Born in the early 1980's as a multilingual agricultural thesaurus, AGROVOC has steadily evolved over the last fifteen years, moving to an electronic version around the year 2000, and embracing the Semantic Web shortly thereafter. Today AGROVOC is a SKOS-XL concept scheme published as Linked Open Data, containing links (as well as backlinks) and references to many other Linked Datasets in the LOD cloud. In this paper we provide a brief historical summary of AGROVOC and detail its specification as a Linked Dataset.

Abstract. We introduce VocBench, an open source web application for editing thesauri complying with the SKOS and SKOS-XL standards. VocBench has a strong focus on collaboration, supported by workflow management for content validation and publication. Dedicated user roles provide a clean separation of competences, addressing different specificities ranging from management aspects to vertical competences on content editing, such as conceptualization versus terminology editing. Extensive support for scheme management allows editors to fully exploit the possibilities of the SKOS model, as well as to fulfill its integrity constraints. We discuss thoroughly the main features of VocBench, detail its architecture, and evaluate it under both a functional and user-appreciation ground, through a comparison with state-of-the-art and user questionnaires analysis, respectively. Finally, we provide insights on future developments.

In Geographic Object-based Image Analysis (GEOBIA), identification of image objects is normally achieved using rule-based classification techniques supported by appropriate domain knowledge. However, GEOBIA currently lacks a systematic method to formalise the domain knowledge required for image object identification. Ontology provides a representation vocabulary for characterising domain-specific classes. This study proposes an ontological framework that conceptualises domain knowledge in order to support the application of rule-based classifications.The proposed ontological framework is tested with a landslide case study. The Web Ontology Language (OWL) is used to construct an ontology in the landslide domain. The segmented image objects with extracted features are incorporated into the ontology as instances. The classification rules are written in Semantic Web Rule Language (SWRL) and executed using a semantic reasoner to assign instances to appropriate landslide classes. Machine learning techniques are used to predict new threshold values for feature attributes in the rules. Our framework is compared with published work on landslide detection where ontology was not used for the image classification. Our results demonstrate that a classification derived from the ontological framework accords with non-ontological methods. This study benchmarks the ontological method providing an alternative approach for image classification in the case study of landslides.

Ontology-driven Geographic Object-Based Image Analysis (O-GEOBIA) contributes to the identification of meaningful objects. In fusing data from multiple sensors, the number of feature variables is increased and object identification becomes a challenging task. We propose a methodological contribution that extends feature variable characterisation. This method is illustrated with a case study in forest-type mapping in Tasmania, Australia. Satellite images, airborne LiDAR (Light Detection and Ranging) and expert photo-interpretation data are fused for feature extraction and classification. Two machine learning algorithms, Random Forest and Boruta, are used to identify important and relevant feature variables. A variogram is used to describe textural and spatial features. Different variogram features are used as input for rule-based classifications. The rule-based classifications employ (i) spectral features, (ii) vegetation indices, (iii) LiDAR, and (iv) variogram features, and resulted in overall classification accuracies of 77.06%, 78.90%, 73.39% and 77.06% respectively. Following data fusion, the use of combined feature variables resulted in a higher classification accuracy (81.65%). Using relevant features extracted from the Boruta algorithm, the classification accuracy is further improved (82.57%). The results demonstrate that the use of relevant variogram features together with spectral and LiDAR features resulted in improved classification accuracy.

The Food and Agriculture Organization is developing a concept based multilingual vocabulary management tool to manage thesauri, authority lists and glossaries expressed as concept schemes ready to be used in a linked data environment.In this paper, we described the evolution of the AGROVOC thesaurus to AGROVOC Concept Scheme based on OWL (web ontology language) model and now shifting to SKOS (simple knowledge organization system) model. The paper explained why and how it evolved highlighting the key differences between different models. The system architecture and significant set of features available in the VocBench was discussed in the paper.