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Article
INTERPRETABLE ONTOLOGY META-MATCHING IN THE BIOMEDICAL DOMAIN USING MAMDANI
FUZZY INFERENCE
* February 2022
* Expert Systems with Applications 188:116025
DOI:10.1016/j.eswa.2021.116025
Authors:
Jorge Martinez-Gil
* Software Competence Center Hagenberg
Jose M. Chaves-González
* Universidad de Extremadura
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authors.
References (56)
ABSTRACT
Ontology meta-matching techniques have been consolidated as one of the best
approaches to face the problem of discovering semantic relationships between
knowledge models that belong to the same domain but have been developed
independently. After more than a decade of research, the community has reached a
stage of maturity characterized by increasingly better results and aspects such
as the robustness and scalability of solutions have been solved. However, the
resulting models remain practically intelligible to a human operator. In this
work, we present a novel approach based on Mamdani fuzzy inference exploiting a
model very close to natural language. This fact has a double objective: to
achieve results with high degrees of accuracy but at the same time to guarantee
the interpretability of the resulting models. After validating our proposal with
several ontological models popular in the biomedical field, we can conclude that
the results obtained are promising.
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CITATIONS (0)
REFERENCES (56)
ResearchGate has not been able to resolve any citations for this publication.
Solving Ontology Meta-Matching Problem Through An Evolutionary Algorithm with
Approximate Evaluation Indicators And Adaptive Selection Pressure
Article
Full-text available
* Dec 2020
* Qing Lv
* Chengcai Jiang
* He Li
Ontology applies commonly to solve the problem of heterogeneity of data in the
Semantic Web, but the heterogeneity problem between two ontologies seriously
affects their communication. As an effective method, ontology matching can
address the problem above, whose core technique is the similarity measure. A
single similarity measure calculates the similarity value about a feature
between two concepts, but none of the similarity measures can ensure their
effectiveness in all context due to the diverse heterogeneous features between
two ontologies. Therefore, multiple similarity measures are usually aggregated
to improve the result’s confidence. The problem that how to determine the
optimal aggregating weights for the different similarity measures to obtain a
high-quality alignment is called the meta-matching problem of ontology, which is
modeled as a nonlinear problem with many local optimal solutions. Evolutionary
Algorithm (EA) can represent an efficient methodology to address the ontology
meta-matching problem, but EA-based ontology matching techniques suffer from the
premature convergence and the requirement of a reference alignment to evaluate
the solutions. To overcome the defects mentioned above, in this work, an
improved EA-based matching approach is proposed, where two approximate
evaluation indicators, i.e. pseudo-recall and pseudo-precision, are presented to
evaluate the solution’s quality, and an adaptive selection pressure is utilized
to overcome the premature convergence. The experiment utilizes the Ontology
Alignment Evaluation Initiative (OAEI)’s benchmark, and the experimental results
will prove the effectiveness of our proposed method.
View
Show abstract
DAEOM: A Deep Attentional Embedding Approach for Biomedical Ontology Matching
Article
Full-text available
* Nov 2020
* Jifang Wu
* Jianghua Lv
* Haoming Guo
* Shilong Ma
Ontology Matching (OM) is performed to find semantic correspondences between the
entity elements of different ontologies to enable semantic integration, reuse,
and interoperability. Representation learning techniques have been introduced to
the field of OM with the development of deep learning. However, there still
exist two limitations. Firstly, these methods only focus on the
terminological-based features to learn word vectors for discovering mappings,
ignoring the network structure of ontology. Secondly, the final alignment
threshold is usually determined manually within these methods. It is difficult
for an expert to adjust the threshold value and even more so for a non-expert
user. To address these issues, we propose an alternative ontology matching
framework called Deep Attentional Embedded Ontology Matching (DAEOM), which
models the matching process by embedding techniques with jointly encoding
ontology terminological description and network structure. We propose a novel
inter-intra negative sampling skill tailored for the structural relations
asserted in ontologies, and further improve our iterative final alignment method
by introducing an automatic adjustment of the final alignment threshold. The
preliminary result on real-world biomedical ontologies indicates that DAEOM is
competitive with several OAEI top-ranked systems in terms of F-measure.
View
Show abstract
Biomedical ontology alignment: An approach based on representation learning
Article
Full-text available
* Aug 2018
* Barry Smith
* Prodromos Kolyvakis
* Alexandros Kalousis
* Dimitris Kiritsis
Background: 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. Results: 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. Conclusions: Our proposed representation
learning approach leverages terminological embeddings to capture semantic
similarity. Our results provide evidence that the approach produces embeddings
that are especially well tailored to the ontology matching task, demonstrating a
novel pathway for the problem.
View
Show abstract
Matching biomedical ontologies based on formal concept analysis
Article
Full-text available
* Mar 2018
* Mengyi Zhao
* Songmao Zhang
* Weizhuo Li
* Guowei Chen
Background: The goal of ontology matching is to identify correspondences between
entities from different yet overlapping ontologies so as to facilitate semantic
integration, reuse and interoperability. As a well developed mathematical model
for analyzing individuals and structuring concepts, Formal Concept Analysis
(FCA) has been applied to ontology matching (OM) tasks since the beginning of OM
research, whereas ontological knowledge exploited in FCA-based methods is
limited. This motivates the study in this paper, i.e., to empower FCA with as
much as ontological knowledge as possible for identifying mappings across
ontologies. Methods: We propose a method based on Formal Concept Analysis to
identify and validate mappings across ontologies, including one-to-one mappings,
complex mappings and correspondences between object properties. Our method,
called FCA-Map, incrementally generates a total of five types of formal contexts
and extracts mappings from the lattices derived. First, the token-based formal
context describes how class names, labels and synonyms share lexical tokens,
leading to lexical mappings (anchors) across ontologies. Second, the
relation-based formal context describes how classes are in taxonomic, partonomic
and disjoint relationships with the anchors, leading to positive and negative
structural evidence for validating the lexical matching. Third, the positive
relation-based context can be used to discover structural mappings. Afterwards,
the property-based formal context describes how object properties are used in
axioms to connect anchor classes across ontologies, leading to property
mappings. Last, the restriction-based formal context describes co-occurrence of
classes across ontologies in anonymous ancestors of anchors, from which extended
structural mappings and complex mappings can be identified. Results: Evaluation
on the Anatomy, the Large Biomedical Ontologies, and the Disease and Phenotype
track of the 2016 Ontology Alignment Evaluation Initiative campaign demonstrates
the effectiveness of FCA-Map and its competitiveness with the top-ranked
systems. FCA-Map can achieve a better balance between precision and recall for
large-scale domain ontologies through constructing multiple FCA structures,
whereas it performs unsatisfactorily for smaller-sized ontologies with less
lexical and semantic expressions. Conclusions: Compared with other FCA-based OM
systems, the study in this paper is more comprehensive as an attempt to push the
envelope of the Formal Concept Analysis formalism in ontology matching tasks.
