BUSCADOR RECOLECTA

This dataset introduces HESML V1R5 which is the fifth release of the Half-Edge Semantic Measures Library (HESML) detailed in [13]. HESML V1R5 is a linearly scalable and efficient Java software library of ontology-based semantic similarity measures and Information Content (IC) models for ontolgies like WordNet, SNOMED-CT, MeSH, GO and any other ontologies based on the OBO file format. HESML V1R5 implements most ontology-based semantic similarity measures and Information Content (IC) models reported in the literature, as well as the evaluation of three pre-trained word embedding models. It also provides a XML-based input file format in order to specify the execution of reproducible word/concept similarity experiments based on WordNet, SNOMED-CT, MeSH, or GO without software coding. HESML V1R5 introduces the following novelties: (1) the parsing and in-memory representation of the SNOMED-CT, MeSH and any other ontologies based on the OBO file format such as the Gene Ontology (GO); (2) a new collection of efficient path-based similarity measures based on the reformulation of previous path-based measures which are based on the new Ancestors-based Shortest-Path Length (AncSPL) algorithm; and (3) a collection of groupwise similarity measures. HESML library is freely distributed for any non-commercial purpose under a CC By-NC-SA-4.0 license, subject to the citing of the two mains HESML papers as attribution requirement. However, HESML distribution also includes other datasets, databases or data files whose use require the attribution acknowledgement by any user of HEMSL. Thus, we urge to the HESML users to fulfill with licensing terms related to other resources distributed with the library as detailed in its companion release notes.

Semantic Textual Similarity (also known as Semantic Short-text Similarity) is a research problem that aims to calculate the similarity among text units (phrases, sentences, paragraphs or texts) focusing on the semantic content. The importance of Semantic Similarity in Natural Language Processing has increased in the last years due to its relevance in many tasks and applications, such as Automatic Summarization, Machine Translation, Question Answering or Semantic Indexing. UB-NER is a self-contained Java software library for benchmarking state-of-the-art STS measures in the biomedical domain. It allows to define and execute a set of experiments combining different measures and preprocessing methods. This dataset contains the reproducibility framework and dependencies, whose aim is to allow the exact replication of unsupervised named entity recognition experiment in the biomedical domain as detailed in "ReproductionProtocol.pdf" file.

This dataset introduces a set of reproducibility resources with the aim of allowing the exact replication of the experiments introduced by our companion paper, which compare the performance of the three UMLS-based semantic similarity libraries reported in the literature as follows: (1) UMLS::Similarity [20], (2) Semantic Measures Library (SML) [3], and the latest version of our Half-Edge Semantic Measures Library (HESML) introduced in our aforementioned companion paper. HESML V1R5 is the fifth release of our Half-Edge Semantic Measures Library (HESML) detailed in [15] which is a linearly scalable and efficient Java software library of ontology-based semantic similarity measures and Information Content (IC) models for ontologies like WordNet, SNOMED-CT, MeSH and GO. This dataset sets a self-contained reproducibility platform which contains the Java source code and binaries of our main benchmark program, as well as a Docker image which allows the exact replication of our experiments in any software platform supported by Docker, such as all Linux-based operating systems, Windows or MacOS. Our benchmark program is distributed with the UMLS SNOMED-CT and MeSH ontologies by courtesy of the US National Library of Medicine (NLM), as well as all needed software components with the aim of making the setup process easier. Our Docker image provides an exact virtual replica of the machine in which we ran our experiments, thus removing the need to carry-out any tedious setup process, such as the setup of the UMLS Metathesaurus on MySQL database, UMLS::Similarity library and other software components.