Lightweight Semantic Annotation of Geospatial RESTful Services

Saquicela, Víctor; Vilches‐Blázquez, Luis M.; Corcho, Óscar

doi:10.1007/978-3-642-21064-8_23

Cited by 6 publications

(6 citation statements)

References 15 publications

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“…Most of the proposals found in the literature focus on enhancing service descriptions with semantics, such as SDWS (Bravo et al, 2014) and ASSARS (Luo et al, 2016). Saquicela et al (2011) propose an approach for generating semantic annotation of RESTful services using external resources, such as DBpedia ontology and a synonymous database. Nevertheless, semantic data is added only to the service description.…”

Section: Related Workmentioning

confidence: 99%

OntoGenesis: an architecture for automatic semantic enhancement of data services

Oliveira

Huf

Salvadori

et al. 2019

IJWIS

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Purpose This paper describes a software architecture that automatically adds semantic capabilities to data services. The proposed architecture, called OntoGenesis, is able to semantically enrich data services, so that they can dynamically provide both semantic descriptions and data representations. Design/methodology/approach The enrichment approach is designed to intercept the requests from data services. Therefore, a domain ontology is constructed and evolved in accordance with the syntactic representations provided by such services in order to define the data concepts. In addition, a property matching mechanism is proposed to exploit the potential data intersection observed in data service representations and external data sources so as to enhance the domain ontology with new equivalences triples. Finally, the enrichment approach is capable of deriving on demand a semantic description and data representations that link to the domain ontology concepts. Findings Experiments were performed using real-world datasets, such as DBpedia, GeoNames as well as open government data. The obtained results show the applicability of the proposed architecture and that it can boost the development of semantic data services. Moreover, the matching approach achieved better performance when compared with other existing approaches found in the literature. Research limitations/implications This work only considers services designed as data providers, i.e., services that provide an interface for accessing data sources. In addition, our approach assumes that both data services and external sources – used to enhance the domain ontology – have some potential of data intersection. Such assumption only requires that services and external sources share particular property values. Originality/value Unlike most of the approaches found in the literature, the architecture proposed in this paper is meant to semantically enrich data services in such way that human intervention is minimal. Furthermore, an automata-based index is also presented as a novel method that significantly improves the performance of the property matching mechanism.

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Section: Related Workmentioning

confidence: 99%

OntoGenesis: an architecture for automatic semantic enhancement of data services

Oliveira

Huf

Salvadori

et al. 2019

IJWIS

View full text Add to dashboard Cite

show abstract

“…We explain the problem of learning semantic models by giving a concrete example that will be used throughout this paper to illustrate different steps of our approach. In this example, the goal is to model a set of museum data sources using EDM 5 , AAC 6 , SKOS 7 , Dublin Core Metadata Terms 8 , FRBR 9 , FOAF, ORE 10 , and ElementsGr2 11 ontologies and then use the created semantic models to publish their data as RDF [21]. Suppose that we have three data sources.…”

Section: Motivating Examplementioning

confidence: 99%

“…Although desirable, generating these models automatically is a challenging problem. In Semantic Web research, there is much work on mapping data sources to ontologies [2][3][4][5][6][7][8][9][10][11][12][13], but most focus on semantic labeling or are very limited in automatically inferring the relationships. Our goal is to construct semantic models that not only include the semantic types of the source attributes, but also describe the relationships between them.…”

Section: Introductionmentioning

confidence: 99%

Learning the semantics of structured data sources

Taheriyan

Knoblock

Szekely

et al. 2016

Journal of Web Semantics

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Information sources such as relational databases, spreadsheets, XML, JSON, and Web APIs contain a tremendous amount of structured data that can be leveraged to build and augment knowledge graphs. However, they rarely provide a semantic model to describe their contents. Semantic models of data sources represent the implicit meaning of the data by specifying the concepts and the relationships within the data. Such models are the key ingredients to automatically publish the data into knowledge graphs. Manually modeling the semantics of data sources requires significant effort and expertise, and although desirable, building these models automatically is a challenging problem. Most of the related work focuses on semantic annotation of the data fields (source attributes). However, constructing a semantic model that explicitly describes the relationships between the attributes in addition to their semantic types is critical.We present a novel approach that exploits the knowledge from a domain ontology and the semantic models of previously modeled sources to automatically learn a rich semantic model for a new source. This model represents the semantics of the new source in terms of the concepts and relationships defined by the domain ontology. Given some sample data from the new source, we leverage the knowledge in the domain ontology and the known semantic models to construct a weighted graph that represents the space of plausible semantic models for the new source. Then, we compute the top k candidate semantic models and suggest to the user a ranked list of the semantic models for the new source. The approach takes into account user corrections to learn more accurate semantic models on future data sources. Our evaluation shows that our method generates expressive semantic models for data sources and services with minimal user input. These precise models make it possible to automatically integrate the data across sources and provide rich support for source discovery and service composition. They also make it possible to automatically publish semantic data into knowledge graphs.

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“…There are many studies on mapping data sources to ontologies. Most of this work [2]- [6] focus on semantic annotation, but is limited in learning relationships. Carman and Knoblock [20] use known source descriptions to generate a mapping for an unknown target source.…”

Section: Related Workmentioning

confidence: 99%

A Scalable Approach to Learn Semantic Models of Structured Sources

Taheriyan

Knoblock

Szekely

et al. 2014

2014 IEEE International Conference on Semantic Computing

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Abstract-Semantic models of data sources describe the meaning of the data in terms of the concepts and relationships defined by a domain ontology. Building such models is an important step toward integrating data from different sources, where we need to provide the user with a unified view of underlying sources. In this paper, we present a scalable approach to automatically learn semantic models of a structured data source by exploiting the knowledge of previously modeled sources. Our evaluation shows that the approach generates expressive semantic models with minimal user input, and it is scalable to large ontologies and data sources with many attributes.I. INTRODUCTION A significant amount of information available on the Web is available in sources such as relational databases, spreadsheets, XML, JSON, and Web APIs. A common approach to integrate these sources involves building a domain model and constructing source descriptions that represent the intended meaning of the data by specifying mappings between the sources and the domain model [1]. In the Semantic Web, the domain model is an ontology that defines concepts and relationships within a domain, and source descriptions are formal specifications of semantic models. Semantic models can be viewed as graphs with ontology classes as the nodes and ontology properties as the links between the nodes.Manually constructing semantic models is a timeconsuming task that requires significant effort and expertise. Automatically generating these models involves two steps. The first step is specifying the semantic types, i.e., labeling each data field, or source attribute with a class or a data property of the domain ontology. However, simply annotating the attributes is not sufficient. A precise semantic model needs a second step that specifies the relationships between the source attributes in terms of the properties in the ontology. In Semantic Web research, there are many studies on mapping data sources to ontologies [2]- [7], but most focus on the first step of the modeling process or are very limited in automatically inferring the relationships.In our previous work [8], we presented a novel approach to learn semantic models of data sources from known semantic models, semantic models of sources that have already been modeled. The work is inspired by the idea that different sources in the same domain often provide similar or overlapping data and have similar semantic models. Given sample data from the new source, we use an existing machine learning

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Lightweight Semantic Annotation of Geospatial RESTful Services

Cited by 6 publications

References 15 publications

OntoGenesis: an architecture for automatic semantic enhancement of data services

OntoGenesis: an architecture for automatic semantic enhancement of data services

Learning the semantics of structured data sources

A Scalable Approach to Learn Semantic Models of Structured Sources

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