On the Discovery of Subsumption Relations for the Alignment of Ontologies

Spiliopoulos, Vassilis; Vouros, George A.; Karkaletsis, Vangelis

doi:10.2139/ssrn.3199467

Cited by 11 publications

(11 citation statements)

References 21 publications

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“…If the design of matchers consists of tuning further similarity measures or issuing other combinations of matchers, it is not to be expected a revolutionary progress, but most likely only an incremental one, as §5 also suggests. Other open issues are the computation of expressive alignments, e.g., correspondences across classes and properties [47,68], oriented alignments (with non equivalence relations) [69,70], or cross-lingual matching [71,72]. Such issues are gradually progressing within the ontology matching field.…”

Section: Towards the Challengesmentioning

confidence: 99%

Ontology Matching: State of the Art and Future Challenges

Shvaiko¹,

Euzenat

2013

IEEE Trans. Knowl. Data Eng.

878

480

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After years of research on ontology matching, it is reasonable to consider several questions: is the field of ontology matching still making progress? Is this progress significant enough to pursue further research? If so, what are the particularly promising directions? To answer these questions, we review the state of the art of ontology matching and analyze the results of recent ontology matching evaluations. These results show a measurable improvement in the field, the speed of which is albeit slowing down. We conjecture that significant improvements can be obtained only by addressing important challenges for ontology matching. We present such challenges with insights on how to approach them, thereby aiming to direct research into the most promising tracks and to facilitate the progress of the field.

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Section: Towards the Challengesmentioning

confidence: 99%

Ontology Matching: State of the Art and Future Challenges

Shvaiko¹,

Euzenat

2013

IEEE Trans. Knowl. Data Eng.

878

480

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show abstract

“…Thus, while the data set might provide enough variability for concepts related to the other relationship types, the associated relationship may require more data. Overall, the precision is 87.94%, which is considered a good performance [within the range of 80% to 90% (Spiliopoulos et al 2010)].…”

Section: Discussionmentioning

confidence: 99%

Extending Building Information Models Semi-Automatically Using Semantic Natural Language Processing Techniques

Zhang

El-Gohary

2014

Computing in Civil and Building Engineering (2014)

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Automated compliance checking (ACC) of building designs requires automated extraction of information from building information models (BIMs). However, current Industry Foundation Classes (IFC)-based BIMs provide limited support for ACC, because they lack the necessary information that is needed to perform compliance checking (CC). In this paper, the authors propose a new method for extending the IFC schema to incorporate CC-related information, in an objective and semi-automated manner. The method utilizes semantic natural language processing (NLP) techniques and machine learning techniques to extract concepts from documents that are related to CC (e.g., building codes) and match the extracted concepts to concepts in the IFC class hierarchy. The proposed method includes a set of methods/algorithms that are combined into one computational platform: (1) a method for concept extraction that utilizes pattern-matching-based rules to extract regulatory concepts from CC-related regulatory documents, (2) a method for concept matching and semantic similarity (SS) assessment to select the most related IFC concepts to the extracted regulatory concepts, and (3) a machine learning classification method for predicting the relationship between the extracted regulatory concepts and their most related IFC concepts. The proposed method enables the extension of the IFC schema, in an objective way, using any construction regulatory document. To test and evaluate the proposed method, two chapters were randomly selected from the International Building Code

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“…A few systems, including BLOOMS [Jain et al 2010] and PARIS [Suchanek et al 2011b], are capable of finding subsumption relationships across ontologies. CSR [Spiliopoulos et al 2010] and TaxoMap [Hamdi et al 2010] attempt to find 1-to-m equivalence and subsumption relationships. In general though, most research activity in the field of ontology alignment remains focused on finding 1-to-1 equivalence relations between ontologies.…”

Section: Alignment Thresholdmentioning

confidence: 99%

The properties of property alignment on the semantic web

Cheatham

2018

IJMSO

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2014. The Properties of Property Alignment on the Semantic Web. Ontology alignment is an important step in enabling computers to query and reason across the many linked datasets on the semantic web. This is a difficult challenge because the ontologies underlying different linked datasets can vary in terms of subject area coverage, level of abstraction, ontology modeling philosophy, and even language. The alignment approach presented here centers on string similarity metrics. Nearly all ontology alignment systems use a string similarity metric in one form or another, but it seems that the choice of a particular metric is often arbitrary. We begin this dissertation with the most comprehensive survey to date on the performance of string similarity metrics and string preprocessing strategies for ontology alignment. Based on this work we present practical guidelines for choosing string metrics in the face of different types of ontologies and different alignment goals. Additionally, we show that string similarity metrics alone can perform competitively with state-of-the-art alignment systems on the most popular benchmarks in the field. One of the contributions of our string similarity metric survey is quantification of the difference in performance between aligning classes and aligning properties (relations between classes). Put simply: aligning properties is hard, and existing string similarity metrics are not of great help. We therefore take on the task of developing a new string-based alignment approach that performs better on properties. Unfortunately, evaluating that approach is difficult because the only existing alignment benchmark that includes properties is, in our view, unrealistic since all relations in the reference alignment are presented as completely certain. Human experts do not have this degree of confidence when asked to align an ontology. We therefore present a more nuanced version of this benchmark that we have created through a combination of expert survey and crowdsourcing. We then present our new string-based property alignment system and evaluate its performance on both the current benchmark and our proposed revision. Our property-centric string metric can be configured for either high precision or high recall. The results show a five-fold increase in precision and a doubling of recall over an approach based on the best current string metric. Finally, we apply our system to a real-world test case and analyze the results.

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On the Discovery of Subsumption Relations for the Alignment of Ontologies

Cited by 11 publications

References 21 publications

Ontology Matching: State of the Art and Future Challenges

Ontology Matching: State of the Art and Future Challenges

Extending Building Information Models Semi-Automatically Using Semantic Natural Language Processing Techniques

The properties of property alignment on the semantic web

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