural hazards knowledge graphs is either disaster problem-oriented [18][19][20][21] or user-oriented [22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Global Flood Disaster Research Graph Analysis Based on Literature Mining

Zhang

Wang

2022

Applied Sciences

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Floods are the most frequent and highest-impact among the natural disasters caused by global climate change. A large number of flood disaster knowledge were buried in the scientific literature. This study mines research trends and hotspots on flood disasters and identifies their quantitative and spatial distribution features using natural language process technology. The abstracts of 14,076 studies related to flood disasters from 1990 to 2020 were used for text mining. The study used logistic regression to classify themes, adopted the dictionary matching method to analyze flood disaster subcategories, analyzed the spatial distribution characteristics of research institutions, and used Stanford named entity recognition to identify hot research areas. Finally, the disaster information was integrated and visualized as a knowledge graph. The main findings are as follows. (1) The research hotspots are concentrated on flood disaster risks and prediction. Rainfall, coastal floods, and flash floods are the most-studied flood disaster sub-categories. (2) There are some connections and differences between the physical occurrence and research frequency of flood disasters. Occurrence frequency and research frequency of flood disasters are correlated. However, the spatial distribution at the global and intercontinental scales is geographically imbalanced. (3) The study’s flood disaster knowledge graph contains 39,679 nodes and 64,908 edges, reflecting the literature distribution and field information on the research themes. Future research will extract more disaster information from the full texts of the studies to enrich the flood disaster knowledge graph and obtain more knowledge on flood disaster risk and reduction.

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Ontology and NLP support for building disaster knowledge base

Cited by 10 publications

References 12 publications

Practical AI Cases for Solving ESG Challenges

Practical AI Cases for Solving ESG Challenges

Ontology-Based Methodology for Knowledge Acquisition from Groupware

Global Flood Disaster Research Graph Analysis Based on Literature Mining

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