ExploreDB 2016 - 3rd International Workshop on Exploratory Search in Databases and the Web

The traditional way of interaction between a user and a database system is through queries, for which the correctness and completeness of their answers are key challenges. Structured query languages, such as SQL for relational data, XQuery for XML, and SPARQL for RDF data, allow users to submit queries that may precisely identify their information needs, but often require users to be familiar with formal logic representation and with the underlying ontology and data structure. Moreover, this interaction mode assumes that users are to some extent familiar with the content of the database and also have a clear understanding of their information needs. As databases get larger and accessible to a more diverse and less technically oriented audience, new forms of data exploration and interaction become increasingly more attractive to aid users navigate through the information space and overcome the challenges of "information overload".
The World Wide Web represents the largest and arguably the most complex repository of content. Users seek information on the web through two predominant modes: by browsing or by searching. In the first mode, the interaction between the user and the data repository is driven directly by the user’s interpretation of their information need and their information foraging constraints. In the latter mode, a search engine typically mediates the user-data interactions and the process starts with the user entering query-terms that act as surrogates for the user information goals. Free-text queries allow end-users a simple way to express their information needs independently from the underlying data model and structure, as well as from a specific query language. Given a query, the most common strategy has been to present the results as a ranked list. Users have to subsequently peruse the list to satisfy their information needs through browsing the links and/or by issuing further queries.
Additionally, the information in the Web gets rapidly diversified both in terms of its complexity, as well as in terms of the media through which the information is encoded, spanning from large amounts of unstructured and semi-structured data to semantically rich information. Increasing demands for sophisticated discovery capabilities are now being imposed by numerous applications in various domains, such as social media, healthcare, telecommunication, e-commerce and Web analytics, business intelligence, and cyber-security. Data generated from these domains are high dimensional, dynamic and change rapidly over time. Yet, many of these data are hidden behind barriers of language constraints, data heterogeneity, ambiguity, and the lack of proper query interfaces.
Furthermore, the complexity and heterogeneity of the information implies that the associated semantics is often user-dependent and emergent. Individual aspects like age, gender, profession or experience are often not taken into account, for example, the difference in searching between children and adults. Although long challenged by works, such as Bates' berrypicking model, common systems still assume that the user has a static information need, which remains unchanged during the seeking process. Hence, many systems are strongly optimized for lookup searches, expecting that the user is only interested in facts and not in complex problem solving.
Thus, there is a need to develop novel paradigms for exploratory user-data interactions that emphasize user context and interactivity with the goal of facilitating exploration, interpretation, retrieval, and assimilation of information. A huge number of applications need an exploratory form of querying. Ranked retrieval techniques for relational databases, XML, RDF and graph databases, text and multimedia databases, scientific and statistical databases, social networks and many others, is a first step towards this direction. Recently, several new aspects for exploratory search, such as preferences, diversity, novelty, surprise and serendipity, are gaining increasing importance. Not only that, if the user query is too generic or specific, or the user is unsure about the domain, or even her goal, a traditional ranked retrieval method may fail to effectively distinguish a smaller subset of results from there. Therefore, the query-answering task needs to be further enhanced to capture additional context and intent that the user may have in mind during querying. Exploratory search techniques are of great assistance that facilitates and guides users to focus on the relevant aspects of her search results.
From a different perspective, recommendation applications tend to anticipate user needs by automatically suggesting the information which is most appropriate to the users and their current context. Also, a new line of research in the area of exploratory search is fueled by the growth of online social interactions within social networks and Web communities. Many useful facts about entities (e.g., people, locations, organizations) and their relationships can be found in a multitude of semi-structured and structured data sources such as Wikipedia, Linked Data Cloud, Freebase, and many others. Therefore, novel discovery methods are required to provide highly expressive discovery capabilities over large amounts of entity-relationship data, which are yet intuitive for end-users.