Deciding which vocabulary terms to use when modeling data as Linked Open Data (LOD) is far from trivial. Choosing too general vocabulary terms, or terms from vocabularies that are not used by other LOD datasets, is likely to lead to a data representation, which will be harder to understand by humans and to be consumed by Linked data applications. In this technical report, we propose TermPicker: a novel approach for vocabulary reuse by recommending RDF types and properties based on exploiting the information on how other data providers on the LOD cloud use RDF types and properties to describe their data. To this end, we introduce the notion of so-called schema-level patterns (SLPs). They capture how sets of RDF types are connected via sets of properties within some data collection, e.g., within a dataset on the LOD cloud. TermPicker uses such SLPs and generates a ranked list of vocabulary terms for reuse. The lists of recommended terms are ordered by a ranking model which is computed using the machine learning approach Learning To Rank (L2R). TermPicker is evaluated based on the recommendation quality that is measured using the Mean Average Precision (MAP) and the Mean Reciprocal Rank at the first five positions (MRR@5). Our results illustrate an improvement of the recommendation quality by 29 - 36 when using SLPs compared to the beforehand investigated baselines of recommending solely popular vocabulary terms or terms from the same vocabulary. The overall best results are achieved using SLPs in conjunction with the Learning To Rank algorithm Random Forests.
Again, the input for these recommendation services is a single string or a set of strings specifying a vocabulary term or a domain of interest. Whereas these services provide recommendations based on string analyzes, they do not exploit any structural information on how vocabulary terms are connected to each other. In contrast, Falcons' Ontology Search @cite_26 provides the engineer with such information. Compared to traditional ontology matching approach, which align ontologies based on , the authors of Falcons' Ontology Search use different kinds of relatedness, in order to identify which vocabulary terms might express similar semantics. However, it is mainly designed to establish a general relatedness between vocabularies specifying that different vocabularies contain terms that describe similar data. Thus, it does not investigate how data providers on the LOD cloud use vocabulary terms to describe their data and individual relations as it is done by TermPicker.
@cite_26
110961551
When thousands of vocabularies having been published on the SemanticWeb by various authorities, a question arises as to how they are related to each other. Existing work has mainly analyzed their similarity. In this paper, we inspect the more general notion of relatedness, and characterize it from four angles: well-defined semantic relatedness, lexical similarity in contents, closeness in expressivity and distributional relatedness. We present an empirical study of these measures on a large, real data set containing 2,996 vocabularies, and 15 million RDF documents that use them. Then, we propose to apply vocabulary relatedness to the problem of post-selection vocabulary recommendation. We implement such a recommender service as part of a vocabulary search engine, and test its effectiveness against a handcrafted gold standard.