Five types of formal contexts are constructed incrementally, and their derived
concept lattices are used to cluster the commonalities among classes at lexical
and structural level, respectively. Experiments on large, real-world domain
ontologies show promising results and reveal the power of FCA.
View
Show abstract
A Compact Co-Evolutionary Algorithm for sensor ontology meta-matching
Article
Full-text available
* Aug 2018
* KNOWL INF SYST
* Jeng-Shyang Pan
* Xingsi Xue
With the proliferation of sensors, semantic web technologies are becoming
closely related to sensor network. The linking of elements from semantic web
technologies with sensor networks is called semantic sensor web whose main
feature is the use of sensor ontologies. However, due to the subjectivity of
different sensor ontology designer, different sensor ontologies may define the
same entities with different names or in different ways, raising so-called
sensor ontology heterogeneity problem. There are many application scenarios
where solving the problem of semantic heterogeneity may have a big impact, and
it is urgent to provide techniques to enable the processing, interpretation and
sharing of data from sensor web whose information is organized into different
ontological schemes. Although sensor ontology heterogeneity problem can be
effectively solved by Evolutionary Algorithm (EA)-based ontology meta-matching
technologies, the drawbacks of traditional EA, such as premature convergence and
long runtime, seriously hamper them from being applied in the practical dynamic
applications. To solve this problem, we propose a novel Compact Co-Evolutionary
Algorithm (CCEA) to improve the ontology alignment’s quality and reduce the
runtime consumption. In particular, CCEA works with one better probability
vector (PV) \(PV_{better}\) and one worse PV \(PV_{worse}\), where
\(PV_{better}\) mainly focuses on the exploitation which dedicates to increase
the speed of the convergence and \(PV_{worse}\) pays more attention to the
exploration which aims at preventing the premature convergence. In the
experiment, we use Ontology Alignment Evaluation Initiative (OAEI) test cases
and two pairs of real sensor ontologies to test the performance of our approach.
The experimental results show that CCEA-based ontology matching approach is both
effective and efficient when matching ontologies with various scales and under
different heterogeneous situations, and compared with the state-of-the-art
sensor ontology matching systems, CCEA-based ontology matching approach can
significantly improve the ontology alignment’s quality.
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BIOSSES: a semantic sentence similarity estimation system for the biomedical
domain
Article
Full-text available
* Jul 2017
* BIOINFORMATICS
* Gizem soğancıoğlu
* Hakime Öztürk
* Arzucan Ozgur
Motivation: The amount of information available in textual format is rapidly
increasing in the biomedical domain. Therefore, natural language processing
(NLP) applications are becoming increasingly important to facilitate the
retrieval and analysis of these data. Computing the semantic similarity between
sentences is an important component in many NLP tasks including text retrieval
and summarization. A number of approaches have been proposed for semantic
sentence similarity estimation for generic English. However, our experiments
showed that such approaches do not effectively cover biomedical knowledge and
produce poor results for biomedical text. Methods: We propose several approaches
for sentence-level semantic similarity computation in the biomedical domain,
including string similarity measures and measures based on the distributed
vector representations of sentences learned in an unsupervised manner from a
large biomedical corpus. In addition, ontology-based approaches are presented
that utilize general and domain-specific ontologies. Finally, a supervised
regression based model is developed that effectively combines the different
similarity computation metrics. A benchmark data set consisting of 100 sentence
pairs from the biomedical literature is manually annotated by five human experts
and used for evaluating the proposed methods. Results: The experiments showed
that the supervised semantic sentence similarity computation approach obtained
the best performance (0.836 correlation with gold standard human annotations)
and improved over the state-of-the-art domain-independent systems up to 42.6% in
terms of the Pearson correlation metric. Availability and implementation: A
web-based system for biomedical semantic sentence similarity computation, the
source code, and the annotated benchmark data set are available at:
http://tabilab.cmpe.boun.edu.tr/BIOSSES/ . Contact: gizemsogancioglu@gmail.com
or arzucan.ozgur@boun.edu.tr.
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Selection and Combination of Heterogeneous Mappings to Enhance Biomedical
Ontology Matching
Conference Paper
Full-text available
* Nov 2016
* Lect Notes Comput Sci
* Amina Annane
* Zohra Bellahsene
* Faical Azouaou
* Clement Jonquet
This paper presents a novel background knowledge approach which selects and
combines existing mappings from a given biomedical ontology repository to
improve ontology alignment. Current background knowledge approaches usually
select either manually or automatically a limited number of di�erent ontologies
and use them as a whole for back- ground knowledge. Whereas in our approach, we
propose to pick up only relevant concepts and relevant existing mappings linking
these concepts all together in a speci�c and customized background knowledge
graph. Paths within this graph will help to discover new mappings. We have im-
plemented and evaluated our approach using the content of the NCBO BioPortal
repository and the Anatomy benchmark from the Ontology Alignment Evaluation
Initiative. We used the mapping gain measure to assess how much our �nal
background knowledge graph improves results of state-of-the-art alignment
systems. Furthermore, the evaluation shows that our approach produces a high
quality alignment and discovers map- pings that have not been found by
state-of-the-art systems.
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CoTO: A Novel Approach for Fuzzy Aggregation of Semantic Similarity Measures
Article
Full-text available
* Feb 2016
* COGN SYST RES
* Jorge Martinez-Gil
Semantic similarity measurement aims to determine the likeness between two text
expressions that use different lexicographies for representing the same real
object or idea. There are a lot of semantic similarity measures for addressing
this problem. However, the best results have been achieved when aggregating a
number of simple similarity measures. This means that after the various
similarity values have been calculated, the overall similarity for a pair of
text expressions is computed using an aggregation function of these individual
semantic similarity values. This aggregation is often computed by means of
statistical functions. In this work, we present CoTO (Consensus or Trade-Off) a
solution based on fuzzy logic that is able to outperform these traditional
approaches.
