Towards Tailored Domain Ontologies Cheikh Niang1,2 , Béatrice Bouchou1 , and Moussa Lo2 1 Université François Rabelais Tours - Laboratoire d’Informatique 2 Université Gaston Berger de Saint-Louis - LANI Introduction. The goal of domain ontology is to provide a common concep- tual vocabulary to members of a virtual community of users who need to share their information in a particular domain (such as medical, tourism, banking, agricultural). The identification and definition of concepts that describe the do- main knowledge requires a certain consensus. Generally, each member or sub- community holds some knowledge, he has its own view on the domain, and he describes it with his own vocabulary. Thus, to reach a consensus allowing to reflect a common view of the domain can be a difficult task and even more harder if members are geographically dispersed. One way very widely used is to start from pre-existent elements in the domain: text corpus, taxonomies, ontol- ogy fragments, and to exploit them as a basis for gradually defining the domain ontology [2][7]. In this short paper, we present an approach using Ontology Matching tech- niques [1][5][6][3] for building a tailored domain ontology, starting from a general domain taxonomy and several pieces of knowledge given by different partners. Our strategy is to design a mediator, firstly to reach an agreement with each partner on their knowledge fragments that will be part to the shared do- main ontology, and secondly to conciliate these various fragments by linking and structuring the concepts that compose them. As a mediator ontology, in our case study we use a public taxonomy that exists for describing subject fields in agriculture, forestry, fisheries, food and related domains (e.g. environment), called AGROVOC3 . The resulting domain ontology combines the following two features: (i) it is the portion of the general taxonomy that is relevant to the considered application domain as seen by each partner, (ii) it is completed and tailored by relations and properties coming from partner’s data. Fig. 1 shows an example of a domain ontology DO built starting from two local ontologies LO1 and LO2 . DO’s concepts prefixed with ag are from AGROVOC. One can see that in DO Plan products and Varieties are related and also that they are related to attributes price and surface, which is not the case in AGROVOC. Reaching an agreement with a partner. This is the first step of our general approach. Each partner’s fragment knowledge is represented by a Local On- tology, denoted by LO. The agreement between the mediator and the partner is concluded based on a matching between LO and the mediator ontology M O. It is consented by the partner that each concept of LO which can be associated with a concept of M O, called its anchor, will be a concept of the tailored domain 3 http ://www4.fao.org/agrovoc/ 4 This work is supported by ANR-08-DEFIS-04 surface price sd:Tomato literal ad:Onion literal sd:Varieties ad:Varieties surface price sd:Rice literal ad:Sorghum literal LO1 e LO2 e ag:Tomato surface ag:Vegetables literal ag:Onion price ag:Plan_products literal ag:Varieties ag:Rice surface ag:Cereals literal ag:Sorghum price literal DO Fig. 1. Domain Ontology built starting from LO1 and LO2 . ontology DO. This agreement is also an ontology composed by the anchored con- cepts of LO with their anchor, as well as the local relationships between them. Conciliation. Once the mediator has found an agreement with each partner on the concepts which must be part to the domain ontology, it applies a conciliation phase at the end of which the domain ontology is built. This is an incrementaly phase, the local ontologies are conciliated by integrating their agreement into the domain ontology DO, one after another. To achieve efficiently this phase, (i) the mediator ontology is partitioned into blocks, according to Falcon-AO method [4] and (ii) conflict resolution strategies are applied. Each block is a sub-ontology of M O containing semantically close concepts. Our algorithm relies on this clas- siffication in order to find links that exist between the concepts already present in the domain ontology and those of the new local ontology to conciliate. References 1. Zharko Aleksovski, Warner ten Kate, and Frank van Harmelen. Exploiting the struc- ture of background knowledge used in ontology matching. In Ontology Matching, 2006. 2. Loris Bozzato, Mauro Ferrari, and Alberto Trombetta. Building a domain ontology from glossaries: A general methodology. In SWAP, 2008. 3. Jérôme Euzenat and Pavel Shvaiko. Ontology matching. Springer, 2007. 4. Wei Hu, Yuanyuan Zhao, and Yuzhong Qu. Partition-based block matching of large class hierarchies. In ASWC, pages 72–83, 2006. 5. Yannis Kalfoglou and Marco Schorlemmer. Ontology mapping: The state of the art. The Knowledge Engineering Review, 18:2003, 2003. 6. Pavel Shvaiko and Jerome Euzenat. Ten challenges for ontology matching. In Proceedings of The 7th International Conference on Ontologies, DataBases, and Applications of Semantics (ODBASE), 2008. 7. Jie Yang, Lei Wang, Song Zhang, Xin Sui, Ning Zhang 0003, and Zhuoqun Xu. Building domain ontology based on web data and generic ontology. In Web Intelli- gence, pages 686–689, 2004.