HESML V1R3 Java software library of ontology-based semantic similarity measures and information content models
Juan J. Lastra-Díaz · Data Archiving and Networked Services (DANS) · 2017
HESML V1R3 is the third release of the Half-Edge Semantic Measures Library (HESML) detailed in [1], which is a new, scalable and efficient Java software library of ontology-based semantic similarity measures and Information Content (IC) models based on WordNet. HESML V1R3 implements most ontology-based semantic similarity measures and Information Content (IC) models based on WordNet reported in the literature. It also provides a XML-based input file format in order to specify the execution of reproducible experiments on WordNet-based similarity, even with no software coding. 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. HESML V1R3 introduces two minor novelties as follows: the vertex ID has been updated from Integer to Long type in order to support a larger number of vertexes, and it includes five new similarity measures introduced by Hao et al (2011), Liu et al (2007), Pekar&Staab (2002) and Stojanovic et al (2001). 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 paper [1] 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. References: [1] Lastra-Díaz, J. J., García-Serrano, A., Batet, M., Fernández, M., & 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. http: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. [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. [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). [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.