Input Ontologies Research Articles

Alignment Conservativity Under the Ontology Change

Recently, many methods have appeared to solve the problem of the evolution of alignment under the change of ontologies. The main challenge for them is to maintain consistency of alignment after applying the change. An alignment is consistent if and only if the ontologies remain consistent even when used in conjunction with the alignment. The objective of this work is to take a step forward by considering the alignment evolution according to the conservativity principle under the change of ontologies. In this context, an alignment is conservative if the ontological change should not introduce new semantic relationships between concepts from one of the input ontologies. The authors give methods for the conservativity violation detection and repair under the change of ontologies and they carry out an experiment on a dataset adapted from the Ontology Alignment Evaluation Initiative. The experiment demonstrates both the practical applicability of the proposed approach and shows the limits of the alignment evolution methods compared to the alignment conservativity under the change of ontologies.

Deep Embedding Learning With Auto-Encoder for Large-Scale Ontology Matching

Ontology matching is an efficient method to establish interoperability among heterogeneous ontologies. Large-scale ontology matching still remains a big challenge for its long time and large memory space consumption. The actual solution to this problem is ontology partitioning which is also challenging. This paper presents DeepOM, an ontology matching system to deal with this large-scale heterogeneity problem without partitioning using deep learning techniques. It consists on creating semantic embeddings for concepts of input ontologies using a reference ontology, and use them to train an auto-encoder in order to learn more accurate and less dimensional representations for concepts. The experimental results of its evaluation on large ontologies, and its comparison with different ontology matching systems which have participated to the same test challenge, are very encouraging with a precision score of 0.99. They demonstrate the higher efficiency of the proposed system to increase the performance of the large-scale ontology matching task.

AccTEF: A Transparency and Accountability Evaluation Framework for Ontology-Based Systems

This paper proposes a new accountability and transparency evaluation framework (AccTEF) for ontology-based systems (OSysts). AccTEF is based on an analysis of the relation between a set of widely accepted data governance principles, i.e. findable, accessible, interoperable, reusable (FAIR) and accountability and transparency concepts. The evaluation of accountability and transparency of input ontologies and vocabularies of OSysts are addressed by analyzing the relation between vocabulary and ontology quality evaluation metrics, FAIR and accountability and transparency concepts. An ontology-based knowledge extraction pipeline is used as a use case in this study. Discovering the relation between FAIR and accountability and transparency helps in identifying and mitigating risks associated with deploying OSysts. This also allows providing design guidelines that help accountability and transparency to be embedded in OSysts. We found that FAIR can be used as a transparency indicator. We also found that the studied vocabulary and ontology quality evaluation metrics do not cover FAIR, accountability and transparency. Accordingly, we suggest these concepts should be considered as vocabulary and ontology quality evaluation aspects. To the best of our knowledge, it is the first time that the relation between FAIR and accountability and transparency concepts has been found and used for evaluation.

An Alignment-Based Implementation of a Holistic Ontology Integration Method

Despite the intense research activity in the last two decades, ontology integration still presents a number of challenging issues. As ontologies are continuously growing in number, complexity and size and are adopted within open distributed systems such as the Semantic Web, integration becomes a central problem and has to be addressed in a context of increasing scale and heterogeneity. In this paper, we describe a holistic alignment-based method for customized ontology integration. The holistic approach proposes additional challenges as multiple ontologies are jointly integrated at once, in contrast to most common approaches that perform an incremental pairwise ontology integration. By applying consolidated techniques for ontology matching, we investigate the impact on the resulting ontology. The proposed method takes multiple ontologies as well as pairwise alignments and returns a refactored/non-refactored integrated ontology that faithfully preserves the original knowledge of the input ontologies and alignments. We have tested the method on large biomedical ontologies from the LargeBio OAEI track. Results show effectiveness, and overall, a decreased integration cost over multiple ontologies.•OIAR and AROM are two implementations of the proposed method.•OIAR creates a bridge ontology, and AROM creates a fully merged ontology.•The implementation includes the option of ontology refactoring.

