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Many museums are currently providing online access to their collections. The state of the art research in the last decade shows that it is beneficial for institutions to provide their datasets as Linked Data in order to achieve easy cross-referencing, interlinking and integration. In this paper, we present the Rijksmuseum linked dataset (accessible at http://datahub.io/dataset/rijksmuseum), along with collection and vocabulary statistics, as well as lessons learned from the process of converting the collection to Linked Data. The version of March 2016 contains over 350,000 objects, including detailed descriptions and high-quality images released under a public domain license.

An increasing number of cultural heritage institutions publish data online. Ontologies can be used to structure published data, thereby increasing interoperability. To achieve widespread adoption of ontologies, institutions such as libraries, archives and museums have to be able to assess whether an ontology can adequately capture information about their artifacts. We identify six requirements that should be met by ontologies in the cultural heritage domain, based upon modeling challenges encountered while publishing data of the Rijksmuseum Amsterdam and challenges observed in related work. These challenges regard specialization, object- and event-centric approaches, temporality, representations, views and subject matter. For each challenge, we investigate common modeling approaches, by discussing two models regularly used in the museum sector: the CIDOC Conceptual Reference Model and the Europeana Data Model. The outlined approaches and requirements provide insights for data modeling practices reaching beyond the cultural heritage sector.

An increasing number of cultural heritage institutions publish data online. Ontologies can be used to structure published data, thereby increasing interoperability. To achieve widespread adoption of ontologies, institutions such as libraries, archives and museums have to be able to assess whether an ontology can adequately capture information about their artifacts. We identify six requirements that should be met by ontologies in the cultural heritage domain, based upon modeling challenges encountered while publishing data of the Rijksmuseum Amsterdam and challenges observed in related work. These challenges regard specialization, object- and event-centric approaches, temporality, representations, views and subject matter. For each challenge, we investigate common modeling approaches, by discussing two models regularly used in the museum sector: the CIDOC Conceptual Reference Model and the Europeana Data Model. The outlined approaches and requirements provide insights for data modeling practices reaching beyond the cultural heritage sector.

Many museums are currently providing online access to their collections. The state of the art research in the last decade shows that it is beneficial for institutions to provide their datasets as Linked Data in order to achieve easy cross-referencing, interlinking and integration. In this paper, we present the Rijksmuseum linked dataset (accessible at http://datahub.io/dataset/rijksmuseum), along with collection and vocabulary statistics, as well as lessons learned from the process of converting the collection to Linked Data. The version of March 2016 contains over 350,000 objects, including detailed descriptions and high-quality images released under a public domain license.

Games with a purpose (GWAPs) are increasingly used in audio-visual collections as a mechanism for annotating videos through tagging. One such GWAP is Waisda?, a video labeling game where players tag streaming video and win points by reaching consensus on tags with other players. The open-ended and unconstrained manner of tagging in the fast-paced setting of the game has fundamental impact on the resulting tags. We find that Waisda? tags predominately describe visual objects and rarely refer to the topics of the videos. In this study we evaluate to what extent the tags entered by players can be regarded as topical descriptors of the video material. Moreover, we characterize the quality of the user tags as topical descriptors with the aim to detect and filter out the bad ones. Our results show that after filtering, game tags perform equally well compared to the manually crafted metadata when it comes to accessing the videos based on topic. An important consequence of this finding is that tagging games can provide a cost-effective alternative in situations when manual annotation by professionals is too costly.

Games with a purpose (GWAPs) are increasingly used in audio-visual collections as a mechanism for annotating videos through tagging. One such GWAP is Waisda?, a video labeling game where players tag streaming video and win points by reaching consensus on tags with other players. The open-ended and unconstrained manner of tagging in the fast-paced setting of the game has fundamental impact on the resulting tags. We find that Waisda? tags predominately describe visual objects and rarely refer to the topics of the videos. In this study we evaluate to what extent the tags entered by players can be regarded as topical descriptors of the video material. Moreover, we characterize the quality of the user tags as topical descriptors with the aim to detect and filter out the bad ones. Our results show that after filtering, game tags perform equally well compared to the manually crafted metadata when it comes to accessing the videos based on topic. An important consequence of this finding is that tagging games can provide a cost-effective alternative in situations when manual annotation by professionals is too costly.

