Jesús R. Campaña

On Storing Ontologies including Fuzzy Datatypes in Relational Databases

This work deals with the need for managing large amounts of fuzzy data in the context of the Semantic Web. A schema to store ontologies with fuzzy datatypes into a database is presented as part of a framework designed to perform tasks of fuzzy information extraction and publishing. The database schema allows the storage of an ontology along with its instances preserving all information. Ontology and instances are stored in different schemas in order to improve the access to instances while retaining the capacity of reasoning over the ontology. This sets the foundations of a research opportunity on the definition of a ontology reasoner over these structures. The paper also presents a brief description of the framework on which the database is included, and the structures conforming the storage schema proposed.

An automatic system for identifying authorities in digital libraries

In this paper we introduce an automatic system to perform authority control in digital libraries based on data mining techniques. This system is able to find the different representations for an author name as well as to distinguish between different authors sharing the same name. Using that information, the system shows the user the results of a search over a digital library properly grouped according to their authorship. To accomplish this task, it only uses information that can be directly obtained from the digital library itself without any kind of external data. The system has been tested using different digital libraries on the web.

ImmoSoftWeb: a Web Based Fuzzy Application for Real Estate Management

The paper describes ImmoSoftWeb, a web based application which takes advantage of fuzzy sets to apply them on the area of real estate management. ImmoSoftWeb is built on a FRDB, initially using a prototype called FSQL Server, which provides application capabilities for fuzzy handling, but ImmoSoftWeb is independent from FRDB by means of XML technologies usage. Moreover, the paper shows the way real estate attributes can be expressed using fuzzy data, and how fuzzy queries can be used to apply typical real estate customer requirements on a fuzzy real estate database.

MTCIR: A multi-term tag cloud information retrieval system

Abstract Processing and accessing database resources available on Internet is sometimes complex, specially when textual content is involved. A new user may need a general description of the contents available in the database in order to determine if the information is useful for his/her search needs. In this paper we present MTCIR, a system that summarizes the content of a database and provides the user with simple interfaces to access the information. The system uses a visual interface based on multi-term tag clouds, which presents the content of the database and can be used as assistance in the search process. The novelty of this approach is the underlying structure which provides the text with certain semantics and is able to retrieve the most relevant information. We test our proposal in four datasets and discuss the tag clouds obtained and the metrics computed for each of them.

On the Use of a Fuzzy Object-Relational Database for Flexible Retrieval of Medical Images

This paper introduces a novel approach to medical image retrieval using a fuzzy object-relational database management system (FORDBMS). The system stores medical images along with information about the content of the image, such as the presence or absence of certain indicators of pathologies. It allows us to flexibly retrieve them on the basis of these indicators, making it possible to obtain images from patients with similar diagnosis and thus, following a common visual pattern. To illustrate the capabilities of the FORDBMS, this paper focuses on X-ray images of patients suffering from scoliosis (a medical condition in which the patients spine is curved) from which spine descriptions are obtained. Then queries are performed to obtain a set of images with a certain curvature pattern. Results show high accuracy when evaluated by medical experts. Compared with other ad hoc content-based image retrieval systems, the one presented here is easily adaptable to other application domains, customizable, and very scalable.

INDEXING FUZZY NUMERICAL DATA WITH A B+ TREE FOR FAST RETRIEVAL USING NECESSITY-MEASURED FLEXIBLE CONDITIONS

This paper proposes an indexing procedure for improving the performance of query processing on a fuzzy database. It focuses on the case when a necessity-measured atomic flexible condition is imposed on the values of a fuzzy numerical attribute. The proposal is to apply a classical indexing structure for numerical crisp data, a B+-tree combined with a Hilbert curve. The use of such a common indexing technique makes its incorporation into current systems straightforward. The efficiency of the proposal is compared with that of another indexing procedure for similar fuzzy data and flexible query types. Experimental results reveal that the performance of the proposed method is similar and more stable than that of its competitor.

Semantic Data Management Using Fuzzy Relational Databases

This chapter presents a schema and a transformation algorithm to store OWL ontologies in Object Relational Databases. The database schema allows the storage of an ontology structure, while the transformation algorithm creates an appropriate schema to store its instances preserving all information. We allow the use of instance data of imprecise nature, mostly fuzzy numerical data. An OWL ontology is defined allowing numerical fuzzy datatypes as the range of properties. In order to manage all the information, instance data handling is delegated onto a Fuzzy ORDBMS, which is briefly described. We present here a complete description of the structures conforming the storage schema proposed, and the algorithms used to transform the OWL ontology to a database schema. We also discuss the role of ontologies as relational database design tools.

Semantic Enrichment of Database Textual Attributes

Textual attributes in relational databases contain useful information that sometimes is not handled in a proper way. Syntactic querying of textual fields provides only superficial information, avoiding issues related to the semantics of data. In order to extract relevant information from these fields, a text mining process is conducted. The AP-Sets mining structure allows the building of an intermediate representation for texts based on the Apriori property. This intermediate form has the shape of a lattice of text itemsets, and can be translated into a basic ontology using semantic relations extracted from WordNet lexical database. The ontology generated can be seen as a summarized structured description of the contents of the textual attribute. Furthermore, it adds semantic capabilities to the query process on textual attributes.

Text Retrieval and Visualization in Databases Using Tag Clouds

This paper proposes a novel text information retrieval and visualization approach. This method allows users to browse and query texts in databases in a simple and efficient way. To facilitate content identification, the attractive design of tag clouds has been combined with an innovative text representation structure developed by our research group. This structure stems from the semantics in text representation since it keeps terms together and does not activate them separately. This structure has also been improved by the inclusion of both term order and term weight. Our proposal thus provides a more accurate form of querying and visualization by means of tag clouds.

Semantic processing of database textual attributes using wikipedia

Text attributes in databases contain rich semantic information that is seldom processed or used. This paper proposes a method to extract and semantically represent concepts from texts stored in databases. This process relies on tools such as WordNet and Wikipedia to identify concepts extracted from texts and represent them as a basic ontology whose concepts are annotated with search terms. This ontology can play diverse roles. It can be seen as a conceptual summary of the content of an attribute, which can be used as a means to navigate through the textual content of an attribute. It can also be used as a profile for text search using the terms associated to the ontology concepts. The ontology is built as a subset of Wikipedia category graph, selected using diverse metrics. Category selection using these metrics is discussed and an example application is presented and evaluated.