Domain-specific Knowledge Graphs: A survey

Bilal Abu-Salih

AI summary · arXiv: 2011.00235v1 · cs.DB

Domain-specific Knowledge Graphs: A survey

Bilal Abu-Salih

Original paper Submitted to arXiv on 31 Oct. 2020 License: NONEXCLUSIVE-DISTRIB 1.0

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Knowledge Graphs (KGs) have revolutionized knowledge representation
KGs enable deeper understanding, reasoning, and interpretation of knowledge for humans and machines
KGs are crucial in addressing real-life problems across different domains
No consensus on a definitive definition of domain-specific KGs
Survey paper aims to provide an inclusive definition of domain-specific KGs and review state-of-the-art approaches
Current approaches have limitations such as lack of standardization, limited scalability, and inadequate representation capabilities
Proposed improvements include incorporating diverse data sources, utilizing advanced machine learning techniques, and implementing standardized ontologies
Recommendations for future research directions include integrating heterogeneous data sources and developing efficient algorithms for constructing large-scale KGs
The survey serves as an important resource for researchers and practitioners working with KGs

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, , , , In the field of knowledge representation, Knowledge Graphs (KGs) have brought about a significant advancement and revolution. The underlying structure of KGs enables a deeper understanding, reasoning, and interpretation of knowledge for both humans and machines. As a result, KGs have become a crucial tool in addressing various real-life problems across different domains. However, despite their widespread use, there is still no consensus on a definitive definition of domain-specific KGs. To address this issue, this survey paper aims to provide an inclusive definition of domain-specific KGs and offers a comprehensive review of state-of-the-art approaches. The survey focuses on seven distinct domains of knowledge and draws insights from academic works in each domain. Through the examination of current approaches, the study uncovers a range of correlated limitations and deficiencies that hinder the effectiveness of domain-specific KG construction. These include lack of standardization, limited scalability, and inadequate representation capabilities. To overcome these challenges, the paper proposes a set of improvements that can be made to enhance the current approaches. These include incorporating more diverse data sources, utilizing advanced machine learning techniques, and implementing standardized ontologies. Furthermore, the survey presents recommendations for future research directions in order to advance the field of domain-specific KG construction. This includes exploring new methods for integrating heterogeneous data sources and developing more efficient algorithms for constructing large-scale KGs. Overall,<b> this survey serves as an important resource for researchers and practitioners working with KGs by providing them with a clear understanding of domain-specific KGs </b>and highlighting areas for improvement and further exploration.

Introduction

Knowledge Graphs (KGs) have emerged as a powerful tool in the field of knowledge representation, enabling a deeper understanding and interpretation of knowledge for both humans and machines. They have been widely used in various domains such as healthcare, finance, and e-commerce to address real-life problems. However, there is still no consensus on a definitive definition of domain-specific KGs. This survey paper aims to provide an inclusive definition of domain-specific KGs and offers a comprehensive review of state-of-the-art approaches.

What are Domain-Specific Knowledge Graphs?

Domain-specific Knowledge Graphs refer to KGs that are constructed for a specific domain or subject area. These graphs contain structured data about entities and their relationships within that particular domain. For example, a healthcare KG would contain information about diseases, treatments, medications, and their connections.

The Importance of Domain-Specific Knowledge Graphs

The use of domain-specific KGs has become increasingly important due to the growing amount of data available in different domains. These graphs allow for better organization and understanding of complex data by providing context and relationships between entities. They also enable more efficient search capabilities by linking related information together.

State-of-the-Art Approaches

This survey focuses on seven distinct domains: healthcare, finance, e-commerce, social media, education, transportation, and energy. It draws insights from academic works in each domain to provide an overview of current approaches used in constructing domain-specific KGs.

Lack of Standardization

One common limitation found across all domains is the lack of standardization in constructing KGs. Different researchers use different methods for entity extraction and relationship identification which can lead to inconsistencies in the resulting graph.

Limited Scalability

Another challenge faced by current approaches is limited scalability when dealing with large datasets. As the amount of data increases, it becomes more difficult to maintain and update the KG, leading to a decrease in performance.

Inadequate Representation Capabilities

Many current approaches also have limited representation capabilities, meaning they are not able to capture all aspects of a domain. This can result in incomplete or inaccurate knowledge graphs.

Improvements for Domain-Specific Knowledge Graph Construction

To overcome these challenges, this survey proposes several improvements that can be made to enhance the construction of domain-specific KGs:

Incorporating More Diverse Data Sources

One way to improve the quality and completeness of domain-specific KGs is by incorporating diverse data sources. This includes structured data from databases as well as unstructured data from text documents and social media.

Utilizing Advanced Machine Learning Techniques

The use of advanced machine learning techniques such as natural language processing (NLP) and deep learning can aid in entity extraction and relationship identification, improving the accuracy and efficiency of constructing KGs.

Implementing Standardized Ontologies

Standardized ontologies provide a common vocabulary for describing entities and relationships within a specific domain. By implementing these ontologies, it will ensure consistency across different knowledge graphs within the same domain.

Future Research Directions

This survey also presents recommendations for future research directions in order to advance the field of domain-specific KG construction:

Exploring New Methods for Integrating Heterogeneous Data Sources

As mentioned earlier, incorporating diverse data sources can improve the quality and completeness of domain-specific KGs. Future research should focus on developing new methods for integrating heterogeneous data sources efficiently.

Developing More Efficient Algorithms for Constructing Large-Scale Knowledge Graphs

With an increasing amount of data available in different domains, there is a need for more efficient algorithms to construct large-scale KGs. Future research should focus on developing algorithms that can handle large datasets without compromising performance.

Conclusion

In conclusion, this survey provides a comprehensive review of state-of-the-art approaches in constructing domain-specific knowledge graphs. It highlights the challenges and limitations faced by current methods and proposes improvements that can be made to enhance the construction of these graphs. The paper also presents recommendations for future research directions, providing valuable insights for researchers and practitioners working with KGs. By addressing these challenges and implementing improvements, we can advance the field of domain-specific KG construction and unlock its full potential in various domains.

Created on 18 Jan. 2024

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