An evaluation of ontology matching in geo-service applications

Vaccari, Lorenzino; Shvaiko, Pavel; Pane, Juan; Besana, Paolo; Marchese, Maurizio

doi:10.1007/s10707-011-0125-8

Cited by 24 publications

(11 citation statements)

References 43 publications

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“…This task comes in different forms, depending on the kind of information that is concerned. While the use of semantic technologies for enhancing classical information retrieval tasks has not been the subject of systematic evaluation, there is some work from the area of web service discovery and composition, see, e.g., [66]. In particular, the task of selecting appropriate web services based on a user request and semantic annotations was investigated in detail and a comprehensive benchmarking suite is available [41].…”

Section: Related Work On Evaluationsmentioning

confidence: 99%

Ontology Alignment Evaluation Initiative: Six Years of Experience

Euzenat

Meilicke

Stuckenschmidt

et al. 2011

Lecture Notes in Computer Science

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230

174

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Abstract. In the area of semantic technologies, benchmarking and systematic evaluation is not yet as established as in other areas of computer science, e.g., information retrieval. In spite of successful attempts, more effort and experience are required in order to achieve such a level of maturity. In this paper, we report results and lessons learned from the Ontology Alignment Evaluation Initiative (OAEI), a benchmarking initiative for ontology matching. The goal of this work is twofold: on the one hand, we document the state of the art in evaluating ontology matching methods and provide potential participants of the initiative with a better understanding of the design and the underlying principles of the OAEI campaigns. On the other hand, we report experiences gained in this particular area of semantic technologies to potential developers of benchmarking for other kinds of systems. For this purpose, we describe the evaluation design used in the OAEI campaigns in terms of datasets, evaluation criteria and workflows, provide a global view on the results of the campaigns carried out from 2005 to 2010 and discuss upcoming trends, both specific to ontology matching and generally relevant for the evaluation of semantic technologies. Finally, we argue that there is a need for a further automation of benchmarking to shorten the feedback cycle for tool developers.

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Section: Related Work On Evaluationsmentioning

confidence: 99%

Ontology Alignment Evaluation Initiative: Six Years of Experience

Euzenat

Meilicke

Stuckenschmidt

et al. 2011

Lecture Notes in Computer Science

Self Cite

230

174

View full text Add to dashboard Cite

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“…As shown in the previous sections, massive outstanding achievements for the entity alignment of GKBs, in terms of similarity metrics, similarity combination, alignment judgement, and result evaluation, have been made. Meanwhile, this semantic technique has been widely used in the integration and conflation of geographic data or knowledge [113,114], toponym resolution [26,43,44,66], correlation and discovery of geographic information [58,71,[115][116][117][118], web service chain composition [119,120], and personalized recommendations [121,122]. However, there are still some challenges, which need to be addressed in the future.…”

Section: Challenges and Future Researchmentioning

confidence: 99%

Progress and Challenges on Entity Alignment of Geographic Knowledge Bases

Sun

Zhu

Song

2019

IJGI

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Geographic knowledge bases (GKBs) with multiple sources and forms are of obvious heterogeneity, which hinders the integration of geographic knowledge. Entity alignment provides an effective way to find correspondences of entities by measuring the multidimensional similarity between entities from different GKBs, thereby overcoming the semantic gap. Thus, many efforts have been made in this field. This paper initially proposes basic definitions and a general framework for the entity alignment of GKBs. Specifically, the state-of-the-art of algorithms of entity alignment of GKBs is reviewed from the three aspects of similarity metrics, similarity combination, and alignment judgement; the evaluation procedure of alignment results is also summarized. On this basis, eight challenges for future studies are identified. There is a lack of methods to assess the qualities of GKBs. The alignment process should be improved by determining the best composition of heterogeneous features, optimizing alignment algorithms, and incorporating background knowledge. Furthermore, a unified infrastructure, techniques for aligning large-scale GKBs, and deep learning-based alignment techniques should be developed. Meanwhile, the generation of benchmark datasets for the entity alignment of GKBs and the applications of this field need to be investigated. The progress of this field will be accelerated by addressing these challenges.

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“…The tool incorporates two structure-based methods, Descendant's Similarity Inheritance and Sibling's Similarity Contribution, presented in [19,5]. Another work applies the structure-preserving semantic matching approach to facilitate semantic interoperability of GIS web services [20]. Finally, a survey of semantic similarity measures for geospatial data has been presented in [16].…”

Section: Related Workmentioning

confidence: 99%

Extraction, integration and analysis of crowdsourced points of interest from multiple web sources

Lamprianidis

Skoutas

Papatheodorou

et al. 2014

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information

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The amount of user-generated geospatial content on the Web is constantly increasing, making it a valuable source of information for enabling, enriching and enhancing geospatial applications and services. However, this content is highly heterogeneous and diverse, varying significantly in quality and accuracy. Extracting, integrating, and mining these crowdsourced geospatial data from the Web is far from trivial. Among the main challenges are to retrieve data from multiple sources, each one providing its own access methods and restrictions, to deal with different schemas and taxonomies, and to find matching entities across multiple sources. In this paper, we present our work for retrieving and integrating crowdsourced Points of Interest (POIs) from popular Web sources. We retrieve POIs from different Web sources and we describe the steps taken for mapping the source categories to a common schema, detecting duplicate POIs, and eventually clustering them to identify hotspots. We present the results of this process applied to six major Web sources to retrieve and integrate POIs located in the metropolitan areas of three European capital cities.

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An evaluation of ontology matching in geo-service applications

Cited by 24 publications

References 43 publications

Ontology Alignment Evaluation Initiative: Six Years of Experience

Ontology Alignment Evaluation Initiative: Six Years of Experience

Progress and Challenges on Entity Alignment of Geographic Knowledge Bases

Extraction, integration and analysis of crowdsourced points of interest from multiple web sources

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