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Constructing geo-ontologies by reification of observation data
- In ACM GIS
, 2011
"... The semantic integration of heterogeneous, spatiotemporal information is a major challenge for achieving the vision of a multithematic and multi-perspective Digital Earth. The Semantic Web technology stack has been proposed to address the integration problem by knowledge representation languages and ..."
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The semantic integration of heterogeneous, spatiotemporal information is a major challenge for achieving the vision of a multithematic and multi-perspective Digital Earth. The Semantic Web technology stack has been proposed to address the integration problem by knowledge representation languages and reasoning. However approaches such as the Web Ontology Languages (OWL) were developed with decidability in mind. They do not integrate well with established modeling paradigms in the geosciences that are dominated by numerical and geometric methods. Additionally, work on the Semantic Web is mostly feature-centric and a fieldbased view is difficult to integrate. A layer specifying the transition from observation data to classes and relations is missing. In this work we combine OWL with geometric and topological language constructs based on similarity spaces. Our approach provides three main benefits. First, class constructors can be built from a larger palette of mathematical operations based on vector algebra. Second, it affords the representation of prototype-based classes. Third, it facilitates the representation of classes derived from machine learning classifiers that utilize a multi-dimensional feature space. Instead of following a one-size-fits-all approach, our work allows one to derive contextualized OWL ontologies by reification of observation data.
A Geo-Semantics Flyby
"... Abstract. Geospatial semantics as a research field studies how to publish, retrieve, reuse, and integrate geo-data, how to describe geo-data by conceptual models, and how to develop formal specifications on top of data structures to reduce the risk of incompatibilities. Geo-data is highly heterogene ..."
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Abstract. Geospatial semantics as a research field studies how to publish, retrieve, reuse, and integrate geo-data, how to describe geo-data by conceptual models, and how to develop formal specifications on top of data structures to reduce the risk of incompatibilities. Geo-data is highly heterogeneous and ranges from qualitative interviews and thematic maps to satellite imagery and complex simulations. This makes ontologies, semantic annotations, and reasoning support essential ingredients towards a Geospatial Semantic Web. In this paper, we present an overview of major research questions, recent findings, and core literature. 1
A semantic geo- catalogue implementation for a regional SDI.InProc
- of the INPSIRE Conference
, 2010
"... Abstract. In this paper we report our work on the implementation of a semantic geo-catalogue for the spatial data infrastructure (SDI) of the Trentino region, Italy. Introduction. Within the INSPIRE directive [4], the recently approved regulation on network services [3] establishes minimum criteria ..."
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Abstract. In this paper we report our work on the implementation of a semantic geo-catalogue for the spatial data infrastructure (SDI) of the Trentino region, Italy. Introduction. Within the INSPIRE directive [4], the recently approved regulation on network services [3] establishes minimum criteria for the discovery service to support search within the INSPIRE metadata elements. However, discovery capabilities are often limited by only syntactically matching the user terminology to the details published by the metadata compiler. The needs to overcome this limitation, both
Semantic geo-catalog: a scenario and requirements
"... Abstract. In this short paper we present a scenario and requirements for ontology matching posed by a geographical application, namely a semantic geocatalog, which is an integral part of any spatial data infrastructure (SDI). It enables semantic interoperability among various geo-data and geo-servic ..."
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Abstract. In this short paper we present a scenario and requirements for ontology matching posed by a geographical application, namely a semantic geocatalog, which is an integral part of any spatial data infrastructure (SDI). It enables semantic interoperability among various geo-data and geo-service providers, and thus, contributes to the harmonization of geo-information. Introduction. The need for coherent and contextual use of geographic information between different stakeholders, such as departments in public administrations, formed the basis for a number of initiatives aiming at sharing of spatial information, e.g., the INfrastructure for SPatial InfoRmation in Europe (INSPIRE) 1, see also [8, 10]. In this paper, we focus on a particular component of the INSPIRE architecture, which is a discovery
Establishing similarity across multi-granular topological-relation ontologies
- In D. Fritsch & K. Rothermel (Eds.), QuaCon 2009. First International Workshop on Quality of Context
, 2009
"... Abstract. Within the Geospatial Semantic Web, selecting a different ontology for a spatial data set will enable that data’s analysis in a different context. Analyses of multiple data sets, each based on a different ontology, require appropriate bridges across the ontologies. This paper focuses on es ..."
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Abstract. Within the Geospatial Semantic Web, selecting a different ontology for a spatial data set will enable that data’s analysis in a different context. Analyses of multiple data sets, each based on a different ontology, require appropriate bridges across the ontologies. This paper focuses on establishing such a bridge across two topological-relation ontologies of different granularity—the standard eight detailed toplogical relations and five coarse topological relations. By mapping the conceptual neighborhood graphs onto a zonal representation, the different granularities are aligned spatially, yielding a reasoned approach to determining similarity values for the bridges across the two ontologies. A comparison with bridge lengths from an averaged model shows the better quality of zonal model. 1.
Semantic Referencing – Determining Context Weights for Similarity Measurement
"... Abstract. Semantic similarity measurement is a key methodology in various domains ranging from cognitive science to geographic information retrieval on the Web. Meaningful notions of similarity, however, cannot be determined without taking additional contextual information into account. One way to m ..."
