SKOS and semantic web best practice to access terminological resources: NatureSDIPlus and CHRONIOUS hand-on experience

Editor's Notes

• #20: Check out:http://www.slideshare.net/jordanhatcher/linked-data-licensing-introduction-isemantics-2010
• #27: The interlinking procedure among KOS aims at harmonizes the use of their terms when the terms are referring to the same concepts in more than one thesaurus. It enables the access to the same concept from a multicultural point of view allowing to move from a KOS to another. The interlinking is not aimed to connect all KOS to each others, but connections between KOS should be created only where they are useful and sound. The interlinking is one of the hot topic addressed by the semantic web/linked data research community. As far as we know there are no consolidated practices, but different strategies are employed according to the characteristics of the resources that have to be interlinked. These strategies can be roughly grouped as follows: Strategy A: “Exploitation of automatic tools”. The idea behind these tools is to compare concepts belonging to distinct KOS assessing their similarity, and then they link the concepts whose similarity is higher than a given threshold. Different similarity measures can be employed considering the concept definition, their broader, narrow, related concepts. In particular, as it recently pointed out by (Heath et al, 2009), two research frameworks have been developed by the linked data community so far: SILK (Volz et al., 2009) and LinQL (Hassanzadeh et al., 2009). The Silk framework works against local and remote SPARQL endpoints and it is designed to be employed in distributed environments without having to replicate data sets locally. The LinQL framework works over relational databases and it is designed to be used together with database to RDF mapping tools such as D2R Server tools. Strategy B: “Exploitation of a priori knowledge”. Very often KOS have been created by common origins or they have been built including part of other pre-existing resources. Knowledge about these interrelations can be crucial to link different KOS, since it suggests a good starting point to find comparable concepts. It is even more effective when they refer to generally accepted naming schemata. For instance, in the domain of Nature Conservation different agencies have produced data referring to habitat classification as NATURA 2000 A I. If the link source and the link target data sets already support one of these identification schemas, the implicit relationships between entities in data sets can easily be made explicit.Strategy C: “Exploitation of domain experts”. The interlinking can be defined manually by the domain experts. The management of this activity requires a huge effort. In particular, it is very tricky to reach a consensus especially when a large group of experts are involved. The process can result in a high quality mapping, but only if domain experts are very willing and knowledgeable. In order to reach a high quality, it is highly recommendable the involvement of experts who have originally defined the thesauri and classifications which are going to be mapped. All these strategies have been adopted to realise the interlinking within our Common Thesaurus Framework. In the first stage, we have considered SILK and LinQL as automatic tool to interlink the sub-thesauri according to the strategy A. The rationale behind these tools is very promising and interesting especially from a research point of view but they are available only as research prototypes (alpha/beta release) and they have not yet reached the technological maturity required for our purpose and to scale up the huge number of concepts that are provided in the framework. Since, the quest for this kind of tools came from a large set of applications and projects, we foresee that the aforementioned tools will be soon improved or brand new more exploitable and stable tools will be soon available. Anyway, the attempt of using these prototypic automatic tools has been very inspiring in order to outline the approach adopted in our activity. In particular, inspired by LinQL which work at the database level, we have combined the automatic and the a priori strategies (strategies A and B) to interlink habitats and species. Habitats in the EUNIS database were equipped with descriptions listing the species that they guest. Although different taxonomies about species exist, we assumed the species used in the habitat description were coherent with the species taxonomy because both the resources were provided by EUNIS. Exploiting such a priori knowledge and inspired by the approach followed by LinQL, we have developed a store procedure adding an link between a specie and a habitat whenever the species were mentioned in the habitat descriptions.The link between EARTh and Biogeographical regions has been obtained exploiting the domain experts (strategyC). We have exploited EARTh developers’ knowledge to find out a mapping between EARTh and the resources pertaining to the biogeographic regions. This activity was feasible because the moderate number of concepts contained in the biogeographical region (about 80) and the deep expert’s knowledge of EARTh.Finally, the strategy B has been deployed to interlink the EARTH and GEMET, as well as to map the EUNIS Species published in our framework to the official linked data EUNIS species dataset. In the former, EARTh is a significantly extension of GEMET [Plini et al., 2009], and it contains explicit references to the GEMET ID for the EARTh concepts that are in common with GEMET. Exploiting these references and the fact that GEMET has been published assigning URI derived by the original GEMET ID, it has been possible to materialize the links from EARTh to GEMET. In the latter, we have exposed as SKOS concepts the species and their whole taxonomic information (e.g., Kingdom, phylum, class, order, family and genus). After our exposing, EUNIS provided Species as linked data. Since we know how we exploited the internal species identifiers to provide an URI to each species we provided, we know the correspondence between the EUNIS official species and those we have provided. So we have been able to interlink these two datasets.
• #37: AGGIUNGERE void:uriRegexPattern ”http://linkeddata.ge.imati.cnr.it:2020/page/EARTh/d”
• #38: In the case of GEMET I was not able to find a URI set by the EEA which refers to GEMETSo we are forced to rely on homepage IFP to discover that EARTh is connected to GEMET
• #42: Resource selection. It is the phase that identifies and selects a list of the relevant resources, i.e., classifications, terminologies and thesauri. Criteria adopted in the selection such as relevance of resource content, issues concerning the copyright constraints and their availability as structured digital form will be introduced.Resource translation in SKOS (SKOSifycation) is the phase where the data model of the selected resources is mapped in the SKOS model. The deployed strategies will be described considering that further RDF vocabularies might be also considered to complement the SKOS model whenever information relevant for the final application does not completely fit the SKOS Model;KOS PublicationAccess is fundamental to make the SKOSsifyied KOS available to third parties. The strategies exploited in the two mentioned projects will be presented. In particular we will detail the Linked Data Best Practices echnologies adopted in NatureSDIPlus and the JAVA API developed to grant the access in CHONIOUS. Knowledge organization systems interlinking: Some specific mappings among KOS, and between KOS and Ontologies, have been defined in the projects relying on a-priori knowledge (e.g., Knowledge about how KOS have been originated) andor developing appropriate semi-automatic procedure. The interlinking procedure and their results will be shown in the presentation.KOS advertisement: Once KOS have been interlinked and stable versions have been reached, specific strategies to advertise them in related communities should be foreseen. In this direction there are different actions that can be undertaken to advertise the KOS availability. The actions as registering KOS in existing websites, producing VOID descriptions, submitting the KOS to semantic web search engines as SINDICE will be briefly introduced as strategies that we are going to be deployed.