Pairs Of Ontologies Research Articles

Integrating Heterogeneous Ontologies in Asian Languages Through Compact Genetic Algorithm with Annealing Re-sample Inheritance Mechanism

An ontology is a state-of-the-art knowledge modeling technique in the natural language domain, which has been widely used to overcome the linguistic barriers in Asian and European countries’ intelligent applications. However, due to the different knowledge backgrounds of ontology developers, the entities in the ontologies could be defined in different ways, which hamper the communications among the intelligent applications built on them. How to find the semantic relationships among the entities that are lexicalized in different languages is called the Cross-lingual Ontology Matching problem (COM), which is a challenge problem in the ontology matching domain. To face this challenge, being inspired by the success of the Genetic Algorithm (GA) in the ontology matching domain, this work proposes a Compact GA with Annealing Re-sample Inheritance mechanism (CGA-ARI) to efficiently address the COM problem. In particular, a Cross-lingual Similarity Metric (CSM) is presented to distinguish two cross-lingual entities, a discrete optimal model is built to define the COM problem, and the compact encoding mechanism and the Annealing Re-sample Inheritance mechanism (ARI) are introduced to improve CGA’s searching performance. The experiment uses Multifarm track to test CGA-ARI’s performance, which includes 45 ontology pairs in different languages. The experimental results show that CGA-ARI is able to significantly improve the performance of GA and CGA and determine better alignments than state-of-the-art ontology matching systems.

Performance assessment of ontology matching systems for FAIR data

BackgroundOntology matching should contribute to the interoperability aspect of FAIR data (Findable, Accessible, Interoperable, and Reusable). Multiple data sources can use different ontologies for annotating their data and, thus, creating the need for dynamic ontology matching services. In this experimental study, we assessed the performance of ontology matching systems in the context of a real-life application from the rare disease domain. Additionally, we present a method for analyzing top-level classes to improve precision.ResultsWe included three ontologies (NCIt, SNOMED CT, ORDO) and three matching systems (AgreementMakerLight 2.0, FCA-Map, LogMap 2.0). We evaluated the performance of the matching systems against reference alignments from BioPortal and the Unified Medical Language System Metathesaurus (UMLS). Then, we analyzed the top-level ancestors of matched classes, to detect incorrect mappings without consulting a reference alignment. To detect such incorrect mappings, we manually matched semantically equivalent top-level classes of ontology pairs. AgreementMakerLight 2.0, FCA-Map, and LogMap 2.0 had F1-scores of 0.55, 0.46, 0.55 for BioPortal and 0.66, 0.53, 0.58 for the UMLS respectively. Using vote-based consensus alignments increased performance across the board. Evaluation with manually created top-level hierarchy mappings revealed that on average 90% of the mappings’ classes belonged to top-level classes that matched.ConclusionsOur findings show that the included ontology matching systems automatically produced mappings that were modestly accurate according to our evaluation. The hierarchical analysis of mappings seems promising when no reference alignments are available. All in all, the systems show potential to be implemented as part of an ontology matching service for querying FAIR data. Future research should focus on developing methods for the evaluation of mappings used in such mapping services, leading to their implementation in a FAIR data ecosystem.

Improved Convergence of Iterative Ontology Alignment using Block-Coordinate Descent

A wealth of ontologies, many of which overlap in their scope, has made aligning ontologies an important problem for the semantic Web. Consequently, several algorithms now exist for automatically aligning ontologies, with mixed success in their performances. Crucial challenges for these algorithms involve scaling to large ontologies, and as applications of ontology alignment evolve, performing the alignment in a reasonable amount of time without compromising on the quality of the alignment. A class of alignment algorithms is iterative and often consumes more time than others while delivering solutions of high quality. We present a novel and general approach for speeding up the multivariable optimization process utilized by these algorithms. Specifically, we use the technique of block-coordinate descent in order to possibly improve the speed of convergence of the iterative alignment techniques. We integrate this approach into three well-known alignment systems and show that the enhanced systems generate similar or improved alignments in significantly less time on a comprehensive testbed of ontology pairs. This represents an important step toward making alignment techniques computationally more feasible.

