On Suggesting Entities as Web Search Queries

The Web of Data is growing in popularity and dimension, and named entity exploitation is gaining importance in many research fields. In this paper, we explore the use of entities that can be extracted from a query log to enhance query recommendation. In particular, we extend a state-of-the-art recommendation algorithm to take into account the semantic information associated with submitted queries. Our novel method generates highly related and diversified suggestions that we as- sess by means of a new evaluation technique. The manually annotated dataset used for performance comparisons has been made available to the research community to favor the repeatability of experiments

Text Mining for User Query Analysis: A 5-Step Method for Cultural Heritage Institutions

Peer reviewe

Improving Search Effectiveness through Query Log and Entity Mining

The Web is the largest repository of knowledge in the world. Everyday people contribute to make it bigger by generating new web data. Data never sleeps. Every minute someone writes a new blog post, uploads a video or comments on an article. Usually people rely on Web Search Engines for satisfying their information needs: they formulate their needs as text queries and they expect a list of highly relevant documents answering their requests. Being able to manage this massive volume of data, ensuring high quality and performance, is a challenging topic that we tackle in this thesis. In this dissertation we focus on the Web of Data: a recent approach, originated from the Semantic Web community, consisting in a collective effort to augment the existing Web with semistructured-data. We propose to manage the data explosion shifting from a retrieval model based on documents to a model enriched with entities, where an entity can describe a person, a product, a location, a company, through semi-structured information. In our work, we combine the Web of Data with an important source of knowledge: query logs, which record the interactions between the Web Search Engine and the users. Query log mining aims at extracting valuable knowledge that can be exploited to enhance users’ search experience. According to this vision, this dissertation aims at improving Web Search Engines toward the mutual use of query logs and entities. The contributions of this work are the following: we show how historical usage data can be exploited for improving performance during the snippet generation process. Secondly, we propose a query recommender system that, by combining entities with queries, leads to significant improvements to the quality of the suggestions. Furthermore, we develop a new technique for estimating the relatedness between two entities, i.e., their semantic similarity. Finally, we show that entities may be useful for automatically building explanatory statements that aim at helping the user to better understand if, and why, the suggested item can be of her interest

Interface Language, User Language and Success Rates in The European Library

In this paper, TEL 2010 action logs are analyzed with a particular focus on the impact of language (user native language and interface language) on the success of a search session. Particular user actions are defined as success indicators for searches and sessions are divided into “successful” and “unsuccessful” sessions with respect to their outcomes. Two approaches for studying the impact of the language of the search interface are pursued: (1) the effect of concurrent language choice when associating the user language (determined by IP address) with the interface language and (2) the consequences of interface language changes during a session. The challenges of country and language identification via IP addresses are also discussed

Evaluation: Thinking Outside the (Search) Box

Evaluation of IR systems has typically focused on the system and specifically assessing the quality of a ranked list of results with respect to a query. However, IR functionality is typically just one component amongst many that are used to help support users' wider information seeking activities. Many systems that include a search box also provide features, such as faceted lists, subject hierarchies, visualizations and recommendations to help users find information. In this paper I discuss experiences gained from developing a system to support exploration and discovery in digital cultural heritage. In particular I focus on the development of system components to support search and navigation and how the different components were evaluated within the development life-cycle of the project. The importance of taking a holistic approach to evaluation, as well as utilising evaluation approaches from domains other than IR, is emphasized. In short, we need to be thinking outside the (search) box when it comes to evaluation in IR

B!SON: A Tool for Open Access Journal Recommendation

Finding a suitable open access journal to publish scientific work is a complex task: Researchers have to navigate a constantly growing number of journals, institutional agreements with publishers, funders’ conditions and the risk of Predatory Publishers. To help with these challenges, we introduce a web-based journal recommendation system called B!SON. It is developed based on a systematic requirements analysis, built on open data, gives publisher-independent recommendations and works across domains. It suggests open access journals based on title, abstract and references provided by the user. The recommendation quality has been evaluated using a large test set of 10,000 articles. Development by two German scientific libraries ensures the longevity of the project