Heterogeneity Management Using OAEI Benchmark Dataset

The evolution of ontologies and itsapplications are in various fields like artificial intelligence, reasoning, philosophy, biological science, and medical field. The components of ontologiesare concepts, instance, relationships, constraints, axioms and inference mechanism. Ontology is a main source for enabling interoperability in the semantic web. In this paper heterogeneities are identified between information systems and the possible rectification are carried out using OAEI benchmark datasets. Proposed method is compared with S-Match algorithm. The evaluation results shows that proposed method is performed better and structure changes of input ontologies not affect the results.

Ontologies’ mappings validation and annotation enrichment through tagging

Pay as you go ontology matching, the technique of first executing an automatic matching tool and then engaging users to improve the quality of an alignment produced by the tool is gaining popularity. Most of the existing techniques employ a single user to validate mappings by annotating them using terms from a controlled set such as “correct” or “incorrect”. This single user based approach of validating mappings using a controlled set of vocabulary is restrictive. First, the use of controlled vocabulary does not maximize the user’s effort since it restrains him/her from adding more meaning to the concepts participating in low-quality mappings using his/her own terms. Secondly, a single user approach of validating a wide range of mappings is error prone since even the most experienced user may not be familiar with all subtopics contained in the input ontologies. We demonstrate in this research that through tagging of concepts participating in mappings flagged as low-quality, we can achieve both mappings’ validation and ontology’s metadata enrichment by adding quality annotations to the ontology.

Procedure Based on Semantic Similarity for Merging Ontologies by Non-Redundant Knowledge Enrichment

The merging procedures of two ontologies are mostly related to the enrichment of one of the input ontologies, i.e. the knowledge of the aligned concepts from one ontology are copied into the other ontology. As a consequence, the resulting new ontology extends the original knowledge of the base ontology, but the unaligned concepts of the other ontology are not considered in the new extended ontology. On the other hand, there are experts-aided semi-automatic approaches to accomplish the task of including the knowledge that is left out from the resulting merged ontology and debugging the possible concept redundancy. With the aim of facing the posed necessity of including all the knowledge of the ontologies to be merged without redundancy, this article proposes an automatic approach for merging ontologies, which is based on semantic similarity measures and exhaustive searching along of the closest concepts. The authors' approach was compared to other merging algorithms, and good results are obtained in terms of completeness, relationships and properties, without creating redundancy.

Optimizing Ontology Alignments by Using Neural NSGA-II

In this article, the authors propose a new hybrid approach based on a continuous Non-dominated Sorting Genetic Algorithm II (NSGA-II) and a neural network to refine the alignment results. This approach consists of three phases: (i) pre-alignment phase which allows to identify the formats of input ontologies, to adapt them and to transform them into Ontology Web Language (OWL) in order to solve the problem of heterogeneity of representation. (ii) alignment phase which combines syntactic and linguistic matching techniques and methods, based on the relevant attributes per different points of syntactic and structural technic. (iii) The post-alignment phase which optimizes the matching by a hybrid technique of continuous NSGA-II and networks of neurons. This approach is compared with the greatest systems per the Ontology Alignment Evaluation Initiative (OAEI) standard. The experimental results appear that the proposed approach is effective.

PSOM2—partitioning-based scalable ontology matching using MapReduce

The growth and use of semantic web has led to a drastic increase in the size, heterogeneity and number of ontologies that are available on the web. Correspondingly, scalable ontology matching algorithms that will eliminate the heterogeneity among large ontologies have become a necessity. Ontology matching algorithms generally do not scale well due to the massive number of complex computations required to achieve matching. One of the methods used to address this problem is the use of partition-based systems to reduce the matching space. In this paper, we propose a new partitioning-based scalable ontology matching system called PSOM2. We have designed a new neighbour-based intra-similarity measure to increase the quality of the cluster set formation for the partition-based ontology matching process. These sets of clusters or sub-ontologies are matched across the input ontologies to identify matchable cluster pairs, based on anchors that are efficiently discovered through a new light-weight linguistic matcher (EI-sub). However, in order to further increase the efficiency of the time-consuming anchor discovery process we have designed a MapReduce-based EI-sub process where anchors are discovered in distributed and parallel fashion. Experiments on benchmark OAEI (Ontology Alignment Evaluation Initiative) large scale ontologies demonstrate that the new PSOM2 system achieves, on an average, 31% decrease in entropy of the clusters and 54.5% reduction in overall run time. Based on the experimental results, it is evident that the new PSOM2 achieves better quality clusters and a major reduction in execution time, leading to an effective and scalable ontology matching system.