The results of our exploratory study provide new insights to crowdsourcing knowledge intensive tasks. We designed and performed an annotation task on a print collection of the Rijksmuseum Amsterdam, involving experts and crowd workers in the domain-specific description of depicted flowers. We created a testbed to collect annotations from flower experts and crowd workers and analyzed these in regard to user agreement. The findings show promising results, demonstrating how, for given categories, nichesourcing can provide useful annotations by connecting crowdsourcing to domain expertise.

The results of our exploratory study provide new insights to crowdsourcing knowledge intensive tasks. We designed and performed an annotation task on a print collection of the Rijksmuseum Amsterdam, involving experts and crowd workers in the domain-specific description of depicted flowers. We created a testbed to collect annotations from flower experts and crowd workers and analyzed these in regard to user agreement. The findings show promising results, demonstrating how, for given categories, nichesourcing can provide useful annotations by connecting crowdsourcing to domain expertise.

Diversity and profundity of the topics in cultural heritage collections make experts from outside the institution indispensable for acquiring qualitative and comprehensive annotations. We define the concept of nichesourcing and present challenges in the process of obtaining qualitative annotations from people in these niches. We believe that experts provide better annotations if this process is personalized. We present a framework called Accurator, that allows to realize and evaluate strategies and applications for personalized nichesourcing.

Diversity and profundity of the topics in cultural heritage collections make experts from outside the institution indispensable for acquiring qualitative and comprehensive annotations. We define the concept of nichesourcing and present challenges in the process of obtaining qualitative annotations from people in these niches. We believe that experts provide better annotations if this process is personalized. We present a framework called Accurator, that allows to realize and evaluate strategies and applications for personalized nichesourcing.

This volume contains the papers presented at the 1st International Workshop on ”Crowdsourcing the Semantic Web” that was held in conjunction with the 12th International Semantic Web Conference (ISWC 2013), 21-25 October 2013, in Sydney, Australia. This interactive workshop takes stock of the emergent work and chart the research agenda with interactive sessions to brainstorm ideas and potential applications of collective intelligence to solving AI hard semantic web problems.

Most digitised and online available objects from GLAMs (Galleries, Libraries, Archives, Museums) can be browsed through a predefined set of formal metadata, such as its creator, year of creation, and type of material. Standards for metadata management and exchange have matured and are being adopted widely. They enable intra-collection search and exploration, and are also main drivers behind supporting domain and cross-boundary access to collections. However, these formal metadata often do not give access to information pertaining to the content of the object, such as its topic, or what is depicted. This information is often given through textual descriptions which are mostly only accessible through keyword search. Keyword search is limited in the sense that it does not facilitate sorting, or retrieving objects whose descriptions contain terms that are synonymous to the search term. This paper provides results of an interdisciplinary research project, Agora, that is taking collection access one step further by enabling users to search and browse museum collections through the content descriptions of objects in a structured way. The three-year Agora project is funded by the Netherlands Organisation for Scientific Research and brings together computer scientists, cultural heritage experts, and humanities researchers.

Recently, various crowdsourcing initiatives showed that targeted efforts of user communities result in massive amou

Most digitised and online available objects from GLAMs (Galleries, Libraries, Archives, Museums) can be browsed through a predefined set of formal metadata, such as its creator, year of creation, and type of material. Standards for metadata management and exchange have matured and are being adopted widely. They enable intra-collection search and exploration, and are also main drivers behind supporting domain and cross-boundary access to collections. However, these formal metadata often do not give access to information pertaining to the content of the object, such as its topic, or what is depicted. This information is often given through textual descriptions which are mostly only accessible through keyword search. Keyword search is limited in the sense that it does not facilitate sorting, or retrieving objects whose descriptions contain terms that are synonymous to the search term. This paper provides results of an interdisciplinary research project, Agora, that is taking collection access one step further by enabling users to search and browse museum collections through the content descriptions of objects in a structured way. The three-year Agora project is funded by the Netherlands Organisation for Scientific Research and brings together computer scientists, cultural heritage experts, and humanities researchers.

Recently, various crowdsourcing initiatives showed that targeted efforts of user communities result in massive amounts of tags. For example, the Netherlands Institute for Sound and Vision collected a large number of tags with the video labeling game \emph{Waisda?}. To successfully utilize these tags, a better understanding of their characteristics is required. The goal of this paper is twofold: (i) to investigate the vocabulary that users employ when describing videos and compare it to the vocabularies used by professionals; and (ii) to establish which aspects of the video are typically described and what type of tags are used for this. We report on an analysis of the tags collected with \emph{Waisda?}. With respect to the first goal, we compared the the tags with a typical domain thesaurus used by professionals, as well as with a more general vocabulary. With respect to the second goal, we compare the tags to the video subtitles to determine how many tags are derived from the audio signal. In addition, we perform a qualitative study in which a tag sample is interpreted in terms of an existing annotation classification framework. The results suggest that the tags complement the metadata provided by professional cataloguers, the tags describe both the audio and the visual aspects of the video, and the users primarily describe objects in the video using general descriptions.

