Dividing the Ontology Alignment Task with Semantic Embeddings and Logic-based Modules

Large ontologies still pose serious challenges to state-of-the-art ontology alignment systems. In this paper we present an approach that combines a neural embedding model and logic-based modules to accurately divide an input ontology matching task into smaller and more tractable matching (sub)tasks. We have conducted a comprehensive evaluation using the datasets of the Ontology Alignment Evaluation Initiative. The results are encouraging and suggest that the proposed method is adequate in practice and can be integrated within the workflow of systems unable to cope with very large ontologies

On partitioning for ontology alignment

On Partitioning for Ontology Alignment?Sunny Pereira1, Valerie Cross1, Ernesto Jiménez-Ruiz21Miami University, Oxford, OH 45056, United States2University of Oslo, Norway1 IntroductionOntology Alignment (OA) is the process of determining the mappings between twoontologies. A number of systems currently exists and many of them are participating inthe annual Ontology Alignment Evaluation Initiative (OAEI).3Ontology alignment for two very large ontologies becomes time consuming andmemory intensive. For example, thelargebiotrack in the OAEI campaign still posesserious challenges to participants and only 4 out of 11 systems managed to completethe largestlargebiotask. A general approach to address these challenges is to partitioneach ontology into cohesive blocks. The matching task is then divided into smaller tasksinvolving only relevant pair of blocks (i.e., partitions). Ontology partitioning brings newchallenges: how best to partition each ontology into blocks and whether the partitioningprocess on each ontology should be independent of each other. Three main strategiesexist:(i)totally independent partitioning of both ontologies using various clusteringalgorithms,(ii)independent partitioning of the better structured ontology and then useits partitioning to direct the partitioning of the other, and(iii)dependent partitioningbetween the two using a quick and efficient initial mapping of the two and then thismapping directs their partitioning.A preliminary study of these three partitioning strategies and their effects on ontol-ogy alignment is presented. The objective of this preliminary work is to determine thesuitability of these strategies to improve the performance of OA systems when dealingwith large ontologies, especially those unable to cope with the largest tasks

Exploring and linking biomedical resources through multidimensional semantic spaces

Background The semantic integration of biomedical resources is still a challenging issue which is required for effective information processing and data analysis. The availability of comprehensive knowledge resources such as biomedical ontologies and integrated thesauri greatly facilitates this integration effort by means of semantic annotation, which allows disparate data formats and contents to be expressed under a common semantic space. In this paper, we propose a multidimensional representation for such a semantic space, where dimensions regard the different perspectives in biomedical research (e.g., population, disease, anatomy and protein/genes). Results This paper presents a novel method for building multidimensional semantic spaces from semantically annotated biomedical data collections. This method consists of two main processes: knowledge and data normalization. The former one arranges the concepts provided by a reference knowledge resource (e.g., biomedical ontologies and thesauri) into a set of hierarchical dimensions for analysis purposes. The latter one reduces the annotation set associated to each collection item into a set of points of the multidimensional space. Additionally, we have developed a visual tool, called 3D-Browser, which implements OLAP-like operators over the generated multidimensional space. The method and the tool have been tested and evaluated in the context of the Health-e-Child (HeC) project. Automatic semantic annotation was applied to tag three collections of abstracts taken from PubMed, one for each target disease of the project, the Uniprot database, and the HeC patient record database. We adopted the UMLS Meta-thesaurus 2010AA as the reference knowledge resource. Conclusions Current knowledge resources and semantic-aware technology make possible the integration of biomedical resources. Such an integration is performed through semantic annotation of the intended biomedical data resources. This paper shows how these annotations can be exploited for integration, exploration, and analysis tasks. Results over a real scenario demonstrate the viability and usefulness of the approach, as well as the quality of the generated multidimensional semantic spaces

LogMap family participation in the OAEI 2020

We present the participation of LogMap and its variants in the OAEI 2020 campaign. The LogMap project started in January 2011 with the objective of developing a scalable and logic-based ontology matching system. This is the ninth participation in the OAEI and the experience has so far been very positive. LogMap is one of the few systems that participates in (almost) all OAEI tracks

LogMap Family Participation in the OAEI 2023

We present the participation of LogMap and its variants in the OAEI 2023 campaign. The LogMap project started in January 2011 with the objective of developing a scalable and logic-based ontology matching system

LogMap family participation in the OAEI2018

We present the participation of LogMap and its variants in the OAEI 2018 campaign. The LogMap project started in January 2011 with the objective of developing a scalable and logic-based ontology matching system. This is our eight participation in the OAEI and the experience has so far been very positive. LogMap is one of the few systems that participates in (almost) all OAEI tracks

Building conceptual spaces for exploring and linking biomedical resources

The establishment of links between data (e.g., patient records) and Web resources (e.g., literature) and the proper visualization of such discovered knowledge is still a challenge in most Life Science domains (e.g., biomedicine). In this paper we present our contribution to the community in the form of an infrastructure to annotate information resources, to discover relationships among them, and to represent and visualize the new discovered knowledge. Furthermore, we have also implemented a Web-based prototype tool which integrates the proposed infrastructure

Human-centric Transfer Learning Explanation via Knowledge Graph [Extended Abstract]

Transfer learning which aims at utilizing knowledge learned from one problem (source domain) to solve another different but related problem (target domain) has attracted wide research attentions. However, the current transfer learning methods are mostly uninterpretable, especially to people without ML expertise. In this extended abstract, we brief introduce two knowledge graph (KG) based frameworks towards human understandable transfer learning explanation. The first one explains the transferability of features learned by Convolutional Neural Network (CNN) from one domain to another through pre-training and fine-tuning, while the second justifies the model of a target domain predicted by models from multiple source domains in zero-shot learning (ZSL). Both methods utilize KG and its reasoning capability to provide rich and human understandable explanations to the transfer procedure

Aplicación de nuevas técnicas docentes en la asignatura Sistemas Cliente/Servidor

En este trabajo mostramos nuestras experiencias en la aplicación de metodologías de aprendizaje cooperativo y basado en proyectos en la asignatura Sistemas Cliente/Servidor en los cursos académicos 2008/2009 y 2009/2010.SUMMARY: In this work we present our teaching experience in the aplication of cooperative and project-based learning methodologies within the subject Client/Server Systems in the academic years 2008/2009 and 2009/2010.Peer Reviewe

We divide, you conquer: From large-scale ontology alignment to manageable subtasks with a lexical index and neural embeddings

Large ontologies still pose serious challenges to state-of-the-art on-tology alignment systems. In this paper we present an approach that combines alexical index, a neural embedding model and locality modules to effectively di-vide an input ontology matching task into smaller and more tractable matchingsubtasks. We have conducted a comprehensive evaluation using the datasets ofthe Ontology Alignment Evaluation Initiative. The results are encouraging andsuggest that the proposed methods are adequate in practice and can be integratedwithin the workflow of state-of-the-art systems