A statistically-based ontology matching tool

Ontologies have become a popular means of knowledge sharing and reuse. This has motivated development of large independent ontologies within the same or different domains with some overlapping information among them. In order to match such large ontologies, automatic matchers become an inevitable solution. This work explores the use of a predictive statistical model to establish an alignment between two input ontologies. We demonstrate how to integrate ontology partitioning and parallelism in the ontology matching process in order to make the statistical predictive model scalable to large ontology matching tasks. Unlike most ontology matching tools which establish 1:1 cardinality mappings, our statistical model generates one-to-many cardinality mappings.

CroMatcher: An ontology matching system based on automated weighted aggregation and iterative final alignment

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.

Minimizing conservativity violations in ontology alignments: algorithms and evaluation

In order to enable interoperability between ontology-based systems, ontology matching techniques have been proposed. However, when the generated mappings lead to undesired logical consequences, their usefulness may be diminished. In this paper, we present an approach to detect and minimize the violations of the so-called conservativity principle where novel subsumption entailments between named concepts in one of the input ontologies are considered as unwanted. The practical applicability of the proposed approach is experimentally demonstrated on the datasets from the Ontology Alignment Evaluation Initiative.

A fine-grained load balancing technique for improving partition-parallel-based ontology matching approaches

Currently, the use of large ontologies in various areas of knowledge is increasing. Since these ontologies can present overlapping of content, the identification of correspondences between entities becomes necessary for different tasks, for example, data integration and data linkage. Matching large ontologies is a challenge since it involves an excessive number of comparisons between entities which leads to high execution times and requires a considerable amount of computing resources. This work proposes a fine-grained load balancing technique which can be applied to Partition-Parallel-based Ontology Matching (PPOM) approaches. A PPOM approach partitions the input ontologies into sub-ontologies and executes the comparisons between entities in parallel (for instance, using MapReduce). In this sense, the fine-grained load balancing technique aims to guide the even distribution of comparisons among the nodes of a cluster infrastructure. Experimental results indicate that the proposed load balancing technique is able to reduce the overall execution time of PPOM approaches.

Symbiosis of evolutionary and combinatorial ontology mapping approaches

The problem of identifying semantically aligned entities in different ontologies known as ontology mapping is an outstanding research area and lies at the heart of many semantic applications. The overarching goal of ontology mapping is to discover a valid and comprehensive mapping with the aim of maximizing the number of reasonable alignments of ontological entities. Recently many efforts to automate the ontology mapping have been carried out, with some problems such as scalability and efficiency still evident. In this paper, ontology mapping in heterogeneous knowledge bases is formalized as an optimization problem, and an efficient method called Harmony Search based Ontology Mapping (HSOMap) is proposed, that effectively finds a near-optimal mapping for two input ontologies. In this approach, we make use of many kinds of rating functions, which are also called base matchers to evaluate the similarity of entities. Each base matcher captures the similarity between entities from a different perspective and is able to exploit the available side information about the entities effectively. Also, a novel weighted harmonic-mean method is proposed to aggregate different metrics into a single similarity metric among all pairs of entities from two ontologies. After obtaining the combined similarity metric between ontological entities, a discrete harmony search algorithm is proposed to extract the best alignment. To demonstrate the merits and advantages of the HSOMap algorithm, we conduct a set of experiments on benchmark data sets and compare its performance to other state-of-the-other methods. Our experimental results demonstrate that applying harmony search in the context of ontology mapping is a feasible approach and improves the mapping effectiveness significantly.

Cromatcher: An Ontology Matching System Based on Automated Weighted Aggregation and Iterative Final Alignment

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.

Ontology Alignment Repair through Modularization and Confidence-Based Heuristics.

