Searching in the cultural heritage domain: capturing cultural heritage expert information seeking needs

We report the results of a user study that captures knowledge on how cultural heritage experts search for information. We use a qualitative study technique with participants from four cultural heritage institutions in the Netherlands who were interviewed and asked to answer questionnaires about their daily work. Our goal is to acquire knowledge of their information seeking needs and the information sources they use. The paper provides a

‘Give me a hug': the effects of touch and autonomy on people's responses to embodied social agents

Embodied social agents are programmed to display human-like social behaviour to increase intuitiveness of interacting with these agents. It is not yet clear to what extent people respond to agents’ social behaviours. One example is touch. Despite robots’ embodiment and increasing autonomy, the effect of communicative touch has been a mostly overlooked aspect of human-robot interaction. This video-based, 2x2 betweensubject survey experiment (N=119) found that the combination of touch and proactivity influenced whether people saw the robot as machine-like and dependable. Participants’ attitude towards robots in general also influenced perceived closeness between humans and robots. Results show that communicative touch is considered a more appropriate behaviour for proactive agents rather than reactive agents. Also, people that are generally more positive towards robots find robots that interact by touch less machine-like. These effects illustrate that careful consideration is necessary when incorporating social behaviours in agents’ physical interaction design

Understanding cultural heritage experts’ information seeking needs

We report on our user study on the information seeking behavior of cultural heritage experts and the sources they use to carry out search tasks. Seventeen experts from nine cultural heritage institutes in the Netherlands were interviewed and asked to answer questionnaires about their daily search activities. The interviews helped us to better understand their search motivations, types, sources and tools. A key finding of our study is that the majority of search tasks involve relatively complex information gathering. This is in contrast to the relatively simple fact-finding oriented support provided by current tools. We describe a number of strategies that experts have developed to overcome the inadequacies of their tools. Finally, based on the analysis, we derive general trends of cultural heritage experts’ information seeking needs and discuss our preliminary experiences with potential solutions

List, group or menu: Organizing Suggestions in Autocompletion Interfaces

We describe two user studies that investigate organization strategies of autocompletion in a known-item search task: searching for terms taken from a thesaurus. In Study 1, we explored ways of grouping term suggestions from two different thesauri (TGN and WordNet) and found that different thesauri may require different organization strategies. Users found Group organization more appropriate to organize location names from TGN, while Alphabetical works better for WordNet. In Study 2, we compared three different organization strategies (Alphabetical, Group and Composite) for location name search tasks. The results indicate that for TGN autocompletion interfaces help improve the quality of keywords, Group and Composite organization help users search faster, and is perceived easier to understand and to use than Alphabetical

The design space of a configurable autocompletion component

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

The design space of a configurable autocompletion component

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

MultimediaN E-Culture demonstrator

The main objective of the MultimediaN E-Culture project is to demonstrate how novel semantic-web and presentation technologies can be deployed to provide better indexing and search support within large virtual collections of cultural-heritage resources. The architecture is fully based on open web standards, in particular XML, SVG, RDF/OWL and SPARQL. One basic hypothesis underlying this work is that the use of explicit background knowledge in the form of ontologies/vocabularies/thesauri is in particular useful in information retrieval in knowledge-rich domains