In this paper, we propose a novel data valuation method for a Dataset Retrieval (DR) use case in Ireland's National mapping agency. To the best of our knowledge, data valuation has not yet been applied to Dataset Retrieval. By leveraging metadata and a user's preferences, we estimate the personal value of each dataset to facilitate dataset retrieval and filtering. We then validated the data value-based ranking against the stakeholders' ranking of the datasets. The proposed data valuation method and use case demonstrated that data valuation is promising for dataset retrieval. For instance, the outperforming dataset retrieval based on our approach obtained 0.8207 in terms of NDCG@5 (the truncated Normalized Discounted Cumulative Gain at 5). This study is unique in its exploration of a data valuation-based approach to dataset retrieval and stands out because, unlike most existing methods, our approach is validated using the stakeholders ranking of the datasets.

Building Information Modelling (BIM) is a key enabler to support integration of building data within the buildings life cycle (BLC) and is an important aspect to support a wide range of use cases, related to intelligent automation, navigation, energy efficiency, sustainability and so forth. Open building data faces several challenges related to standardization, data interdependency, data access, and security. In addition to these technical challenges, there remains the barrier among BIM developers who wish to protect their intellectual property, as full 3D BIM development requires expertise and effort. This means that there is often limited availability of building data. However, a Linked Data approach to BIM, combined with a supporting national geospatial identifier infrastructure makes interlinking and controlled sharing of BIM models possible. In Ireland, the Ordnance Survey Ireland (OSi) maintains a substantial data set, called Prime2, which includes not only building GIS data (polygon footprint, geodetic coordinate), but also additional building specific data (e.g. form, function and status). The data set also includes change information, recording when changes took place and who captured and validated those changes. This paper presents the development of a national geospatial identifier infrastructure based on an OSi building ontology that supports capturing OSi building data using Resource Description Framework (RDF). The paper details the different steps required to generate the ontology and publish the data. First, an initial analysis of the data set to generate the ontology is discussed. This includes identification of mappings to existing standards, e.g. GeoSPARQL to handle geometries and PROV-O to handle provenance, to the development of R2RML mappings to generate the RDF and the method for deploying the ontology and the building graphs. This data is then made available dependent on different licensing agreements handled by an access control approach. Methods are then presented to support the interlinking of the authoritative data with other building data standards and data sets using geolocation, followed finally by discussion and future work.

Geoff, the geospatial form and function vocabulary, is a comprehensive RDF-based spatial object classification scheme based on a separation of the concepts of form and function. Geoff is based on our analysis of the extensive (over 50 million spatial object instances) Digital Landscape Model (DLM) Core model maintained by Ordnance Survey Ireland (OSi). We propose Geoff as there are currently no open geospatial form and function classification systems that cover the full range of geospatial objects (from buildings and roads to lakes and other natural features) modelled as Linked Data or in any other formalism. Geoff is a generalization of the DLM Core schema and adopts the GeoSPARQL ontology. Geoff was initially developed to make these classifications available for OSi’s geospatial Linked Data as they facilitate the publications of more expressive models of spatial features. For example, to state that a church building (form) is now used as apartments (function). Geoff is now presented to the wider community for reuse and extension to meet their own needs. Geoff supports geospatial queries based on form and function and interlinking of geo-information datasets using different form and function code lists. The Geoff ontology follows Linked Data publishing best practice in terms of available metadata, documentation, and quality assurance.

Ordnance Survey Ireland (OSi) is Ireland’s national mapping agency that is responsible for the digitisation of the island’s infrastructure in terms of mapping. Generating data from various sensors (e.g. spatial sensors), OSi build its knowledge in the Prime2 framework, a subset of which is transformed into geo-Linked Data. In this paper we discuss how the quality of the generated sematic data fares against datasets in the LOD cloud. We set up Luzzu, a scalable Linked Data quality assessment framework, in the OSi pipeline to continuously assess produced data in order to tackle any quality problems prior to publishing.

This dataset contains ontologies, full dump of the administrative boundary dataset (in NQUADS) of data.geohive.ie, and metadata about the dataset using VOID. Data are derived from Ordnance Survey Ireland (OSi), Ireland’s national mapping agency.fulldump.nq - full dump of the rdf administrative boundary dataset in N-Quads, a line-based, plain text RDF format.osi_prov.rdf - OSi Provenance Ontology in RDF formatosi_voc.rdf - OSi Boundary Ontology in RDF formatfulldumpmetadata.ttl - metadata in Terse RDF Triple Language describing the full dump of the rdf administrative boundary dataset.All data files can be accessed through openly-available text edit software. URIs resolve to either HTML pages or RDF serialization by means of content negotiation.Background:Data.geohive.ie aims to provide an authoritative service for serving Ireland’s national geospatial data as Linked Data. The service currently provides information on Irish administrative boundaries and the boundaries used for the Irish 2011 census. The service is designed to support two use cases: serving boundary data of geographic features at various level of detail and capturing the evolution of administrative boundaries. In the associated paper, we report on the development of the service and elaborate on some of the informed decisions concerned with the URI strategy and use of named graphs for the support of aforementioned use cases – relating those with similar initiatives.

“Place” is an important concept providing a useful dimension to explore, align and analyze data on the Linked Data Web. Though Linked Data datasets can use standardized geospatial predicates such as GeoSPARQL, access to SPARQL endpoints that supports these is not guaranteed. When not available, one needs to load the data into their own GeoSPARQL-enabled triplestores in order to avail of those predicates. Triple Pattern Fragments (TPF) is a proposal to make clients more intelligent in processing RDF, thereby lessening the burden carried by servers. In this paper, we propose to extend TPF to support GeoSPARQL. The contribution is a minimal extension of the TPF client that does not rely on a spatial database such that the extension can be run from within a browser. Even though our approach will unlikely outperform GeoSPARQL-enabled triplestores in terms of query execution time, we demonstrate its feasibility by means of a couple of use cases using data provided by data.geohive.ie , an initiative to publish authoritative, high-resolution geospatial data for The Republic of Ireland as Linked Data on the Web. This high-resolution data does cause a lot of network traffic, but related work showed how extending the communication between a TPF client and server reduces the number HTTP calls and some network traffic. The integration of our extension in one such optimization did reduce the overhead. We, however, decided to stick to our first implementation as it only extended the client in a minimal way. Future work includes investigating how our approach scales, and its usefulness of adding and using a spatial component to datasets.

Data.geohive.ie aims to provide an authoritative service for serving Ireland?s national geospatial data as Linked Data. The service currently provides information on Irish administrative boundaries and the boundaries used for the Irish 2011 census. The service is designed to support two use cases: serving boundary data of geographic features at various level of detail and capturing the evolution of administrative boundaries. In this paper, we report on the development of the service and elaborate on some of the informed decisions concerned with the URI strategy and use of named graphs for the support of aforementioned use cases ? relating those with similar initiatives. While clear insights on how the data is being used are still being gathered, we provide examples of how and where this geospatial Linked Data dataset is used.

We present data.geohive.ie, which aims to provide an authoritative platform for serving Ireland’s national geospatial data, including Linked Data. Currently, the platform provides information on Irish administrative boundaries and was designed to support two use cases: serving boundary data of geographic features at various level of detail and capturing the evolution of administrative boundaries. We report on the decisions taken for modeling and serving the data such as the adoption of an appropriate URI strategy, the development of necessary ontologies, and the use of (named) graphs to support aforementioned use cases.