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.

This dataset is a companion reproducibility package of the related paper submitted for publication, whose aim is to allow the exact replication of a very large experimental survey on word similarity between the families of ontology-based semantic similarity measures and word embedding models as detailed in ‘appendix-reproducible-experiments.pdf’ file. Our experiments are based on the evaluation of all methods with the HESML V1R4 semantic measures library and the recording of these experiments with Reprozip. HESML is a self-contained Java software library of semantic measures based on WordNet whose latest version, called HESML V1R4, also supports the evaluation of pre-trained word embedding files. HESML is a self-contained experimentation platform on word similarity which is especially well suited to run large experimental surveys by supporting the execution of automatic reproducible experiment files on word similarity based on a XML-based file format called (*.exp). On the other hand, ReproZip is a virtualisation tool whose aim is to warrant the exact replication of experimental results onto a different system from that originally used in their creation. Reprozip captures all the program dependencies and is able to reproduce the packaged experiments on any host platform, regardless of the hardware and software configuration used in their creation. Thus, ReproZip warrants the reproduction of the experiments introduced herein in the long-term. Finally, other very valuable feature of Reprozip is that it allows to modify the input files of any Reprozip package with the aim of evaluating a set of experiments using originally unconsidered methods, configuration parameters or datasets. This dataset contains a Reprozip package to reproduce our experiments in any supported platform, as well as all pre-trained word embedding models and word similarity datasets used in our experiments. In addition, this dataset also contains all raw output files generated by our experiments, and a R script file to generate all output processed files corresponding to the data tables in our related paper. Finally, we provide a very detailed experimental setup in the aforementioned PDF file to allow all our experiments to be reproduced exactly.

This dataset introduces HESML V2R1 which is the sixth release of the Half-Edge Semantic Measures Library (HESML) detailed in [24]. HESML V2R1 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, GO and any other ontologies based on the OBO file format. HESML V2R1 also implements most of the sentence similarity methods in the biomedical domain together with a set of sentence pre-processing configurations, the integration of the three main biomedical NER tools, Metamap [3], MetamapLite [7] and cTAKES [31]. HESML V2R1 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 for the general domain and 33 pre-trained embeddings and language 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, and the necessary software clients to run the sentence-based experiments in the biomedical domain. HESML V2R1 introduces the following novelties: (1) the software implementation of a new package for the evaluation of sentence similarity methods; (2) the software implementation of most of the sentence similarity methods in the biomedical domain; (3) the implementation of a new package for sentence pre-processing together with a set of sentence pre-processing configurations; (4) the integration of the three main biomedical NER tools, Metamap [3], MetamapLite [7] and cTAKES [31]; (5) the software implementation of a parser based on the averaging Simple Word EMbeddings (SWEM) models introduced by Shen et al. [32] for efficiently loading and evaluating FastText-based [4] and other word embedding models; (6) the integration of Python wrappers for the evaluation of BERT [8], Universal Sentence Encoder (USE) [5] and Flair [1] models; and finally, (7) the software implementation of a new string-based sentence similarity method based on the aggregation of the Li et al. [29] similarity and Block distance [9] measures, called LiBlock, as well as eight new variants of the ontology-based methods proposed by Sogancioglu et al. [33], and a new pre-trained word embedding model based on FastText [4] and trained on the full-text of the articles in the PMC-BioC corpus [6]. 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 [24] 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., HESML V2R1 is a Java library developed with NetBeans 8 which compiles and runs in any Docker-based complaint platform., This work was partially supported by the UNED predoctoral grant started in April 2019 (BICI N7, November 19th, 2018)., Esta librerı́a estará disponible de forma permanente y perpetua.

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.