A framework for evaluating ontology meta-matching approaches

Ontology matching has become a key issue to solve problems of semantic heterogeneity. Several researchers propose diverse techniques that can be used in distinct scenarios. Ontology meta-matching approaches are a specialization of ontology matching and have achieved good results in pairs of ontologies with different types of heterogeneities. However, developing a new ontology meta-matcher can be a costly process and a lot of experiments are often carried out to analyze the behavior of the matcher. This article presents a modularized framework that covers the main stages of the ontology meta-matching evaluation process. This framework aims to aid researchers to develop and analyze algorithms for ontology meta-matching, mainly metaheuristic-based supervised and unsupervised approaches. As the main contribution of the research, the framework proposed will facilitate the evaluation of ontology meta-matching approaches and, as the secondary contribution, a data provenance model that captures the main information generated and consumed throughout experiments is presented in the framework.

Towards evaluating complex ontology alignments

Abstract The development of semi-automated and automated ontology alignment techniques is an important part of realizing the potential of the Semantic Web. Until very recently, most existing work in this area was focused on finding simple (1:1) equivalence correspondences between two ontologies. However, many real-world ontology pairs involve correspondences that contain multiple entities from each ontology. These ‘complex’ alignments pose a challenge for existing evaluation approaches, which hinders the development of new systems capable of finding such correspondences. This position paper surveys and analyzes the requirements for effective evaluation of complex ontology alignments and assesses the degree to which these requirements are met by existing approaches. It also provides a roadmap for future work on this topic taking into consideration emerging community initiatives and major challenges that need to be addressed.

Alignment of surface water ontologies: a comparison of manual and automated approaches

Studying the surface water systems of the earth is important for many fields, from biology to agriculture to tourism. Much of the data relevant to surface water systems are stored in isolated repositories that interface with different ontologies, such as the US Geological Survey’s Surface Water Ontology or the Environment Ontology (ENVO). Effectively using these data requires integrating the ontologies so that the data can be seamlessly queried and analyzed. Automated alignment algorithms exist to facilitate this data integration challenge. In this paper we examine the utility of two leading automated alignment systems to integrate four pairs of ontologies from the surface water domain. We show that the performance of such systems in this domain lags behind their results on popular benchmarks, and therefore incorporate the alignment task described here into the set of benchmarks used by the alignment community. We also show that with minor modifications, existing alignment algorithms can be used effectively within a semi-automated system for the surface water domain. In addition, we analyze the unique challenges of this domain with respect to data integration and discuss possible solutions to pursue in order to address these challenges.

Networked Ontologies with Contextual Alignments

The problem of knowledge heterogeneity in the Semantic Web or in the context of information systems remains a major challenge for the scientific community, in particular when several ontologies developed independently and separately have to be exploited to exchange their knowledge. Several works have addressed the semantic heterogeneity issue in ontologies and proposed to align them with additional knowledge. Recently a formalism taking into account the challenge of applied techniques to represent and reason on aligned ontologies was proposed by the authors. The authors proposed a contribution that can be seen as an extension of existing work on the heterogeneous ontologies integration. This formalism allows dealing with contextual representation and reasoning where ontologies and alignments by pairs of ontologies are developed in different and incompatible context. In this paper, some aspects of multi-level networked knowledge are recalled, detailing its semantics and discussing the comparison of the two semantics, DL-approach and DDL-approach, according to certain criteria, in order to measure their relevance and to give to readers a way to choose one semantics rather than another according to the context or the intended application.

Extended Analysis of Topological-Pattern-Based Ontology Enrichment.

Maintenance of biomedical ontologies is difficult. We have previously developed a topological-pattern-based method to deal with the problem of identifying concepts in a reference ontology that could be of interest for insertion into a target ontology. Assuming that both ontologies are parts of the Unified Medical Language System (UMLS), the method suggests approximate locations where the target ontology could be extended with new concepts from the reference ontology. However, the final decision about each concept has to be made by a human expert. In this paper, we describe the universe of cross-ontology topological patterns in quantitative terms. We then present a theoretical analysis of the number of potential placements of reference concepts in a path in a target ontology, allowing for new cross-ontology synonyms. This provides a rough estimate of what expert resources need to be allocated for the task. One insight in previous work on this topic was the large percentage of cases where importing concepts was impossible, due to a configuration called "alternative classification." In this paper, we confirm this observation. Our target ontology is the National Cancer Institute thesaurus (NCIt). However, the methods can be applied to other pairs of ontologies with hierarchical relationships from the UMLS.