The goal of the paper is assessing the quality of end-user tags from a video labeling game as a first step in the process of integrating them with the annotations made by professionals. Tags lack precise meaning, whereas the terms and concepts the professionals are used to have a clearly defined semantics given by structured vocabularies. Hence, we explore the possibility of mapping user tags to their semantic counterparts from domain and lexical vocabularies. Furthermore, we report on an analysis performed by a senior cataloguer regarding the general usefulness of the user tags in terms of video and fragment retrieval. Finally, we investigate the distribution of tags with respect to audio and visual portion of the video content.

The goal of the paper is assessing the quality of end-user tags from a video labeling game as a first step in the process of integrating them with the annotations made by professionals. Tags lack precise meaning, whereas the terms and concepts the professionals are used to have a clearly defined semantics given by structured vocabularies. Hence, we explore the possibility of mapping user tags to their semantic counterparts from domain and lexical vocabularies. Furthermore, we report on an analysis performed by a senior cataloguer regarding the general usefulness of the user tags in terms of video and fragment retrieval. Finally, we investigate the distribution of tags with respect to audio and visual portion of the video content.

Cultural heritage involves rich and highly heterogeneous collections of different people, organizations and collections. Preserved mainly by professionals it is challenging to convey this diversity of perspectives and information to the general public. Professionals also experience a great deal of obstacles archiving digital collections. This special issue presents current trends in employing AI and Web technologies to overcome such problems.

Cultural heritage involves rich and highly heterogeneous collections of different people, organizations and collections. Preserved mainly by professionals it is challenging to convey this diversity of perspectives and information to the general public. Professionals also experience a great deal of obstacles archiving digital collections. This special issue presents current trends in employing AI and Web technologies to overcome such problems.

This paper discusses accuracy in processing ratings of and recommendations for item features. Such processing facilitates featurebased user navigation in recommender system interfaces. Item features, often in the form of tags, categories or meta-data, are becoming important hypertext components of recommender interfaces. Recommending features would help unfamiliar users navigate in such environments. This work explores techniques for improving feature recommendation accuracy. Conversely, it also examines possibilities for processing user ratings of features to improve recommendation of both features and items. This work’s illustrative implementation is a web portal for a museum collection that lets users browse, rate and receive recommendations for both artworks and interrelated topics about them. Accuracy measurements compare proposed techniques for processing feature ratings and recommending features. Resulting techniques recommend features with relative accuracy. Analysis indicates that processing ratings of either features or items does not improve accuracy of recommending the other.

Web 2.0 enables increased access to the museum digital collection. More and more, users will spend time preparing their visits to the museums and reflecting on them after the visits. In this context, the CHIP (Cultural Heritage Information Personalization) project offers tools to the users to be their own curator, e.g. planning a personalized museum tour, discovering interesting artworks they want to see in a 'virtual' or a 'real' tour and quickly finding their ways in the museum. In this paper we present the new additions to the CHIP tools, which target the above functionality - a Web-based Tour Preparation Wizard and an export of a personalized tour to an interactive Mobile Guide used in the physical museum space. In addition, the user interactions during a real museum visit are stored and synchronized with the user model, which is maintained at the museum Web site.

Web 2.0 enables increased access to the museum digital collection. More and more, users will spend time preparing their visits to the museums and reflecting on them after the visits. In this context, the CHIP (Cultural Heritage Information Personalization) project offers tools to the users to be their own curator, e.g. planning a personalized museum tour, discovering interesting artworks they want to see in a 'virtual' or a 'real' tour and quickly finding their ways in the museum. In this paper we present the new additions to the CHIP tools, which target the above functionality - a Web-based Tour Preparation Wizard and an export of a personalized tour to an interactive Mobile Guide used in the physical museum space. In addition, the user interactions during a real museum visit are stored and synchronized with the user model, which is maintained at the museum Web site.