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Using MOEA/D for optimizing ontology alignments
Article
Full-text available
* Aug 2013
* Weichen Hao
* Xingsi Xue
* Yuping Wang
This paper proposes a novel approach which uses a multi-objective evolutionary
algorithm based on decomposition to address the ontology alignment optimization
problem. Comparing with the approach based on Genetic Algorithm (GA), our method
can simultaneously optimize three goals (maximizing the alignment recall, the
alignment precision and the f-measure). The experimental results shows that our
approach is able to provide various alignments in one execution which are less
biased to one of the evaluations of the alignment quality than GA approach, thus
the quality of alignments are obviously better than or equal to those given by
the approach based on GA which considers precision, recall and f-measure only,
and other multi-objective evolutionary approach such as NSGA-II approach. In
addition, the performance of our approach outperforms NSGA-II approach with the
average improvement equal to 32.79 \(\%\) . Through the comparison of the
quality of the alignments obtained by our approach with those by the state of
the art ontology matching systems, we draw the conclusion that our approach is
more effective and efficient.
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An effective method of large scale ontology matching
Article
Full-text available
* Oct 2014
* Gayo Diallo
We are currently facing a proliferation of heterogeneous biomedical data sources
accessible through various knowledge-based applications. These data are
annotated by increasingly extensive and widely disseminated knowledge
organisation systems ranging from simple terminologies and structured
vocabularies to formal ontologies. In order to solve the interoperability issue,
which arises due to the heterogeneity of these ontologies, an alignment task is
usually performed. However, while significant effort has been made to provide
tools that automatically align small ontologies containing hundreds or thousands
of entities, little attention has been paid to the matching of large sized
ontologies in the life sciences domain. We have designed and implemented
ServOMap, an effective method for large scale ontology matching. It is a fast
and efficient high precision system able to perform matching of input ontologies
containing hundreds of thousands of entities. The system, which was included in
the 2012 and 2013 editions of the Ontology Alignment Evaluation Initiative
campaign, performed very well. It was ranked among the top systems for the large
ontologies matching. We proposed an approach for large scale ontology matching
relying on Information Retrieval (IR) techniques and the combination of lexical
and machine learning contextual similarity computing for the generation of
candidate mappings. It is particularly adapted to the life sciences domain as
many of the ontologies in this domain benefit from synonym terms taken from the
Unified Medical Language System and that can be used by our IR strategy. The
ServOMap system we implemented is able to deal with hundreds of thousands
entities with an efficient computation time.
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jFuzzyLogic: A Java Library to Design Fuzzy Logic Controllers According to the
Standard for Fuzzy Control Programming
Article
Full-text available
* Jun 2013
* Pablo Cingolani
* Jesus Alcala-Fdez
Fuzzy Logic Controllers are a specific model of Fuzzy Rule Based Systems
suitable for engineering applications for which classic control strategies do
not achieve good results or for when it is too difficult to obtain a
mathematical model. Recently, the International Electrotechnical Commission has
published a standard for fuzzy control programming in part 7 of the IEC 61131
norm in order to offer a well defined common understanding of the basic means
with which to integrate fuzzy control applications in control systems. In this
paper, we introduce an open source Java library called jFuzzyLogic which offers
a fully functional and complete implementation of a fuzzy inference system
according to this standard, providing a programming interface and Eclipse plugin
to easily write and test code for fuzzy control applications. A case study is
given to illustrate the use of jFuzzyLogic.
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An overview of current ontology meta-matching solutions
Article
Full-text available
* Dec 2012
* KNOWL ENG REV
* Jorge Martinez-Gil
* Jose F Aldana Montes
Nowadays, there are a lot of techniques and tools for addressing the ontology
matching problem; however, the complex nature of this problem means that the
existing solutions are unsatisfactory. This work intends to shed some light on a
more flexible way of matching ontologies using ontology meta-matching. This
emerging technique selects appropriate algorithms and their associated weights
and thresholds in scenarios where accurate ontology matching is necessary. We
think that an overview of the problem and an analysis of the existing
state-of-the-art solutions will help researchers and practitioners to identify
the most appropriate specific features and global strategies in order to build
more accurate and dynamic systems following this paradigm.
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Biomedical Ontology Matching Using the AgreementMaker System
Article
Full-text available
* Jan 2011
* Isabel F. Cruz
* C. Stroe
* Catia Pesquita
* V. Cross
View
Evaluation of two heuristic approaches to solve the ontology meta-matching
problem
Article
Full-text available
* Jan 2010
* Jorge Martinez-Gil
* Jose F Aldana Montes
Nowadays many techniques and tools are available for addressing the ontology
matching problem, however, the complex nature of this problem causes existing
solutions to be unsatisfactory. This work aims to shed some light on a more
flexible way of matching ontologies. Ontology meta-matching, which is a set of
techniques to configure optimum ontology matching functions. In this sense, we
propose two approaches to automatically solve the ontology meta-matching
problem. The first one is called maximum similarity measure, which is based on a
greedy strategy to compute efficiently the parameters which configure a
composite matching algorithm. The second approach is called genetics for
ontology alignments and is based on a genetic algorithm which scales better for
a large number of atomic matching algorithms in the composite algorithm and is
able to optimize the results of the matching process. KeywordsOntology
meta-matching–Knowledge management–Information integration
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Improving ontology alignment through memetic algorithms
Conference Paper
Full-text available
* Jul 2011
* Giovanni Acampora
* Pasquale Avella
* Vincenzo Loia
* Autilia Vitiello
Born primarily as means to model knowledge, ontologies have successfully been
exploited to enable knowledge exchange among people, organizations and software
agents. However, because of strong subjectivity of ontology modeling, a matching
process is necessary in order to lead ontologies into mutual agreement and
obtain the relative alignment, i.e., the set of correspondences among them. The
aim of this paper is to propose a memetic algorithm to perform an automatic
matching process capable of computing a suboptimal alignment between two
ontologies. To achieve this aim, the ontology alignment problem has been
formulated as a minimum optimization problem characterized by an objective
function depending on a fuzzy similarity. As shown in the performed experiments,
the memetic approach results more suitable for ontology alignment problem than
other evolutionary techniques such as genetic algorithms.
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Optimizing Ontology Alignments by Using Genetic Algorithms
Conference Paper
Full-text available
* Jan 2008
* Jorge Martinez-Gil
* Enrique Alba
* Jose F Aldana Montes
In this work we present GOAL (Genetics for Ontology Align- ments) a new approach
to compute the optimal ontology alignment func- tion for a given ontology input
set. Although this problem could be solved by an exhaustive search when the
number of similarity measures is low, our method,is expected to scale better for
a high number,of measures. Our approach is a genetic algorithm which is able to
work with several goals: maximizing the alignment precision, maximizing the
alignment re- call, maximizing the f-measure or reducing the number of false
positives. Moreover, we test it here by combining some cutting-edge similarity
mea- sures over a standard benchmark, and the results obtained show several
advantages in relation to other techniques. Key words: ontology alignment;
genetic algorithms; semantic integra-
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Towards an automatic parameterization of ontology matching tools based on
example mappings
Conference Paper
Full-text available
* Jan 2011
* Dominique Ritze
* Heiko Paulheim
With a growing number of ontologies and datasets using those ontologies,
ontology mappings become an essential building block of the Semantic Web. In the
last years, a larger number of sophisticated ontology matching tools for
generating such mappings has been developed. The quality of the mappings
provided by those tools typically depends on the settings of the tools'
parameters. As this is a non-trivial task for an end user, we propose the
ECOMatch approach, which asks the user to provide example mappings instead of
parameter settings, and automatically determines a suitable parameter setting
based on those examples. We show how the preliminary result quality of ontology
mappings can be improved by applying automatic, example-based configuration of
ontology matching tools.
