Carlos Bobed

An approach to process continuous location-dependent queries on moving objects with support for location granules

Location-based services have attracted the attention of important research in the field of mobile computing. Specifically, different mechanisms have been proposed to process location-dependent queries. In the above mentioned context, it is usually assumed that the location data are expressed at a fine geographic precision. However, a different granularity may be more appropriate in certain situations. Thus, a location resolution higher than required may even be inconvenient or not understandable by the user (for example, if the user expects a city name as an answer and instead the system provides the latitude/longitude coordinates). Moreover, if the locations presented to the user need to be refreshed automatically as the objects move, it is obvious that maintaining up-to-date GPS-like geographic coordinates would be more expensive in terms of processing and communication. Unfortunately, the existing approaches assume queries whose locations are always given with maximum precision (i.e., GPS locations). In this paper, a distributed query processing approach that adapts itself to the level of the location resolution required is presented. Thus, it supports continuous location-dependent queries based on the required terminology for the locations, depending on the granularity used (e.g., GPS, cities, states, provinces, or any other predefined geographic area). For the above mentioned purpose, location granules can be defined to specify the semantics appropriate for the queries and/or the way the results should be presented. A prototype showing the functionality and benefits of the approach has been implemented and used in an extensive experimental evaluation. The proposal not only increases the flexibility and expressive power of the queries considerably but also performs efficiently.

Semantic reasoning on mobile devices

The massive spread of mobile computing in our daily lives has attracted a huge community of mobile application (apps) developers. These developers can take advantage of the benefits of semantic technologies (such as knowledge sharing and reusing, and knowledge decoupling) to enhance their applications. Moreover, the use of semantic reasoners would enable them to create more intelligent applications capable of discovering new knowledge, inferred from the available information.However, using semantic APIs and reasoners on current mobile devices is not a trivial task. In this paper, we show that the most popular current available Description Logics (DL) reasoners can be used on Android-based devices, and detail the efforts needed to port them to the Android platform. We also analyze the performance of these reasoners on current smartphones/tablets against more than 300 ontologies from the ORE?2013 ontology set, showing that, despite a notable difference with respect to desktop computers, their use is feasible.

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.

Enabling keyword search on Linked Data repositories: An ontology-based approach

The Web is experiencing a continuous change that is leading to the realization of the Semantic Web. Initiatives such as Linked Data have made a huge amount of structured information publicly available, encouraging the rest of the Internet community to tag their resources with it. Unfortunately, the amount of interlinked domains and information is so big that handling it efficiently has become really difficult for final users. Thus, we have to provide them with tools to search the needed resources in an easy way. In this paper, we propose an approach to provide users with different domain views on a general data repository, enabling them to perform both keyword and refinement searches. Our system exploits the knowledge stored in ontologies to 1 perform efficient keyword searches over a specified domain, and 2 refine the users domain searches. In this way, we enable the definition of different semantic views on Linked Data datasets without having to change the original semantics. We present a prototype of our approach that focuses on the case of DBpedia, which provides a semantic way to access to Wikipedia.

QueryGen: Semantic interpretation of keyword queries over heterogeneous information systems

Abstract In the last years, users have become used to keyword-based search interfaces due to their ease of use. By matching input keywords against huge amounts of textual information and labeled multimedia files, current search engines satisfy most of users’ information needs. However, the principal problem of this kind of search is the semantic gap between the input and the real user need, as keywords are a simplification of the query intended by the user. Moreover, different users could use the same set of keywords to search different information; even the same user could do it at different times. The search system, before accessing any data, should discover first the intended semantics behind the user keywords, in order to return only data fulfilling such semantics. The use of formal query languages is not an option for non-expert users, so a semantic keyword-based search based on semantic interpretation of keyword queries could be the solution, i.e., a search that starts discovering the semantics intended for the input user keywords, and then only data relevant to that semantics are returned as answer. In this paper we present a system that performs semantic keyword interpretation on different data repositories. Our system (1) discovers the meaning of the input keywords by consulting a generic pool of ontologies and applying different disambiguation techniques; (2) once the meaning of each keyword has been established, the system combines them in a formal query that captures the semantics intended by the user, considering different formal query languages and possibilities that could arise, but avoiding inconsistent and semantically equivalent queries; and, finally, (3) after the user has validated the generated query that best fits her/his intended meaning, the system routes the query to the appropriate data repositories that will retrieve data according to the semantics of such a query. Experimental results show the semantic interpretation capabilities and the feasibility of our approach.