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Abstract. Semantic similarity measurement is a key methodology in various domains ranging from cognitive science to geographic information retrieval on the Web. Meaningful notions of similarity, however, cannot be determined without taking additional contextual information into account. One way to make similarity measures context-aware is by introducing weights for specific characteristics. Existing approaches to automatically determine such weights are rather limited or require application specific adjustments. In the past, the possibility to tweak similarity theories until they fit a specific use case has been one of the major criticisms for their evaluation. In this work, we propose a novel approach to semi-automatically adapt similarity theories to the user’s needs and hence make them context-aware. Our methodology is inspired by the process of georeferencing images in which known control points between the image and geographic space are used to compute a suitable transformation. We propose to semi-automatically calibrate weights to compute inter-instance and inter-concept similarities by allowing the user to adjust pre-computed similarity rankings. These known control similarities are then used to reference other similarity values. Keywords: Semantic Similarity, Geo-Semantics, Information Retrieval 1
AgreementMakerLight Results for OAEI 2013
"... Abstract. AgreementMakerLight (AML) is an automated ontology matching framework based on element-level matching and the use of external resources as background knowledge. This paper describes the configuration of AML for the OAEI 2013 competition and discusses its results. Being a newly developed an ..."
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Abstract. AgreementMakerLight (AML) is an automated ontology matching framework based on element-level matching and the use of external resources as background knowledge. This paper describes the configuration of AML for the OAEI 2013 competition and discusses its results. Being a newly developed and still incomplete system, our focus in this year’s OAEI were the anatomy and large biomedical ontologies tracks, wherein background knowledge plays a critical role. Nevertheless, AML was fairly successful in other tracks as well, showing that in many ontology matching tasks, a lightweight approach based solely on element-level matching can compete with more complex approaches. 1 Presentation of the system 1.1 State, purpose, general statement AgreementMakerLight (AML) is an automated ontology matching framework derived from the AgreementMaker system [2, 4]. It was developed with the main goal of tackling very large ontology matching problems such as those in the life science domain,
Using Semantic Similarity in Ontology Alignment
- OM Workshop, 10th Int. Semantic Web Conference (ISWC 2011
, 2011
"... Abstract. Many approaches to measure the similarity between concepts that exist in two different ontologies are used in the matchers of ontology alignment systems. These matchers belong to various categories depending on the context of the similarity measurement, such as lexical, structural, or exte ..."
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Abstract. Many approaches to measure the similarity between concepts that exist in two different ontologies are used in the matchers of ontology alignment systems. These matchers belong to various categories depending on the context of the similarity measurement, such as lexical, structural, or extensional matchers. Although OA systems have used various forms of similarity measures along with some background knowledge sources, not many have incorporated the use of semantic similarity measures. This paper first reviews the use of semantic similarity in current OA systems, presents a unique application of such measures to assess the semantic alignment quality (SAQ) of OA systems and reports on the results of a study done using SAQ measures on the OAEI 2010 results from the anatomy track
Semantic Enablement for Spatial Data
"... Building on abstract reference models, the Open Geospatial Consortium (OGC) has established standards for storing, discovering, and processing geographical information. These standards act as basis for the implementation of specific services and Spatial Data Infrastructures (SDI). Research on geo-se ..."
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Building on abstract reference models, the Open Geospatial Consortium (OGC) has established standards for storing, discovering, and processing geographical information. These standards act as basis for the implementation of specific services and Spatial Data Infrastructures (SDI). Research on geo-semantics plays an increasing role to support complex queries and retrieval across heterogeneous information sources, as well as for service orchestration, semantic translation, and on-the-fly integration. So far, this research targets individual solutions or focuses on the Semantic Web, leaving the integration into SDI aside. What is missing is a shared and transparent Semantic Enablement Layer for Spatial Data Infrastructures which also integrates reasoning services known from the Semantic Web. Instead of developing new semantically enabled services from scratch, we propose to create profiles of existing services that implement a transparent mapping between the OGC and the Semantic Web world. Finally, we point out how to combine SDI with linked data. 1
Pay-As-You-Go Multi-User Feedback Model for Ontology Matching
"... Abstract. Using our multi-user model, a community of users provides feedback in a pay-as-you-go fashion to the ontology matching process by validating the mappings found by automatic methods, with the following advantages over having a single user: the e↵ort required from each user is reduced, user ..."
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Abstract. Using our multi-user model, a community of users provides feedback in a pay-as-you-go fashion to the ontology matching process by validating the mappings found by automatic methods, with the following advantages over having a single user: the e↵ort required from each user is reduced, user errors are corrected, and consensus is reached. We propose strategies that dynamically determine the order in which the candidate mappings are presented to the users for validation. These strategies are based on mapping quality measures that we define. Further, we use a propagation method to leverage the validation of one mapping to other mappings. We use an extension of the AgreementMaker ontology match-ing system and the Ontology Alignment Evaluation Initiative (OAEI) Benchmarks track to evaluate our approach. Our results show how F-measure and robustness vary as a function of the number of user valida-tions. We consider di↵erent user error and revalidation rates (the latter measures the number of times that the same mapping is validated). Our results highlight complex trade-o↵s and point to the benefits of dynam-ically adjusting the revalidation rate. 1