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LogMap: Logic-Based and Scalable Ontology Matching
Conference Paper
Full-text available
* Oct 2011
* Ernesto Jiménez-Ruiz
* Bernardo Cuenca Grau
In this paper, we present LogMap--a highly scalable ontology matching system
with 'built-in' reasoning and diagnosis capabilities. To the best of our
knowledge, LogMap is the only matching system that can deal with semantically
rich ontologies containing tens (and even hundreds) of thousands of classes. In
contrast to most existing tools, LogMap also implements algorithms for 'on the
fly' unsatisfiability detection and repair. Our experiments with the ontologies
NCI, FMA and SNOMED CT confirm that our system can efficiently match even the
largest existing bio-medical ontologies. Furthermore, LogMap is able to produce
a 'clean' set of output mappings in many cases, in the sense that the ontology
obtained by integrating LogMap's output mappings with the input ontologies is
consistent and does not contain unsatisfiable classes.
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Combining Ontology Mapping Methods Using Bayesian Networks.
Conference Paper
Full-text available
* Jan 2006
* Ondrej Sváb
* Vojtěch Svátek
Bayesian networks (BNs) can capture interdependencies among ontology mapping
methods and thus possibly improve the way they are combined. Experiments on
ontologies from the OAEI collection are shown, and the possibility of modelling
explicit mapping patterns in combination with methods is discussed. Most
existing systems for ontology mapping combine various methods for achiev- ing
higher performance in terms of recall and precision. Our approach relies on
Bayesian networks (BNs) as well-known formal technique that can capture in-
terdependencies among random variables. A Bayesian network (BN) (3) is a di-
rected acyclic graph with attached local probability distributions. Nodes in the
graph represent random variables with mutually exclusive and exhaustive sets of
values (states). Edges in the graph represents direct interdependences between
two random variables. We believe that this approach can bring additional ben-
efits compared to ad hoc combination of methods, mainly resulting from better
adaptability (training from data within a well-established formal framework).
Two approaches that use BNs for Ontology Mapping have recently been re- ported.
The first is OMEN (4), which mainly serves for enhancing existing map- pings.
Its input are results of another mapping tool, while its output are more precise
mappings as well as and new mappings. Nodes in the BN represent pairs of
concepts that can potentially be mapped. Edges follow the taxonomy given in
original ontologies. The network structure thus mimics that of ontologies them-
selves, though heuristics for graph pruning are employed in this transformation.
For constructing conditional probability tables (CPTs) for each node meta-rules
are used, such as : "if two nodes match and so do two arrows coming out of these
nodes then the probability that nodes at the other end of the arrows match is
increased". The second project, BayesOWL ((5)), is rather a framework for on-
tology mapping than a mapping method per se. The probabilistic ontological
information is assumed to be learnt (in forms of probabilistic constraints) from
web data using a text-classification-based learner; this information is
translated to BNs. Mappings among concepts from two dierent ontologies then can
be discovered using so-called evidential reasoning across two BNs.
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Improving Ontology Matching Using Meta-level Learning
Conference Paper
Full-text available
* May 2009
* Kai Eckert
* Christian Meilicke
* Heiner Stuckenschmidt
Despite serious research efforts, automatic ontology matching still suffers from
severe problems with respect to the quality of matching results. Existing
matching systems trade-off precision and recall and have their specific
strengths and weaknesses. This leads to problems when the right matcher for a
given task has to be selected. In this paper, we present a method for improv-
ing matching results by not choosing a specific matcher but applying machine
learning techniques on an ensemble of matchers. Hereby we learn rules for the
correctness of a correspondence based on the output of different matchers and
additional information about the nature of the elements to be matched, thus
lever- aging the weaknesses of an individual matcher. We show that our method
always performs significantly better than the median of the matchers used and in
most cases outperforms the best matcher with an optimal threshold for a given
pair of ontologies. As a side product of our experiments, we discovered that the
major- ity vote is a simple but powerful heuristic for combining matchers that
almost reaches the quality of our learning results.
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ETuner: Tuning schema matching software using synthetic scenarios
Article
Full-text available
* Jan 2007
* Yoonkyong Lee
* Mayssam Sayyadian
* AnHai Doan
* Arnon Rosenthal
Most recent schema matching systems assemble multiple components, each employing
a particular matching technique. The domain user mustthen tune the system:
select the right component to be executed and correctly adjust their numerous
“knobs” (e.g., thresholds, formula coefficients). Tuning is skill and time
intensive, but (as we show) without it the matching accuracy is significantly
inferior. We describe eTuner, an approach to automatically tune schema matching
systems. Given a schema S, we match S against synthetic schemas, for which the
ground truth mapping is known, and find a tuning that demonstrably improves the
performance of matching S against real schemas. To efficiently search the huge
space of tuning configurations, eTuner works sequentially, starting with tuning
the lowest level components. To increase the applicability of eTuner, we develop
methods to tune a broad range of matching components. While the tuning process
is completely automatic, eTuner can also exploit user assistance (whenever
available) to further improve the tuning quality. We employed eTuner to tune
four recently developed matching systems on several real-world domains. The
results show that eTuner produced tuned matching systems that achieve higher
accuracy than using the systems with currently possible tuning methods.
