HESML V1R5 Java software library of ontology-based semantic similarity measures and information content (IC) models. Main novelty of V1R5 version is the implementation of the most important biomedical ontologies, such as SNOMED-CT, MeSH, GO and OBO-based ontologies, in addition to WordNet.
This is the GitHub public repository for the HESML Java software library of ontology-based semantic similarity measures and Information Content (IC) models.
The initial master code of this repository matches the HESML V1R2 version available as Mendeley dataset at https://dx.doi.org/10.17632/t87s78dg78.2.
HESML V1R5 is the fifth release of the Half-Edge Semantic Measures Library (HESML) [1], which is a new, scalable and efficient Java software library of ontology-based semantic similarity measures and Information Content (IC) models, which also implements the evaluation of pre-trained word embedding models.
HESML V1R5 implements most ontology-based semantic similarity measures and Information Content (IC) models reported in the literature which are based on WordNet, SNOMED-CT, MeSH, GO and OBO-based ontologies. In addition, it provides a XML-based input file format in order to specify the execution of reproducible experiments on word/concept similarity based on WordNet, SNOMED-CT, MeSH, and GO ontologies, even with no software coding.
HESML is introduced and detailed in a companion reproducibility paper [1, 7] of the methods and experiments introduced in [2,3,4, 6].
The main features of HESML are as follows: (1) it is based on an efficient and linearly scalable representation for taxonomies called PosetHERep introduced in [1], (2) its performance exhibits a linear scalability as regards the size of the taxonomy, and (3) it does not use any caching strategy of vertex sets.
This is a minor service release of HESML V1R5 that introduces a minor performance improvement of the AncSPL algorithm introduced in the HESML V1R5 paper [7] and the HESML_UMLS_bencmark program, which implements the experiments reported in the aforementioned paper [7]. IMPORTANT NOTE: the HESML_UMLS_bencmark program does not compile because it uses METAMAP Java libraries, which require a license from the National Library of Medicine (NLM) of the United States, and thus, they cannot be included in the HESML distribution. We refer any reader interested in these benchmarks to the HESML V1R5 paper [7] and its reproducibility dataset [12].
Main novelties provided by HESML V1R5 are as follows:
(1) implementation of the SNOMED-CT, MeSH, GO and OBO-based ontologies for the biomedical doman [5].
(2) software implementation of six new groupwise similarity measures as follows: SimUI [8], SimLP [8], SimGIC [9], Average [10], Maximum [11], Best-match-Average (BMA) [10].
(3) the introduction of a new family of efficient path-based semantic similarity measures based on the reformulation of path-based measures using the new AncSPL [7] algorithm for the real-time computation of the length of the shortest path between concepts.
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 main HESML papers [1, 7] as attribution requirement. On other hand, the commercial use of the similarity measures introduced in [2], as well as part of the intrinsic IC models introduced in [3] and [4], is protected by a patent application [5]. In addition, any user of HESML must fulfill other licensing terms described in [1] related to other resources distributed with the library, such as WordNet and a dataset of corpus-based IC models, among others.
[1] Lastra-Díaz, J. J., García-Serrano, A., Batet, M., Fernández, M. and Chirigati, F. (2017). HESML: a scalable ontology-based semantic similarity measures library with a set of reproducible experiments and a replication dataset. Information Systems 66, 97-118. https://dx.doi.org/10.1016/j.is.2017.02.002
[2] Lastra-Díaz, J. J., & García-Serrano, A. (2015). A novel family of IC-based similarity measures with a detailed experimental survey on WordNet. Engineering Applications of Artificial Intelligence Journal, 46, 140-153. https://dx.doi.org/10.1016/j.engappai.2015.09.006
[3] Lastra-Díaz, J. J., & García-Serrano, A. (2015). A new family of information content models with an experimental survey on WordNet. Knowledge-Based Systems, 89, 509-526. https://dx.doi.org/10.1016/j.knosys.2015.08.019
[4] Lastra-Díaz, J. J., & García-Serrano, A. (2016). A refinement of the well-founded Information Content models with a very detailed experimental survey on WordNet. Universidad Nacional de Educación a Distancia (UNED). https://e-spacio.uned.es/fez/view/bibliuned:DptoLSI-ETSI-Informes-Jlastra-refinement
[5] Lastra Díaz, J. J., & García Serrano, A. (2016). System and method for the indexing and retrieval of semantically annotated data using an ontology-based information retrieval model. United States Patent and Trademark Office (USPTO) Application, US2016/0179945 A1.
