Victor de Boer

Interactive vocabulary alignment

In many heritage institutes, objects are routinely described using terms from predefined vocabularies. When object collections need to be merged or linked, the question arises how those vocabularies relate. In practice it often unclear for data providers how well alignment tools will perform on their specific vocabularies. This creates a bottleneck to align vocabularies, as data providers want to have tight control over the quality of their data. We will discuss the key limitations of current tools in more detail and propose an alternative approach. We will show how this approach has been used in two alignment use cases, and demonstrate how it is currently supported by our Amalgame alignment platform.

Amsterdam Museum Linked Open Data

In this document we describe the Amsterdam Museum Linked Open Data set. The dataset is a five-star Linked Data representation and comprises the entire collection of the Amsterdam Museum consisting of more than 70,000 object descriptions. Furthermore, the institutions thesaurus and person authority files used in the object metadata are included in the Linked Data set. The data is mapped to the Europeana Data Model, utilizing Dublin Core, SKOS, RDA-group2 elements and the OAI-ORE model to represent the museum data. Vocabulary concepts are mapped to GeoNames and DBpedia. The two main contributions of this dataset are the inclusion of internal vocabularies and the fact that the complexity of the original dataset is retained.

RadioMarché: distributed voice- and web-interfaced market information systems under rural conditions

Despite its tremendous success, the World Wide Web is still inaccessible to 4.5 billion people - mainly in developing countries - who lack a proper internet infrastructure, a reliable power supply, and often the ability to read and write. Hence, alternative or complementary technologies are needed to make the Web accessible to all, given the limiting conditions. These technologies must serve a large audience, who then may start contributing to the Web by creating content and services. In this paper we propose RadioMarche, a voice- and web-based market information system aimed at stimulating agricultural trade in Sahel countries. To overcome interfacing and infrastructural issues, RadioMarche has a mobile-voice interface and is easy to deploy. Furthermore, we will show how data from regionally distributed instances of RadioMarche, can be aggregated and exposed using Linked Data approaches, so that new opportunities for product and service innovation in agriculture and other domains can be unleashed.

Dutch Ships and Sailors Linked Data

We present the Dutch Ships and Sailors Linked Data Cloud. This heterogeneous dataset brings together four curated datasets on Dutch Maritime history as five-star linked data. The individual datasets use separate datamodels, designed in close collaboration with maritime historical researchers. The individual models are mapped to a common interoperability layer, allowing for analysis of the data on the general level. We present the datasets, modeling decisions, internal links and links to external data sources. We show ways of accessing the data and present a number of examples of how the dataset can be used for historical research. The Dutch Ships and Sailors Linked Data Cloud is a potential hub dataset for digital history research and a prime example of the benefits of Linked Data for this field.

The BigDataEurope Platform – Supporting the Variety Dimension of Big Data

The management and analysis of large-scale datasets – described with the term Big Data – involves the three classic dimensions volume, velocity and variety. While the former two are well supported by a plethora of software components, the variety dimension is still rather neglected. We present the BDE platform – an easy-to-deploy, easy-to-use and adaptable (cluster-based and standalone) platform for the execution of big data components and tools like Hadoop, Spark, Flink, Flume and Cassandra. The BDE platform was designed based upon the requirements gathered from seven of the societal challenges put forward by the European Commission in the Horizon 2020 programme and targeted by the BigDataEurope pilots. As a result, the BDE platform allows to perform a variety of Big Data flow tasks like message passing, storage, analysis or publishing. To facilitate the processing of heterogeneous data, a particular innovation of the platform is the Semantic Layer, which allows to directly process RDF data and to map and transform arbitrary data into RDF. The advantages of the BDE platform are demonstrated through seven pilots, each focusing on a major societal challenge.

