M. Hildebrand

Supporting linked data production for cultural heritage institutes: the amsterdam museum case study

Within the cultural heritage field, proprietary metadata and vocabularies are being transformed into public Linked Data. These efforts have mostly been at the level of large-scale aggregators such as Europeana where the original data is abstracted to a common format and schema. Although this approach ensures a level of consistency and interoperability, the richness of the original data is lost in the process. In this paper, we present a transparent and interactive methodology for ingesting, converting and linking cultural heritage metadata into Linked Data. The methodology is designed to maintain the richness and detail of the original metadata. We introduce the XMLRDF conversion tool and describe how it is integrated in the ClioPatria semantic web toolkit. The methodology and the tools have been validated by converting the Amsterdam Museum metadata to a Linked Data version. In this way, the Amsterdam Museum became the first ‘small cultural heritage institution with a node in the Linked Data cloud.

Nichesourcing: harnessing the power of crowds of experts

In this position paper we identify nichesourcing, a specific form of human-based computation that harnesses the computational efforts from niche groups rather than the faceless crowd. We claim that nichesourcing combine the strengths of the crowd with those of professionals, optimizing the result of human-based computation for certain tasks. We illustrate our claim using scenarios in two domains: cultural heritage and regreening in Africa. The contribution of this paper is to provide a definition of the main characteristics of nichesourcing as a natural extension of crowdsourcing and to outline research challenges for realizing nichesourcing applications.

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.

Waisda?: video labeling game

The Waisda? video labeling game is a crowsourcing tool to collect user-generated metadata for video clips. It follows the paradigm of games-with-a-purpose, where two or more users play against each other by entering tags that describe the content of the video. Players score points by entering the same tags as one of the other players. As a result each video that is played in the game is annotated with tags that are anchored to a time point in the video. Waisda? has been deployed in two projects with videos from Dutch broadcasters. With the open source version of Waisda? crowdsourcing of video annotation becomes available for any online video collection.

Linking user generated video annotations to the web of data

In the audiovisual domain tagging games are explored as a method to collect user-generated metadata. For example, the Netherlands Institute for Sound and Vision deployed the video labelling game Waisda? to collect user tags for videos from their collection. These tags are potentially useful to improve the access to the content within the videos. However, the uncontrolled tags allow for multiple interpretations, preventing long term access. In this paper we investigate a semi-automatic process to define the interpretation of the tags by linking them to concepts from the Linked Open Data cloud. More specifically, we investigate if existing web services are suited to find a number of candidate concepts, and if human users can select the most appropriate concept from these suggestions. We present a prototype application that supports this process and discuss the results of a user experiment where this application is used with different data sources.

Interactive Agents Learning Their Environment

In this paper we describe the implementation of interactive agents capable of gathering and extending their knowledge. Interactive agents are designed to perform tasks requested by a user in natural language. Using simple sentences the agent can answer questions and in case a task can not be fulfilled the agent must communicate with the user. In particular, an interactive agent can tell when necessary information for a task is missing, giving the user a chance to supply this information, which may in effect result in teaching the agent. The interactive agent platform is implemented in DLP, a tool for the implementation of 3D web agents. In this paper we discuss the motivation for interactive agents, the learning mechanisms and it’s realization in the DLP platform.

An evaluation of labelling-game data for video retrieval

Games with a purpose (GWAPs) are increasingly used in audio-visual collections as a mechanism for annotating videos through tagging. This trend is driven by the assumption that user tags will improve video search. In this paper we study whether this is indeed the case. To this end, we create an evaluation dataset that consists of: (i) a set of videos tagged by users via video labelling game, (ii) a set of queries derived from real-life query logs, and (iii) relevance judgements. Besides user tags from the labelling game, we exploit the existing metadata associated with the videos (textual descriptions and curated in-house tags) and closed captions. Our findings show that search based on user tags alone outperforms search based on all other metadata types. Combining user tags with the other types of metadata yields an increase in search performance of 33%. We also find that the search performance of user tags steadily increases as more tags are collected.