Partial Ontology Matching Using Instance Features

Purpose – Ontologies are used to formally describe the concepts within a domain in a machine-understandable way. Matching of heterogeneous ontologies is often essential for many applications like semantic annotation, query answering or ontology integration. Some ontologies may include a large number of entities which make the ontology matching process very complex in terms of the search space and execution time requirements. The purpose of this paper is to present a technique for finding degree of similarity between ontologies that trims down the search space by eliminating the ontology concepts that have less likelihood of being matched. Design/methodology/approach – Algorithms are written for finding key concepts, concept matching and relationship matching. WordNet is used for solving synonym problems during the matching process. The technique is evaluated using the reference alignments between ontologies from ontology alignment evaluation initiative benchmark in terms of degree of similarity, Pearson’s correlation coefficient and IR measures precision, recall and F-measure. Findings – Positive correlation between the degree of similarity and degree of similarity (reference alignment) and computed values of precision, recall and F-measure showed that if only key concepts of ontologies are compared, a time and search space efficient ontology matching system can be developed. Originality/value – On the basis of the present novel approach for ontology matching, it is concluded that using key concepts for ontology matching gives comparable results in reduced time and space.

Ontologies are widely used in various domain such as medical, e-commerce and semantic web. However, heterogeneous ontologies are one of the main challenges in realizing the semantic interoperation in the domain of ontology. Ontology matching is proposed as the solution to realize the semantic interoperation. In general, three main categories of ontology matching strategies proposed by researchers: string based matching; structural based matching and also instance-based matching. Instances in ontology contain lot of semantic information which can be used for the matching purposes. However, some of the ontology contains superfluous concepts/classes which should be removed in order to increase the performance of matching. In this paper, an idea for instance-based matching with rough set feature selection capability approach is proposed to perform the ontology matching task and further increase the matching's efficiency is presented.

With the increasing number of ontologies being designed to represent and manage knowledge in all sorts of sectors, ontology alignment and integration become more and more important in aggregating intelligent efforts on homogenous and heterogeneous data. From the computational perspective, it is challenging due to the ubiquitous existence of diverse classifications of same data. In this paper, we propose an active ontology integration and alignment system, which plugs in expandable learning reference context pool. In the reference context pool, we have integrated WordNet, MeSH, and external curated mapping sources (ICD9 to SNOMEDCT) with an extension to injecting UMLS. The active ontology integration and alignment system takes account of not only subsumption tree but also directed acyclic graph underlying ontologies. It allows 1) finding exact one-to-one matching terms of pairwise ontologies, 2) finding inexact one-to-one term mappings, where two terms have at least a concept in common on basis of the lexical context, and 3) finding one-to-many concept mappings, where one concept can be lexically mapped to the combination of multiple exclusive concepts.

In this paper, we describe a mechanism for ontology alignment using instance based matching of types (or classes). Instance-based matching is known to be a useful technique for matching ontologies that have different names and different structures. A key problem in instance matching of types, however, is scaling the matching algorithm to (a) handle types with a large number of instances, and (b) efficiently match a large number of type pairs. We propose the use of state-of-the art locality-sensitive hashing (LSH) techniques to vastly improve the scalability of instance matching across multiple types. We show the feasibility of our approach with DBpedia and Freebase, two different type systems with hundreds and thousands of types, respectively. We describe how these techniques can be used to estimate containment or equivalence relations between two type systems, and we compare two different LSH techniques for computing instance similarity.

Decision support has gradually evolved in both the fields of theoretical decision support studies and practical assisting tools for decision makers since the 1960s. Ontology Engineering (OE) brings new synergy to decision support. On the one hand, it will change (and actually now is changing) the decision support landscape, as it will enable new breeds of decision models, decision support systems (DSS) to be developed. On the other hand, DSS can bring theories and applications that support OE, such as ontology integration, ontology matching and ontology integration.

This work targets the issue of ontology matching in a grid environment. For this, similarity values are considered in order to qualify the similarity between several ontologies, concepts and properties. Based on the similarity values a matching is proposed in order to extend one priority ontology. The ontology matching is proposed in a grid environment with the aim to manage ontology matching for big data sets at same time.

This paper reflects on some major aspects related to library subject access systems in the era of networked information. The main argument builds on the fact that we nowadays witness the strongest demand and expectation on subject access tools, coming from far beyond the traditional library world, but the field remains the weakest side of international 66librarianship. While the emergence to cope with the practical challenges of a wider environment is emphasized, the need to reinforce the internationalization of knowledge organization as a professional library matter is stressed, not only at the pragmatic level but also, more importantly, in theoretical terms.

