In the last decades, the use of ontologies in information systems has become more and more popular in various fields, such as web technologies, database integration, multi agent systems, natural language processing, etc. Artificial intelligent researchers have initially borrowed the word “ontology” from Philosophy, then the word spread in many scientific domain and ontologies are now used in several developments.

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an Introduction to Ontologies and Ontology Engineering

Environmental sensors have continuously improved by becoming smaller, cheaper, and more intelligent over the past years. As consequence of these technological advancements, sensors are increasingly deployed to monitor our environment. The large variety of available sensor types with often incompatible protocols complicates the integration of sensors into observing systems. The standardized Web service interfaces and data encodings defined within OGC’s Sensor Web Enablement (SWE) framework make sensors available over the Web and hide the heterogeneous sensor protocols from applications. So far, the SWE framework does not describe how to integrate sensors on-the-fly with minimal human intervention. The driver software which enables access to sensors has to be implemented and the measured sensor data has to be manually mapped to the SWE models. In this article we introduce a Sensor Plug & Play infrastructure for the Sensor Web by combining (1) semantic matchmaking functionality, (2) a publish/subscribe mechanism underlying the SensorWeb, as well as (3) a model for the declarative description of sensor interfaces which serves as a generic driver mechanism. We implement and evaluate our approach by applying it to an oil spill scenario. The matchmaking is realized using existing ontologies and reasoning engines and provides a strong case for the semantic integration capabilities provided by Semantic Web research.

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Semantically-Enabled Sensor Plug & Play for the Sensor Web

With the increased use of ontologies in semantically-enabled applications, the issues of debugging and aligning ontologies have become increasingly important. The quality of the results of such applications is directly dependent on the quality of the ontologies and mappings between the ontologies they employ. A key step towards achieving high quality ontologies and mappings is discovering and resolving modeling defects, e.g., wrong or missing relations and mappings. In this paper we present a unified framework for aligning taxonomies, the most used kind of ontologies, and debugging taxonomies and their alignments, where ontology alignment is treated as a special kind of debugging. Our framework supports the detection and repairing of missing and wrong is-a structure in taxonomies, as well as the detection and repairing of missing (alignment) and wrong mappings between ontologies. Further, we implemented a system based on this framework and demonstrate its benefits through experiments with ontologies from the Ontology Alignment Evaluation Initiative.

The Semantic Web: Semantics and Big Data

In this paper, we present a generic ontology evolution approach guided by a pattern-oriented process and a quality evaluation activity. Pattern modeling aims to drive and control change application while maintaining consistency of the evolved ontology. Evaluation activity ---supported by an ontology quality model--- is used to guide inconsistency resolution by assessing the impact of resolution alternatives ---proposed by the evolution process- on ontology quality and selecting the resolution that preserves the quality of the evolved ontology.

ONTO-EVO A L an ontology evolution approach guided by pattern modeling and quality evaluation

Debugging inconsistent OWL ontologies is a tedious and time-consuming task where a combination of ontology engineers and domain experts is often required to understand whether the changes to be performed are actually dealing with formalisation errors or changing the intended meaning of the original knowledge model. Debugging services from existing ontology engineering tools and debugging strategies available in the literature aid in this task. However, in complex cases they are still far from providing adequate support to ontology developers, due to their lack of efficiency or precision when explaining the main causes for unsatisfiable classes, together with little support for proposing solutions for them. We claim that it is possible to provide additional support to ontology developers, based on the identification of common antipatterns and a debugging strategy, which can be combined with the use of existing tools in order to make this task more effective.

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Pattern-based OWL ontology debugging guidelines

This article describes the relationship between Urban Civil
Engineering and other domains, specifically the hydrographic domain. The
process of building HydrOntology and the portion of the model relating to
urban features are described. This ontology emerges with the intent of settling
as a framework in the GI domain, very closely interrelating to Towntology.

Towntology & hydrOntology: Relationship between Urban and Hydrographic Features in the Geographic Information Domain.

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Towntology & hydrOntology: Relationship between Urban and Hydrographic Features in the Geographic Information Domain

In this paper we report on an ongoing process aimed at publishing hydrographical data on the Web with a Spanish GeoLinked Data Use Case Moreover, we discuss the process we followed, and propose methodological guidelines for all the activities involved within the process

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An approach to publish spatial data on the web: The geolinked data case

Geospatial data is increasingly being made available on the Web as knowledge graphs using Linked Data principles. This entails adopting the best practices for publishing, retrieving, and using data, providing relevant initiatives that play a prominent role in the Web of Data. Despite the appropriate progress related to the amount of geospatial data available, knowledge graphs still face significant limitations in the GIScience community since their use, consumption, and exploitation are scarce, especially considering that just a few developments retrieve and consume geospatial knowledge graphs from within GIS. To overcome these limitations and address some critical challenges of GIScience, standards and specific best practices for publishing, retrieving, and using geospatial data on the Web have appeared. Nevertheless, there are few developments and experiences that support the possibility of expressing queries across diverse knowledge graphs to retrieve and process geospatial data from different and distributed sources. In this scenario, we present an approach to request, retrieve, and consume (geospatial) knowledge graphs available at diverse and distributed platforms, prototypically implemented on Apache Marmotta, supporting SPARQL 1.1 and GeoSPARQL standards. Moreover, our approach enables the consumption of geospatial knowledge graphs through a lightweight web application or QGIS. The potential of this work is shown with two examples that use GeoSPARQL-based knowledge graphs.

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Luis Manuel Vilches Blázquez

Papers

Pattern-based OWL ontology debugging guidelines

Towntology & hydrOntology: Relationship between Urban and Hydrographic Features in the Geographic Information Domain.

Towntology & hydrOntology: Relationship between Urban and Hydrographic Features in the Geographic Information Domain

An approach to publish spatial data on the web: The geolinked data case

Bringing Federated Semantic Queries to the GIS-Based Scenario