Reasoning with large ontologies stored in relational databases: The OntoMinD approach

Al-Jadir, Lina; Parent, Christine; Spaccapietra, Stefano

doi:10.1016/j.datak.2010.07.006

Cited by 28 publications

(13 citation statements)

References 4 publications

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“…OntoDB maps the metaschema of the ontology and the metadata of the database. is is a reverse process of generating an ontology through the metadata, which can guarantee high query performance based on the OBDB [24][25][26].…”

Section: Database-based Large-scale Ontology Data Managementmentioning

confidence: 99%

Methodology for Automatic Ontology Generation Using Database Schema Information

Park

2018

Mobile Information Systems

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An ontology is a model language that supports the functions to integrate conceptually distributed domain knowledge and infer relationships among the concepts. Ontologies are developed based on the target domain knowledge. As a result, methodologies to automatically generate an ontology from metadata that characterize the domain knowledge are becoming important. However, existing methodologies to automatically generate an ontology using metadata are required to generate the domain metadata in a predetermined template, and it is difficult to manage data that are increased on the ontology itself when the domain OWL (Ontology Web Language) individuals are continuously increased. The database schema has a feature of domain knowledge and provides structural functions to efficiently process the knowledge-based data. In this paper, we propose a methodology to automatically generate ontologies and manage the OWL individual through an interaction of the database and the ontology. We describe the automatic ontology generation process with example schema and demonstrate the effectiveness of the automatically generated ontology by comparing it with existing ontologies using the ontology quality score.

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Section: Database-based Large-scale Ontology Data Managementmentioning

confidence: 99%

Methodology for Automatic Ontology Generation Using Database Schema Information

Park

2018

Mobile Information Systems

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“…Currently, several systems like Minerva [Heymans et al, 2008] and OntoMind [Al-Jadir et al, 2010] have been designed to store ontology classes and relationships (called TBox in ontology literature) as well as instances (called ABox). Currently, several systems like Minerva [Heymans et al, 2008] and OntoMind [Al-Jadir et al, 2010] have been designed to store ontology classes and relationships (called TBox in ontology literature) as well as instances (called ABox).…”

Section: Storing Ontological Structuresmentioning

confidence: 99%

Managing Event Information: Modeling, Retrieval, and Applications

Gupta¹,

Jain²

2011

Synthesis Lectures on Data Management

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All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means-electronic, mechanical, photocopy, recording, or any other except for brief quotations in printed reviews, without the prior permission of the publisher. Managing Event Information: Modeling, Retrieval, and Applications ABSTRACTWith the proliferation of citizen reporting, smart mobile devices, and social media, an increasing number of people are beginning generate information about events they observe and participate in. A significant fraction of this information contain multimedia data to share the experience with their audience. A systematic information modeling and management framework is necessary to capture this widely heterogeneous, schemaless, potentially humongous information produced by many different people. This book is an attempt to examine the modeling, storage, querying, and applications of such an event management system in a holistic manner. It uses a semantic-web style graph-based view of events, and shows how this event model, together with its query facility, can be used toward emerging applications like semi-automated storytelling.

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“…Utilizing semantic reasoning enables transforming the integrated low-level data into highlevel knowledge, allowing accurate and intelligent decisions. However, expressive logics such as Description Logic (DL)-Reasoning have EXPTIME complexity [30], resulting in slow reasoning times with growing datasets [26,3]. This is inconsistent with the vision that events in the IoT should be processed in a timely manner [8].…”

Section: Introductionmentioning

confidence: 96%

The MASSIF platform: a modular and semantic platform for the development of flexible IoT services

et al. 2016

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In the Internet of Things (IoT), data-producing entities sense their environment and transmit these observations to a data-processing platform for further analysis. Applications can have a notion of context-awareness by combining this sensed data, or by processing the combined data. The processes of combining data can consist both of merging the dynamic sensed data, as well as fusing the sensed data with background and historical data. Semantics can aid in this task, as they have proven their use in data integration, knowledge exchange and reasoning. Semantic services performing reasoning on the integrated sensed data, combined with background knowledge, such as profile data, allow extracting useful information and support intelligent decision making. However, advanced reasoning on the combination of this sensed data and background knowledge is still hard to achieve. Furthermore, the collaboration between semantic services allows to reach complex decisions. The dynamic composition of such collaborative workflows that can adapt to the current context, has not received much attention yet.In this paper, we present MASSIF, a data-driven platform for the semantic annotation of and reasoning on IoT data. It allows the integration of multiple modular reasoning services, that can collaborate in a flexible manner to facilitate complex decision making processes. Data-driven workflows are enabled by letting services specify the data they would like to consume. After thorough processing, these services can decide to share their decisions with other consumers. By defining the data these services would like to consume, they can operate on a subset of data, improving reasoning efficiency. Furthermore, each of these services can integrate the consumed data with background knowledge in its own context model, for rapid intelligent decision making. To show the strengths of the platform, two use cases are detailed and thoroughly evaluated.

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Reasoning with large ontologies stored in relational databases: The OntoMinD approach

Cited by 28 publications

References 4 publications

Methodology for Automatic Ontology Generation Using Database Schema Information

Methodology for Automatic Ontology Generation Using Database Schema Information

Managing Event Information: Modeling, Retrieval, and Applications

The MASSIF platform: a modular and semantic platform for the development of flexible IoT services

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