Domain-aware ontology matching

During the last years, technological advances have created new ways of communication, which have motivated governments, companies and institutions to digitalise the data they have in order to make it accessible and transferable to other people. Despite the millions of digital resources that are currently available, their diversity and heterogeneous knowledge representation make complex the process of exchanging information automatically. Nowadays, the way of tackling this heterogeneity is by applying ontology matching techniques with the aim of finding correspondences between the elements represented in different resources. These approaches work well in some cases, but in scenarios when there are resources from many different areas of expertise (e.g. emergency response) or when the knowledge represented is very specialised (e.g. medical domain), their performance drops because matchers cannot find correspondences or find incorrect ones. In our research, we have focused on tackling these problems by allowing matchers to take advantage of domain-knowledge. Firstly, we present an innovative perspective for dealing with domain-knowledge by considering three different dimensions (specificity - degree of specialisation -, linguistic structure - the role of lexicon and grammar -, and type of knowledge resource - regarding generation methodologies). Secondly, domain-resources are classified according to the combination of these three dimensions. Finally, there are proposed several approaches that exploit each dimension of domain-knowledge for enhancing matchers’ performance. The proposals have been evaluated by matching two of the most used classifications of diseases (ICD-10 and DSM-5), and the results show that matchers considerably improve their performance in terms of f-measure. The research detailed in this thesis can be used as a starting point to delve into the area of domain-knowledge matching. For this reason, we have also included several research lines that can be followed in the future to enhance the proposed approaches

Automatic Positional Accuracy Assessment of Imagery Segmentation Processes: A Case Study

There are many studies related to Imagery Segmentation (IS) in the field of Geographic Information (GI). However, none of them address the assessment of IS results from a positional perspective. In a field in which the positional aspect is critical, it seems reasonable to think that the quality associated with this aspect must be controlled. This paper presents an automatic positional accuracy assessment (PAA) method for assessing this quality component of the regions obtained by means of the application of a textural segmentation algorithm to a Very High Resolution (VHR) aerial image. This method is based on the comparison between the ideal segmentation and the computed segmentation by counting their differences. Therefore, it has the same conceptual principles as the automatic procedures used in the evaluation of the GI's positional accuracy. As in any PAA method, there are two key aspects related to the sample that were addressed: (i) its size-specifically, its influence on the uncertainty of the estimated accuracy values-and (ii) its categorization. Although the results obtained must be taken with caution, they made it clear that automatic PAA procedures, which are mainly applied to carry out the positional quality assessment of cartography, are valid for assessing the positional accuracy reached using other types of processes. Such is the case of the IS process presented in this study

Publisher Correction : Pancreatic duct ligation reduces premalignant pancreatic lesions in a Kras model of pancreatic adenocarcinoma in mice

Correction to: Scientific Reports, https://doi.org/10.1038/s41598-020-74947-4, published online 27 October 2020 The original version of this Article contained a typographical error in the spelling of the author Patricia Sánchez- Velázquez, which was incorrectly given as Patricia Sánchez Velazquez. Additionally, the author Patricia Sánchez- Velázquez was incorrectly indexed. These errors have now been corrected in the PDF and HTML versions of the ArticleS

Illustrative Example ER extension for WordNet

Example for ISCRAM 2017, describing an Emergency Response (ER) extension for WordNe

Emergency Response extension for WordNet (100 terms from the UKCP lexicon)

Dataset that includes 100 terms from the UKCP lexicon. This is written in LMF format in order to be compatible with WordNet.<div><br></div><div>This resource has been developed to extend WordNet with Emergency Response (ER) terminologies and so to improve semantic matching between organisations in ER scenarios<br><div><br></div></div

Towards Building Ontologies with the Wisdom of the Crowd

Crowdsourcing provides a valuable source of input that reflects the human diversity of domain knowledge. It has increasingly been used in ontology engineering and evaluation, however, few approaches consider different types of crowdsourcing for data acquisition. In this paper, we compare two crowdsourcing techniques - a mechanized labor-based task and a game-based approach - to acquire shared knowledge from which we semi-automatically build an ontology. This paper focuses on the first two steps of ontology engineering, the forming of concepts and their hierarchical relations. To this end, we adapt a distributional semantic and class-based word sense disambiguation approach and a knowledge-intensive tree traversal algorithm. Each step along the process and the final resources are evaluated manually and by a gold standard created from Wikipedia data. Our results show that the ontology resulting from data obtained with the mechanized labor-based approach provides a higher level of granularity than the game-based one. However, the latter is faster and seems more enticing to participants

Deep learning methods applied to digital elevation models: state of the art

Deep Learning (DL) has a wide variety of applications in various thematic domains, including spatial information. Although with limitations, it is also starting to be considered in operations related to Digital Elevation Models (DEMs). This study aims to review the methods of DL applied in the field of altimetric spatial information in general, and DEMs in particular. Void Filling (VF), Super-Resolution (SR), landform classification and hydrography extraction are just some of the operations where traditional methods are being replaced by DL methods. Our review concludes that although these methods have great potential, there are aspects that need to be improved. More appropriate terrain information or algorithm parameterisation are some of the challenges that this methodology still needs to face

Permanently magnetic micro-composite material without rare earth elements and production method thereof

[ES] Material microcompuesto magnético permanente sin tierras raras y su método de obtención. Se reivindica un material microcompuesto basado en imanes permanentes compuestos de partículas cerámicas de ferrita hexagonal magnéticamente duras y partículas de aleaciones metálicas magnéticamente blandas y su método de fabricación por aleado mecánico en presencia de un agente de acoplamiento. Los imanes permanentes sin tierras raras basados en nanopartículas presentan un método de producción industrial complejo y costoso. La invención describe la obtención un material composite basado en ferritas y aleaciones metálicas de tamaño micrométrico (no-nanométrico), mediante métodos de molienda mecánica. Se consigue un producto mediante un método de fabricación fácilmente escalable, industrializable y de bajo coste, con el que se obtienen materiales compuestos con propiedades magnéticas y microestructurales favorables caracterizados por presentar un aumento del producto máximo de energía (BHmax) de hasta un 30% respecto al componente mayoritario de ferrita.[EN] The invention relates to a micro-composite material based on permanent magnets formed from magnetically hard ceramic particles of hexagonal ferrite and magnetically soft particles of metal alloys, and to the method for producing same by means of mechanical alloying in the presence of a coupling agent. Nanoparticle-based permanent magnets without rare earth elements have a complex and costly industrial production method. The invention describes the production of a composite material based on micro-scale (non-nano-scale) ferrite and metallic alloys, by means of mechanical grinding methods. A product is obtained using a low-cost, industrialisable, easily scalable production method, with which composite materials with favourable micro-structural and magnetic properties are obtained, said materials being characterised in that they have an increase in maximum energy product (BHmax) of up to 30% with respect to the main component of ferrite.Universidad Complutense de Madrid, Institute for Energy Technology, IMDEA Naciociencias, Consejo Superior de Investigaciones Científicas (España)B2 Patente con examen previ