3 research outputs found
Memetic algorithms for ontology alignment
2011 - 2012Semantic interoperability represents the capability of two or more systems to
meaningfully and accurately interpret the exchanged data so as to produce
useful results. It is an essential feature of all distributed and open knowledge
based systems designed for both e-government and private businesses, since it
enables machine interpretation, inferencing and computable logic.
Unfortunately, the task of achieving semantic interoperability is very difficult
because it requires that the meanings of any data must be specified in an
appropriate detail in order to resolve any potential ambiguity.
Currently, the best technology recognized for achieving such level of precision
in specification of meaning is represented by ontologies. According to the
most frequently referenced definition [1], an ontology is an explicit
specification of a conceptualization, i.e., the formal specification of the
objects, concepts, and other entities that are presumed to exist in some area of
interest and the relationships that hold them [2]. However, different tasks or
different points of view lead ontology designers to produce different
conceptualizations of the same domain of interest. This means that the
subjectivity of the ontology modeling results in the creation of heterogeneous
ontologies characterized by terminological and conceptual discrepancies.
Examples of these discrepancies are the use of different words to name the
same concept, the use of the same word to name different concepts, the
creation of hierarchies for a specific domain region with different levels of
detail and so on. The arising so-called semantic heterogeneity problem
represents, in turn, an obstacle for achieving semantic interoperability... [edited by author]XI n.s
Exploiting general-purpose background knowledge for automated schema matching
The schema matching task is an integral part of the data integration process. It is usually the first step in integrating data. Schema matching is typically very complex and time-consuming. It is, therefore, to the largest part, carried out by humans. One reason for the low amount of automation is the fact that schemas are often defined with deep background knowledge that is not itself present within the schemas. Overcoming the problem of missing background knowledge is a core challenge in automating the data integration process.
In this dissertation, the task of matching semantic models, so-called ontologies, with the help of external background knowledge is investigated in-depth in Part I. Throughout this thesis, the focus lies on large, general-purpose resources since domain-specific resources are rarely available for most domains. Besides new knowledge resources, this thesis also explores new strategies to exploit such resources.
A technical base for the development and comparison of matching systems is presented in Part II. The framework introduced here allows for simple and modularized matcher development (with background knowledge sources) and for extensive evaluations of matching systems.
One of the largest structured sources for general-purpose background knowledge are knowledge graphs which have grown significantly in size in recent years. However, exploiting such graphs is not trivial. In Part III, knowledge graph em- beddings are explored, analyzed, and compared. Multiple improvements to existing approaches are presented.
In Part IV, numerous concrete matching systems which exploit general-purpose background knowledge are presented. Furthermore, exploitation strategies and resources are analyzed and compared. This dissertation closes with a perspective on real-world applications
A FML-based fuzzy tuning for a memetic ontology alignment system
Ontology alignment systems are software tools aimed at producing a set of correspondences, called alignment, between two heterogeneous ontologies in order to bring them in a mutual agreement. Performing this task is an essential step to allow the exchange of information between people, organizations and web applications using ontologies for representing their view of the world. Currently, in spite of several ontology alignment systems have been developed, there is no a robust solution that seems capable of producing alignments with the same high quality on different alignment task instances. Mainly, this weakness of ontology alignment systems is due to the dependence of their behavior on a set of specific instance parameters. This work proposes to improve performance of a well-known memetic algorithm based ontology alignment system by adaptively regulating its specific instance parameters through a FML-based fuzzy tuning. The validity of our proposal is shown by aligning ontologies belonging to two well-known OAEI datasets and by performing a Wilcoxon's signed rank test which highlights that our proposal statistically outperforms its not fuzzy adaptive counterpart