Johann Schaible

LOVER: support for modeling data using linked open vocabularies

Various best practices and principles are provided to guide an ontology engineer when modeling Linked Data. The choice of appropriate vocabularies is one essential aspect in the guidelines, as it leads to better interpretation, querying, and consumption of the data by Linked Data applications and users. In this paper, we propose LOVER: a novel approach to support the ontology engineer in modeling a Linked Data dataset. We illustrate the concept of LOVER, which supports the engineer by recommending appropriate classes and properties from existing and actively used vocabularies. The recommendations are made on the basis of on an iterative multimodal search. It uses different, orthogonal information sources for finding vocabulary terms, e.g. based on a best string match or schema information on other datasets published in the Linked Open Data cloud. We describe LOVERs recommendation mechanism in general and illustrate it along a real-life example from the social sciences domain.

TheSoz: A SKOS representation of the thesaurus for the social sciences

The Thesaurus for the Social Sciences TheSoz is a Linked Dataset in SKOS format, which serves as a crucial instrument for information retrieval based on e.g. document indexing or search term recommendation. Thesauri and similar controlled vocabularies build a linking bridge for datasets from the Linked Open Data cloud. In this article the conversion process of the TheSoz to SKOS is described including the analysis of the original dataset and its structure, the mapping to adequate SKOS classes and properties, and the technical conversion. In order to create a semantically full representation of TheSoz in SKOS, extensions based on SKOS-XL had to be defined. These allow the modeling of special relations like compound equivalences and terms with ambiguities. Additionally, mappings to other datasets and the appliance of the TheSoz are presented. Finally, limitations and modeling issues encountered during the creation process are discussed.

A novel combined term suggestion service for domain-specific digital libraries

Interactive query expansion can assist users during their query formulation process. We conducted a user study with over 4,000 unique visitors and four different design approaches for a search term suggestion service. As a basis for our evaluation we have implemented services which use three different vocabularies: (1) user search terms, (2) terms from a terminology service and (3) thesaurus terms. Additionally, we have created a new combined service which utilizes thesaurus term and terms from a domain-specific search term recommender. Our results show that the thesaurus-based method clearly is used more often compared to the other single-method implementations. We interpret this as a strong indicator that term suggestion mechanisms should be domainspecific to be close to the user terminology. Our novel combined approach which interconnects a thesaurus service with additional statistical relations outperformed all other implementations. All our observations show that domainspecific vocabulary can support the user in finding alternative concepts and formulating queries.

Survey on Common Strategies of Vocabulary Reuse in Linked Open Data Modeling

The choice of which vocabulary to reuse when modeling and publishing Linked Open Data (LOD) is far from trivial. There is no study that investigates the different strategies of reusing vocabularies for LOD modeling and publishing. In this paper, we present the results of a survey with 79 participants that examines the most preferred vocabulary reuse strategies of LOD modeling. The participants, LOD publishers and practitioners, were asked to assess different vocabulary reuse strategies and explain their ranking decision. We found significant differences between the modeling strategies that range from reusing popular vocabularies, minimizing the number of vocabularies, and staying within one domain vocabulary. A very interesting insight is that the popularity in the meaning of how frequent a vocabulary is used in a data source is more important than how often individual classes and properties are used in the LOD cloud. Overall, the results of this survey help in better understanding the strategies how data engineers reuse vocabularies and may also be used to develop future vocabulary engineering tools.

TermPicker: Enabling the Reuse of Vocabulary Terms by Exploiting Data from the Linked Open Data Cloud

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.

Comparing Vocabulary Term Recommendations Using Association Rules and Learning to Rank: A User Study

When modeling Linked Open Data LOD, reusing appropriate vocabulary terms to represent the data is difficult, because there are many vocabularies to choose from. Vocabulary term recommendations could alleviate this situation. We present a user study evaluating a vocabulary term recommendation service that is based on how other data providers have used RDF classes and properties in the LOD cloud. Our study compares the machine learning technique Learning to Rank L2R, the classical data mining approach Association Rule mining AR, and a baseline that does not provide any recommendations. Results show that utilizing AR, participants needed less time and less effort to model the data, which in the end resulted in models of better quality.

Applying Linked Data Technologies in the Social Sciences

In recent years, Linked Open Data (LOD) has matured and gained acceptance across various communities and domains. Large potential of Linked Data technologies is seen for an application in scientific disciplines. In this article, we present use cases and applications for an application of Linked Data in the social sciences. They focus on (a) interlinking domain-specific information, and (b) linking social science data to external LOD sources (e.g. authority data) from other domains. However, several technical and research challenges arise, when applying Linked Data technologies to a scientific domain with its specific data, information needs and use cases. We discuss these challenges and show how they can be addressed.