From keywords to queries: discovering the user's intended meaning

Regarding web searches, users have become used to keyword-based search interfaces due to their ease of use. However, this implies a semantic gap between the users information need and the input of search engines, as keywords are a simplification of the real user query. Thus, the same set of keywords can be used to search different information. Besides, retrieval approaches based only on syntactic matches with user keywords are not accurate enough when users look for information not so popular on the Web. So, there is a growing interest in developing semantic search engines that overcome these limitations. In this paper, we focus on the front-end of semantic search systems and propose an approach to translate a list of user keywords into an unambiguous query, expressed in a formal language, that represents the exact semantics intended by the user. We aim at not sacrificing any possible interpretation while avoiding generating semantically equivalent queries. To do so, we apply several semantic techniques that consider the properties of the operators and the semantics behind the keywords. Moreover, our approach also allows us to present the queries to the user in a compact representation. Experimental results show the feasibility of our approach and its effectiveness in facilitating the users to express their intended query.

Semantic Discovery of the User Intended Query in a Selectable Target Query Language

The syntactic approach of most of Web search engines still has the drawback of not considering the semantics of the keywords entered by the user. So, users usually have to browse many hits looking for the information they want. In this paper, we present a system that, given a set of keywords with well defined semantics, automatically generates a set of formal queries, in the query language of the users choice, which attempt to capture what the user had in mind when she or he wrote those keywords. The system uses ontologies and a description logics reasoner to perform a semantic enrichment of user keywords to improve the discovering of possible user queries and to reject semantically inconsistent queries.

VOX system: a semantic embodied conversational agent exploiting linked data

In the last few years, the use of ontologies has spread thanks to the irruption of the Semantic Web. They have become a crucial tool in information systems as they explicitly state the meaning of information, making it possible to share it and to achieve higher levels of interoperability. However, being knowledge representation models as they are, other fields can take advantage of their characteristics to extend their capabilities. In particular, in the context of Embodied Conversational Agents, they can be used to provide them with semantic knowledge and, therefore, enhance their intellectual skills. In this paper, we propose an approach to explore the synergies between these technologies. Thus, we have developed a multimodal ECA that exploits the knowledge provided by the Linked Data initiative to help users in their search information tasks. Based on a semantic-guided keyword search, our approach is flexible enough to: 1) deal with different Linked Data repositories and 2) handle different search/knowledge domains in a multilingual way. To illustrate the potential of our approach, we have focused on the case of DBpedia, as it mirrors the information stored in the Wikipedia, providing a semantic entry to it.

Exploiting the semantics of location granules in location-dependent queries

The need for location-based services has motivated an important research effort in the efficient processing of location-dependent queries. Most of the existing approaches only deal with locations at maximum precision (e.g., GPS coordinates). However, due to imprecision or expressivity requirements, there are situations in which locations must be handled at different granularity levels (e.g., neighborhoods, cities, states, etc.). Indeed, whenever a set of locations are represented together as a granule, a meaning is implicitly given to the set. So, the use of different granularities brings different semantics to the location data. In this paper, we propose the use of semantic location granules to enhance the expressivity of location-dependent queries. This is done by exploiting the semantic information that is asserted about different granularity levels. This information could be, for example, the cost incurred by a moving object to traverse a spatial area or a requirement to traverse a connection (e.g., need of a visa or passport). In particular, we propose: 1) an ontological model for describing the semantics inherent to location granules; 2) an upper-level ontology that can be extended and adapted to different scenarios; and 3) the use of a reasoner to exploit the semantics expressed in the ontologies, to make it possible to add new query constraints and so extend the expressivity of the queries.

Probabilistic Granule-Based Inside and Nearest Neighbor Queries

The development of location-based services and advances in the field of mobile computing have motivated an intensive research effort devoted to the efficient processing of location-dependent queries. In this context, it is usually assumed that location data are expressed at a fine geographic precision. Adding support for location granules means that the user is able to use his/her own terminology for locations (e.g., GPS, cities, states, provinces, etc.), which may have an impact in the semantics of the query, the way the results are presented, and the performance of the query processing. Along with its advantages, the management of the so-called location granules introduces new challenges for query processing. In this paper, we analyze two popular location-dependent constraints, inside and nearest neighbors, and enhance them with the possibility to specify location granules. In this context, we study the problem that arises when the locations of the objects are subject to some imprecision.