Ontological evaluation in the knowledge based system

In the last few years, several studies have emphasized the use of ontologies as an alternative to organization of the information. The notion of ontology has become popular in fields such as intelligent information integration, information retrieval on the Internet, and knowledge management. Different groups use different approaches to develop and verify de effectiveness of ontologies. This diversity can be a factor that makes the formularization difficult of formal methodologies of evaluation. This paper intends to provide a way to identify the effectiveness of knowledge representation based on ontology that was developed through Knowledge Based System tools. The reason is that all processing and storage of gathered information and knowledge base organization is performed using this structure. Our evaluation is based on case studies of the KMAI system, involving real world ontology for the money laundry domain. Our results indicate that modification of ontology structure can effectively reveal faults, as long as they adversely affect the program state.Applications in Artificial Intelligence - Knowledge EngineeringRed de Universidades con Carreras en Informática (RedUNCI

A model for concepts extraction and context identification in knowledge based systems

Information Retrieval Systems normally deal with keywordbased technologies. Although those systems reach satisfactory results, they aren’t able to answer more complex queries done by users, especially those directly in natural language. To do that, there are the KnowledgeBased Systems, which use ontologies to represent the knowledge embedded in texts. Currently, the construction of ontologies is based on the participation of three components: the knowledge engineer, the domain specialist, and the system analyst. This work demands time due to the various studies that should be made do determine which elements must participate of the knowledge base and how these elements are interrelated. In this way, using computational systems that, at least, accelerate this work is fundamental to create systems to the market. A model, that allows a computer directly represents the knowledge, just needing a minimal human intervention, or even no one, enlarges the range of domains a system can maintain, becoming it more efficient and userfriendly.Applications in Artificial Intelligence - Knowledge EngineeringRed de Universidades con Carreras en Informática (RedUNCI

Process of ontology construction for the development of an intelligent system for the organization and retrieval of knowledge in biodiversity – SISBIO

This work describes the ontology construction process for the development of an Intelligent System for the Organization and Retrieval of Knowledge in Biodiversity – SISBIO. The system aims at the production of strategic information for the biofuel chain Two main methodologies are used for the construction of the ontologies: knowledge engineering and ontology engineering. The first one consists of extracting and organizing the biofuel specialists´ knowledge, and ontology engineering is used to represent the knowledge through indicative expressions and its relations, developing a semantic network of relationships.Applications in Artificial Intelligence - Ontologies and Intelligent WebRed de Universidades con Carreras en Informática (RedUNCI

Using competence modeling to create knowledge engineering team

The present paper is about applying competence modeling for a knowledge engineer in the case of the company WBSA Sistemas Inteligentes S.A. The process was based on Lucia and Lepsinger model, by which competences are characterized through the identification of situations and behaviors considered relevant to the engineer performance. As one of the different techniques suggested by the model for collecting data, a number of individual interviews were undertaken and at the end it was defined and validated a set of eleven competence regarded as necessary for a satisfactory performance of a knowledge engineerApplications in Artificial Intelligence - Knowledge EngineeringRed de Universidades con Carreras en Informática (RedUNCI

Ontological evaluation in the knowledge based system

In the last few years, several studies have emphasized the use of ontologies as an alternative to organization of the information. The notion of ontology has become popular in fields such as intelligent information integration, information retrieval on the Internet, and knowledge management. Different groups use different approaches to develop and verify de effectiveness of ontologies. This diversity can be a factor that makes the formularization difficult of formal methodologies of evaluation. This paper intends to provide a way to identify the effectiveness of knowledge representation based on ontology that was developed through Knowledge Based System tools. The reason is that all processing and storage of gathered information and knowledge base organization is performed using this structure. Our evaluation is based on case studies of the KMAI system, involving real world ontology for the money laundry domain. Our results indicate that modification of ontology structure can effectively reveal faults, as long as they adversely affect the program state.Applications in Artificial Intelligence - Knowledge EngineeringRed de Universidades con Carreras en Informática (RedUNCI

A model for concepts extraction and context identification in knowledge based systems

Information Retrieval Systems normally deal with keywordbased technologies. Although those systems reach satisfactory results, they aren’t able to answer more complex queries done by users, especially those directly in natural language. To do that, there are the KnowledgeBased Systems, which use ontologies to represent the knowledge embedded in texts. Currently, the construction of ontologies is based on the participation of three components: the knowledge engineer, the domain specialist, and the system analyst. This work demands time due to the various studies that should be made do determine which elements must participate of the knowledge base and how these elements are interrelated. In this way, using computational systems that, at least, accelerate this work is fundamental to create systems to the market. A model, that allows a computer directly represents the knowledge, just needing a minimal human intervention, or even no one, enlarges the range of domains a system can maintain, becoming it more efficient and userfriendly.Applications in Artificial Intelligence - Knowledge EngineeringRed de Universidades con Carreras en Informática (RedUNCI

Process of ontology construction for the development of an intelligent system for the organization and retrieval of knowledge in biodiversity – SISBIO

This work describes the ontology construction process for the development of an Intelligent System for the Organization and Retrieval of Knowledge in Biodiversity – SISBIO. The system aims at the production of strategic information for the biofuel chain Two main methodologies are used for the construction of the ontologies: knowledge engineering and ontology engineering. The first one consists of extracting and organizing the biofuel specialists´ knowledge, and ontology engineering is used to represent the knowledge through indicative expressions and its relations, developing a semantic network of relationships.Applications in Artificial Intelligence - Ontologies and Intelligent WebRed de Universidades con Carreras en Informática (RedUNCI

Using competence modeling to create knowledge engineering team

The present paper is about applying competence modeling for a knowledge engineer in the case of the company WBSA Sistemas Inteligentes S.A. The process was based on Lucia and Lepsinger model, by which competences are characterized through the identification of situations and behaviors considered relevant to the engineer performance. As one of the different techniques suggested by the model for collecting data, a number of individual interviews were undertaken and at the end it was defined and validated a set of eleven competence regarded as necessary for a satisfactory performance of a knowledge engineerApplications in Artificial Intelligence - Knowledge EngineeringRed de Universidades con Carreras en Informática (RedUNCI

Reusing cases to the automatic index assignment from textual documents

Paper presented at the Sixth German Workshop on Case-Based Reasoning: Foundations, Systems, and Applications, Rostock, Germany.This paper describes one solution developed to convert textual documents into formlike representations of cases. The experiences described by cases are textual descriptions of legal decisions. Indexing vocabulary and assignment theory contributed in gathering expert knowledge to define attributes and values as well as the required elements to employ template mining. Most index values are automatically extracted by the use of template mining. The multi-purpose index Theme is automatically assigned by reusing cases through an elaboration process. Seed cases are used to indicate values if the new case is a partial match to one in the case base

Representing cases from texts in case-based reasoning

Paper presented at the Third International Conference of Industrial Engineering and XVII ENEGEP, Rio Grande do Sul, Brazil.Case representation is a Case-Based Reasoning (CBR) problem area that refers to selecting proper descriptors to describe and index cases. The complexity of case representation has been preventing CBR systems from solving problems when large case bases are required. We present the development and implementation of a methodology to automatically convert legal texts into cases based on indexing methods and domain expert knowledge. The methodology is tailored to the domain of law although it can be extended to be applied to other domains as well