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

and problem specific facts to drift by itself the fact we are interested in knowing. There are different inference algorithms like model checking, theorem proving, forward-chaining, Davis-Putnam algorithm, Hill-climbing search [1]. They have respectively their advantages and their drawbacks but their studies are beyond the scope of the present article. An upcoming article will be dedicated to the explanation as well as the demonstration of the operating mode of an inference algorithm. So far we have studied knowledge as an abstract and a general concept with an abstract representation language. In this abstract form, knowledge reuse and knowledge sharing will not be possible. In the following section, we will learn knowledge implementation through the use of ontologies. III. O ntology In philosophy, the term ontology refers to the science that studies being as being . With the emergence of knowledge engineering and the Semantic Web, and emphasizing the importance of knowledge sharing and reuse, this definition has been extended. “An ontology is a formal specification of a shared conceptualization” [6]. Ontology represents a means of materializing knowledge in a form and in a structure that makes its reuse and its sharing possible. a) Taxonomy A general ontology organizes everything in the world into a hierarchy of categories - called a taxonomy - such as Events, Time, Physical objects and beliefs. The organization of objects into categories is a vital part of knowledge representation since much reasoning takes place at the level of categories. Categories permit also to make predictions about classified objects. A taxonomy has a tree structure. Categories (or classes) serve to organize and simplify the knowledge base through inheritance. If we say that all instances of the category Persons have the property are Mortal , and if we assert that Women is a subclass of Persons and Mothers is a Subclass of Women , then our agent will know that every mother is mortal. We say that the individual woman inherit the property of mortality, in this case from their membership in the Persons category. b) Relationships It is possible to bring more precisions into the characterization of the relations between categories. To state that two categories that are not subclasses of each other (e.g. Males and Females) have no members in common, we use the relation Disjoint: Disjoint ( {Males, Females}). We can go further and precise that an animal that is not a male must be a female, and say therefore that males and females constitute an exhaustive decomposition of the animals. A disjoint exhaustive decomposition is known as a partition. Partition ({Males, Females}, Animals). We use the general Part Of relation to state that one thing is part of another. Through the introduction of such types of relations between categories, the tree becomes a graph. c) Named Entities For the purpose of our project, we need an ontology that should not only contain categories or concepts but also named entities designating among other people, organizations, places and other important things. It should be possible to establish relations between individuals of different kinds: What is located where? Who was born where? Which sovereign reigned during which period? d) Ontology Language The best known languages to write ontologies are OWL (Web Ontology Language) and RDFS (Resource Description Framework Schema). Both are computational logic-based languages such that knowledge expressed in OWL or in RDFS can be exploited by computer programs. In the following section, we present the YAGO model that is a slight extension of RDFS. it is designed to be extendable by other sources (high quality sources, domain-specific extensions, or data gathered through information extraction from Web pages), what makes it highly interesting for our project. IV. Y ago ( Y et- A nother- G reat- O ntology) YAGO was developed at the Max-Planck- Institute for Informatics in Germany. It is able to express entities, facts, relations between facts and properties of relations, while it is at the same time simple and decidable.In contrast to other existing ontologies, that are limited to a single source of background knowledge, YAGOcombines high coverage with high quality [8]. Its latest version YAGO 4 (2022) is a cleaned version of Wikidata that contains more than 50 million entities and 2 billion facts. a) The Components of YAGO In YAGO, all objects (concepts) are represented as entities which are organized according to a taxonomy. The higher classes come from schema.org 2 and the lower classes from Wikidata. In the leaves of the tree, we no longer have classes but named entities, that is, concrete objects, individuals. We will refer to entities that are neither facts nor relations as common entities . An ontology represents also relationships between entities that have no hierarchical link between them. Example: In “An author writes a book”, there is no hierarchical link between author and book. The two concepts are related by “writing”. YAGO does not only allow relationships between entities but even between © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue II Version I 4 ( ) Year 2023 D Guideline for Including Unperceivable Knowledge in a Universal Ontology Experimentation Field: Ontology Malagasy