AgreementMakerLight (AML) is an automated ontology matching system based primarily on element-level matching and on the use of external resources as background knowledge. This paper describes its configuration for the OAEI 2016 competition and discusses its results. For this OAEI edition, we tackled instance matching for the first time, thus expanding the coverage of AML to all types of ontolog...

The amount of research papers published nowadays related to ontology matching is remarkable and we believe that reflects the growing interest of the research community. However, for new practitioners that approach the field, this amount of information might seem overwhelming. Therefore, the purpose of this work is to help in guiding new practitioners get a general idea on the state of the field...

Fulfilling needs through internal and external resources is a key business requirement. To better enable this, description of both needs and resources, using a common domain language is required. Using techniques from Social Network Analysis (SNA) this paper describes a SENSUS-based methodology which generates domain ontology that can provide the breadth and depth of coverage required for autom...

Ontology is the main infrastructure of the Semantic Web which provides facilities for integration, searching and sharing of information on the web. Development of ontologies as the basis of semantic web and their heterogeneities have led to the existence of ontology matching. By emerging large-scale ontologies in real domain, the ontology matching systems faced with some problem like memory con...

As more and more ontology designers follow the patternbased approach, automatic analysis of those structures and their exploitation in semantic tools is becoming more doable and important. We present an approach to ontology transformation based on transformation patterns, which could assist in many semantic tasks (such as reasoning, modularisation or matching). Ontology transformation can be ap...

BACKGROUND Biomedical ontologies pose several challenges to ontology matching due both to the complexity of the biomedical domain and to the characteristics of the ontologies themselves. The biomedical tracks in the Ontology Matching Evaluation Initiative (OAEI) have spurred the development of matching systems able to tackle these challenges, and benchmarked their general performance. In this s...

Ontology matching approaches have mostly worked on the schema level so far. With the advent of Linked Open Data and the availability of a massive amount of instance information, instance-based approaches become possible. This position paper discusses approaches and challenges for using those instances as input for machine learning algorithms, with a focus on rule learning algorithms, as a means...

We think of ontology matching as a dialogue-based interactive mediation process for which we propose a three stage model. A preliminary evaluation shows how we applied this method of eliciting input for ontology matching in the medical domain. Especially, we address the challenge how to use dialogue-based interactivity with the user to rate partial alignments between two ontologies.

In this paper, we provide a novel solution for ontology matching by using the ordered weighted average (OWA) operator to aggregate multiple values obtained from different similarity measures. We have implemented the solution in the ontology matching system FOAM. Using the base matchers in FOAM, we analyze the way to choose different OWA operators and compare our system with others.

An ontology defines a common vocabulary that facilitates the sharing of information in a domain. It includes machine-interpretable definitions of basic concepts in the domain and their relations. In the Grid environment (GE) where so many different implementations are available, the need for semantic matching based on a defined ontology becomes increasingly important. Especially for service dis...