Enterprise information integration: on discovering links using genetic programming

Both established and emergent business rely heavily on data, chiefly those that wish to become game changers. The current biggest source of data is the Web, where there is a large amount of sparse data. The Web of Data aims at providing a unified view of these islands of data. To realise this vision, it is required that the resources in different data sources that refer to the same real-world entities must be linked, which is they key factor for such a unified view. Link discovery is a trending task that aims at finding link rules that specify whether these links must be established or not. Currently there are many proposals in the literature to produce these links, especially based on meta-heuristics. Unfortunately, creating proposals based on meta-heuristics is not a trivial task, which has led to a lack of comparison between some well-established proposals. On the other hand, it has been proved that these link rules fall short in cases in which resources that refer to different real-world entities are very similar or vice versa. In this dissertation, we introduce several proposals to address the previous lacks in the literature. On the one hand we, introduce Eva4LD, which is a generic framework to build genetic programming proposals for link discovery; which are a kind of meta-heuristics proposals. Furthermore, our framework allows to implement many proposals in the literature and compare their results fairly. On the other hand, we introduce Teide, which applies effectively the link rules increasing significantly their precision without dropping their recall significantly. Unfortunately, Teide does not learn link rules, and applying all the provided link rules is computationally expensive. Due to this reason we introduce Sorbas, which learns what we call contextual link rules.Las empresas que desean establecer un precedente en el panorama actual tienden a recurrir al uso de datos para mejorar sus modelos de negocio. La mayor fuente de datos disponible es la Web, donde una gran cantidad es accesible aunque se encuentre fragmentada en islas de datos. La Web de los Datos tiene como objetivo dar una visión unificada de dichas islas, aunque el almacenamiento de los mismos siga siendo distribuido. Para ofrecer esta visión es necesario enlazar los recursos presentes en las islas de datos que hacen referencia a las mismas entidades del mundo real. Link discovery es el nombre atribuido a esta tarea, la cual se basa en generar reglas de enlazado que permiten establecer bajo qué circunstancias dos recursos deben ser enlazados. Se pueden encontrar diferentes propuestas en la literatura de link discovery, especialmente basadas en meta-heurísticas. Por desgracia comparar propuestas basadas en meta-heurísticas no es trivial. Por otro lado, se ha probado que estas reglas de enlazado no funcionan bien cuando los recursos que hacen referencia a dos entidades distintas del mundo real son muy parecidos, o por el contrario, cuando dos recursos muy distintos hacen referencia a la misma entidad. En esta tesis presentamos varias propuestas. Por un lado, Eva4LD es un framework genérico para desarrollar propuestas de link discovery basadas en programación genética, que es un tipo de meta-heurística. Gracias a nuestro framework, hemos podido implementar distintas propuestas de la literatura y comprar justamente sus resultados. Por otro lado, en la tesis presentamos Teide, una propuesta que recibiendo varias reglas de enlazado las aplica de tal modo que mejora significativamente la precisión de las mismas sin reducir significativamente su cobertura. Por desgracia, Teide es computacionalmente costoso debido a que no aprende reglas. Debido a este motivo, presentamos Sorbas que aprende un tipo de reglas de enlazado que denominamos reglas de enlazado con contexto

On learning context-aware rules to link RDF datasets

Integrating RDF datasets has become a relevant problem for both researchers and practitioners. In the literature, there are many genetic proposals that learn rules that allow to link the resources that refer to the same real-world entities, which is paramount to integrating the datasets. Unfortunately, they are context-unaware because they focus on the resources and their attributes but forget about their neighbours. This implies that they fall short in cases in which different resources have similar attributes but refer to different real-world entities or cases in which they have dissimilar attributes but refer to the same real-world entities. In this article, we present a proposal that learns context-aware rules that take into account both the attributes of the resources and their neighbours. We have conducted an extensive experimentation that proves that it outperforms the most advanced genetic proposal. Our conclusions were checked using statistically sound methods.Ministerio de Economía y Competitividad TIN2013-40848-RMinisterio de Economía y Competitividad TIN2016-75394-RJunta de Andalucía P18- RT-106

Improving Link Specifications using Context-Aware Information

There is an increasing interest in publishing data using the Linked Open Data philosophy. To link the RDF datasets, a link discovery task is performed to generate owl:sameAs links. There are two ways to perform this task: by means of a classi er or a link speci cation; we focus in the latter approach. Current link speci cation techniques only use the data properties of the instances that they are linking, and they do not take the context information into account. In this paper, we present a proposal that aims to generate context-aware link speci cations to improve the regular link speci cations, increasing the e ectiveness of the results in several real-world scenarios where the context is crucial. Our context-aware link speci cations are independent from similarity functions, transformations or aggregations. We have evaluated our proposal using two real-world scenarios in which we improve precision and recall with respect to regular link speci cations in 23% and 58%, respectively.Ministerio de Economía y Competitividad TIN2013-40848-