Improving the interoperability of biomedical ontologies with compound alignments

BackgroundOntologies are commonly used to annotate and help process life sciences data. Although their original goal is to facilitate integration and interoperability among heterogeneous data sources, when these sources are annotated with distinct ontologies, bridging this gap can be challenging. In the last decade, ontology matching systems have been evolving and are now capable of producing high-quality mappings for life sciences ontologies, usually limited to the equivalence between two ontologies. However, life sciences research is becoming increasingly transdisciplinary and integrative, fostering the need to develop matching strategies that are able to handle multiple ontologies and more complex relations between their concepts.ResultsWe have developed ontology matching algorithms that are able to find compound mappings between multiple biomedical ontologies, in the form of ternary mappings, finding for instance that “aortic valve stenosis”(HP:0001650) is equivalent to the intersection between “aortic valve”(FMA:7236) and “constricted” (PATO:0001847). The algorithms take advantage of search space filtering based on partial mappings between ontology pairs, to be able to handle the increased computational demands. The evaluation of the algorithms has shown that they are able to produce meaningful results, with precision in the range of 60-92% for new mappings. The algorithms were also applied to the potential extension of logical definitions of the OBO and the matching of several plant-related ontologies.ConclusionsThis work is a first step towards finding more complex relations between multiple ontologies. The evaluation shows that the results produced are significant and that the algorithms could satisfy specific integration needs.

Optimizing ontology alignments through a Memetic Algorithm using both MatchFmeasure and Unanimous Improvement Ratio

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.

Towards maximal unification of semantically diverse ontologies for controversial domains

Purpose – Ontologies are prone to wide semantic variability due to subjective points of view of their composers. The purpose of this paper is to propose a new approach for maximal unification of diverse ontologies for controversial domains by their relations. Design/methodology/approach – Effective matching or unification of multiple ontologies for a specific domain is crucial for the success of many semantic web applications, such as semantic information retrieval and organization, document tagging, summarization and search. To this end, numerous automatic and semi-automatic techniques were proposed in the past decade that attempt to identify similar entities, mostly classes, in diverse ontologies for similar domains. Apparently, matching individual entities cannot result in full integration of ontologies’ semantics without matching their inter-relations with all other-related classes (and instances). However, semantic matching of ontological relations still constitutes a major research challenge. Therefore, in this paper the authors propose a new paradigm for assessment of maximal possible matching and unification of ontological relations. To this end, several unification rules for ontological relations were devised based on ontological reference rules, and lexical and textual entailment. These rules were semi-automatically implemented to extend a given ontology with semantically matching relations from another ontology for a similar domain. Then, the ontologies were unified through these similar pairs of relations. The authors observe that these rules can be also facilitated to reveal the contradictory relations in different ontologies. Findings – To assess the feasibility of the approach two experiments were conducted with different sets of multiple personal ontologies on controversial domains constructed by trained subjects. The results for about 50 distinct ontology pairs demonstrate a good potential of the methodology for increasing inter-ontology agreement. Furthermore, the authors show that the presented methodology can lead to a complete unification of multiple semantically heterogeneous ontologies. Research limitations/implications – This is a conceptual study that presents a new approach for semantic unification of ontologies by a devised set of rules along with the initial experimental evidence of its feasibility and effectiveness. However, this methodology has to be fully automatically implemented and tested on a larger dataset in future research. Practical implications – This result has implication for semantic search, since a richer ontology, comprised of multiple aspects and viewpoints of the domain of knowledge, enhances discoverability and improves search results. Originality/value – To the best of the knowledge, this is the first study to examine and assess the maximal level of semantic relation-based ontology unification.

