Open Information Extraction for Knowledge Graph Construction

Muhammad, Iqra; Kearney, Anna; Gamble, Carrol; Coenen, Frans; Williamson, Paula

doi:10.1007/978-3-030-59028-4_10

Cited by 16 publications

(7 citation statements)

References 19 publications

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“…An OIE system extracts different triples (arg1, rel, arg2) from each sentence in a text, usually in RDF format, that represent key propositions or claims [44][45][46][47][48].…”

Section: Open Iementioning

confidence: 99%

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Examining Knowledge Extraction Processes from Heterogeneous Data Sources

Sarıkoz¹

2023

BEN

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In the last 20 years, e-mail, instant messaging, documents, blogs, news, text communication in the transfer of information over the web, as a result of the presentation and transmission of information as a result of the Web the dramatic increase in the amount of data in digital environments has increased the importance of studies in the field of knowledge extraction from unstructured data. Since the 2000s, one of the primary goals of researchers in the field of artificial intelligence has been to extract knowledge from heterogeneous data sources on the World Wide Web, including real-life entities and semantic relationships between entities, and to display them in machine-readable format. Advances in natural language processing and information extraction have increased the importance of large-scale knowledge bases in complex applications, resulting in scalable information extraction from semi-structured and unstructured heterogeneous data sources on the Web, and the detection of entities and relationships; It enabled the automatic creation of prominent knowledge bases in this field such as DbPedia, YAGO, NELL, Freebase, Probase, Google Knowledge Vault, IBM Watsons, which contain millions of semantic relationships between hundreds of thousands of entities, and displaying the created information in machine-readable format. Within the scope of this article; Web-scale(end-to-end) knowledge extraction from heterogeneous data sources, methods, challenges and opportunities are provided.

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“…An OIE system extracts different triples (arg1, rel, arg2) from each sentence in a text, usually in RDF format, that represent key propositions or claims [44][45][46][47][48].…”

Section: Open Iementioning

confidence: 99%

“…However, when Open IE studies are examined in general, it is seen that these studies are also divided into four classes according to the technique they use [46][47][48]:…”

Section: Open Iementioning

confidence: 99%

Examining Knowledge Extraction Processes from Heterogeneous Data Sources

Sarıkoz¹

2023

BEN

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“…Various studies handle these tasks with the help of OpenIE tools where each KG is generated. In this context, a literature knowledge graph for clinical research methodology dataset OIE4KGC [77] is generated using the concept of open information extraction. In this paper, spacy's Noun chunker is used to retrain noun phrases and filtered triple such as< study, determine, cardiovascularrisk f actors >.…”

Section: Schema Basedmentioning

confidence: 99%

Scholarly knowledge graphs through structuring scholarly communication: a review

Verma

Bhatia

Harit

et al. 2022

Complex Intell. Syst.

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The necessity for scholarly knowledge mining and management has grown significantly as academic literature and its linkages to authors produce enormously. Information extraction, ontology matching, and accessing academic components with relations have become more critical than ever. Therefore, with the advancement of scientific literature, scholarly knowledge graphs have become critical to various applications where semantics can impart meanings to concepts. The objective of study is to report a literature review regarding knowledge graph construction, refinement and utilization in scholarly domain. Based on scholarly literature, the study presents a complete assessment of current state-of-the-art techniques. We presented an analytical methodology to investigate the existing status of scholarly knowledge graphs (SKG) by structuring scholarly communication. This review paper investigates the field of applying machine learning, rule-based learning, and natural language processing tools and approaches to construct SKG. It further presents the review of knowledge graph utilization and refinement to provide a view of current research efforts. In addition, we offer existing applications and challenges across the board in construction, refinement and utilization collectively. This research will help to identify frontier trends of SKG which will motivate future researchers to carry forward their work.

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“…Knowledge acquisition first extracts the information from a large number of structured, semi-structured or unstructured data, and then enhances the logic and representation of knowledge base through a series of post-processing procedures, such as entity disambiguation, entity integration, etc. [ 27 , 28 ]. Knowledge application utilizes existing knowledge in the constructed KG for further knowledge reasoning and quality evaluation, which can be realized by the description logic reasoning, rule-based reasoning and case-based reasoning, etc.…”

Section: Related Workmentioning

confidence: 99%

Knowledge graph of alpine skiing events: A focus on meteorological conditions

et al. 2022

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Alpine skiing, as an outdoor winter sport, is particularly vulnerable to the variation of meteorological conditions. Scattered and multi-source big data cannot be fully utilized to conduct effective decision analyses by conventional data analysis methods. Presently, knowledge graphs are the most advanced organization form of knowledge base, which can make explicit the complex relationships among different objects. Thus, introducing knowledge graph to the event management of alpine skiing is significant to improve the ability of risk prediction and decision-making. In this research, we analyze the components and dynamic characteristics of alpine skiing, and construct an “Object-Characteristic-Relation” representation model to express multi-level knowledge. Moreover, we propose a “Characteristic-value- Relationship” representation method based on the multi-source data, to construct the knowledge graph of alpine skiing. With the proposed method, comprehensive relationships between meteorological conditions and alpine skiing can be represented clearly, and support further knowledge reasoning for the event management under meteorological conditions. We have tested the utility of the proposed method in a case study of 2018 Winter Olympics in PyeongChang. The case study realizes an semi-automatic construction of knowledge graph for alpine skiing, provides decision supports for event risk managements, according to different meteorological conditions, and grounds a foundation for future knowledge graph construction of other large-scale sport events.

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Open Information Extraction for Knowledge Graph Construction

Cited by 16 publications

References 19 publications

Examining Knowledge Extraction Processes from Heterogeneous Data Sources

Examining Knowledge Extraction Processes from Heterogeneous Data Sources

Scholarly knowledge graphs through structuring scholarly communication: a review

Knowledge graph of alpine skiing events: A focus on meteorological conditions

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