Knowledge Representation Reasoning

Key Points in the KRR

In the field of artificial intelligence, knowledge representation and reasoning are essential areas of research. Knowledge representation involves converting information into a machine-readable format, while reasoning involves utilizing this information to make informed decisions and solve problems [1].

Here are some main points about knowledge representation and reasoning:

1. Methods of presenting information, such as ontologies, semantic networks, and rule-based systems, can be used to represent knowledge [2].

2. Various reasoning algorithms, including deductive, inductive, and abductive reasoning, exist [3].

3. Methods for managing incomplete information and uncertainty, such as fuzzy logic and probabilistic reasoning, can be employed [4].

4. The implementation of knowledge representation and reasoning in artificial intelligence has numerous applications, including expert systems, natural language processing, and robotics [5].

5. Frameworks and tools, like Protégé and RDFLib, are available to develop knowledge representation and reasoning systems [6][7].

6. Modern research trends in knowledge representation and reasoning include machine learning-based methods and deep learning techniques [8].

7. Illustrative case studies and instances of knowledge representation and reasoning systems are evident in different fields, including medicine, finance, and manufacturing.

References

[1] “Knowledge representation – what is it?,” AI is here to support you., 11-Apr-2023. [Online]. Available: https://ai-info.org/learn-about-ai/knowledge-representation-what-is-it/. [Accessed: 16-Apr-2023].

[2] “Ai techniques of Knowledge Representation - Javatpoint,” www.javatpoint.com. [Online]. Available: https://www.javatpoint.com/ai-techniques-of-knowledge-representation. [Accessed: 16-Apr-2023].

[3] “Deductive, inductive and abductive reasoning - tip sheet,” Butte College. [Online]. Available: https://www.butte.edu/departments/cas/tipsheets/thinking/reasoning.html. [Accessed: 16-Apr-2023].

[4] “Probabilistic reasoning in intelligent systems:networks of plausible inference,” Guide books. [Online]. Available: https://dl.acm.org/doi/10.5555/534975. [Accessed: 16-Apr-2023].

[5] Expert systems and Applied Artificial Intelligence. [Online]. Available: https://www.umsl.edu/~joshik/msis480/chapt11.htm. [Accessed: 16-Apr-2023].

[6] Stanford Center for Biomedical Informatics Research, “A free, open-source ontology editor and framework for Building Intelligent Systems,” protégé. [Online]. Available: https://protege.stanford.edu/. [Accessed: 16-Apr-2023].

[7] “Rdflib 6.3.2¶,” rdflib 6.3.2 - rdflib 6.3.2 documentation. [Online]. Available: https://rdflib.readthedocs.io/. [Accessed: 16-Apr-2023].

[8] A. S. Garcez, “Advances in neural-symbolic learning systems: Modal and temporal reasoning,” Perspectives of Neural-Symbolic Integration, pp. 265–282, 2007.