Speeding Up Iterative Ontology Alignment using Block-Coordinate Descent

In domains such as biomedicine, ontologies are prominently utilized for annotating data. Consequently, aligning ontologies facilitates integrating data. Several algorithms exist for automatically aligning ontologies with diverse levels of performance. As alignment applications evolve and exhibit online run time constraints, performing the alignment in a reasonable amount of time without compromising the quality of the alignment is a crucial challenge. A large class of alignment algorithms is iterative and often consumes more time than others in delivering solutions of high quality. We present a novel and general approach for speeding up the multivariable optimization process utilized by these algorithms. Specifically, we use the technique of block-coordinate descent (BCD), which exploits the subdimensions of the alignment problem identified using a partitioning scheme. We integrate this approach into multiple well-known alignment algorithms and show that the enhanced algorithms generate similar or improved alignments in significantly less time on a comprehensive testbed of ontology pairs. Because BCD does not overly constrain how we partition or order the parts, we vary the partitioning and ordering schemes in order to empirically determine the best schemes for each of the selected algorithms. As biomedicine represents a key application domain for ontologies, we introduce a comprehensive biomedical ontology testbed for the community in order to evaluate alignment algorithms. Because biomedical ontologies tend to be large, default iterative techniques find it difficult to produce a good quality alignment within a reasonable amount of time. We align a significant number of ontology pairs from this testbed using BCD-enhanced algorithms. Our contributions represent an important step toward making a significant class of alignment techniques computationally feasible.

Bridging the multilingualism gap in ontology alignment

In the semantic web context, research in ontology alignment has focused primarily on the development of monolingual matching algorithms. As more and more resources become available in more than one natural language, novel algorithms are required. The latter are capable of aligning ontologies which share more than one language. Indeed, translation techniques are often used as an intermediate step to translate ontology labels. This treatment essentially removes the natural language barrier in the alignment environment. To meet this challenge, we propose in this paper a new cross-lingual alignment method, designed to take into account the goal of dealing with ontologies expressed in different natural languages. The proposed method implements a strategy of a direct alignment based on the use of external resources. The results of performed tests with our method on different pairs of ontologies and according to several scenarios show their effectiveness.

Speeding up Batch Alignment of Large Ontologies Using MapReduce.

Real-world ontologies tend to be very large with several containing thousands of entities. Increasingly, ontologies are hosted in repositories, which often compute the alignment between the ontologies. As new ontologies are submitted or ontologies are updated, their alignment with others must be quickly computed. Therefore, aligning several pairs of ontologies quickly becomes a challenge for these repositories. We project this problem as one of batch alignment and show how it may be approached using the distributed computing paradigm of MapReduce. Our approach allows any alignment algorithm to be utilized on a MapReduce architecture. Experiments using four representative alignment algorithms demonstrate flexible and significant speedup of batch alignment of large ontology pairs using MapReduce.

OntoPart: at the cross-roads of ontology partitioning and scalable ontology alignment systems

In the semantic web register, ontologies are the cornerstones for many applications thanks to their expressiveness ability to explicate shared conceptualisations. Neverthless, the steady growth of these ontologies made their management beyond human capabilities. Thus, ontology partitioning seems to be a thriving issue for tackling such oversized ontologies and their administration through several systems. In this respect, ontology alignment task is an important component of the integration systems by providing a solution for data heterogeneousness by allowing their interoperability. In addition, such used alignment techniques implement complex computations that are losing their effectiveness against the scalability problem. To meet this challenge, we introduce in this paper a partitioning method, designed to take into account the goal of the alignment task. This method allows to split both ontologies to be aligned in two reduced size block sets, containing the elements capable to be matched. The results of tests performed with our method on different pairs of ontologies show their effectiveness.

Automated weighted aggregation in an ontology matching system

Ontology matching is the process of finding semantic relationships between entities of different ontologies. It plays an important role in the integration of heterogeneous data sources. To find semantic correspondences between entities, various matchers can be used. When multiple matchers are integrated into a matching system, the issue of combining these matchers together and determining the importance of every basic matcher arises. In a parallel composition of basic matchers, several matchers are used independently and then their results are aggregated. In this paper, we propose a method for automatically determining weights of basic matchers in their parallel composition and the architecture of the matching system. An evaluation of the method is performed on case study examples concerning the matching of four ontology pairs and a comparison with other aggregation methods is given. Additionally, the architecture of the matching system based on our automatic weighted aggregation method is presented.

