A Proposed Model for Improving the Performance of Knowledge Bases in Real-World Applications by Extracting Semantic Information

Knowledge Bases are information resources that convert factual knowledge to machine-readable formats to allow users to extract their desired data from multiple sources. The objective of knowledge base population frameworks is to extend KBs with semantic information to solve fundamental artificial intelligence problems such as understanding human knowledge. Information extraction entails the discovery of critical knowledge facts from unstructured text, which is important in the population of knowledge bases. The objective of this paper is to explore the concept of information extraction as a technique for accelerating the performance of knowledge bases with minimal annotation efforts for real-world applications such as content recommendation during a web search. This entails performing slot filling operations for data collection from large KBs and applying probabilistic estimations to determine the accuracy of the new information. The results are then used to explore the feasibility of applying knowledge bases to real-world tasks such as user-centric information access by encoding entities with deep semantic knowledge.

@article{Karrar2022,
  title     = {A Proposed Model for Improving the Performance of Knowledge Bases in Real-World Applications by Extracting Semantic Information},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {2},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Abdelrahman Elsharif Karrar},
  doi       = {10.14569/IJACSA.2022.0130214},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130214}
}