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Discrete particle swarm optimisation for ontology alignment
Article
Full-text available
* Jun 2012
* INFORM SCIENCES
* Juergen Bock
* Jan Hettenhausen
Particle swarm optimisation (PSO) is a biologically-inspired, population-based
optimisation technique that has been successfully applied to various problems in
science and engineering. In the context of semantic technologies, optimisation
problems also occur but have rarely been considered as such. This work addresses
the problem of ontology alignment, which is the identification of overlaps in
heterogeneous knowledge bases backing semantic applications. To this end, the
ontology alignment problem is revisited as an optimisation problem. A discrete
particle swarm optimisation algorithm is designed in order to solve this
optimisation problem and compute an alignment of two ontologies. A number of
characteristics of traditional PSO algorithms are partially relaxed in this
article, such as fixed dimensionality of particles. A complex fitness function
based on similarity measures of ontological entities, as well as a tailored
particle update procedure are presented. This approach brings several benefits
for solving the ontology alignment problem, such as inherent parallelisation,
anytime behaviour, and flexibility according to the characteristics of
particular ontologies. The presented algorithm has been implemented under the
name MapPSO (ontology mapping using particle swarm optimisation). Experiments
demonstrate that applying PSO in the context of ontology alignment is a feasible
approach. Yes Yes
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The Unified Medical Language System (UMLS): Integrating Biomedical Terminology
Article
Full-text available
* Feb 2004
* NUCLEIC ACIDS RES
* Olivier Bodenreider
The Unified Medical Language System (http://umlsks.nlm.nih.gov) is a repository
of biomedical vocabularies developed by the US National Library of Medicine. The
UMLS integrates over 2 million names for some 900 000 concepts from more than 60
families of biomedical vocabularies, as well as 12 million relations among these
concepts. Vocabularies integrated in the UMLS Metathesaurus include the NCBI
taxonomy, Gene Ontology, the Medical Subject Headings (MeSH), OMIM and the
Digital Anatomist Symbolic Knowledge Base. UMLS concepts are not only
inter‐related, but may also be linked to external resources such as GenBank. In
addition to data, the UMLS includes tools for customizing the Metathesaurus
(MetamorphoSys), for generating lexical variants of concept names (lvg) and for
extracting UMLS concepts from text (MetaMap). The UMLS knowledge sources are
updated quarterly. All vocabularies are available at no fee for research
purposes within an institution, but UMLS users are required to sign a license
agreement. The UMLS knowledge sources are distributed on CD‐ROM and by FTP.
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NCI Thesaurus: using science-based terminology to integrate cancer research
results
Article
Full-text available
* Feb 2004
* Stud Health Tech Informat
* Sherri de Coronado
* Margaret W Haber
* Nicholas Sioutos
* Lawrence W Wright
Cancer researchers need to be able to organize and report their results in a way
that others can find, build upon, and relate to the specific clinical conditions
of individual patients. NCI Thesaurus is a description logic terminology based
on current science that helps individuals and software applications connect and
organize the results of cancer research, e.g., by disease and underlying
biology. Currently containing some 34,000 concepts--covering chemicals, drugs
and other therapies, diseases, genes and gene products, anatomy, organisms,
animal models, techniques, biologic processes, and administrative
categories--NCI Thesaurus serves applications and the Web from a terminology
server. As a scalable, formal terminology, the deployed Thesaurus, and
associated applications and interfaces, are a model for some of the standards
required for the NHII (National Health Information Infrastructure) and the
Semantic Web.
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The Adult Mouse Anatomical Dictionary: A tool for annotating and integrating
data
Article
Full-text available
* Feb 2005
* Mary Mangan
* John Corradi
* Terry F Hayamizu
* Martin Ringwald
We have developed an ontology to provide standardized nomenclature for
anatomical terms in the postnatal mouse. The Adult Mouse Anatomical Dictionary
is structured as a directed acyclic graph, and is organized hierarchically both
spatially and functionally. The ontology will be used to annotate and integrate
different types of data pertinent to anatomy, such as gene expression patterns
and phenotype information, which will contribute to an integrated description of
biological phenomena in the mouse.
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Of Mice and Men: Aligning Mouse and Human Anatomies
Article
Full-text available
* Feb 2005
* Olivier Bodenreider
* Terry F Hayamizu
* Martin Ringwald
* Songmao Zhang
This paper reports on the alignment between mouse and human anatomies, a
critical resource for comparative science as diseases in mice are used as
mod-els of human disease. The two ontologies under investigation are the NCI
Thesaurus (human anatomy) and the Adult Mouse Anatomical Dictionary, each
comprising about 2500 anatomical concepts. This study compares two approaches to
aligning ontologies. One is fully automatic, based on a combination of lexical
and structural similarity; the other is manual. The resulting mappings were
evaluated by an expert. 715 and 781 mappings were identified by each method
respectively, of which 639 are common to both and all valid. The applications of
the map-ping are discussed from the perspective of biology and from that of
ontology.
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Metaheuristics-based ontology meta-matching approaches
Article
* Jan 2021
* EXPERT SYST APPL
* Nicolas Ferranti
* Stenio Soares
* Jairo Souza
Ontologies have emerged to establish a well-defined meaning for information,
solving problems of heterogeneity in data semantics and facilitating the process
of information exchange. However, ontologies have generated a new semantic
problem, since using more than one ontology can generate ambiguity in the
meaning of a given data. The problem of ontology matching is to search for
relationships between entities of distinct ontologies, solving the problem of
semantic heterogeneity of the data. The problem is a relevant issue in the area
of knowledge representation and several approaches have been proposed to solve
it. This work presents a systematic mapping of the main works published in the
area with an emphasis on metaheuristics-based meta-matching approaches.
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Fast knot optimization for multivariate adaptive regression splines using hill
climbing methods
Article
* Jan 2021
* EXPERT SYST APPL
* Xinglong Ju
* Victoria C P Chen
* Jay M. Rosenberger
* Feng Liu
Multivariate adaptive regression splines (MARS) is a statistical modeling
approach with wide real-world applications. In the MARS model building process,
knot positioning is a critical step that potentially affects the accuracy of the
final MARS model. Identifying well-positioned knots entails assessing the
quality of many knots in each model building iteration, which requires intensive
computational effort. By exploring the change in the residual sum of squares
(RSS) within MARS, we find that local optima from previous iterations can be
very close to those of the current iteration. In our approach, the prior change
in RSS information is used to “warm start” an optimal knot positioning. We
propose two methods for MARS knot positioning. The first method is a hill
climbing method (HCM), which ignores prior change in RSS information. The second
method is a hill climbing method using prior change in RSS information (PHCM).
Numerical experiments are conducted on data with up to 30 dimensions. Our
results show that both versions of hill climbing methods outperform a standard
MARS knot selection method on datasets with different noise levels. Further,
PHCM using prior change in RSS information performs best in both accuracy and
computational speed. In addition, an open source Python code will be available
upon acceptance of the paper on GitHub (https://github.com/JuXinglong/MARSHC).
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Fuzzy Systems Interpretability: What, Why and How
Chapter
* Oct 2020
* Luis Magdalena
Interpretability has been always present in Machine Learning and Artificial
Intelligence. However, it is difficult to measure it (even to define it), and
quite commonly it collides with other properties as accuracy, with a clear
meaning and well defined metrics. This situation has reduced its influence in
the area. But due to different external reasons, interpretability is now gaining
importance in Artificial Intelligence, and particularly in Machine Learning.
