This paper investigates the experiences of Italian and Dutch children while interacting with a social robot that is designed to support their diabetes self-management. Observations of children's behaviors and analyses of questionnaires at diabetes camps, showed positive experiences with variation (e.g., Italian children seemed to be more open and expressive, and more close to the robot compared to the Dutch children). A culture-aware robot should be sensitive to such differences.

Abstract This article studies one of the main bottlenecks in providing more effective information access: the poverty on the query end. We explore whether users can classify keyword queries into categories from the DMOZ directory on different levels and whether this topical context can help retrieval performance. We have conducted a user study to let participants classify queries into DMOZ categories, either by freely searching the directory or by selection from a list of suggestions. Results of the study show that DMOZ categories are suitable for topic categorization. Both free search and list selection can be used to elicit topical context. Free search leads to more specific categories than the list selections. Participants in our study show moderate agreement on the categories they select, but broad agreement on the higher levels of chosen categories. The free search categories significantly improve retrieval effectiveness. The more general list selection categories and the top‐level categories do not lead to significant improvements. Combining topical context with blind relevance feedback leads to better results than applying either of them separately. We conclude that DMOZ is a suitable resource for interacting with users on topical categories applicable to their query, and can lead to better search results.

In this paper we investigate the task of Entity Ranking on the Web. Searchers looking for entities are arguably better served by presenting a ranked list of entities directly, rather than a list of web pages with relevant but also potentially redundant information about these entities. Since entities are represented by their web homepages, a naive approach to entity ranking is to use standard text retrieval. Our experimental results clearly demonstrate that text retrieval is effective at finding relevant pages, but performs poorly at finding entities. Our proposal is to use Wikipedia as a pivot for finding entities on the Web, allowing us to reduce the hard web entity ranking problem to easier problem of Wikipedia entity ranking. Wikipedia allows us to properly identify entities and some of their characteristics, and Wikipedia's elaborate category structure allows us to get a handle on the entity's type.

We investigate the task of finding links from Wikipedia pages to external web pages. Such external links significantly extend the information in Wikipedia with information from the Web at large, while retaining the encyclopedic organization of Wikipedia. We use a language modeling approach to create a full-text and anchor text runs, and experiment with different document priors. In addition we explore whether social bookmarking site Delicious can be exploited to further improve our performance. We have constructed a test collection of 53 topics, which are Wikipedia pages on different entities. Our findings are that the anchor text index is a very effective method to retrieve home pages. Url class and anchor text length priors and their combination leads to the best results. Using Delicious on its own does not lead to very good results, but it does contain valuable information. Combining the best anchor text run and the Delicious run leads to further improvements.

This paper presents a case-study in which we use a large semi-structured data set consisting of official transcripts of meetings of the Dutch parliament for focused retrieval and result aggregation. Transcripts of meetings are a document genre characterized by a complex narrative structure. The essence is not only what is said, but also by who and to whom. We have notes of more than 40 years of Dutch parliamentary debates where this structure is exploited to automatically make semantic annotations. These annotations yield numerous new ways of searching, browsing, mining and summarizing these documents. Concerning result aggregation, we summarise and visualise the structure of meetings into tables of content and interruption graphs. The contents of meetings or parts of meetings are condensed into word clouds that are created using a parsimonious language model. Furthermore, we have developed a search engine that exploits the structure and annotations of our data making it possible to provide entry points, to group search results, and to use faceted search techniques for data-exploration. Evaluation shows that our content and structure summarization tools provide a good first impression of a debate. Users reported that, compared to a standard document retrieval system, our search engine gives a better overview of the data. Search tasks are performed faster and the users felt more certain of their answers.

In this paper we explore the use of category information for ad hoc retrieval in Wikipedia. We show that techniques for entity ranking exploiting this category information can also be applied to ad hoc topics and lead to significant improvements. Automatically assigned target categories are good surrogates for manually assigned categories, which perform only slightly better.

Transcripts of meetings are a document genre characterized by a complex narrative structure. The essence is not only what is said, but also by who and to whom. This paper investigates whether we can use semantic annotations like the speaker in order to capture this debate structure, as well as the related content of the debate. The structure is visualized in a graph, while the content is condensed into word clouds, that are created using a parsimonious language model. Evaluation shows that both tools adequately capture the structure and content of the debate at an aggregated level.

In this paper we explore the use of parsimonious language models for web retrieval. These models are smaller thus more efficient than the standard language models and are therefore well suited for large-scale web retrieval. We have conducted experiments on four TREC topic sets, and found that the parsimonious language model results in improvement of retrieval effectiveness over the standard language model for all data-sets and measures. In all cases the improvement is significant, and more substantial than in earlier experiments on newspaper/newswire data.