Ignacio J. Blanco

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.

Component-based data mining frameworks

OLAP Vs. OLTP in the middle tier.

Text Knowledge Mining: An Alternative to Text Data Mining

In this paper we introduced an alternative view of text mining and we review several alternative views proposed by different authors. We propose a classification of text mining techniques into two main groups: techniques based on inductive inference, that we call text data mining (TDM, comprising most of the existing proposals in the literature), and techniques based on deductive or abductive inference, that we call text knowledge mining (TKM). To our knowledge, the TKM view of text mining is new though, as we shall show, several existing techniques could be considered in this group. We discuss about the possibilities and challenges of TKM techniques. We also discuss about the application of existing theories in possible future research in this field.

A definition for fuzzy approximate dependencies

In the analysis of data stored in databases, a very interesting issue is the detection of possible existing relations between attribute values and, at an upper level, relations between attributes themselves. In case uncertainty is present in data, or it is introduced in a pre-processing step, specific data mining and knowledge discovery techniques and methodologies must be provided. The theory of fuzzy subsets is a helpful tool to reach this goal. In this paper we introduce a new definition and an algorithm for computing fuzzy approximate dependencies, a type of relations that can be found between attributes in a fuzzy database, on the basis of a previous definition of fuzzy association rule. We will discuss about possible applications of this new tool.

A new proposal of aggregation functions: the linguistic summary

This paper presents a new way of giving the summary of a numerical attribute involved in a fuzzy query. It is based on the idea of offering a linguistic interpretation, therefore we propose to use a flat fuzzy number as summary. To obtain it, we optimize any index which measures the relation between the fuzzy bag (which is the answer to the fuzzy query) and the fuzzy number. Several indices should be considered: some of them are based on linguistic quantified sentences, other ones are founded on divergence measures. The method can be also used to summarize other related fuzzy sets such as fuzzy average, maximum, minimum etc.

Softening the object-oriented database model: imprecision, uncertainty, and fuzzy types

Object-oriented databases have proved to be a good alternative to the relational ones of Codd when dealing with applications characterized by their complexity and dynamism. A big part of the effort of researchers in the field of object-oriented databases (OODB) has been focused on the study of the addition of vagueness to this database model. There are different levels where vagueness can arise: uncertain and imprecise attribute values, fuzzy extents in classes, vague relationships between classes (including inheritance), and soft type definitions. We summarize our proposal in this area, showing how these different sources of vagueness can be managed over a traditional OODB system. We explain the new structures to be considered in order to incorporate vagueness and we use the Unified Modeling Language (UML) to make the conceptual representation of this structures clear because of its direct translation to an object-oriented model.

Using association rules to mine for strong approximate dependencies

In this paper we deal with the problem of mining for approximate dependencies (AD) in relational databases. We introduce a definition of AD based on the concept of association rule, by means of suitable definitions of the concepts of item and transaction. This definition allow us to measure both the accuracy and support of an AD. We provide an interpretation of the new measures based on the complexity of the theory (set of rules) that describes the dependence, and we employ this interpretation to compare the new measures with existing ones. A methodology to adapt existing association rule mining algorithms to the task of discovering ADs is introduced. The adapted algorithms obtain the set of ADs that hold in a relation with accuracy and support greater than user-defined thresholds. The experiments we have performed show that our approach performs reasonably well over large databases with real-world data.

An Extension of Data Description Language (DDL) for Fuzzy Data Handling

In this paper, we extend an existing relational language such as SQL with capabilities for representing and handling imprecise information. We add new sentences to manage new types based on a domain concept extension. New values for these types are added, including three ones for a more specialized management of NULL values.

An Implementation for Fuzzy Deductive Relational Databases

This chapter shows how to integrate the representation of deductive rules and fuzzy information stored in a relational DBMS to build a module that can obtain new data from data stored in tables. The deductions can be applied to classical (or precise) data, imprecise data or both of them, so it is necessary to provide a mechanism to find the tuples in the database satisfying a rule, i.e. a mechanism to calculate the precision degree of the answer by means of the combination of the precision degrees of every value into an unified measure. Keywords, relational databases extension, fuzzy deduction, inference.

A tuple-oriented algorithm for deduction in a fuzzy relational database

In this paper, we define the concept of generalized rule for making classical deduction with imprecise data, stored both data and rules in a fuzzy relational database represented in the GEFRED model. We propose a way of measuring the imprecision related to the calculation of a fact based on the matching degree of the facts in the database and the facts calculated while expanding the rules. In order to achieve this, classical algorithms for deduction are not appropriated and we propose the modifications that have to be applied on a classical tuple-oriented algorithm in order to design a new algorithm for deducing from imprecise data with generalized rules.