This new situation has two effects on the field of fuzzy systems. First,
considering the capability of the fuzzy formalism to describe complex phenomena
in terms that are quite close to human language, fuzzy systems have gained
significant presence as an interpretable modeling tool. Second, the attention
paid to interpretability of fuzzy systems, that grew during the first decade of
this century and then experienced a certain decay, is growing again. The present
paper will consider four questions regarding interpretability: what is, why is
it important, how to measure it, and how to achieve it. These questions will be
first introduced in the general framework of Artificial Intelligence, to be then
focused from the point of view of fuzzy systems.
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Measuring semantic similarity of documents with weighted cosine and fuzzy logic
Article
* Jun 2020
* J INTELL FUZZY SYST
* Juan Huetle-Figueroa
* Fernando Perez-Tellez
* David Pinto
Currently, the semantic analysis is used by different fields, such as
information retrieval, the biomedical domain, and natural language processing.
The primary focus of this research work is on using semantic methods, the cosine
similarity algorithm, and fuzzy logic to improve the matching of documents. The
algorithms were applied to plain texts in this case CVs (resumes) and job
descriptions. Synsets of WordNet were used to enrich the semantic similarity
methods such as the Wu-Palmer Similarity (WUP), Leacock-Chodorow similarity
(LCH), and path similarity (hypernym/hyponym). Additionally, keyword extraction
was used to create a postings list where keywords were weighted. The task of
recruiting new personnel in the companies that publish job descriptions and
reciprocally finding a company when workers publish their resumes is discussed
in this research work. The creation of a new gold standard was required to
achieve a comparison of the proposed methods. A web application was designed to
match the documents manually, creating the new gold standard. Thereby the new
gold standard confirming benefits of enriching the cosine algorithm
semantically. Finally, the results were compared with the new gold standard to
check the efficiency of the new methods proposed. The measures used for the
analysis were precision, recall, and f-measure, concluding that the cosine
similarity weighted semantically can be used to get better similarity scores.
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A novel meta-matching approach for ontology alignment using grasshopper
optimization
Article
* May 2020
* KNOWL-BASED SYST
* Zhaoming Lv
* Rong Peng
Ontology alignment is a fundamental task to support information sharing and
reuse in heterogeneous information systems. Optimizing the combination of
matchers through evolutionary algorithms to align ontology is an effective
method. However, such methods have two significant shortcomings: weights need to
be set manually to combine matchers, and a reference alignment is required
during the optimization process. In this paper, a meta-matching approach GSOOM
for automatically configuring weights and threshold using grasshopper
optimization algorithm (GOA) has been proposed. In this approach, the ontology
alignment problem is modeled as optimizing individual fitness of GOA. A fitness
function is proposed, which includes two goals: maximizing the number of
matching and the average similarity score. Since it does not require an expert
to provide a reference alignment, it is more suitable for real-world scenarios.
To demonstrate the advantages of the approach, we conduct exhaustive experiments
tasks on several standard datasets and compare its performance to other
state-of-the-other methods. The experimental results illustrate that our
approach is more efficiently and is significantly superior to other
metaheuristic-based methods.
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An Ontology-based approach to Knowledge-assisted Integration and Visualization
of Urban Mobility Data
Article
* Jul 2020
* EXPERT SYST APPL
* Thiago Sobral
* Teresa Galvão Dias
* José Borges
This paper proposes an ontology-based framework to support integration and
visualization of data from Intelligent Transportation Systems. These activities
may be technically demanding for transportation stakeholders, due to technical
and human factors, and may hinder the use of visualization tools in practice.
The existing ontologies do not provide the necessary semantics for integration
of spatio-temporal data from such systems. Moreover, a formal representation of
the components of visualization techniques and expert knowledge can leverage the
development of visualization tools that facilitate data analysis. The proposed
Visualization-oriented Urban Mobility Ontology (VUMO) provides a semantic
foundation to knowledge-assisted visualization tools (KVTs). VUMO contains three
facets that interrelate the characteristics of spatio-temporal mobility data,
visualization techniques and expert knowledge. A built-in rule set leverages
semantic technologies standards to infer which visualization techniques are
compatible with analytical tasks, and to discover implicit relationships within
integrated data. The annotation of expert knowledge encodes qualitative and
quantitative feedback from domain experts that can be exploited by
recommendation methods to automate part of the visualization workflow. Data from
the city of Porto, Portugal were used to demonstrate practical applications of
the ontology for each facet. As a foundational domain ontology, VUMO can be
extended to meet the distinctiveness of a KVT.
View
Show abstract
A framework to aggregate multiple ontology matchers
Article
* Oct 2019
* Int J Web Inform Syst
* Jairo Souza
* Sean Wolfgand Matsui Siqueira
* Bernardo Pereira Nunes
Purpose Although ontology matchers are annually proposed to address different
aspects of the semantic heterogeneity problem, finding the most suitable
alignment approach is still an issue. This study aims to propose a computational
solution for ontology meta-matching (OMM) and a framework designed for
developers to make use of alignment techniques in their applications.
Design/methodology/approach The framework includes some similarity functions
that can be chosen by developers and then, automatically, set weights for each
function to obtain better alignments. To evaluate the framework, several
simulations were performed with a data set from the Ontology Alignment
Evaluation Initiative. Simple similarity functions were used, rather than
aligners known in the literature, to demonstrate that the results would be more
influenced by the proposed meta-alignment approach than the functions used.
Findings The results showed that the framework is able to adapt to different
test cases. The approach achieved better results when compared with existing
ontology meta-matchers. Originality/value Although approaches for OMM have been
proposed, it is not easy to use them during software development. On the other
hand, this work presents a framework that can be used by developers to align
ontologies. New ontology matchers can be added and the framework is extensible
to new methods. Moreover, this work presents a novel OMM approach modeled as a
linear equation system which can be easily computed.
