Linguistic based matching of local ontologies

This paper describes an automatic algorithm of meaning negotiation that enables semantic interoperability between local overlapping and heterogeneous ontologies. Rather than reconciling differences between heterogeneous ontologies, this algorithm searches for mappings between concepts of different ontologies. The algorithm is composed of three main steps: (i) computing the linguistic meaning of the label occurring in the ontologies via natural language processing, (ii) contextualization of such a linguistic meaning by considering the context, i.e. the ontologies, where a label occurs; (iii) comparing contextualized linguistic meaning of two ontologies in in order to find a possible matching between them

Results of the Ontology Alignment Evaluation Initiative 2015

cheatham2016aInternational audienceOntology matching consists of finding correspondences between semantically related entities of two ontologies. OAEI campaigns aim at comparing ontology matching systems on precisely defined test cases. These test cases can use ontologies of different nature (from simple thesauri to expressive OWL ontologies) and use different modalities, e.g., blind evaluation, open evaluation and consensus. OAEI 2015 offered 8 tracks with 15 test cases followed by 22 participants. Since 2011, the campaign has been using a new evaluation modality which provides more automation to the evaluation. This paper is an overall presentation of the OAEI 2015 campaign

How to Find Suitable Ontologies Using an Ontology-based WWW Broker

Knowledge reuse by means of outologies now faces three important problems: (1) there are no standardized identifying features that characterize ontologies from the user point of view; (2) there are no web sites using the same logical organization, presenting relevant information about ontologies; and (3) the search for appropriate ontologies is hard, time-consuming and usually fruitless. To solve the above problems, we present: (1) a living set of features that allow us to characterize ontologies from the user point of view and have the same logical organization; (2) a living domain ontology about ontologies (called ReferenceOntology) that gathers, describes and has links to existing ontologies; and (3) (ONTO)2Agent, the ontology-based www broker about ontologies that uses the Reference Ontology as a source of its knowledge and retrieves descriptions of ontologies that satisfy a given set of constraints. (ONTO)~Agent is available at http://delicias.dia.fi.upm.es/REFERENCE ONTOLOGY

An Approach to Cope with Ontology Changes for Ontology-based Applications

Keeping track of ontology changes is becoming a critical issue for ontology-based applications because updating an ontology that is in use may result in inconsistencies between the ontology and the knowledge base, dependent ontologies and dependent applications/services. Current research concentrates on the creation of ontologies and how to manage ontology changes in terms of the attempts to ease the communications between ontology versions and keep consistent with the instances, and there is little work available on controlling the impact to dependent applications/services which is the aims of the system presented in this paper. The approach we propose in this paper is to manually capture and log ontology changes, use this log to analyse incoming RDQL queries and amend them as necessary. Revised queries can then be used to query the knowledge base of the applications/services. We present the infrastructure of our approach based on the problems and scenarios identified within ontology-based systems. We discuss the issues met during our design and implementation, and consider some problems whose solutions will be beneficial to the development of our approach

How much semantic data on small devices?

Semantic tools such as triple stores, reasoners and query en- gines tend to be designed for large-scale applications. However, with the rise of sensor networks, smart-phones and smart-appliances, new scenar- ios appear where small devices with restricted resources have to handle limited amounts of data. It is therefore important to assess how ex- isting semantic tools behave on such small devices, and how much data they can reasonably handle. There exist benchmarks for comparing triple stores and query engines, but these benchmarks are targeting large-scale applications and would not be applicable in the considered scenarios. In this paper, we describe a set of small to medium scale benchmarks explicitly targeting applications on small devices. We describe the re- sult of applying these benchmarks on three different tools (Jena, Sesame and Mulgara) on the smallest existing netbook (the Asus EEE PC 700), showing how they can be used to test and compare semantic tools in resource-limited environments

Expliciting semantic relations between ontologies in large ontology repositories

and other research outputs Expliciting semantic relations between ontologies in large ontology repositorie

A platform for semantic web studies

The Semantic Web can be seen as a large, heterogeneous network of ontologies and semantic documents. Characterizing these ontologies, the way they relate and the way they are organized can help in better understanding how knowledge is produced and published online. It also provides new ways to explore and exploit this large collection of ontologies. In this paper, we present the foundation of a research platform for characterizing the Semantic Web, relying on the collection of ontologies and the functionalities provided by the Watson Semantic Web search engine. We more specifically focus on formalizing and monitoring relationships between ontologies online, considering a variety of different relations (similarity, versioning, agreement, modularity) and how they can help us obtaining meaningful overviews of the current state of the Semantic Web