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

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

OBOME - Ontology based opinion mining in UBIPOL

Ontologies have a special role in the UBIPOL system, they help to structure the policy related context, provide conceptualization for policy domain and use in the opinion mining process. In this work we presented a system called Ontology Based Opinion Mining Engine (OBOME) for analyzing a domain-specific opinion corpus by first assisting the user with the creation of a domain ontology from the corpus. We determined the polarity of opinion on the various domain aspects. In the former step, the policy domain aspect has are identified (namely which policy category is represented by the concept). This identification is supported by the policy modelling ontology, which describe the most important policy – related classes and structure. Then the most informative documents from the corpus are extracted and asked the user to create a set of aspects and related keywords using these documents. In the latter step, we used the corpus specific ontology to model the domain and extracted aspect-polarity associations using grammatical dependencies between words. Later, summarized results are shown to the user to analyze and store. Finally, in an offline process policy modeling ontology is updated

Development of an ontology for aerospace engine components degradation in service

This paper presents the development of an ontology for component service degradation. In this paper, degradation mechanisms in gas turbine metallic components are used for a case study to explain how a taxonomy within an ontology can be validated. The validation method used in this paper uses an iterative process and sanity checks. Data extracted from on-demand textual information are filtered and grouped into classes of degradation mechanisms. Various concepts are systematically and hierarchically arranged for use in the service maintenance ontology. The allocation of the mechanisms to the AS-IS ontology presents a robust data collection hub. Data integrity is guaranteed when the TO-BE ontology is introduced to analyse processes relative to various failure events. The initial evaluation reveals improvement in the performance of the TO-BE domain ontology based on iterations and updates with recognised mechanisms. The information extracted and collected is required to improve service k nowledge and performance feedback which are important for service engineers. Existing research areas such as natural language processing, knowledge management, and information extraction were also examined

Ontologies and Information Extraction

This report argues that, even in the simplest cases, IE is an ontology-driven process. It is not a mere text filtering method based on simple pattern matching and keywords, because the extracted pieces of texts are interpreted with respect to a predefined partial domain model. This report shows that depending on the nature and the depth of the interpretation to be done for extracting the information, more or less knowledge must be involved. This report is mainly illustrated in biology, a domain in which there are critical needs for content-based exploration of the scientific literature and which becomes a major application domain for IE