View
Show abstract
DeepAlignment: Unsupervised Ontology Matching with Refined Word Vectors
Conference Paper
* Jan 2018
* Prodromos Kolyvakis
* Alexandros Kalousis
* Dimitris Kiritsis
View
An experiment in linguistic synthesis witch a fuzzy logic controller
Article
* Jan 1995
* Int J Man Mach Stud
* E. Mamdani
View
Fuzzy identification of systems and its application to modeling and control
Article
* Jan 1985
* T. Takagi
* M. Sugeno
View
CroMatcher: An ontology matching system based on automated weighted aggregation
and iterative final alignment
Article
* Nov 2016
* J WEB SEMANT
* Marko Gulić
* Boris Vrdoljak
* Marko Banek
In order to perform ontology matching with high accuracy, while at the same time
retaining applicability to most diverse input ontologies, the matching process
generally incorporates multiple methods. Each of these methods is aimed at a
particular ontology component, such as annotations, structure, properties or
instances. Adequately combining these methods is one of the greatest challenges
in designing an ontology matching system. In a parallel composition of basic
matchers, the ability to dynamically set the weights of the basic matchers in
the final output, thus making the weights optimal for the given input, is the
key breakthrough for obtaining first-rate matching performance. In this paper we
present CroMatcher, an ontology matching system, introducing several novelties
to the automated weight calculation process. We apply substitute values for
matchers that are inapplicable for the particular case and use thresholds to
eliminate low-probability alignment candidates. We compare the alignments
produced by the matchers and give less weight to the matchers producing mutually
similar alignments, whereas more weight is given to those matchers whose
alignment is distinct and rather unique. We also present a new, iterative method
for producing one-to-one final alignment of ontology structures, which is a
significant enhancement of similar non-iterative methods proposed in the
literature. CroMatcher has been evaluated against other state-of-the-art
matching systems at the OAEI evaluation contest. In a large number of test cases
it achieved the highest score, which puts it among the state-of-the-art leaders.
View
Show abstract
Lecture Notes in Computer Science
Conference Paper
* Oct 2013
* Cosmin Stroe
* Catia Pesquita
* Daniel Faria
* Francisco M Couto
To bring the Life Sciences domain closer to a Semantic Web realization it is
fundamental to establish meaningful relations between biomedical ontologies. The
successful application of ontology matching techniques is strongly tied to an
effective exploration of the complex and diverse biomedical terminology
contained in biomedical ontologies. In this paper, we present an overview of the
lexical components of several biomedical ontologies and investigate how
different approaches for their use can impact the performance of ontology
matching techniques. We propose novel approaches for exploring the different
types of synonyms encoded by the ontologies and for extending them based both on
internal synonym derivation and on external ontologies. We evaluate our
approaches using AgreementMaker, a successful ontology matching platform that
implements several lexical matchers, and apply them to a set of four benchmark
biomedical ontology matching tasks. Our results demonstrate the impact that an
adequate consideration of ontology synonyms can have on matching performance,
and validate our novel approach for combining internal and external synonym
sources as a competitive and in many cases improved solution for biomedical
ontology matching.
View
Show abstract
Optimizing ontology alignments through a Memetic Algorithm using both
MatchFmeasure and Unanimous Improvement Ratio
Article
* Mar 2015
* ARTIF INTELL
* Xingsi Xue
* Yuping Wang
There are three main drawbacks of current evolutionary approaches for
determining the weights of ontology matching system. The first drawback is that
it is difficult to simultaneously deal with several pairs of ontologies, i.e.
finding a universal weight configuration that can be used for different ontology
pairs without adjustment. The second one is that a reference alignment between
two ontologies to be aligned should be given in advance which could be very
expensive to obtain especially when the scale of ontologies is considerably
large. The last one arises from f-measure, a generally used evaluation metric of
the alignment's quality, which may cause the bias improvement of the solution.
To overcome these three defects, in this paper, we propose to use both
MatchFmeasure, a rough evaluation metric on no reference alignment to
approximate f-measure, and Unanimous Improvement Ratio (UIR), a measure that
complements MatchFmeasure, in the process of optimizing the ontology alignments
by Memetic Algorithm (MA). The experimental results have shown that the MA using
both MatchFmeasure and UIR is effective to simultaneously align multiple pairs
of ontologies and avoid the bias improvement caused by MatchFeasure. Moreover,
the comparison with state-of-the-art ontology matching systems further indicates
the effectiveness of the proposed method.
View
Show abstract
The AgreementMakerLight Ontology Matching System
Conference Paper
* Sep 2013
* Daniel Faria
* Emanuel Santos
* Francisco M Couto
* Catia Pesquita
AgreementMaker is one of the leading ontology matching systems, thanks to its
combination of a flexible and extensible framework with a comprehensive user
interface. In many domains, such as the biomedical, ontologies are becoming
increasingly large thus presenting new challenges. We have developed a new core
framework, AgreementMakerLight, focused on computational efficiency and designed
to handle very large ontologies, while preserving most of the flexibility and
extensibility of the original AgreementMaker framework. We evaluated the
efficiency of AgreementMakerLight in two OAEI tracks: Anatomy and Large
Biomedical Ontologies, obtaining excellent run time results. In addition, for
the Anatomy track, AgreementMakerLight is now the best system as measured in
terms of F-measure. Also in terms of F-measure, AgreementMakerLight is
competitive with the best OAEI performers in two of the three tasks of the Large
Biomedical Ontologies track that match whole ontologies.
View
Show abstract
Optimizing ontology alignment through Memetic Algorithm based on Partial
Reference Alignment
Article
* Jun 2014
* EXPERT SYST APPL
* Xingsi Xue
* Yuping Wang
* Aihong Ren
All the state of the art approaches based on evolutionary algorithm (EA) for
addressing the meta-matching problem in ontology alignment require the domain
expert to provide a reference alignment (RA) between two ontologies in advance.
Since the RA is very expensive to obtain especially when the scale of ontology
is very large, in this paper, we propose to use the Partial Reference Alignment
(PRA) built by clustering-based approach to take the place of RA in the process
of using evolutionary approach. Then a problem-specific Memetic Algorithm (MA)
is proposed to address the meta-matching problem by optimizing the aggregation
of three different basic similarity measures (Syntactic Measure, Linguistic
Measure and Taxonomy based Measure) into a single similarity metric. The
experimental results have shown that using PRA constructed by our approach in
most cases leads to higher quality of solution than using PRA built in randomly
selecting classes from ontology and the quality of solution is very close to the
approach using RA where the precision value of solution is generally high.
Comparing to the state of the art ontology matching systems, our approach is
able to obtain more accurate results. Moreover, our approach’s performance is
better than GOAL approach based on Genetic Algorithm (GA) and RA with the
average improvement up to 50.61%. Therefore, the proposed approach is both
effective.
View
Show abstract
An experiment in linguistic synthesis of fuzzy controllers
Article
* Jan 1974
* E.H. Mamdani
* S. Assilian
View
Sugeno, M.: Fuzzy Identification of Systems and its Applications to Modeling and
Control. IEEE Transactions on Systems, Man, and Cybernetics SMC-15(1), 116-132
Article
* Jan 1985
* IEEE Trans Syst Man Cybern Syst Hum
* Tomohiro Takagi
* Michio Sugeno
A mathematical tool to build a fuzzy model of a system where fuzzy implications
and reasoning are used is presented. The premise of an implication is the
description of fuzzy subspace of inputs and its consequence is a linear
input-output relation. The method of identification of a system using its
input-output data is then shown. Two applications of the method to industrial
processes are also discussed: a water cleaning process and a converter in a
steel-making process.
