Global Journal of Computer Science and Technology, D: Neural & Artificial Intelligence, Volume 23 Issue 2

existence of unknown worlds . Existing ontologies are mainly imbued with Western culture, so as immense as they are, they are never complete [2]. Before showing how to add new worlds into pre-established concepts, let us first see how to represent knowledgemathematically for the purpose of its automatic processing. a) Knowledge Base Our goal is to model a knowledge-based agent that can form representations of a real world. The task is not about actually representing everything in the world. New representations are derived from existing ones through inference processes. These new representations are used to deduce what to do. A knowledge base (KB) consists of a set of sentences that are expressed in a knowledge representation language. Each sentence corresponds to some assertion about the world. When the sentence is considered as given without being derived from other sentences, we call it an axiom . The KB may initially contain some background axioms. © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue II Version I 2 ( ) Year 2023 D • The standard operation ASK to query knowledge from KB. • In the following section, we will study, how the expression of sentences and the definition of their semantics are achieved through logic. b) Logic i. Syntax Logic governs the representation language and specifies through a grammar all the sentences that aresyntactically correct (well-formed). According to [1], the syntax of First-Ordered-Logic is given in Figure 01. According to [1], two types of operation are used to manage knowledge in a KB: • The standard operation TELL to add new sentences to the KB Figure 01: Syntax of First-Order-Logic in Backus-Naur-Form → | → | ( , … ) | = → ( )| [ ] | ¬ | | ∨ | ⟹ | ⟺ | , … → ( )| | → ∀ | ∃ → | 1 | John | … → | | | … → | | | | | … → ℎ | | … ii. Semantics A logic must also define the meaning (semantics) of sentences. Depending on the used logic, this task can be simple or more sophisticated. Propositional logic simply assumes that there are facts that either hold or do not hold in the world. Propositional logic has the advantage of using a declarative, context- independent and unambiguous semantics. It is sufficient to illustrate the basic concepts of logic and knowledge-based agents. Nevertheless, it is not suitable to represent knowledge of complex environments in a concise way. For this reason, first-order-logic is preferred. It builds a more expressive logic on the foundation of propositional logic, borrowing representational ideas from natural language and at the same time avoiding its disadvantages. Its language is built around objects and relations. It also forms the foundation of many other representation languages. iii. Model For every sentence, its truth or falsehood is specified through a model. The possible models are just all possible assignments to the concerned variables. If a sentence α is true in model m, we say that m satisfies α or m is a model of α . M( α ) is the set of all models of α . For example, the sentence a * 3 = 6 is true in a world where a is 2 , but false in any other world. iv. Logical Entailment Logical entailment is a relation between two sentences the second of which follows logically from the first one. It is the basis of l ogical reasoning. In mathematical notation, we write α β to mean that the sentence α entails the sentence β . The formal definition of entailment is α β if and only if ( ) ⊆ ( ) In clear: α β if and only if, in every model in which α is true, β is also true. v. Logical inference Entailment is applied to carry out logical inference (to derive conclusions). If an inference Guideline for Including Unperceivable Knowledge in a Universal Ontology Experimentation Field: Ontology Malagasy