In Flanders Fields the Poppies Grow

https://digitalcommons.library.umaine.edu/mmb-vp/1838/thumbnail.jp

On the linguistic linked open data infrastructure

In this paper we describe the current state of development of the Linguistic Linked Open Data (LLOD) infrastructure, an LOD(sub-)cloud of linguistic resources, which covers various linguistic data bases, lexicons, corpora, terminology and metadata repositories.We give in some details an overview of the contributions made by the European H2020 projects “Prêt-à-LLOD” (‘Ready-to-useMultilingual Linked Language Data for Knowledge Services across Sectors’) and “ELEXIS” (‘European Lexicographic Infrastructure’) to the further development of the LLOD

Modelling frequency and attestations for OntoLex-Lemon

The OntoLex vocabulary enjoys increasing popularity as a means of publishing lexical resources with RDF and as Linked Data. The recent publication of a new OntoLex module for lexicography, lexicog, reflects its increasing importance for digital lexicography. However, not all aspects of digital lexicography have been covered to the same extent. In particular, supplementary information drawn from corpora such as frequency information, links to attestations, and collocation data were considered to be beyond the scope of lexicog. Therefore, the OntoLex community has put forward the proposal for a novel module for frequency, attestation and corpus information (FrAC), that not only covers the requirements of digital lexicography, but also accommodates essential data structures for lexical information in natural language processing. This paper introduces the current state of the OntoLex-FrAC vocabulary, describes its structure, some selected use cases, elementary concepts and fundamental definitions, with a focus on frequency and attestations

Recent developments for the linguistic linked open data infrastructure

In this paper we describe the contributions made by the European H2020 project “Pret-a-LLOD” (‘Ready-to-use Multilingual Linked Language Data for Knowledge Services across Sectors’) to the further development of the Linguistic Linked Open Data (LLOD) infrastructure. Pret-a-LLOD aims to develop a new methodology for building data value chains applicable to a wide range of sectors and applications and based around language resources and language technologies that can be integrated by means of semantic technologies. We describe the methods implemented for increasing the number of language data sets in the LLOD. We also present the approach for ensuring interoperability and for porting LLOD data sets and services to other infrastructures, as well as the contribution of the projects to existing standards

Case Reports1. A Late Presentation of Loeys-Dietz Syndrome: Beware of TGFβ Receptor Mutations in Benign Joint Hypermobility

Background: Thoracic aortic aneurysms (TAA) and dissections are not uncommon causes of sudden death in young adults. Loeys-Dietz syndrome (LDS) is a rare, recently described, autosomal dominant, connective tissue disease characterized by aggressive arterial aneurysms, resulting from mutations in the transforming growth factor beta (TGFβ) receptor genes TGFBR1 and TGFBR2. Mean age at death is 26.1 years, most often due to aortic dissection. We report an unusually late presentation of LDS, diagnosed following elective surgery in a female with a long history of joint hypermobility. Methods: A 51-year-old Caucasian lady complained of chest pain and headache following a dural leak from spinal anaesthesia for an elective ankle arthroscopy. CT scan and echocardiography demonstrated a dilated aortic root and significant aortic regurgitation. MRA demonstrated aortic tortuosity, an infrarenal aortic aneurysm and aneurysms in the left renal and right internal mammary arteries. She underwent aortic root repair and aortic valve replacement. She had a background of long-standing joint pains secondary to hypermobility, easy bruising, unusual fracture susceptibility and mild bronchiectasis. She had one healthy child age 32, after which she suffered a uterine prolapse. Examination revealed mild Marfanoid features. Uvula, skin and ophthalmological examination was normal. Results: Fibrillin-1 testing for Marfan syndrome (MFS) was negative. Detection of a c.1270G > C (p.Gly424Arg) TGFBR2 mutation confirmed the diagnosis of LDS. Losartan was started for vascular protection. Conclusions: LDS is a severe inherited vasculopathy that usually presents in childhood. It is characterized by aortic root dilatation and ascending aneurysms. There is a higher risk of aortic dissection compared with MFS. Clinical features overlap with MFS and Ehlers Danlos syndrome Type IV, but differentiating dysmorphogenic features include ocular hypertelorism, bifid uvula and cleft palate. Echocardiography and MRA or CT scanning from head to pelvis is recommended to establish the extent of vascular involvement. Management involves early surgical intervention, including early valve-sparing aortic root replacement, genetic counselling and close monitoring in pregnancy. Despite being caused by loss of function mutations in either TGFβ receptor, paradoxical activation of TGFβ signalling is seen, suggesting that TGFβ antagonism may confer disease modifying effects similar to those observed in MFS. TGFβ antagonism can be achieved with angiotensin antagonists, such as Losartan, which is able to delay aortic aneurysm development in preclinical models and in patients with MFS. Our case emphasizes the importance of timely recognition of vasculopathy syndromes in patients with hypermobility and the need for early surgical intervention. It also highlights their heterogeneity and the potential for late presentation. Disclosures: The authors have declared no conflicts of interes

AUTOMATIC EXTRACTION OF LOGICALLY CONSISTENT ONTOLOGIES FROM TEXT CORPORA

Ontologies provide a structured description of the concepts and terminologyused in a particular domain and provide valuable knowledge for a range of natu-ral language processing applications. However, for many domains and languagesontologies do not exist and manual creation is a difficult and resource-intensiveprocess. As such, automatic methods to extract, expand or aid the constructionof these resources is of significant interest.　　There are a number of methods for extracting semantic information abouthow terms are related from raw text, most notably the approach of Hearst[1992], who used patterns to extract hypernym information. This method wasmanual and it is not clear how to automatically generate patterns, which arespecific to a given relationship and domain. I present a novel method for de-veloping patterns based on the use of alignments between patterns. Alignmentworks well as it is closely related to the concept of a join-set of patterns, whichminimally generalise over-fitting patterns. I show that join-sets can be viewedas an reduction on the search space of patterns, while resulting in no loss ofaccuracy. I then show the results can be combined by a support vector machineto a obtain a classifier, which can decide if a pair of terms are related. I appliedthis to several data sets and conclude that this method produces a precise result,with reasonable recall.　　The system I developed, like many semantic relation systems, produces onlya binary decision of whether a term pair is related. Ontologies have a structure,that limits the forms of networks they represent. As the relation extraction isgenerally noisy and incomplete, it is unlikely that the extracted relations willmatch the structure of the ontology. As such I represent the structure of ontol-ogy as a set of logical statements, and form a consistent ontology by finding thenetwork closest to the relation extraction system\u27s output, which is consistentwith these restrictions. This gives a novel NP-hard optimisation problem, forwhich I develop several algorithms. I present simple greedy approaches, andbranch and bound approaches, which my results show are not sufficient for thisproblem. I then use resolution to show how this problem can be stated as aninteger programming problem, which can be efficiently solved by relaxing it toa linear programming problem. I show that this result can efficiently solve theproblem, and furthermore when applied to the result of the relation extractionsystem, this improves the quality of the extraction as well as converting it to anontological structure

Cardamom: Comparative deep models for minority and historical languages

This paper gives an overview of the Cardamom project, which aims to close the resource gap for minority and under-resourced languages by means of deep-learning-based natural language processing (NLP) and exploiting similarities of closely-related languages. The project further extends this idea to historical languages, which can be considered as closely related to their modern form, and as such aims to provide NLP through both space and time for languages that have been ignored by current approaches