186 research outputs found
Personalization of Queries based on User Preferences
Query Personalization is the process of dynamically enhancing a query with related user preferences stored in a user profile with the aim of providing personalized answers. The underlying idea is that different users may find different things relevant to a search due to different preferences. Essential ingredients of query personalization are: (a) a model for representing and storing preferences in user profiles, and (b) algorithms for the generation of personalized answers using stored preferences. Modeling the plethora of preference types is a challenge. In this paper, we present a preference model that combines expressivity and concision. In addition, we provide algorithms for the selection of preferences related to a query and the progressive generation of personalized results, which are ranked based on user interest
DBMSs Should Talk Back Too
Natural language user interfaces to database systems have been studied for
several decades now. They have mainly focused on parsing and interpreting
natural language queries to generate them in a formal database language. We
envision the reverse functionality, where the system would be able to take the
internal result of that translation, say in SQL form, translate it back into
natural language, and show it to the initiator of the query for verification.
Likewise, information extraction has received considerable attention in the
past ten years or so, identifying structured information in free text so that
it may then be stored appropriately and queried. Validation of the records
stored with a backward translation into text would again be very powerful.
Verification and validation of query and data input of a database system
correspond to just one example of the many important applications that would
benefit greatly from having mature techniques for translating such database
constructs into free-flowing text. The problem appears to be deceivingly
simple, as there are no ambiguities or other complications in interpreting
internal database elements, so initially a straightforward translation appears
adequate. Reality teaches us quite the opposite, however, as the resulting text
should be expressive, i.e., accurate in capturing the underlying queries or
data, and effective, i.e., allowing fast and unique interpretation of them.
Achieving both of these qualities is very difficult and raises several
technical challenges that need to be addressed. In this paper, we first expose
the reader to several situations and applications that need translation into
natural language, thereby, motivating the problem. We then outline, by example,
the research problems that need to be solved, separately for data translations
and query translations.Comment: CIDR 200
From Personalization to Adaptivity: Creating Immersive Visits through Interactive Digital Storytelling at the Acropolis Museum
Storytelling has recently become a popular way to guide museum visitors, replacing traditional exhibit-centric descriptions by story-centric cohesive narrations with references to the exhibits and multimedia content. This work presents the fundamental elements of the CHESS project approach, the goal of which is to provide adaptive, personalized, interactive storytelling for museum visits. We shortly present the CHESS project and its background, we detail the proposed storytelling and user models, we describe the provided functionality and we outline the main tools and mechanisms employed. Finally, we present the preliminary results of a recent evaluation study that are informing several directions for future work
Towards Analytics Aware Ontology Based Access to Static and Streaming Data (Extended Version)
Real-time analytics that requires integration and aggregation of
heterogeneous and distributed streaming and static data is a typical task in
many industrial scenarios such as diagnostics of turbines in Siemens. OBDA
approach has a great potential to facilitate such tasks; however, it has a
number of limitations in dealing with analytics that restrict its use in
important industrial applications. Based on our experience with Siemens, we
argue that in order to overcome those limitations OBDA should be extended and
become analytics, source, and cost aware. In this work we propose such an
extension. In particular, we propose an ontology, mapping, and query language
for OBDA, where aggregate and other analytical functions are first class
citizens. Moreover, we develop query optimisation techniques that allow to
efficiently process analytical tasks over static and streaming data. We
implement our approach in a system and evaluate our system with Siemens turbine
data
The Impact of Name-Matching and Blocking on Author Disambiguation
In this work, we address the problem of blocking in the context of author name disambiguation. We describe a framework that formalizes different ways of name-matching to determine which names could potentially refer to the same author. We focus on name variations that follow from specifying a name with different completeness (i.e. full first name or only initial). We extend this framework by a simple way to define traditional, new and custom blocking schemes. Then, we evaluate different old and new schemes in the Web of Science. In this context we define and compare a new type of blocking schemes. Based on these results, we discuss the question whether name-matching can be used in blocking evaluation as a replacement of annotated author identifiers. Finally, we argue that blocking can have a strong impact on the application and evaluation of author disambiguation
- …