Jorge Bernad

Searching the Web: from keywords to semantic queries

Within the emergent Semantic Web framework, the use of traditional Web search engines based on keywords provided by the users is not adequate any more. Instead, new methods based on the semantics of user keywords must be defined to search in the vast Web space without incurring in an undesirable loss of information. In this paper we propose a system that takes as input a list of plain keywords provided by the user and outputs equivalent semantic queries expressed in a knowledge representation language, that could be used to retrieve relevant data. For the translation task, specialized agents manage a third-party thesaurus and a pool of pre-existing ontologies to obtain the different meanings of the user keywords and discover semantic relationships between them in run-time.

A formalization for semantic location granules

Location-based services have become an increasingly interesting research area in the last two decades. However, in many scenarios, dealing with the most precise location coordinates is not the best solution since people structure the world in geographic areas instead of coordinates. Since humans work with abstractions, and names are the way we refer to those abstractions, introducing semantics in geographic definitions becomes natural. For example, users can be interested in states with vacation resorts and may want to retrieve the state names, instead of the exact geographic limits of such states. Moreover, semantics introduces new challenges, such as how to exploit the location semantics to infer new information from known definitions. For instance, we may want a system to automatically obtain the value added tax (VAT) that should be applied by a shop in Madrid, inferring the applicable tax by considering the economic area where Madrid is included (in this case, Spain); notice that the VAT should not be inferred from a bigger economic area, like Europe, although it also includes Madrid geographically. Thus, the expression of locations at different granularities extends the traditional location-based query processing to consider the most appropriate semantics for each user. In this article, adopting description logics (DLs) as a base formalism, we provide a formalization of the notion of semantic location granule and semantic granule map. We benefit from the underlying semantics of the different granularities to extend the expressivity of location-based queries and automatically discover and infer new knowledge. The model we propose uses a DL reasoner to infer new granules relationships. In particular, a DL reasoner can infer containment and intersection relationships between location granules (and help to obtain several more relationships), which provides the way to introduce semantics in location-based queries. This is done within the logical frame of DLs, thus ensuring that our approach can be supported by existing regular DL reasoners (such as Pellet, Racer Pro, and HermiT) without the need to extend their reasoning capabilities.

Location-aware system based on a dynamic 3D model to help in live broadcasting of sport events

Broadcasting sport events in live is a challenging task because obtaining the best views requires taking into account many dynamic factors, such as: the location and movement of interesting objects, all the views provided by cameras in the scenario (some of them wireless, mobile, or attached to moving objects), possible occlusions, etc. Therefore, a technical director needs to manage a great amount of continuously changing information to quickly select the camera whose view should be broadcasted. In this paper, we present a location-aware system that helps technical directors in the broadcasting task, using a 3D model updated continuously with real-time location data retrieved from the scenario. They can indicate in run-time their interest in certain moving objects and the system is in charge of selecting the cameras that provide the kind of views required.

Handling location uncertainty in probabilistic location-dependent queries

Location-based services have motivated intensive research in the field of mobile computing, and particularly on location-dependent queries. Existing approaches usually assume that the location data are expressed at a fine geographic precision (physical coordinates such as GPS). However, many positioning mechanisms are subject to an inherent imprecision (e.g., the cell-id mechanism used in cellular networks can only determine the cell where a certain moving object is located). Moreover, even a GPS location can be subject to an error or be obfuscated for privacy reasons. Thus, moving objects can be considered to be associated not to an exact location, but to an uncertainty area where they can be located.In this paper, we analyze the problem introduced by the imprecision of the location data available in the data sources by modeling them using uncertainty areas. To do so, we propose to use a higher-level representation of locations which includes uncertainty, formalizing the concept of uncertainty location granule. This allows us to consider probabilistic location-dependent queries, among which we will focus on probabilistic inside (range) constraints. The adopted model allows us to develop a systematic and efficient approach for processing this kind of queries. An experimental evaluation shows that these probabilistic queries can be supported efficiently.

