| References
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| 1. Wang, X., Chen, L., Ban, T., Usman, M., Guan, Y., Liu, S., Wu, T., & Chen, H. (2021b). Knowledge Graph Quality Control: A survey. Fundamental Research, 1(5), 607–626. https://doi.org/10.1016/j.fmre.2021.09.003 |
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| 2. Chen, H., Cao, G., Chen, J., & Ding, J. (2019). A practical framework for evaluating the quality of Knowledge Graph.Communications in Computer and Information Science, 111–122. https://doi.org/10.1007/978-981-15-1956-7_10 |
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| 3. Xue, B., & Zou, L. (2022). Knowledge Graph Quality Management: A comprehensive survey. IEEE Transactions on Knowledge and Data Engineering, 1–1. https://doi.org/10.1109/tkde.2022.3150080 |
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| 4. Li, X., Lyu, M., Wang, Z., Chen, C.-H., & Zheng, P. (2021). Exploiting knowledge graphs in industrial products and services: A survey of key aspects, challenges, and future perspectives. Computers in Industry, 129, 103449. https://doi.org/10.1016/j.compind.2021.103449 |
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| 5. Voskarides, N., Meij, E., Reinanda, R., Khaitan, A., Osborne, M., Stefanoni, G., Kambadur, P., & de Rijke, M. (2018). Weakly-supervised contextualization of knowledge graph facts. The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. https://doi.org/10.1145/3209978.3210031 |
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| 6. Rossi, A., Barbosa, D., Firmani, D., Matinata, A., & Merialdo, P. (2021). Knowledge graph embedding for link prediction. ACM Transactions on Knowledge Discovery from Data, 15(2), 1–49. https://doi.org/10.1145/3424672 |
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| 7. Le-Phuoc, D., Nguyen Mau Quoc, H., Ngo Quoc, H., Tran Nhat, T., & Hauswirth, M. (2016). The graph of things: A step towards the live knowledge graph of connected things. Journal of Web Semantics, 37–38, 25–35. https://doi.org/10.1016/j.websem.2016.02.003 |
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| 8. Li, Xiang, Tur, G., Hakkani-Tur, D., & Li, Q. (2014). Personal knowledge graph population from user utterances in Conversational understanding. 2014 IEEE Spoken Language Technology Workshop (SLT). https://doi.org/10.1109/slt.2014.7078578 |
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| 9. Hechler, E., Weihrauch, M., & Wu, Y. (2023). Data Fabric Architecture Patterns. Data Fabric and Data Mesh Approaches with AI, 231–255. https://doi.org/10.1007/978-1-4842-9253-2_10 |
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In the ever-expanding landscape of data, where complexity reigns and information overload is a constant challenge, emerges a revolutionary paradigm—knowledge graphs (KG).
These intricate webs of interconnected knowledge hold the key to unlocking the untapped potential hidden within our data repositories. A KG uses nodes and edges to express relationships that exist between real-world entities.
The graph manages to store and organize complicated material, so computers and people are able to comprehend and use all the data it contains.
By providing a structured and contextualized representation of knowledge, the KG has bridged the gap between unstructured data and AI applications.
Achieving completeness requires capturing a comprehensive representation of knowledge in a specific domain or across multiple domains.
However, the breadth and depth of human knowledge are vast. Striking a balance between inclusiveness and practicality is essential, as attempting to achieve absolute completeness could lead to an unmanageably large graph with diminishing returns.
On the other hand, ensuring accuracy involves verifying the correctness and reliability of the information stored in the KG. It requires rigorous data validation processes, fact-checking, and resolving conflicts or inconsistencies.
Validating the accuracy of the data can be complex, especially when dealing with heterogeneous data sources, conflicting information, or subjective knowledge.
Achieving both completeness and accuracy requires a combination of automated processes, human curation, and ongoing maintenance to address the inherent challenges.
Vast amounts of data are created every minute on various social media platforms [10].
Timeliness presents a significant challenge when it comes to maintaining a KG.
The difficulty lies in the dynamic nature of the real world and the need to reflect the latest information accurately. The sheer volume and variety of data sources make it challenging to keep pace with updates [4].
KGs often draw information from diverse and constantly changing sources such as databases, APIs, websites, and real-time data feeds. The rate of data change and the velocity of updates further complicate the issue.
New information can emerge rapidly, necessitating frequent updates to the KG. Monitoring and integrating updates from such a vast array of sources require robust mechanisms and continuous monitoring to ensure timely data ingestion.
Navigating the path to reliable knowledge: identifying prominent obstacles to knowledge graph quality.Contextualization involves understanding and incorporating the appropriate context for the stored information. Context plays a crucial role in the interpretation and relevance of knowledge.
However, context can be multifaceted and dynamic, making it difficult to capture and represent accurately in a KG [5].
Contextual factors such as time, location, user preferences, cultural nuances, and domain-specific considerations impact the meaning and interpretation of information.
The same piece of knowledge may have different implications or relevance depending on the context in which it is used. It also necessitates the ability to dynamically adapt and personalize the presentation of knowledge based on the specific context of the user or application.
Furthermore, contextualization often requires a deep understanding of the domain and the ability to integrate diverse sources of information.
Comparative analysis plays a vital role in evaluating the accuracy of a KG. By juxtaposing the graph's content with external sources or reference datasets, analysts can discern inconsistencies and conflicting information.
This allows one to cross-reference the graph's assertions with reliable sources to determine their accuracy. Comparative analysis not only highlights discrepancies but also serves as a mechanism for validation and enhancement. By relying on external sources, analysts gain a broader perspective and establish a foundation for improving the accuracy of the KG.
