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.

There are multiple ontology matching approaches that use domain-specific background knowledge to match labels in domain ontologies or classifications. However, they tend to rely on lexical knowledge and do not consider the specificities of domain grammar. In this paper, we demonstrate the usefulness of both lexical and grammatical linguistic domain knowledge for ontology matching through examples from multiple domains. We also provide an evaluation of the impact of such knowledge on a real-world problem of matching classifications of mental illnesses from the health domain. Our experimentation with two matcher tools that use very different matching mechanisms—LogMap and SMATCH—shows that both lexical and grammatical knowledge improve matching results.

Ontology matching consists of finding correspondences between semantically related entities of different ontologies. The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity (from simple thesauri to expressive OWL ontologies) and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2017 campaign offered 9 tracks with 23 test cases, and was attended by 21 participants. This paper is an overall presentation of that campaign.

A number of ontology matching techniques have been proposed that rely on full disclosure of their ontological models prior to the construction of the alignment. However, within open and opportunistic environments, such approaches may not always be pragmatic or even acceptable (due to privacy concerns). Several studies have focussed on collaborative, decentralised approaches to ontology alignment, where agents negotiate the acceptability of correspondences (i.e. mappings between corresponding entities in different ontologies) acquired from past encounters, or try to ascertain novel correspondences on the fly. However, such approaches can lead to logical flaws that may undermine their utility. In this paper, we extend a dialogical approach to correspondence negotiation, whereby agents not only exchange details of possible correspondences, but also identify potential violations to the so-called conservativity principle, where novel but undesirable entailments between named concepts in one of the input ontologies emerge. We present a formal model of the dialogue, and show how \conservativity violations can be repaired (using an existing correspondence repair system) during the dialogue through the exchange of repairs. We then illustrate how agents negotiate over possible correspondences and repairs by means of a walkthrough example.

Nowadays, several efforts are focused on building very large ontologies that are continuously growing as new knowledge is added to them by the respective communities. This happens mainly in the biomedical domain (e.g. GO, GALEN, FMA, NCI, Tambis, etc.). However, the very large size of these ontologies as well their variety makes it difficult to deploy them in particular applications. A first problem is scalability. Most of these ontologies are expressed in OWL-DL, with different degrees of expressivity. The classification of new concepts, queries and assertions requires the use of reasoners (e.g. Fact, Pellet, etc.), but they are not able to handle even medium-size ontologies [1]. A second problem is the use of these ontologies in concrete applications. Such applications usually do not require comprehensive descriptions of the domains but rather a handful subset of concepts and properties from them (i.e. a local view of the domain [2]). Another important issue is their visualization in ontology editors, in which large ontologies have difficulties to be loaded and properly displayed. In this paper, a tool extending Protégé-OWL has been developed with the aim of allowing the management of a collection of related ontologies and the extraction of personalized modules (views) by means of a query language named OntoPath [3].

. We present the saturation-based reasoning system Lethe. Lethe is a tool that can be used for uniform interpolation, forgetting, TBox abduction and logical di(cid:27)erence. To solve these problems, Lethe uses saturation-based reasoning to eliminate certain symbols from an ontology, such that entailments in the remaining vocabulary are preserved. This is known as forgetting or uniform interpolation. Lethe is an implementation of our forgetting methods for various expressive description logics, and can be used as a Java library and as a standalone tool for the mentioned reasoning tasks. We give a high level description of the calculi used by Lethe , describe the reasoning algorithm implemented in Lethe , and give an evaluation of the system on realistic ontologies.