Ontology Matching aims at identifying a set of semantic correspondences, called an alignment, between related ontologies. In recent years, there has been a growing interest in efficient and effective matching methods for large ontologies. However, alignments produced for large ontologies are often logically incoherent. It was only recently that the use of repair techniques to improve the coherence of ontology alignments began to be explored. This paper presents a novel modularization technique for ontology alignment repair which extracts fragments of the input ontologies that only contain the necessary classes and relations to resolve all detectable incoherences. The paper presents also an alignment repair algorithm that uses a global repair strategy to minimize both the degree of incoherence and the number of mappings removed from the alignment, while overcoming the scalability problem by employing the proposed modularization technique. Our evaluation shows that our modularization technique produces significantly small fragments of the ontologies and that our repair algorithm produces more complete alignments than other current alignment repair systems, while obtaining an equivalent degree of incoherence. Additionally, we also present a variant of our repair algorithm that makes use of the confidence values of the mappings to improve alignment repair. Our repair algorithm was implemented as part of AgreementMakerLight, a free and open-source ontology matching system.

A Hybrid Layered Approach for Ontology Matching

Ontologies act as a key technology for the visualization of the semantic web and their existence in different domains produces a heterogeneity problem for information integration. The process of ontology matching solves the heterogeneity problem by identifying the semantically related identical entities available from different ontologies. Finding similarity between the class names across the input ontologies is the major step performed in every ontology matching process. Most of the existing methods compare the class names only based on their similarity irrespective of their real meaning. This paper suggests a hybrid layered approach for matching the ontology classes. This approach calculates syntactic, semantic and structural similarity between the classes from input ontologies in successive layers. Finally, the alignment layer generates the final matching results by combining the results obtained from the previous layers and generates semantic mappings between them. This approach can also be applied to match other entities of the ontology. The results obtained prove that the proposed approach overtakes the other existing methods, thereby improving the accuracy of the results achieved.

An effective method of large scale ontology matching.

BackgroundWe 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.ResultsWe 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.ConclusionsWe 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.

Português

Ontology alignment is a common and successful way to reduce the semantic heterogeneity among ontologies, relying on the application of similarity functions to decide whether a pair of entities from two input ontologies corresponds to each other. There are several similarity functions proposed in the literature capturing distinct and complementary perspectives, but the challenge is on how to combine their use. This paper presents a methodology to automatically learn a classifier that combines distinct string-based similarity functions for the ontology alignment task, through machine learning. The proposed approach was evaluated experimentally on sixteen scenarios defined on top of the Ontology Alignment Evaluation Initiative (OAEI). 800x600 Ontology alignment is a common and successful way to reduce the semantic heterogeneity among ontologies, relying on the application of similarity functions to decide whether a pair of entities from two input ontologies corresponds to each other. There are several similarity functions proposed in the literature capturing distinct and complementary perspectives, but the challenge is on how to combine their use. This paper presents a methodology to automatically learn a classifier that combines distinct string-based similarity functions for the ontology alignment task, through machine learning. The proposed approach was evaluated experimentally on sixteen scenarios defined on top of the Ontology Alignment Evaluation Initiative (OAEI). Normal 0 21 false false false PT-BR X-NONE X-NONE MicrosoftInternetExplorer4

An Algorithmic Approach to Inferring Cross-Ontology Links while Mapping Anatomical Ontologies

Automated and semi-automated mapping and the subsequently merging of two (or more) anatomical ontologies can be achieved by (at least) two direct procedures. The first concerns syntactic matching between the terms of the two ontologies; in this paper, we call this direct matching (DM). It relies on identities between the terms of the two input ontologies in order to establish cross-ontology links between them. The second involves consulting one or more external knowledge sources and utilizing the information available in them, thus providing additional information as to how terms (concepts) from the two input ontologies are related/linked to each other. Each of the two ontologies is aligned to an external knowledge source and links representing synonymy, is-a parent-child, and part-of parent-child relations, are drawn between the ontology and the knowledge source. These links are then run through a set of simple logical rules in order to come up with cross-ontology links between the two input ontologies. This method is known as semantic matching. It proves useful and reasonably accurate; in this paper, we call it the source matching predictions (SMP) procedure.Not all cross-ontology links that semantically (i.e., from a biological/anatomical standpoint) exist between the two input ontologies will be discovered by either DM or SMP. To improve the discovery of cross-ontology links we propose a novel algorithmic procedure which involves a probability-like scoring scheme. This procedure is called the child matching predictions (CMP) procedure. Describing the DM, SMP, CMP procedures, and particularly the CMP procedure in formal terms is the main goal of this paper.