Impact of Ngrams-based indexing on XML retrieval

We present in this paper a statistical approach of term clustering. This approach is based on a statistical analysis of NGrams shared by a pair of terms and is inspired from the t f × idf criterion commonly used in information retrieval. Being statistical, the approach is completely independent from the lexical and grammatical characteristics of the language in which documents to be indexed are written. Classical indexing is often based on stemming, which consists of transforming a term into its radical. This allows to provide large issues for customized information access. As for us, we consider that this can be made by building term clusters and perform information retrieval based on this concept. This approach is used for XML retrieval, therefore some experiments have been undertaken into a dataset provided by INEX to show its impact compared to Porter stemming method

Patient Safety Culture in Tunisia: Defining Challenges and Opportunities

Background: Although adverse events in health care have been a center of attention recently, patient safety culture in primary care is relatively neglected. This study aimed to provide a baseline assessment of patient safety culture in the primary healthcare centers and explore its associated factors

XML Retrieval

Non

A Decade of Scholarly Research on Open Knowledge Graphs

The proliferation of open knowledge graphs has led to a surge in scholarly research on the topic over the past decade. This paper presents a bibliometric analysis of the scholarly literature on open knowledge graphs published between 2013 and 2023. The study aims to identify the trends, patterns, and impact of research in this field, as well as the key topics and research questions that have emerged. The work uses bibliometric techniques to analyze a sample of 4445 scholarly articles retrieved from Scopus. The findings reveal an ever-increasing number of publications on open knowledge graphs published every year, particularly in developed countries (+50 per year). These outputs are published in highly-referred scholarly journals and conferences. The study identifies three main research themes: (1) knowledge graph construction and enrichment, (2) evaluation and reuse, and (3) fusion of knowledge graphs into NLP systems. Within these themes, the study identifies specific tasks that have received considerable attention, including entity linking, knowledge graph embedding, and graph neural networks

Ortho­rhom­bic polymorph of (6,7-dimeth­oxy-1,2,3,4-tetra­hydro­isoquinolin-1-yl)methanol

The asymmetric unit of the title compound, C12H17NO3, contains two mol­ecules with different conformations. It is a polymorph of the monoclinic form [El Antri et al. (2004 ▶). Mol­ecules, 9, 650–657]; the samples were crystallized at different temperatures from the same solvent. In both structures, mol­ecules are linked by O—H⋯N hydrogen bonds, forming chains. The conformations of the chains and their packing differ markedly in the two polymorphs

Evaluation of the Overall Potential Toxicity of Shallow Marine Sediment from Oran Bay by Means of Bioassays of Acute Toxicity

This study investigates the ecotoxicological evaluation of sediment contamination from west Algerian Mediterranean coast (Oran harbour-Ain Franin).The toxicity of sediment and interstitials waters was estimated using bioessays of acute toxicity with brine shrim

Global visibility of publications through Digital Object Identifiers

This brief research report analyzes the availability of Digital Object Identifiers (DOIs) worldwide, highlighting the dominance of large publishing houses and the need for unique persistent identifiers to increase the visibility of publications from developing countries. The study reveals that a considerable amount of publications from developing countries are excluded from the global flow of scientific information due to the absence of DOIs, emphasizing the need for alternative publishing models. The authors suggest that the availability of DOIs should receive more attention in scholarly communication and scientometrics, contributing to a necessary debate on DOIs relevant for librarians, publishers, and scientometricians

HESML: A scalable ontology-based semantic similarity measures library with a set of reproducible experiments and a replication dataset

This work is a detailed companion reproducibility paper of the methods and experiments proposed by Lastra-Díaz and García-Serrano in (2015, 2016) [56–58], which introduces the following contributions: (1) a new and efficient representation model for taxonomies, called PosetHERep, which is an adaptation of the half-edge data structure commonly used to represent discrete manifolds and planar graphs; (2) a new Java software library called the Half-Edge Semantic Measures Library (HESML) based on PosetHERep, which implements most ontology-based semantic similarity measures and Information Content (IC) models reported in the literature; (3) a set of reproducible experiments on word similarity based on HESML and ReproZip with the aim of exactly reproducing the experimental surveys in the three aforementioned works; (4) a replication framework and dataset, called WNSimRep v1, whose aim is to assist the exact replication of most methods reported in the literature; and finally, (5) a set of scalability and performance benchmarks for semantic measures libraries. PosetHERep and HESML are motivated by several drawbacks in the current semantic measures libraries, especially the performance and scalability, as well as the evaluation of new methods and the replication of most previous methods. The reproducible experiments introduced herein are encouraged by the lack of a set of large, self-contained and easily reproducible experiments with the aim of replicating and confirming previously reported results. Likewise, the WNSimRep v1 dataset is motivated by the discovery of several contradictory results and difficulties in reproducing previously reported methods and experiments. PosetHERep proposes a memory-efficient representation for taxonomies which linearly scales with the size of the taxonomy and provides an efficient implementation of most taxonomy-based algorithms used by the semantic measures and IC models, whilst HESML provides an open framework to aid research into the area by providing a simpler and more efficient software architecture than the current software libraries. Finally, we prove the outperformance of HESML on the state-of-the-art libraries, as well as the possibility of significantly improving their performance and scalability without caching using PosetHERep