WoTHive: Enabling Syntactic and Semantic Discovery in the Web of Things

In the last decade the Internet of Things (IoT) has experienced a significant growth and its adoption has become ubiquitous in either business and private life. As a result, several initiatives have emerged for addressing specific challenges and provide a standard or a specification to address them; like CoRE, Web of Things (WoT), oneM2M, or OGC among others. One of these challenges revolves around the discovery procedures to find IoT devices within IoT infrastructures and whether the discovery performed is semantic or syntactic. This article focusses on the WoT initiative and reports the benefits that Semantic Web technologies bring to discovery in WoT. In particular, one of the implementations for the WoT discovery is presented, which is named WoTHive and provides syntactic and semantic discovery capabilities. WoTHive is the only candidate implementation that addresses at the same time the syntactic and semantic functionalities specified in the discovery described by WoT. Several experiments have been carried out to test WoTHive; these advocate that the implementation is technically sound for CRUD operations and that its semantic discovery outperforms the syntactic one implemented. Furthermore, an experiment has been carried out to compare whether syntactic discovery is faster than semantic discovery using the Link Smart implementation for syntactic discovery and WoTHive for semantic

Atypical Fibroxanthoma-Like Amelanotic Melanoma: A Diagnostic Challenge

Atypical fibroxanthoma-like amelanotic melanoma is a very rare variant of melanoma that can, if not correctly recognized and framed, lead to diagnostic errors that can potentially cause problems of extreme relevance to patients. Correct knowledge of this entity and the execution of adequate immunohistochemical investigations are the basic conditions for the correct management of this lesion. We report on a case of atypical fibroxanthoma-like amelanotic melanoma, which clinically simulated a fibrohistiocytic lesion, and which created differential diagnostic problems, and finally, we conduct a short review of the literature

A Digital Twin Platform generating Knowledge Graphs for construction projects

Construction projects combine activities across diverse domains and involve various entities which exchange information in different formats. To connect such diverse entities supporting their data exchanges using semantic web technologies, a Digital Twin Platform (DTP) is introduced, as a part of a greater ICT framework called COGITO, which aims at optimizing and supervising real construction projects from the conceptual to their implementation stages. To perform these connections, DTP creates a digital twin model designed to be the main data pool of COGITO’s application tools. DTP’s digital twin model is populated based on a well-defined ontology combining different data sources such as OpenBIM, time schedule, and construction resource files into a single RDF file. The digital twin model generation and access are demonstrated successfully on simple 4D OpenBIM data

Transcatheter aortic valve implantation with the novel-generation Navitor device. Procedural and early outcomes

Transcatheter aortic valve implantation (TAVI) has proved beneficial in patients with severe aortic stenosis, especially when second-generation devices are used. We aimed at reporting our experience with Navitor, a third-generation device characterized by intrannular, large cell, and cuffed design, as well as high deliverability and minimization of paravalvular leak. Between June and December 2021, a total of 39 patients underwent TAVI with Navitor, representing 20% of all TAVI cases. Mean age was 80.0 +/- 6.7 years, and 14 (36.8%) women were included. Severe aortic stenosis was the most common indication to TAVI (37 [97.4%] cases), whereas 2 (5.3%) individuals were at low surgical risk. Device and procedural success was obtained in all patients, with a total hospital stay of 6.6 +/- 4.5 days. One (2.9%) patient required permanent pacemaker implantation, but no other hospital events occurred. At 1-month follow-up, a cardiac death was adjudicated in an 87-year-old man who had been at high surgical risk. Echocardiographic follow-up showed no case of moderate or severe aortic regurgitation, with mild regurgitation in 18 (47%), and none or trace regurgitation in 20 (53%). The Navitor device, thanks to its unique features, is a very promising technology suitable to further expand indications and risk-benefit profile of TAVI

L-Proline Induces a Mesenchymal-like Invasive Program in Embryonic Stem Cells by Remodeling H3K9 and H3K36 Methylation

SummaryMetabolites are emerging as key mediators of crosstalk between metabolic flux, cellular signaling, and epigenetic regulation of cell fate. We found that the nonessential amino acid L-proline (L-Pro) acts as a signaling molecule that promotes the conversion of embryonic stem cells into mesenchymal-like, spindle-shaped, highly motile, invasive pluripotent stem cells. This embryonic-stem-cell-to-mesenchymal-like transition (esMT) is accompanied by a genome-wide remodeling of the H3K9 and H3K36 methylation status. Consistently, L-Pro-induced esMT is fully reversible either after L-Pro withdrawal or by addition of ascorbic acid (vitamin C), which in turn reduces H3K9 and H3K36 methylation, promoting a mesenchymal-like-to-embryonic-stem-cell transition (MesT). These findings suggest that L-Pro, which is produced by proteolytic remodeling of the extracellular matrix, may act as a microenvironmental cue to control stem cell behavior

Oxidized Low-Density Lipoproteins Induce Tissue Factor Expression in T-Lymphocytes via activation of Lectin-Like Oxidized Low-Density Lipoprotein Receptor-1

T-lymphocytes plays an important role in the pathophysiology of acute coronary syndromes (ACS). T-cell activation in vitro by pro-inflammatory cytokines may lead to functional Tissue Factor (TF) expression, indicating a possible contribution of immunity to thrombosis. Oxidized low-density lipoproteins (oxLDLs) are found abundantly in atherosclerotic plaques. We aimed at evaluating the effects of oxLDLs on TF expression in T-cells and the role of the lectin-like oxidized low-density lipoprotein receptor-1 (LOX-1)