11,384 research outputs found
Biomedical ontology alignment: An approach based on representation learning
While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic similarity information becomes inscribed onto fields of pre-trained word vectors. The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance. An ontology matching system derived using the proposed framework achieved an F-score of 94% on an alignment scenario involving the Adult Mouse Anatomical Dictionary and the Foundational Model of Anatomy ontology (FMA) as targets. This compares favorably with the best performing systems on the Ontology Alignment Evaluation Initiative anatomy challenge. We performed additional experiments on aligning FMA to NCI Thesaurus and to SNOMED CT based on a reference alignment extracted from the UMLS Metathesaurus. Our system obtained overall F-scores of 93.2% and 89.2% for these experiments, thus achieving state-of-the-art results
Function-based Intersubject Alignment of Human Cortical Anatomy
Making conclusions about the functional neuroanatomical organization of the human brain requires methods for relating the functional anatomy of an individual's brain to population variability. We have developed a method for aligning the functional neuroanatomy of individual brains based on the patterns of neural activity that are elicited by viewing a movie. Instead of basing alignment on functionally defined areas, whose location is defined as the center of mass or the local maximum response, the alignment is based on patterns of response as they are distributed spatially both within and across cortical areas. The method is implemented in the two-dimensional manifold of an inflated, spherical cortical surface. The method, although developed using movie data, generalizes successfully to data obtained with another cognitive activation paradigm—viewing static images of objects and faces—and improves group statistics in that experiment as measured by a standard general linear model (GLM) analysis
How do Ontology Mappings Change in the Life Sciences?
Mappings between related ontologies are increasingly used to support data
integration and analysis tasks. Changes in the ontologies also require the
adaptation of ontology mappings. So far the evolution of ontology mappings has
received little attention albeit ontologies change continuously especially in
the life sciences. We therefore analyze how mappings between popular life
science ontologies evolve for different match algorithms. We also evaluate
which semantic ontology changes primarily affect the mappings. We further
investigate alternatives to predict or estimate the degree of future mapping
changes based on previous ontology and mapping transitions.Comment: Keywords: mapping evolution, ontology matching, ontology evolutio
The Foundational Model of Anatomy Ontology
Anatomy is the structure of biological organisms. The term also denotes the scientific
discipline devoted to the study of anatomical entities and the structural and
developmental relations that obtain among these entities during the lifespan of an
organism. Anatomical entities are the independent continuants of biomedical reality on
which physiological and disease processes depend, and which, in response to etiological
agents, can transform themselves into pathological entities. For these reasons, hard copy
and in silico information resources in virtually all fields of biology and medicine, as a
rule, make extensive reference to anatomical entities. Because of the lack of a
generalizable, computable representation of anatomy, developers of computable
terminologies and ontologies in clinical medicine and biomedical research represented
anatomy from their own more or less divergent viewpoints. The resulting heterogeneity
presents a formidable impediment to correlating human anatomy not only across
computational resources but also with the anatomy of model organisms used in
biomedical experimentation. The Foundational Model of Anatomy (FMA) is being
developed to fill the need for a generalizable anatomy ontology, which can be used and
adapted by any computer-based application that requires anatomical information.
Moreover it is evolving into a standard reference for divergent views of anatomy and a
template for representing the anatomy of animals. A distinction is made between the FMA
ontology as a theory of anatomy and the implementation of this theory as the FMA
artifact. In either sense of the term, the FMA is a spatial-structural ontology of the
entities and relations which together form the phenotypic structure of the human
organism at all biologically salient levels of granularity. Making use of explicit
ontological principles and sound methods, it is designed to be understandable by human
beings and navigable by computers. The FMA’s ontological structure provides for
machine-based inference, enabling powerful computational tools of the future to reason
with biomedical data
Classifying tree structures using elastic matching of sequence encodings
This document is the Accepted Manuscript version of the following article: Angeliki Skoura, Iosif Mporas, Vasileios Megalooikonomou, ‘Classifying tree structures using elastic matching of sequence encodings’, Neurocomputing, Vol. 163, pp. 151-159, February 2015. The Version of Record is available online at: DOI: https://doi.org/10.1016/j.neucom.2014.08.083. This Manuscript version is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.Structures of tree topology are frequently encountered in nature and in a range of scientific domains. In this paper, a multi-step framework is presented to classify tree topologies introducing the idea of elastic matching of their sequence encodings. Initially, representative sequences of the branching topologies are obtained using node labeling and tree traversal schemes. The similarity between tree topologies is then quantified by applying elastic matching techniques. The resulting sequence alignment reveals corresponding node groups providing a better understanding of matching tree topologies. The new similarity approach is explored using various classification algorithms and is applied to a medical dataset outperforming state-of-the-art techniques by at least 6.6% and 3.5% in terms of absolute specificity and accuracy correspondingly.Peer reviewe
Evaluating Pre-trained Word Embeddings in domain specific Ontology Matching
Tese de mestrado, Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2022The ontology matching process focuses on discovering mappings between two concepts from distinct
ontologies, a source and a target. It is a fundamental step when trying to integrate heterogeneous data
sources that are described in ontologies. This data represents an even more challenging problem since
we are working with complex data as biomedical data. Thus, derived from the necessity of keeping on
improving ontology matching techniques, this dissertation focused on implementing a new approach to
the AML pipeline to calculate similarities between entities from two distinct ontologies.
For the implementation of this dissertation, we used some of the OAEI tracks, such as Anatomy
and LargeBio, to apply a new algorithm and evaluate if it improves AML’s results against a refer ence alignment. This new approach consisted of using pre-trained word embeddings of five different
types, BioWordVec Extrinsic, BioWordVec Intrinsic, PubMed+PC, PubMed+PC+Wikipedia and English
Wikipedia. These pre-trained word embeddings use a machine learning technique, Word2Vec, and were
used in this work since it allows to carry the semantic meaning inherent to the words represented with
the corresponding vector. Word embeddings allowed that each concept of each ontology was represented
with a corresponding vector to see if, with that information, it was possible to improve how relations
between concepts were determined in the AML system. The similarity between concepts was calculated
through the cosine distance and the evaluation of the new alignment used the metrics precision recall
and F-measure. Although we could not prove that word embeddings improve AML current results, this
implementation could be refined, and the technique can be still an option to consider in future work if
applied in some other way
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