This article presents the CHIP demonstrator1 for providing personalized access to digital museum collections. It consists of three main components: Art Recommender, Tour Wizard, and Mobile Tour Guide. Based on the semantically enriched Rijksmuseum Amsterdam2 collection, we show how Semantic Web technologies can be deployed to (partially) solve three important challenges for recommender systems applied in an open Web context: (1) to deal with the complexity of various types of relationships for recommendation inferencing, where we take a content-based approach to recommend both artworks and art-history topics; (2) to cope with the typical user modeling problems, such as cold-start for first-time users, sparsity in terms of user ratings, and the efficiency of user feedback collection; and (3) to support the presentation of recommendations by combining different views like a historical timeline, museum map and faceted browser. Following a user-centered design cycle, we have performed two evaluations with users to test the effectiveness of the recommendation strategy and to compare the different ways for building an optimal user profile for efficient recommendations. The CHIP demonstrator received the Semantic Web Challenge Award (third prize) in 2007, Busan, Korea.

In this article we describe a Semantic Web application for semantic annotation and search in large virtual collections of cultural-heritage objects, indexed with multiple vocabularies. During the annotation phase we harvest, enrich and align collection metadata and vocabularies. The semantic-search facilities support keyword-based queries of the graph (currently 20 M triples), resulting in semantically grouped result clusters, all representing potential semantic matches of the original query. We show two sample search scenario’s. The annotation and search software is open source and is already being used by third parties. All software is based on established Web standards, in particular HTML/XML, CSS, RDF/OWL, SPARQL and JavaScript.

Web 2.0 enables increased access to the museum digital collection. More and more, users will spend time preparing their visits to the museums and reflecting on them after the visits. In this context, the CHIP (Cultural Heritage Information Personalization) project offers tools to the users to be their own curator, e.g. planning a personalized museum tour, discovering interesting artworks they want to see in a 'virtual' or a 'real' tour and quickly finding their ways in the museum. In this paper we present the new additions to the CHIP tools, which target the above functionality - a Web-based Tour Preparation Wizard and an export of a personalized tour to an interactive Mobile Guide used in the physical museum space. In addition, the user interactions during a real museum visit are stored and synchronized with the user model, which is maintained at the museum Web site.

This paper discusses accuracy in processing ratings of and recommendations for item features. Such processing facilitates featurebased user navigation in recommender system interfaces. Item features, often in the form of tags, categories or meta-data, are becoming important hypertext components of recommender interfaces. Recommending features would help unfamiliar users navigate in such environments. This work explores techniques for improving feature recommendation accuracy. Conversely, it also examines possibilities for processing user ratings of features to improve recommendation of both features and items. This work’s illustrative implementation is a web portal for a museum collection that lets users browse, rate and receive recommendations for both artworks and interrelated topics about them. Accuracy measurements compare proposed techniques for processing feature ratings and recommending features. Resulting techniques recommend features with relative accuracy. Analysis indicates that processing ratings of either features or items does not improve accuracy of recommending the other.

In this paper we present the CHIP demonstrator aimed at helping users to explore the Rijksmuseum Amsterdam collection both online and inside the museum. Cultural heritage data from various external sources is integrated to provide an enriched semantic knowledge structure. The resulting RDF/OWL graph is the basis for CHIP main functionality for recommendations, search and personalized interaction.

Web 2.0 enables increased access to the museum digital collection. More and more, users will spend time preparing their visits to the museums and reflecting on them after the visits. In this context, the CHIP (Cultural Heritage Information Personalization) project offers tools to the users to be their own curator, e.g. planning a personalized museum tour, discovering interesting artworks they want to see in a 'virtual' or a 'real' tour and quickly finding their ways in the museum. In this paper we present the new additions to the CHIP tools, which target the above functionality - a Web-based Tour Preparation Wizard and an export of a personalized tour to an interactive Mobile Guide used in the physical museum space. In addition, the user interactions during a real museum visit are stored and synchronized with the user model, which is maintained at the museum Web site.