Emerging Semantic-Based Applications

In the last decade, we have witnessed the birth and spread of the Semantic Web and its associated semantic technologies. In this successful scenario, ontologies have played a crucial role. However, being knowledge representation frameworks as they are, the benefits of their use are beyond the WWW, but to many other different kind of systems, making it possible to design and develop smarter information systems which exploit the semantics of data.In this chapter, we present different semantic-based applications and projects that we have developed in the Distributed Information Systems (SID, in Spanish) research group of the University of Zaragoza. They address different application fields benefiting from semantic technologies to broaden their capabilities. In particular, we present our semantic systems for keyword-based search (QueryGen, Doctopush), information extraction (GENIE), fuzzy logic editing (Fuzzy OWL 2) and reasoning (fuzzy DL, DeLorean), and Location-Based Services managing (SHERLOCK).

MultiCAMBA: a system for selecting camera views in live broadcasting of sport events using a dynamic 3D model

For a Technical Director (TD) in charge of a live broadcasting, selecting the best camera shots among the available video sources is a challenging task, even more now that the number of cameras (some of them mobile, or attached to moving objects) in the broadcasting of sport events is increasing. So, the TD needs to manage a great amount of continuously changing information to quickly select the camera whose view should be broadcasted. Besides, the better the decisions made by the TD, the more interesting the content for the audience. Therefore, the development of systems that could help the TD with the selection of camera views is demanded by broadcasting organizations.In this paper, we present the system MultiCAMBA that helps TDs in the live broadcasting task by allowing them to indicate in run-time their interest in certain kind of shots, and the system will show the cameras that are able to provide them. To achieve this task, the system manages location-dependent queries generated according to the interests of the TD. Moreover, to avoid the use of costly on line real-image processing techniques over the camera views, such real camera views are recreated in a 3D engine by using the information contained in a 3D model of the scenario. This model is updated continuously with real-time data retrieved from the real objects and cameras in the scenario. In this way, the system extracts high-level semantic features of 2D projections of the 3D reconstruction of the camera views. We present a prototype of the system and experimental results that show the feasibility of our proposal.

Estimating local coverage areas for location dependent queries

Location-Based Services (LBS) are currently attracting many research efforts due to the pervasiveness of mobile devices in our daily life. Mechanisms to monitor distributedly the remote (and sometimes huge or dynamic) geographic areas referenced from location-based constraints are needed in order to retrieve the required data. In some cases (e.g., ad hoc networks), selecting the best places from which to scan a certain area is still an open problem; but moreover, it depends on another open problem: We should know the actual area that each object can scan (i.e., its communication coverage area) from its location in order to be sure that the whole interesting area is actually covered from a certain set of objects. Different disk-based coverage area models are usually adopted; however, knowing the real communication coverage area of an object is a very difficult task. Therefore, when accuracy is important, there is a need of a mechanism that estimates somehow the unknown real coverage area that a certain object is able to see, anytime and anywhere. In this paper, we face the problem of estimating the real (and unknown) coverage area of a certain (mobile or static) object in a wireless scenario by considering as input data just the location of the devices that can communicate with that object at each time. To do this task we propose different algorithms, including a combination of all of them, and compare their accuracy and efficiency against a set of synthetic and real scenarios with different topologies and obstacles. We show that estimating the real coverage area of an object without assuming a disk of fixed radius is not only possible, but also simple and efficient.

A Fuzzy Ontology-Based System for Gait Recognition Using Kinect Sensor

Gait recognition involves the automatic classification of human people from sequences of data about their movement patterns. This paper describes our ongoing work in the development of a gait recognition system using Microsoft Kinect data and based on fuzzy ontologies to manage the imprecision of the data and to improve the system scalability.

Viewer of Synthetic Scenarios to Evaluate Estimators of Local Coverage Areas Based on Detected Objects

We present a visual tool to test different algorithms that estimate the real coverage area of a certain (mobile or static) object against previously generated scenarios. Such algorithms consider as input data just the location of the devices that can communicate with that object.

Estimation of Local Coverage Areas Based on Detected Objects

We face the problem of estimating the real coverage area of a certain (mobile or static) object in a wireless scenario by considering as input data just the location of the devices that can communicate with that object. We propose and evaluate different algorithms to show that estimating the real coverage area of an object without assuming a disk of fixed radius, is not only possible but also simple and efficient.