Topic
Ontology-based data integration
About: Ontology-based data integration is a research topic. Over the lifetime, 11065 publications have been published within this topic receiving 216888 citations.
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02 May 2013
TL;DR: It is demonstrated that microtask crowdsourcing can become a scalable and efficient component in ontology-engineering workflows and that turkers can achieve accuracy as high as 90% on verifying hierarchy statements form common-sense ontologies such as WordNet.
Abstract: Ontology evaluation has proven to be one of the more difficult problems in ontology engineering. Researchers proposed numerous methods to evaluate logical correctness of an ontology, its structure, or coverage of a domain represented by a corpus. However, evaluating whether or not ontology assertions correspond to the real world remains a manual and time-consuming task. In this paper, we explore the feasibility of using microtask crowdsourcing through Amazon Mechanical Turk to evaluate ontologies. Specifically, we look at the task of verifying the subclass--superclass hierarchy in ontologies. We demonstrate that the performance of Amazon Mechanical Turk workers (turkers) on this task is comparable to the performance of undergraduate students in a formal study. We explore the effects of the type of the ontology on the performance of turkers and demonstrate that turkers can achieve accuracy as high as 90% on verifying hierarchy statements form common-sense ontologies such as WordNet. Finally, we compare the performance of turkers to the performance of domain experts on verifying statements from an ontology in the biomedical domain. We report on lessons learned about designing ontology-evaluation experiments on Amazon Mechanical Turk. Our results demonstrate that microtask crowdsourcing can become a scalable and efficient component in ontology-engineering workflows.
53 citations
Journal Article•
TL;DR: This paper addresses the importance and types of concepts, for priority matching and direct matching between concepts, respectively, and proposes a novel approach to reducing computational complexity in ontology in- tegration.
Abstract: Most previous research on ontology integration has focused on similarity measure- ments between ontological entities, e.g., lexicons, instances, schemas and taxonomies, resulting in high computational costs of considering all possible pairs between two given ontologies. In this paper, we propose a novel approach to reducing computational complexity in ontology in- tegration. Thereby, we address the importance and types of concepts, for priority matching and direct matching between concepts, respectively. Identity-based similarity is computed, to avoid comparisons of all properties related to each concept, while matching between concepts. The problem of conflict in ontology integration has initially been explored on the instance-level and concept-level. This is useful to avoid many cases of mismatching.
53 citations
10 Dec 2007
TL;DR: This work is developing a flexible framework for ontology learning from text which provides a cyclical process that involves the successive application of various NLP techniques and learning algorithms for concept extraction and ontology modelling.
Abstract: Ontology learning refers to extracting conceptual knowledge from several sources and building an ontology from scratch, enriching, or adapting an existing ontology. It uses methods from a diverse spectrum of fields such as Natural Language Processing, Artificial Intelligence and Machine learning. However, a crucial challenging issue is to quantitatively evaluate the usefulness and accuracy of both techniques and combinations of techniques, when applied to ontology learning. It is an interesting problem because there are no published comparative studies. We are developing a flexible framework for ontology learning from text which provides a cyclical process that involves the successive application of various NLP techniques and learning algorithms for concept extraction and ontology modelling. The framework provides support to evaluate the usefulness and accuracy of different techniques and possible combinations of techniques into specific processes, to deal with the above challenge. We show our framework’s efficacy as a workbench for testing and evaluating concept identification. Our initial experiment supports our assumption about the usefulness of our approach.
52 citations
01 Jan 2001
TL;DR: In this article, a general architecture for discovering conceptual structures and engineering ontologies from text and a new approach for discovering non-taxonomic conceptual relations from text are presented. But there remains the problem of engineering large and adequate ontologies within short time frames in order to keep costs low.
Abstract: Ontologies have shown their usefulness in application areas such as information integration, natural language processing, metadata for the world wide, to name but a few. However, there remains the problem of engineering large and adequate ontologies within short time frames in order to keep costs low. We here present a general architecture for discovering conceptual structures and engineering ontologies from text and a new approach for discovering non-taxonomic conceptual relations from text.
52 citations
TL;DR: An ontology and CBR (case-based reasoning) based method which overcomes the difficulty for computers to understand complex structures of various mechanical products and makes the disassembly decision-making process of the products fully automated and cost-saving.
52 citations