When ontology description data by different authors would become widespread in the world, we will be faced with the difficulties of the ontology alignment (OA) problem required for integration and interoperability of ontologies. The OA problem is the problem to find couples of semantically same classes / properties between two ontologies, and includes points of different naming of classes / properties, polysemous naming of classes / properties, different granularity of classes / properties, different hierarchical structures, and so on. We applied our semantic category matching (SCM) tool to the ontology alignment problems. Our approach found pairs of semantically corresponding categories from two different classification hierarchies such as Yahoo directory or library classification as UDC or NDC, based on natural language processing, similarity searching of huge vector spaces, and structural consistency analysis. We tackled problems of the EON2004 Ontology Alignment Contest. For examples, the Contest's random name problems (#201, #202) could not be solved using conventional character string resemblance techniques. However, when we applied SCM to these problems, the results showed that SCM had improved the accuracy as compared to the conventional method (F-measure: 0.021=>0.949, 0.021=>0.580), and exceeded the accuracy average in all problem areas by over 10 % as compared to conventional methods. Our team participated as a competitor in EON OA Contest and could obtain satisfactory results.

The methods and methodologies for building ontologies, called ontology engineering, include ontology development, ontology transformation, ontology merging, ontology integration, ontology mapping, and ontology alignment. During ontology development, the knowledge domain and the scope to cover are determined, and the relevant concepts and their properties collected while considering the vocabulary of existing ontologies, followed by the creation of the concept and role hierarchy with formal grounding. For very expressive ontologies, rules are also defined to complement the DL axioms. The integrity of the resulting ontology should be evaluated with reasoners. This chapter lists both the best practices and the powerful software tools used by ontology engineers to perform the above tasks correctly and efficiently.

With the progressive increase in the number of existing ontologies, ontology matching became a challenging task. Ontology matching is a crucial step in the ontology integration process and its goal is to find correspondent elements in heterogeneous ontologies. A trend of clustering-based solutions for ontology matching has evolved, based on a divide-and-conquer strategy, which partitions ontologies, clusters similar partitions and restricts the matching to ontology elements of similar partitions. Nevertheless, most of these solutions considered solely the terminological aspect, ignoring other ontology aspects that can contribute to the final matching results. In this work, we developed a novel solution for ontology matching based on a consensus clustering of multiple aspects of ontology partitons. We partitioned the ontologies applying Community Detection techniques and applied Bayesian Cluster Ensembles (BCE) to find a consensus clustering among the terminological, topological and extensional aspects of ontology partitions. The matching results of our experimental study indicated that a BCE-based solution with three clusters best captured the contributions of the aspects, in comparison to other consensual solutions. The results corroborated the benefits of the synergy between the ontology aspects to the ontology alignment. We also verified that the BCE-based solution for three clusters yielded higher matching scores than other state-of-the-art solutions. Besides, our proposed methods structurize a configurable framework, which allows adding other ontology aspects and also other techniques.

Ontology alignment plays a critical role in knowledge integration and has been widely investigated in the past decades. State of the art systems, however, still have considerable room for performance improvement especially in dealing with new (industrial) alignment tasks. In this paper we present a machine learning based extension to traditional ontology alignment systems, using distant supervision for training, ontology embedding and Siamese Neural Networks for incorporating richer semantics. We have used the extension together with traditional systems such as LogMap and AML to align two food ontologies, HeLiS and FoodOn, and we found that the extension recalls many additional valid mappings and also avoids some false positive mappings. This is also verified by an evaluation on alignment tasks from the OAEI conference track.

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.

More and more ontologies are emerging across bioinformatics domains to represent and define domain knowledge, such as gene ontology, anatomy ontology and disease ontology. To integrate these heterogeneous ontologies is becoming critically important for applications utilizing multiple ontologies. Because the entities described in the ontology often overlap with other entities in other ontologies, a mapping between two corresponding terms is required and becoming the key to the integration of heterogeneous ontologies. Based on a previously developed schema matching algorithm, this paper presents a framework for mapping and integration of heterogeneous biomedical ontologies. The framework detects possible false mappings intelligently and provides intuitive interfaces for users to customize mappings and for applications to integrate and access biomedical ontologies.

The integration of multiple ontologies and also the interaction between entities using different ontologies is one of the major challenges of the Semantic Web, ontologies play a crucial role in information representation. Significant research effort is directed towards aligning ontologies and identifying matching concepts. In this paper we present a novel method of concept translation and mapping using bridging rules by replacing a concept through an equivalent representation named bag of concepts. By translating concept description between ontologies we obtain the equivalent representation of the same concept using terms from other ontology, which will be used to determine the mapped concept or verify existing ones.

Since different ontologies are mostly developed independently, establishing meaningful links between their entities, so-called ontology matching, is critical to ensure their communication. Due to the complexity of the ontology matching problem, Evolutionary Algorithm (EA) can present a good methodology for determining ontology alignments. However, since none of the similarity measure can distinguish all the identical ontology entities in any context, ontology alignments generated by the automatic matching tools should be validated by the users to ensure their qualities. To improve the quality of the ontology alignment, in this work, a similarity measure is first proposed to calculate the similarity value of two ontology entities; then, an optimal model for ontology matching problem is constructed; after that, an EA-based automatic matcher is presented to solve the ontology matching problem, which can also adaptively determine the timing of getting user involved; and finally, the ontology concept hierarchy graph based reasoning approaches are proposed to tradeoff the user's workload and his work's effect. The experiment is conducted on the Interactive track provided by the Ontology Alignment Evaluation Initiative (OAEI), and the comparisons with OAEI's participants show the effectiveness of our proposal.