AUGMENTING SUBSUMPTION PROPAGATION IN DISTRIBUTED DESCRIPTION LOGICS

Distributed description logics (DDL) enable reasoning with multiple ontologies interconnected by directional semantic mapping, called bridge rules. Bridge rules map concepts of a source ontology into concepts of a target ontology. Concept subsumptions of the source ontology can be propagated according to a propagation pattern expressed by means of bridge rules into concept subsumptions of the target ontology. In the basic formulation of DDL, such a propagation is mostly limited to cases when pairs of ontologies are directly linked by means of bridge rules. However, when more than two ontologies are involved, one would expect that subsumption propagates along chains of ontologies linked by bridge rules, but the semantics of DDL is too weak to support this behavior. In a recent study, an adjusted semantics for DDL that supports subsumption propagation through chains of bridge rules has been introduced. This study makes use of a so-called compositional consistency requirement that has been employed before in package-based description logics. While the results concerning subsumption propagation under the adjusted semantics are encouraging, there are important drawbacks. In this article we take a wider perspective, and propose a study of several different alternative extensions of the DDL semantics. For each of them we study the formal properties, and we select the one that, according to our analysis, constitutes a good compromise, and for this case we provide a sound and complete tableaux decision procedure.

Combining ontologies to automatically generate temporal perspectives of geospatial domains

This paper describes an approach for automatically combining geospatial and temporal ontologies such that a geospatial domain can be analyzed over multiple temporal granularities. Terms from a geospatial ontology are combined with terms from a temporal ontology to form cross products that provide an integrated spatiotemporal framework. This framework is multi-granular, highlighting elements from the geospatial ontology at different domain times. We show how pairs of ontologies represented in Protege can be used as the input for deriving cross products and how the results of this technique can be used as a basis for querying and retrieving new perspectives on geospatial domains. Visualizations of cross product spaces highlight the geospatial---temporal combinations of terms as well as the different relations that link these terms and improve the understanding of the structure of the spatiotemporal framework. Methods for filtering terms from the cross products are also investigated in order to prune the resulting frameworks and remove irrelevant or unnecessary terms.

Application-oriented purely semantic precision and recall for ontology mapping evaluation

To evaluate and compare various ontology mapping algorithms, it is indispensable to apply them to a group of test ontology pairs and compare the results (named mappings, also known as alignments) with some reference. Currently, the most widely used criteria are precision and recall. However, they do not always work well while facing objects with semantics, like ontologies and alignments, because they only consider “exact” correspondences but ignore “close” ones. Some new measures have been proposed to solve the problem. However, they have their own limitations, and do not rely purely on the semantics of ontologies and alignments. A framework for purely semantic precision and recall of ontology mapping is proposed in this paper, which can guarantee that the measures are purely semantic. Then a group of application-oriented measures are proposed to instantiate the framework. In some examples these measures are compared with former ones.

Evaluating ontology mapping techniques: An experiment in public safety information sharing

The public safety community in the United States consists of thousands of local, state, and federal agencies, each with its own information system. In the past few years, there has been a thrust on the seamless interoperability of systems in these agencies. Ontology-based interoperability approaches in the public safety domain need to rely on mapping between ontologies as each agency has its own representation of information. However, there has been little study of ontology mapping techniques in this domain. We evaluate current mapping techniques with real-world data representations from law-enforcement and public safety data sources. In addition, we implement an information theory based tool called MIMapper that uses WordNet and mutual information between data instances to map ontologies. We find that three tools: PROMPT, Chimaera, and LOM, have average F-measures of 0.46, 0.49, and 0.68 when matching pairs of ontologies with the number of classes ranging from 13–73. MIMapper performs better with an average F-measure of 0.84 in performing the same task. We conclude that the tools that use secondary sources (like WordNet) and data instances to establish mappings between ontologies are likely to perform better in this application domain.