Knowledge Graph Construction, Integration, and Reasoning for Enterprise Intelligence Systems
Abstract
: Knowledge graphs (KGs) have emerged as a foundational technology for enterprise intelligence systems by enabling semantic representation, integration, and reasoning over heterogeneous data. In an age where enterprises accumulate vast amounts of structured, semi‑structured, and unstructured information, the ability to unify this data into a coherent, semantically rich structure is essential for advanced analytics, decision support, and automated reasoning. KG construction unifies entity extraction, disambiguation, and schema alignment to produce a graph where entities and their relationships are explicitly modeled. Integration focuses on merging data from multiple sources into a unified model, resolving conflicts, and preserving semantic meaning. Reasoning leverages the graph’s semantics to infer new knowledge, support query answering, and empower automated decision systems. In enterprise environments, KGs enable business intelligence, enhanced search, knowledge discovery, and explainable AI. Despite their promise, challenges persist in scalability, data quality, ontology design, and reasoning performance. This paper presents a comprehensive overview of KG construction, integration, and reasoning tailored for enterprise intelligence systems, surveying existing methodologies, frameworks, advantages, limitations, and future directions. Enterprise KGs will continue to play a central role in next‑generation intelligent systems, bridging symbolic representation and data‑driven analytics. MDPI
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 4 No. 5 (2021): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
7557-7561 |
Published |
September 15, 2021 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Anita Nair (2021). Knowledge Graph Construction, Integration, and Reasoning for Enterprise Intelligence Systems. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 4 No. 5 (2021): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 7557-7561. https://doi.org/10.15662/IJAESIT.2021.0405001 |
References
2. Sowa, J. F. (1984). Conceptual Structures: A Formalism for Knowledge Representation.
3. Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowledge Acquisition.
4. Berners Lee, T., Hendler, J., & Lassila, O. (2001). The Semantic Web. Scientific American.
5. McCarthy, J. (1980). Circumscription—A form of non monotonic reasoning.
6. Hayes, P. (1992). Graphs and Networks in Knowledge Representation.
7. Brachman, R. J., & Levesque, H. J. (1985). Knowledge Representation and Reasoning.
8. Kline, R. R. (1998). Semantic Networks for Enterprise Data Integration.
9. Guarino, N. (1998). Formal ontology in information systems. FOIS.
10. Konakalla, K. (2020). Automated commission calculation and sales quota management in Salesforce: A code-driven approach for sales efficiency. International Journal, 7, 125-127.
11. Lenat, D. B., & Guha, R. V. (1990). Building Large Knowledge Based Systems.
12. Horrocks, I. (2008). Ontologies and the Semantic Web. Communications of the ACM.
13. Auer, S., et al. (2007). DBpedia: A Nucleus for a Web of Open Data.
14. Heath, T., & Bizer, C. (2011). Linked Data: Evolving the Web into a Global Data Space.
15. Paulheim, H. (2017). Knowledge Graph Refinement. Data Mining and Knowledge Discovery.
16. Hogan, A., et al. (2021). Knowledge Graphs. Foundations and Trends in Databases.
17. Kejriwal, M. (2022). Knowledge Graphs: A Practical Review. MDPI
18. Jin, G., Lü, F., & Xiang, Z. (2014). Enterprise integration based on knowledge graphs. Bohrium