The knowledge graph as the default data model for learning on heterogeneous knowledge

In modern machine learning, raw data is the pre-ferred input for our models. Where a decade ago data scien-tists were still engineering features, manually picking out the details they thought salient, they now prefer the data in their raw form. As long as we can assume that all relevant and ir-relevant information is present in the input data, we can de-sign deep models that build up intermediate representations to sift out relevant features. However, these models are often domain specific and tailored to the task at hand, and therefore unsuited for learning on heterogeneous knowledge: informa-tion of different types and from different domains. If we can develop methods that operate on this form of knowledge, we can dispense with a great deal of ad-hoc feature engineering and train deep models end-to-end in many more domains. To accomplish this, we first need a data model capable of ex-pressing heterogeneous knowledge naturally in various do-mains, in as usable a form as possible, and satisfying as many use cases as possible. In this position paper, we argue that the knowledge graph is a suitable candidate for this data model. This paper describes current research and discusses some of the promises and challenges of this approach.

Supporting Exploration of Historical Perspectives Across Collections

The ever growing number of textual historical collections calls for methods that can meaningfully connect and explore these. Different collections offer different perspectives, expressing views at the time of writing or even a subjective view of the author. We propose to connect heterogeneous digital collections through temporal references found in documents as well as their textual content. We evaluate our approach and find that it works very well on digital-native collections. Digitized collections pose interesting challenges and with improved preprocessing our approach performs well. We introduce a novel search interface to explore and analyze the connected collections that highlights different perspectives and requires little domain knowledge. In our approach, perspectives are expressed as complex queries. Our approach supports humanity scholars in exploring collections in a novel way and allows for digital collections to be more accessible by adding new connections and new means to access collections.

Voice-based web access in rural Africa

Despite its tremendous success, the World Wide Web can still not used by large parts of the worlds population. Therefore, many people, especially in rural areas of developing countries, still do not have access to services and information that are available as a result of the World Wide Web. Given the potential of the Web in improving peoples lives, a question is how it can be expanded to serve those living in less privileged conditions. Information must then be reachable regardless of infrastructure, allowing access using also interfaces such as radio and mobile phone. There is widespread use and adoption of radio and mobile telephony in Africa and thus, innovative use of these technologies could help in expanding the reach of the Web. In this paper we present three systems, based on open Web standards, designed and built to fit conditions in remote rural regions in Africa namely, a voice-based (i) trading system, using phone and radio as its interfaces, (ii) a voice-web based interactive news and blogging system and (iii) messaging system. The systems have been developed and have been deployed in Mali. All three systems together showcase the importance that innovation plays in order to make Web technologies relevant in the lives of many rural dwellers in Africa. We show the current status and usage of the systems and discuss how these systems represent our steps into bringing the Web to these contexts.

A dialogue with linked data: Voice-based access to market data in the Sahel

The Linked Data movement has facilitated efficient data sharing in many domains. However, people in rural developing areas are mostly left out. Lack of relevant content and suitable interfaces prohibit potential users in rural communities to produce and consume Linked Data. In this paper, we present a case study exposing locally produced market data as Linked Data, which shows that Linked Data can be meaningful in a rural, development context. We present a way of enriching the market data with voice labels, allowing for the development of applications that (re-)use the data in voice-based applications. Finally, we present a prototype demonstrator that provides access to this linked market data through a voice interface, accessible to first generation mobile phones.

The MIDI Linked Data Cloud

The study of music is highly interdisciplinary, and thus requires the combination of datasets from multiple musical domains, such as catalog metadata (authors, song titles, dates), industrial records (labels, producers, sales), and music notation (scores). While today an abundance of music metadata exists on the Linked Open Data cloud, linked datasets containing interoperable symbolic descriptions of music itself, i.e. music notation with note and instrument level information, are scarce. In this paper, we describe the MIDI Linked Data Cloud dataset, which represents multiple collections of digital music in the MIDI standard format as Linked Data using the novel midi2rdf algorithm. At the time of writing, our proposed dataset comprises 10,215,557,355 triples of 308,443 interconnected MIDI files, and provides Web-compatible descriptions of their MIDI events. We provide a comprehensive description of the dataset, and reflect on its applications for research in the Semantic Web and Music Information Retrieval communities.