Intelligent agents are considered as significant means towards realizing the semantic web vision. On the Semantic Web, integrating ONTOLOGIES AND RULES enables software agents to interoperate between them, however, this leads to a problem, that no studies have focused on effective distributed reasoning for integrating ONTOLOGIES AND RULES in multiple knowledge-bases. The methods that have been presented for distributed reasoning not only get a lot of times AND memory, but also do not lead to a complete AND sound reasoning. In this paper, to solve this problem, we present a distributed reasoning system that deals with the representation of the knowledge-base of order sorted logic. This logic is able to describe the hierarchy of predicates AND inheritance of expressions that there are in our natural language. To have a distributed reasoning, our proposed method uses the expansion of rigid AND valid-non-rigid properties between knowledge-bases. Furthermore, with considering time AND the situation of properties for reasoning, the non-rigid properties have not been ignored, in fact, in their valid time AND situation, they are used. With this method, we achieve a complete reasoning AND, moreover, the extracted knowledge is completely considered in the knowledge-bases AND we have a distributed reasoning with high efficiency AND sound without missing any information.

Ontology AND social tagging, despite the differences they have with each other, are the new ways of organizing, representing AND sharing knowledge in the electronic environment AND can help promote each other. The aim of this research is to describe the relationships AND applications of these two in conjunction with each other. The present study is written with a library AND conceptual approach. Necessary information was gathered using the study of printed AND electronic information resources available in libraries, the Internet, AND Persian AND English databases. Although the position of tags is in Web 2. 0 AND the ontology position is in Semantic Web 2. 0, but we can use tags in the semantic web AND ONTOLOGIES in Web 2. 0. The use of ontology in Web 2. 0 causes creation of semantic structure AND fixes its defects AND weaknesses in organizing AND retrieving that resulting from difficulties related to users, vocabulary as well as system weaknesses in the tagging in Web 2. 0. The use of tags in ontology, also, causes updating, making them more functional AND accepting them in the user community. The lack or deficiency of these is due to the lack of attention paid to end users by ontology AND also the literature AND resources used to create ONTOLOGIES are not up to date.

Background AND Aim: ONTOLOGIES facilitate data integration, exchange, searching AND querying. Open Biomedical ONTOLOGIES (OBO) Foundry is a solution for creating reference ONTOLOGIES. In this foundry, the design of ONTOLOGIES is based on established principles which allow for their interactions as a single system. The purpose of this study is to determine the main features of ONTOLOGIES developed based on foundry principles. Materials AND Methods: This descriptive cross-sectional study (2017) retrieved data about the ontology components, subject, language AND tool from the OBO foundry website, AND OntoBee (the default linked data server for the foundry ONTOLOGIES). The frequency of classes, instances, Object Properties, DataType Properties AND Annotation Properties are classified by the use of descriptive statistics. Results: The main subjects of 151 ONTOLOGIES were Anatomy, health AND Phenotype. Most languages AND tools were, respectively, OBO AND OBO-edit versions used for the development of ONTOLOGIES. Five percent of ONTOLOGIES contained more than 78977 classes, 150 object relations, 39 DataType relations, 110 annotation relations, AND 356 instances. Conclusion: Increasing the number of ONTOLOGIES developed based on OBO foundry principles as well as using OBO language AND OBO-Edit is indicative of the fact that ontology developers have accepted the principles AND tools.

Objective: The aim of this research is to conduct an analysis of qualitative AND quantitative studies on applying thesauri to the construction AND development of ONTOLOGIES AND the identification of subject domains. We will investigate the approaches AND methods used in these studies.Method: This descriptive-analytical research was conducted following the seven stages of systematic review suggested by Petticrew AND Roberts (2008). Data analysis AND visualization were facilitated using the DatawRapper software.Results: The analysis of studies in the area of applying thesauri in the construction AND development of ONTOLOGIES, while identifying AND presenting the subject trends of studies, frequency, methods, languages AND approaches, showed that the studies in this area include six main topics as follows: applying thesauri in the construction AND development of ONTOLOGIES in connection with multilingual information retrieval, applying thesauri in the construction AND development of ONTOLOGIES to cover a specific subject domain, applying the ISO 25964 stANDard in the development of ONTOLOGIES using thesauri, presenting the framework, methods AND tools for the use of thesauri in construction AND development of ONTOLOGIES, improving semantic relations AND concept mapping using thesauri in the construction AND development of ONTOLOGIES AND review studies of thesauri conversion to ONTOLOGIES. Conclusions: Various subject domains, across different time periods, have benefited from applying thesauri for the construction AND development of ONTOLOGIES. Thesauri, as semantic tools, have provided the context for organizing, retrieving AND accessing information more accurately AND easily for stakeholders AND applicants across various domains. Despite these advancements, the lack of a stANDardized AND consensual framework, coupled with the need to update methods, approaches, AND tools due to the reliance on natural language processing (NLP) technologies AND knowledge engineering, has necessitated further research to mature this field.