[6] J.J. Lastra-Díaz, J. Goikoetxea, M. Hadj Taieb, A. García-Serrano, M. Ben Aouicha, E. Agirre, A reproducible survey on word embeddings and ontology-based methods for word similarity: linear combinations outperform the state of the art, Engineering Applications of Artificial Intelligence. 85 (2019) 645–665. https://doi.org/10.1016/j.engappai.2019.07.010
[7] J.J. Lastra-Díaz, A. Lara-Clares, A. García-Serrano, HESML: a real-time semantic measures library for the biomedical domain with a reproducible survey, Submitted for Publication. (2020).
[8] R. Gentleman, Visualizing and distances using GO, URL Http:https://www. Bioconductor. Org/docs/vignettes. Html. 38 (2005). https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.592.2206&rep=rep1&type=pdf.
[9] C. Pesquita, D. Faria, H. Bastos, A. Falcão, F. Couto, Evaluating GO-based semantic similarity measures, in: Proc. 10th Annual Bio-Ontologies Meeting, 2007: p. 38.
[10] F. Azuaje, H. Wang, O. Bodenreider, Ontology-driven similarity approaches to supporting gene functional assessment, in: Proceedings of the ISMB’2005 SIG Meeting on Bio-Ontologies, academia.edu, 2005: pp. 9–10.
[11] J.L. Sevilla, V. Segura, A. Podhorski, E. Guruceaga, J.M. Mato, L.A. Martínez-Cruz, F.J. Corrales, A. Rubio, Correlation between gene expression and GO semantic similarity, IEEE/ACM Trans. Comput. Biol. Bioinform. 2 (2005) 330–338.
[12] J.J. Lastra-Díaz, A. Lara-Clares, A. García-Serrano, Reproducibility dataset for a benchmark of biomedical semantic measures libraries, (2020). https://doi.org/10.21950/OTDA4Z.
Steps to reproduce the library
HESML V1R5 is distributed as a Java class library (HESML-V1R5.0.1.jar) plus three test driver applications (HESMLclient.jar, HESML_GOclien, HESML_UMLScliet), which have been developed using NetBeans 8.2 for Windows, although it has been also compiled and evaluated on Linux-based platforms using the corresponding NetBeans versions.
The HESML-V1R5.0.1.jar file is already included in the HESML_Library\HESML\dist folder of the HESML_Release_V1R5.zip distribution file. In order to compile HESML from its source files, you must follow the following steps:
(1) Download the full distribution of HESML V1R5.
(2) Install Java 8, Java SE Dev Kit 8 and NetBeans 8.0.2 or higher in your workstation.
(3) Launch NetBeans IDE and open the HESML and client projects contained in the root folder. NetBeans automatically detects the presence of a nbproject subfolder with the project files.
(4) Select HESML and client projects in the project treeview respectively. Then, invoke the "Clean and Build project (Shift + F11)" command in order to compile both projects.
In order to remain up to date on new HESML versions, as well as asking for technical support, we invite the readers to visit https://hesml.lsi.uned.es and subscribe to the HESML forum by sending an email to the following address:
Steps to use the library
You can use the HESML client programs to run reproducible experiments or create your own client programs using the client source code as example. For more information, including a detailed description of how to run the reproducible experiments and extending the library, we refer the reader to the introductory paper [1] cited above.