This article presents the CHIP demonstrator1 for providing personalized access to digital museum collections. It consists of three main components: Art Recommender, Tour Wizard, and Mobile Tour Guide. Based on the semantically enriched Rijksmuseum Amsterdam2 collection, we show how Semantic Web technologies can be deployed to (partially) solve three important challenges for recommender systems applied in an open Web context: (1) to deal with the complexity of various types of relationships for recommendation inferencing, where we take a content-based approach to recommend both artworks and art-history topics; (2) to cope with the typical user modeling problems, such as cold-start for first-time users, sparsity in terms of user ratings, and the efficiency of user feedback collection; and (3) to support the presentation of recommendations by combining different views like a historical timeline, museum map and faceted browser. Following a user-centered design cycle, we have performed two evaluations with users to test the effectiveness of the recommendation strategy and to compare the different ways for building an optimal user profile for efficient recommendations. The CHIP demonstrator received the Semantic Web Challenge Award (third prize) in 2007, Busan, Korea.

In this article we describe a Semantic Web application for semantic annotation and search in large virtual collections of cultural-heritage objects, indexed with multiple vocabularies. During the annotation phase we harvest, enrich and align collection metadata and vocabularies. The semantic-search facilities support keyword-based queries of the graph (currently 20 M triples), resulting in semantically grouped result clusters, all representing potential semantic matches of the original query. We show two sample search scenario’s. The annotation and search software is open source and is already being used by third parties. All software is based on established Web standards, in particular HTML/XML, CSS, RDF/OWL, SPARQL and JavaScript.

We have developed a set of small interactive throw pillows containing intelligent touch-sensing surfaces, in order to explore new ways to model the environment, participants, artefacts, and their interactions, in the context of expressive non-verbal interaction. We present the overall architecture of the environment, describing a model of the user, the interface (the interactive pillows and the devices it can interact with) and the context engine. We describe the representation and process modules of the context engine and demonstrate how they support real-time adaptation. We present an evaluation of the current prototype and conclude with plans for future work.

This paper describes ongoing work exploring aspects of personalized access to and presentation of virtual museum collections. The project demonstrator illustrates an interactive approach to collecting data about museum visitors in terms of their interests in and preferences about artefacts from the Rijksmuseum collection. This data is stored in user profiles used further to recommend routes through the museum and to guide the users towards artefacts related to their interests and preferences. The overall goal of the project is to explore different users' characteristics and personalize users' museum experiences within the Rijksmuseum virtual and physical collections.

This paper describes how the combination of recommendation and Semantic Web techniques can enhance user interaction with digital cultural heritage collections. The interface that this paper describes adapts user interaction to individual interest in both artworks and abstract concepts related to them. This paper presents an overview of several studies that evaluate a recommender system for museum collections. The findings and requirements derived from them form a basic framework for personalized exhibition tours that present multiple cultural artifacts as a structured and cohesive whole.

In this paper we present an approach for personalized access to museum collections. We use a RDF/OWL specification of the Rijksmuseum Amsterdam collections as a driver for an interactive dialog. The user gives his/her judgment on the artefacts, indicating likes or dislikes. The elicited user model is further used for generating recommendations of artefacts and topics. In this way we support exploration and discovery of information in museum collections. A user study provided insights in characteristics of our target user group, and showed how novice and expert users employ their background knowledge and implicit interest in order to elicit their art preference in the museum collections.

User-adaptive information filters can be a tool to achieve timely delivery of the right information to the right person, a feat critical in crisis management. This paper ex

Autocompletion is a commonly used interface feature in diverse applications. Semantic Web data has, on the one hand, the potential to provide new functionality by exploiting the semantics in the data used for generating autocompletion suggestions. Semantic Web applications, on the other hand, typically pose extra requirements on the semantic properties of the suggestions given. When the number of syntactic matches becomes too large, some means of selecting a semantically meaningful subset of suggestions to be presented to the user is needed. In this paper we identify a number of key design dimensions of autocompletion interface components. Our hypothesis is that a one-size-fits-all solution to autocompletion interface components does not exist, because different tasks and different data sets require interfaces corresponding to different points in our design space. We present a fully configurable architecture, which can be used to configure autocompletion components to the desired point in this design space. The architecture has been implemented as an open source software component that can be plugged into a variety of applications. We report on the results of a user evaluation that confirms this hypothesis, and describe the need to evaluate semantic autocompletion in a task and application-specific context.

In this paper we present an approach for personalized access to museum collections. We use a RDF/OWL specification of the Rijksmuseum Amsterdam collections as a driver for an interactive dialog. The user gives his/her judgment on the artefacts, indicating likes or dislikes. The elicited user model is further used for generating recommendations of artefacts and topics. In this way we support exploration and discovery of information in museum collections. A user study provided insights in characteristics of our target user group, and showed how novice and expert users employ their background knowledge and implicit interest in order to elicit their art preference in the museum collections.