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3 Papers

Classification of Knowledge Representations using an Ontology-based Approach

Topics: Ontology Engineering; Natural Language Processing; Knowledge Representation; Enterprise Ontology

Authors: Ruben Costa 1 , Paulo Figueiras 1 , Pedro Maló 1 and Celson Lima 2

Affiliations: 1 Universidade Nova de Lisboa, Portugal , 2 Federal University of Western Pará, Brazil

Digital Object Identifier: 10.5220/0004541701840191

Proceedings of KEOD 2013/IC3K, ISBN: 978-989-8565-81-5

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One of the primary research challenges in the knowledge representation domain relates to the process of formalization of document contents using dependent metadata and in particular how the classifiers are derived. Most approaches to determining appropriate classifiers are limited and only take account of the explicit, word-based information in the document. The research described in this paper explores the potential classifier enrichment through incorporation of implicit information derived from the complex relationships (Semantic Associations) in domain ontologies with the addition of information presented in documents for unsupervised document classification. The paper introduces a novel conceptual framework for representation of knowledge sources, where each knowledge source is semantically represented (within its domain of use) by a Semantic Vector (SV), which is enriched using the classical vector space model approach extended with ontological support, employing ontology concepts and their relations in the enrichment process. The test domain for the assessment of the approach is Building and Construction, using an appropriate available Ontology. Preliminary results were collected using a clustering algorithm for document classification, which indicates that the proposed approach does improve the precision and recall of classifications. Future work and open issues are also discussed.
Collapse Button Related Ontology Subjects/Areas/Topics (14)
Applications

Information Systems Analysis and Specification

Ontologies and the Semantic Web

Artificial Intelligence

Knowledge Engineering and Ontology Development

Ontology Engineering

Data Engineering

Knowledge Representation

Pattern Recognition

Enterprise Information Systems

Knowledge-Based Systems

Symbolic Systems
Enterprise Ontology

Natural Language Processing

A STANDARD-BASED SOFTWARE INFRASTRUCTURE TO SUPPORT ENERGY STORAGE IN DISTRIBUTED ENERGY SYSTEMS

Topics: Smart Grid Specific Protocols; Standards for Smart Grids; Energy Monitoring; Renewable Energy Resources; Energy Management Systems (EMS)

Authors: Vasco Delgado-Gomes 1 , José A. Oliveira-Lima 1 , João F. Martins 1 and Celson Lima 2

Affiliations: 1 CTS - Uninova, Portugal , 2 Universidade Federal do Oeste do Pará, Brazil

Digital Object Identifier: 10.5220/0003952600330038

Proceedings of SMARTGREENS 2012, ISBN: 978-989-8565-09-9

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Efficient energy storage in distributed energy systems depends on the operation of a set of coordinated devices spread around the power system. These include not only storage devices, but also production and consumption devices. Usually these devices encompass some communication skills and are able to be operated by energy management systems, regarding the status of the power system. However, despite this interaction readiness, most of these devices rely on non-standard communication protocols and some interoperability issues still remain. This paper presents an innovative plug & play approach for the integration of energy storage devices, supported by a standard-based and service-oriented software infrastructure entitled NEtworked MOnitoring & COntrol, Diagnostic for Electrical Distribution (NEMO). This approach adopts the International Electrotechnical Commission’s 61850 (IEC 61850) semantic information model and the Devices Profile for Web Services (DPWS) specific communication service mapping, to perform a seamless device integration and communication of energy storage devices.
Collapse Button Related Ontology Subjects/Areas/Topics (8)
Energy and Economy

Energy-Aware Systems and Technologies

Smart Grids

Energy Management Systems (EMS)

Renewable Energy Resources

Standards for Smart Grids
Energy Monitoring

Smart Grid Specific Protocols

Information Retrieval in Collaborative Engineering Projects - A Vector Space Model Approach

Topics: Ontology Engineering; Knowledge Representation; Process Knowledge and Semantic Services

Authors: Paulo Figueiras 1 , Ruben Costa 2 , Luis Paiva 3 , Celson Lima 4 and Ricardo Jardim-Gonçalves 2

Affiliations: 1 UNINOVA, Portugal , 2 UNINOVA and Universidade Nova de Lisboa, Portugal , 3 Universidade Nova de Lisboa, Portugal , 4 Federal University of Western Pará, Brazil

Digital Object Identifier: 10.5220/0004139302330238

Proceedings of KEOD 2012/IC3K, ISBN: 978-989-8565-30-3

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This work introduces a conceptual framework and its current implementation to support the classification and discovery of knowledge sources, where every knowledge source is represented through a vector (named Semantic Vector - SV). The novelty of this work addresses the enrichment of such knowledge representations, using the classical vector space model concept extended with ontological support, which means to use ontological concepts and their relations to enrich each SV. Our approach takes into account three different but complementary processes using the following inputs: (1) the statistical relevance of keywords, (2) the ontological concepts, and (3) the ontological relations. SVs are compared against each other, in order to obtain their similarity index, and better support end users with a search/retrieval of knowledge sources capabilities. This paper presents the technical architecture (and respective implementation) supporting the conceptual framework, emphasizing the SV creation process. Moreover, it provides some examples detailing the indexation process of knowledge sources, results achieved so far and future goals pursued here are also presented.
Collapse Button Related Ontology Subjects/Areas/Topics (11)
Artificial Intelligence

Knowledge Engineering and Ontology Development

Ontology Engineering

Data Engineering

Knowledge Representation

Process Knowledge and Semantic Services

Enterprise Information Systems

Knowledge-Based Systems

Symbolic Systems
Information Systems Analysis and Specification

Ontologies and the Semantic Web

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