Additionally, link prediction provides an effective means to evaluate KG completeness. This technique leverages the existing relationships and patterns in the graph to predict missing or future connections between entities [6]. By scrutinizing the graph's structure and using sophisticated algorithms, analysts can identify potential missing links that should logically exist based on the available information.
These predictions act as indicators of potential gaps or areas of limited coverage within the KG. Analysts can then prioritize efforts to address these gaps, enabling the enrichment and enhancement of the graph's completeness.
Combining comparative analysis and link prediction enhances the evaluation of KG quality. Comparative analysis ensures alignment with external sources, improving accuracy by resolving inconsistencies and verifying information.
Meanwhile, link prediction complements this evaluation by identifying missing relationships and providing insights into the graph's completeness.
By predicting connections that should logically exist, analysts gain a holistic view of the graph's comprehensiveness and can focus on integrating missing information.
(Real-Time Data Processing)
Continuous data integration is an essential concept in the realm of data management, particularly in the context of dynamic and rapidly changing data sources. It refers to the process of seamlessly and continuously incorporating new or updated data into a system or database in real-time.
KGs, as complex networks of interconnected information, benefit greatly from continuous data integration. By leveraging this approach, KGs can address the challenges posed by timeliness and data velocity. Firstly, continuous data integration enables the KG to capture and integrate new information as it becomes available, ensuring that the graph remains current and accurate.
Real-time or near real-time updates from various data sources facilitate the timely incorporation of new facts, relationships, and attributes into the KG.
Moreover, continuous data integration contributes to managing the velocity of data in KGs. As KGs interact with a diverse range of data sources, each with its own update frequency and data velocity, it is crucial to maintain synchronization and keep pace with the incoming data.
Continuous integration processes monitor the relevant data sources continuously, extracting and transforming the new or modified data, and integrating it seamlessly into the KG. This enables the graph to remain aligned with the changing data landscape, ensuring that it captures the most recent insights and developments [7].
User-centric customization refers to the process of tailoring knowledge graph experiences and outputs to the specific context of individual users.
It recognizes that different users have varying requirements and expectations when interacting with knowledge graphs and seeks to provide personalized and relevant information based on these factors [8].
User-centric customization addresses this challenge by allowing users to define their context and preferences, such as domain-specific filters.
This customization empowers users to explore and analyze the graph in a way that aligns with their specific objectives, resulting in more meaningful and valuable outcomes.
KGs, as complex networks of interconnected information, provide a flexible and expressive representation of knowledge and relationships.
The relationship between KGs and data fabric lies in their complementary nature, with KGs serving as a valuable component within the broader data fabric framework.
They capture entities, attributes, and relationships, offering a semantic layer that enables advanced querying, reasoning, and analysis.
KGs play a critical role in organizing structured knowledge and bridging the gap between unstructured data and AI applications.
Comparative analysis and link prediction provide approaches to assess accuracy and completeness by cross-referencing KG content with external sources and identifying missing relationships.
User-centric customization enhances the contextualization and value of KGs by tailoring outputs to the specific needs, preferences, and context of users or user groups.
Continued research and innovation in these areas will contribute to the advancement and effectiveness of KGs in various domains and applications.
1. Wang, X., Chen, L., Ban, T., Usman, M., Guan, Y., Liu, S., Wu, T., & Chen, H. (2021b). Knowledge Graph Quality Control: A survey. Fundamental Research, 1(5), 607–626. https://doi.org/10.1016/j.fmre.2021.09.003
3. Xue, B., & Zou, L. (2022). Knowledge Graph Quality Management: A comprehensive survey. IEEE Transactions on Knowledge and Data Engineering, 1–1. https://doi.org/10.1109/tkde.2022.3150080
4. Li, X., Lyu, M., Wang, Z., Chen, C.-H., & Zheng, P. (2021). Exploiting knowledge graphs in industrial products and services: A survey of key aspects, challenges, and future perspectives. Computers in Industry, 129, 103449. https://doi.org/10.1016/j.compind.2021.103449
5. Voskarides, N., Meij, E., Reinanda, R., Khaitan, A., Osborne, M., Stefanoni, G., Kambadur, P., & de Rijke, M. (2018). Weakly-supervised contextualization of knowledge graph facts. The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. https://doi.org/10.1145/3209978.3210031
6. Rossi, A., Barbosa, D., Firmani, D., Matinata, A., & Merialdo, P. (2021). Knowledge graph embedding for link prediction. ACM Transactions on Knowledge Discovery from Data, 15(2), 1–49. https://doi.org/10.1145/3424672
7. Le-Phuoc, D., Nguyen Mau Quoc, H., Ngo Quoc, H., Tran Nhat, T., & Hauswirth, M. (2016). The graph of things: A step towards the live knowledge graph of connected things. Journal of Web Semantics, 37–38, 25–35. https://doi.org/10.1016/j.websem.2016.02.003
8. Li, Xiang, Tur, G., Hakkani-Tur, D., & Li, Q. (2014). Personal knowledge graph population from user utterances in Conversational understanding. 2014 IEEE Spoken Language Technology Workshop (SLT). https://doi.org/10.1109/slt.2014.7078578
9. Hechler, E., Weihrauch, M., & Wu, Y. (2023). Data Fabric Architecture Patterns. Data Fabric and Data Mesh Approaches with AI, 231–255. https://doi.org/10.1007/978-1-4842-9253-2_10