View
Show abstract
A Historical Review of Evolutionary Learning Methods for Mamdani-type Fuzzy
Rule-based Systems: Designing Interpretable Genetic Fuzzy Systems
Article
* Sep 2011
* INT J APPROX REASON
* Oscar Cordon
The need for trading off interpretability and accuracy is intrinsic to the use
of fuzzy systems. The obtaining of accurate but also human-comprehensible fuzzy
systems played a key role in Zadeh and Mamdani’s seminal ideas and system
identification methodologies. Nevertheless, before the advent of soft computing,
accuracy progressively became the main concern of fuzzy model builders, making
the resulting fuzzy systems get closer to black-box models such as neural
networks. Fortunately, the fuzzy modeling scientific community has come back to
its origins by considering design techniques dealing with the
interpretability-accuracy tradeoff. In particular, the use of genetic fuzzy
systems has been widely extended thanks to their inherent flexibility and their
capability to jointly consider different optimization criteria. The current
contribution constitutes a review on the most representative genetic fuzzy
systems relying on Mamdani-type fuzzy rule-based systems to obtain interpretable
linguistic fuzzy models with a good accuracy.
View
Show abstract
Pushing the Envelope: Challenges in a Frame-Based Representation of Human
Anatomy
Article
* Mar 2004
* DATA KNOWL ENG
* Natasha Noy
* Mark Alan Musen
* Jose Mejino
* Cornelius Rosse
One of the main threads in the history of knowledge-representation formalisms is
the trade-off between the expressiveness of first-order logic on the one hand
and the tractability and ease-of-use of frame-based systems on the other hand.
Frame-based systems provide intuitive, cognitively easy-to-understand, and
scalable means for modeling a domain. However, when a domain model is
particularly complex, frame-based representation may lead to complicated and
sometimes awkward solutions. We have encountered such problems when developing
the Digital Anatomist Foundational Model, an ontology aimed at representing
comprehensively the physical organization of the human body. We show that
traditional frame-based techniques such as is-a hierarchies, slots (roles) and
role restrictions are not sufficient for a comprehensive model of this domain.
The diverse modeling challenges and problems in this project required us to use
such knowledge-representation techniques as reified relations, metaclasses and a
metaclass hierarchy, different propagation patterns for template and own slots,
and so on. We posit that even though the modeling structure imposed by
frame-based systems may sometimes lead to complicated solutions, it is still
worthwhile to use frame-based representation for very large-scale projects such
as this one.
View
Show abstract
GAOM: Genetic Algorithm Based Ontology Matching
Conference Paper
* Dec 2006
* Junli Wang
* Zhijun Ding
* Changjun Jiang
In this paper a genetic algorithm-based optimization procedure for ontology
matching problem is presented as a feature-matching process. First, from a
global view, we model the problem of ontology matching as an optimization
problem of a mapping between two compared ontologies, and every ontology has its
associated feature sets. Second, as a powerful heuristic search strategy,
genetic algorithm is employed for the ontology matching problem. Given a certain
mapping as optimizing object for GA, fitness function is defined as a global
similarity measure function between two ontologies based on feature sets.
Finally, a set of experiments are conducted to analysis and evaluate the
performance of GA in solving ontology matching problem.
View
Show abstract
Binary Codes Capable of Correcting Deletions, Insertions, and Reversals
Article
* Nov 1965
* Dokl Akad Nauk SSSR
* V.I. Levenshtein
View
SNOMED-CT: The advanced terminology and coding system for eHealth
Article
* Feb 2006
* Stud Health Tech Informat
* Kevin Donnelly
A clinical terminology is essential for Electronic Health records. It represents
clinical information input into clinical IT systems by clinicians in a
machine-readable manner. Use of a Clinical Terminology, implemented within a
clinical information system, will enable the delivery of many patient health
benefits including electronic clinical decision support, disease screening and
enhanced patient safety. For example, it will help reduce medication-prescribing
errors, which are currently known to kill or injure many citizens. It will also
reduce clinical administration effort and the overall costs of healthcare.
View
Show abstract
A fast and elitist multiobjective genetic algorithm: NSGA-II
Article
* May 2002
* IEEE T EVOLUT COMPUT
* Kalyan Deb
* Amrit Pratap
* Sameer Agarwal
* T. Meyarivan
Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting
and sharing have been criticized mainly for: (1) their O(MN<sup>3</sup>)
computational complexity (where M is the number of objectives and N is the
population size); (2) their non-elitism approach; and (3) the need to specify a
sharing parameter. In this paper, we suggest a non-dominated sorting-based MOEA,
called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates
all of the above three difficulties. Specifically, a fast non-dominated sorting
approach with O(MN<sup>2</sup>) computational complexity is presented. Also, a
selection operator is presented that creates a mating pool by combining the
parent and offspring populations and selecting the best N solutions (with
respect to fitness and spread). Simulation results on difficult test problems
show that NSGA-II is able, for most problems, to find a much better spread of
solutions and better convergence near the true Pareto-optimal front compared to
the Pareto-archived evolution strategy and the strength-Pareto evolutionary
algorithm - two other elitist MOEAs that pay special attention to creating a
diverse Pareto-optimal front. Moreover, we modify the definition of dominance in
order to solve constrained multi-objective problems efficiently. Simulation
results of the constrained NSGA-II on a number of test problems, including a
five-objective, seven-constraint nonlinear problem, are compared with another
constrained multi-objective optimizer, and the much better performance of
NSGA-II is observed
View
Show abstract
POMap++ results for OAEI 2019: Fully automated machine learning approach for
ontology matching
* Jan 2019
* 169-174
* A Laadhar
* F Ghozzi
* I Megdiche
* F Ravat
* O Teste
* F Gargouri
Laadhar, A., Ghozzi, F., Megdiche, I., Ravat, F., Teste, O., & Gargouri, F.
(2019). POMap++ results for OAEI 2019: Fully automated machine learning approach
for ontology matching. In P. Shvaiko, J. Euzenat, E. Jiménez-Ruiz, O.
Hassanzadeh, & C. Trojahn (Eds.), CEUR workshop proceedings: Vol. 2536,
Proceedings of the 14th international workshop on ontology matching co-located
with the 18th international semantic web conference (pp. 169-174). CEUR-WS.org,
URL http://ceur-ws.org/Vol-2536/oaei19_paper13.pdf.
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