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

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

Development of a data model for an Adaptive Multimedia Presentation System (AMPS)

We investigate the requirements and nature of data models for a multimedia learning system that presents adaptable learning objects based on a range of stimuli provided by the student and tutor. A conceptual model is explored together with a proposal for an implementation using the well-known relational data model. We also investigate how to describe the learning objects in the form of hierarchical subject ontology. An ontological calculus is created to allow knowledge metrics to be constructed for evaluation within data models. We further consider the limitations of the relational abstract data model to accurately represent the meaning and understanding of learning objects and contrast this with less structured data models implicit in ontological hierarchies. Our findings indicate that more consideration is needed into how to match traditional data models with ontological structures, especially in the area of database integrity constraints

Evaluation of the Project Management Competences Based on the Semantic Networks

The paper presents the testing and evaluation facilities of the SinPers system. The SinPers is a web based learning environment in project management, capable of building and conducting a complete and personalized training cycle, from the definition of the learning objectives to the assessment of the learning results for each learner. The testing and evaluation facilities of SinPers system are based on the ontological approach. The educational ontology is mapped on a semantic network. Further, the semantic network is projected into a concept space graph. The semantic computability of the concept space graph is used to design the tests. The paper focuses on the applicability of the system in the certification, for the knowledge assessment, related to each element of competence. The semantic computability is used for differentiating between different certification levels.testing, assessment, ontology, semantic networks, certification.

Technology for Ontological Engineering Lifecycle Support

The research is partially supported by Russian Foundation for Basic Research (grants 06-01-81005 and 07-01- 00053)Presented paper describes software system project ONTOLINGE-KAON that provides technological support for the whole lifecycle of ontological engineering. The main stress is put on the evaluation of maturity and quality of ontologies and on the usage of ontologies with the help of automated generation of knowledge portals, based on ontologies. Possibility of creation of knowledge portals built on top of ontologies can become a big step forward in the field of e-learning. The paper presents advantages provided by knowledge portals based on top on ontologies

An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise

Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.Comment: 25 pages, 6 figures, 5 tables, minor revision

An ontology-based universal design knowledge support system

Cataloged from PDF version of article.An effective and efficient knowledge support system is crucial for universal design process, as it has become a major design issue in the last decade with the growth of the elderly population and disabled people. There are a limited number of CAD investigations on the nature of knowledge processing that supports the cognitive activities of universal design process. Therefore, this paper proposes an ontology-based computer-assisted universal design (CAUD) plug-in tool that supports designers in developing satisfactory universal design solutions in the conceptual design phase. The required knowledge processing and representation of the developed tool is motivated by the ontological language. It is based on the multiple divergence-convergence cognitive strategies and cognitive needs of designers in the analysis/synthesis/evaluation operations. The CAUD plug-in tool is the first attempt to interface the universal design knowledge ontologically and respond to the requirements of conceptual design phase. According to the user acceptance study, the tool is assessed as useful, understandable, efficient, supportive and satisfactory

On Implementing Temporal Query Answering in DL-Lite

Ontology-based data access augments classical query answering over fact bases by adopting the open-world assumption and by including domain knowledge provided by an ontology. We implemented temporal query answering w.r.t. ontologies formulated in the Description Logic DL-Lite. Focusing on temporal conjunctive queries (TCQs), which combine conjunctive queries via the operators of propositional linear temporal logic, we regard three approaches for answering them: an iterative algorithm that considers all data available; a window-based algorithm; and a rewriting approach, which translates the TCQs to be answered into SQL queries. Since the relevant ontological knowledge is already encoded into the latter queries, they can be answered by a standard database system. Our evaluation especially shows that implementations of both the iterative and the window-based algorithm answer TCQs within a few milliseconds, and that the former achieves a constant performance, even if data is growing over time

ProCAVIAR: Hybrid Data-Driven and Probabilistic Knowledge-Based Activity Recognition

The recognition of physical activities using sensors on mobile devices has been mainly addressed with supervised and semi-supervised learning. The state-of-the-art methods are mainly based on the analysis of the user\u2019s movement patterns that emerge from inertial sensors data. While the literature on this topic is quite mature, existing approaches are still not adequate to discriminate activities characterized by similar physical movements. The context that surrounds the user (e.g., semantic location) could be used as additional information to significantly extend the set of recognizable activities. Since collecting a comprehensive training set with activities performed in every possible context condition is too costly, if possible at all, existing works proposed knowledge-based reasoning over ontological representation of context data to refine the predictions obtained from machine learning. A problem with this approach is the rigidity of the underlying logic formalism that cannot capture the intrinsic uncertainty of the relationships between activities and context. In this work, we propose a novel activity recognition method that combines semisupervised learning and probabilistic ontological reasoning. We model the relationships between activities and context as a combination of soft and hard ontological axioms. For each activity, we use a probabilistic ontology to compute its compatibility with the current context conditions. The output of probabilistic semantic reasoning is combined with the output of a machine learning classifier based on inertial sensor data to obtain the most likely activity performed by the user. The evaluation of our system on a dataset with 13 types of activities performed by 26 subjects shows that our probabilistic framework outperforms both a pure machine learning approach and previous hybrid approaches based on classic ontological reasoning

Combining ontological and temporal formalisms for composite activity modelling and recognition in smart homes

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Activity recognition is essential in providing activity assistance for users in smart homes. While significant progress has been made for single-user single-activity recognition, it still remains a challenge to carry out real-time progressive composite activity recognition. This paper introduces a hybrid ontological and temporal approach to composite activity modelling and recognition by extending existing ontology-based knowledge-driven approach. The compelling feature of the approach is that it combines ontological and temporal knowledge representation formalisms to provide powerful representation capabilities for activity modelling. The paper describes in detail ontological activity modelling which establishes relationships between activities and their involved entities, and temporal activity modelling which defines relationships between constituent activities of a composite activity. As an essential part of the model, the paper also presents methods for developing temporal entailment rules to support the interpretation and inference of composite activities. In addition, this paper outlines an integrated architecture for composite activity recognition and elaborated a unified activity recognition algorithm which can support the recognition of simple and composite activities. The approach has been implemented in a feature-rich prototype system upon which testing and evaluation have been conducted. Initial experimental results have shown average recognition accuracy of 100% and 88.26% for simple and composite activities, respectively