Carmen Martínez-Cruz

Ontologies versus relational databases: are they so different? A comparison

Two main data models are currently used for representing knowledge and information in computer systems. Database models, especially relational databases, have been the leader in last few decades, enabling information to be efficiently stored and queried. On the other hand, ontologies have appeared as an alternative to databases in applications that require a more ‘enriched’ meaning. However, there is controversy regarding the best information modeling technique, as both models present similar characteristics. In this paper, we present a review of how ontologies and databases are related, of what their main differences are and of the mechanisms used to communicate with each other.

An approximation to the computational theory of perceptions using ontologies

Highlights? CWP provides a framework to develop systems that operate with the meaning of NL expressions. ? A high level ontology is defined to describe the Granular Linguistic Model of Phenomena. ? Application ontologies represent complex phenomena and linguistic descriptions. ? Natural language sentences are obtained after instantiating application ontologies. New technologies allow users to access huge amounts of data about phenomena in their environment. Nevertheless, linguistic description of these available data requires that human experts interpret them highlighting the relevant details and hiding the irrelevant ones. Experts use their personal experience on the described phenomenon and in using the flexibility of natural language to create their reports. In the research line of Computing with Words and Perceptions, this paper deals with the challenge of using ontologies to create a computational representation of the experts knowledge including his/her experience on both the context of the analyzed phenomenon and his/her personal use of language in that specific context. The proposed representation takes as basis the Granular Linguistic Model of a Phenomenon previously proposed by two of the authors. Our approach is explained and demonstrated using a series of practical prototypes with increasing degree of complexity.

A flooding algorithm for extracting drainage networks from unprocessed digital elevation models

A new method for extracting the drainage network from a digital elevation model (DEM) is presented. It is based on the well-known D8 approach that simulates the overland flow but uses a more elaborate water transfer model that is inspired by the natural behaviour of water. The proposed solution has several advantages: it works on unprocessed DEMs avoiding the problems caused by pits and flats, can generate watercourses with a width greater than one cell and detects fluvial landforms like lakes, marshes or river islands that are not directly handled by most previous solutions.

Integrating Ontologies and Fuzzy Logic to Represent User-Trustworthiness in Recommender Systems☆

Abstract Recommender systems can be used to assist users in the process of accessing to relevant information. In the literature we can find sundry approaches for generating personalized recommendations and all of them make use of different users’ and/or items’ features. Building accurate profiles plays an essential role in this context, so that the systems success depend to a large extent on the ability of the learned profiles to represent the users preferences. An ontology works very well to characterize the users profiles. In this paper we develop an ontology to characterize the trust between users using the fuzzy linguistic modelling, this way in the recommendation generation process we do not take into account users with similar ratings history but users in which each user can trust. We present our ontology and provide a method to aggregate the trust information captured in the trust-ontology and to update the user profiles based on the feedback.

A proposal for the hierarchical segmentation of time series. Application to trend-based linguistic description

In this paper we propose methods for obtaining hierarchical segmentations of time series on the basis of the Iterative End-Point Fit Algorithm. We discuss on the utility of the methods for different cases. We illustrate the usefulness of the hierarchical segmentations with an application in linguistic description of trends in time series. A linguistic description based on a segmentation of the time series that do not necessarily corresponds to a level of the hierarchy is obtained by describing segments in different levels that form a segmentation satisfying a quality model.

Flexible queries on relational databases using fuzzy logic and ontologies

Combines fuzzy logic and semantic techniques to perform flexible queries in a relational database.Alternative method to fuzzy logic, to execute flexible queries on non-scalar attributes.Answers are ordered according with the accomplishment degree to the query.Ontologies are used as an attribute domain frame to return semantically similar information to the query.Fuzzy logic are used on scalar attributes. Nowadays there are many proposals that allow users to perform fuzzy queries on relational databases. Regardless of these proposals, fuzzy queries are really useful on scalar values where fuzzy sets can be adjusted to the user needs and domains, but non-scalar values are a more complex task. Here, we extend non-scalar attribute management in fuzzy queries with the use of ontologies. Thus, we allow to compute this kind of queries not only with the similarity relationships defined explicitly on a fuzzy set but with semantically interrelated terms modeled as a domain ontology as well. Moreover, we present the architecture of a novel system that combines both techniques to return an answer as much complete as possible and ordered by a degree of accomplishment. Finally, a qualitative and quantitative study about the use of flexible queries on relational databases is included in this work, as well.

Three main components of experience base in linguistic description of data

In this paper, we present a contribution to solve the problem of organizing the representation of experience in computational systems which are able to generate relevant linguistic descriptions of data for specific users and contexts. We claim, that, typically, the expert knowledge modeled in these systems is limited to one of the dimensions of the meaning of natural language. Here, we model the experience base distinguishing among three types of meaning, namely, Ideational meaning concerning with the technical, impersonal description of the specific phenomenon; Interpersonal meaning concerning with the role of the partners involved in the communication process and Textual meaning concerning with the contribution to the meaning of the specific realization with natural language of both previous types of meaning. In order to organize these types of meaning (also called components of experience base) in a practical computational representation, we have built an ontology that will help designers to model their experience in the application domain. Using this ontology, the computational system is able of identifying the most suitable linguistic descriptions for describing the input data. Our approach is presented with the support of a practical example in the domain of the maintenance of comfort in a room.

An Ontology as a Tool for Representing Fuzzy Data in Relational Databases

Abstract Several applications to represent classical or fuzzy data in databases have been developed in the last two decades. However, these representations present some limitations specially related with the system portability and complexity. Ontologies provides a mechanism to represent data in an implementation-independent and web-accessible way. To get advantage of this, in this paper, an ontology, that represents fuzzy relational database model, has been redefined to communicate users or applications with fuzzy data stored in fuzzy databases. The communication channel established between the ontology and any Relational Database Management System (RDBMS) is analysed in depth throughout the text to justify some of the advantages of the system: expressiveness, portability and platform heterogeneity. Moreover, some tools have been developed to define and manage fuzzy and classical data in relational databases using this ontology. Even an application that performs fuzzy queries using the same technology is ...

An ontology to represent queries in fuzzy relational databases

Fuzzy data management in databases is a complex process because of flexible data nature and heterogeneous database systems. A solution to this problem has been solved using an ontology which isolates the fuzzy database representation of their management platform making fuzzy schemas Relational Database Management System (RDBMS)-independent and Web-accessible. However, queries performed on fuzzy databases present similar problems. In this proposal an ontology which represents a query structure regardless of any RDBMS implementation is defined. This ontology allows generating and executing any query on fuzzy or classical data according to the system where it is executed.

Describing Fuzzy DB Schemas as Ontologies: A System Architecture View

Different communication mechanisms between ontologies and database (DB) systems have appeared in the last few years. However, several problems can arise during this communication, depending on the nature of the data represented and their representation structure, and these problems are often enhanced when a Fuzzy Database (FDB) is involved. An architecture that describes how such communication is established and which attends to all the particularities presented by both technologies, namely ontologies and FDB, is defined in this paper. Specifically, this proposal tries to solve the problems that emerge as a result of the use of heterogeneous platforms and the complexity of representing fuzzy data.