An intelligent system for semi-automatic evolution of ontologies

Lecture Notes in Computer Science, 2010

This paper presents novel algorithms to support the continuous development of ontologies; i.e. the development of ontologies during their use in social semantic bookmarking, semantic wiki or other social semantic applications. Our goal is to assist users in placing a newly added concept in a concept hierarchy. The proposed algorithm is evaluated using a data set from Wikipedia and provides good quality recommendation. These results point to novel possibilities to apply machine learning technologies to support social semantic applications.

Lecture Notes in Computer Science, 2002

The idea of a "Semantic Web" has created a lot of interest in the use of ontologies-formal descriptions of a part of the world-for describing the meaning of information on the web. However, when ontologies are used on the web, several problems appear: how can different ontologies be combined, what happens when they are changed, and how should they be adapted for new tasks. Those questions require the management of ontologies, their use and their evolution. This paper describes a research project that will investigate the management of ontologies in an web-based setting.

Lecture Notes in Computer Science, 2005

Large information repositories as digital libraries, online shops, etc. rely on a taxonomy of the objects under consideration to structure the vast contents and facilitate browsing and searching (e.g., ACM topic classification for computer science literature, Amazon product taxonomy, etc.). As in heterogenous communities users typically will use different parts of such an ontology with varying intensity, customization and personalization of the ontologies is desirable. Of particular interest for supporting users during the personalization are collaborative filtering systems which can produce personal recommendations by computing the similarity between own preferences and the one of other people. In this paper we adapt a collaborative filtering recommender system to assist users in the management and evolution of their personal ontology by providing detailed suggestions of ontology changes. Such a system has been implemented in the context of Bibster, a peer-to-peer based personal bibliography management tool. Finally, we report on an experiment with the Bibster community that shows the performance improvements over nonpersonalized recommendations. 1

2013

Ontology evolution continues to be an important problem that needs further research. Key challenges in ontology evolution and creation of a highly consumable ontology include accomodating: (a) the subtle changes in the meaning of a model element over time, (b) the changing relevance of various parts of the model to the user, and (c) the complexity in representing time-varying semantics of model elements in a dynamic domain. In this work, we address the challenge of evolving an ontology to keep up with the domain changes while focusing on the utility of its content for relevance and imposing constraints for performance. We propose a novel evidence accumulation framework as a principled approach for ontology evolution, which is sufficiently expressive and semantically clear. Our approach classifies model elements (e.g., concepts) into three categories: definitely relevant (that must be included in the ontology), potentially relevant (that can be kept as backup), and irrelevant (that should be removed). Further, our approach dynamically re-classifies models based on external triggers like evidence or internal triggers, like the age of a model in the ontology. As a result, users will have an ontology which is both effective and efficient. We evaluate our approach based on two measures - ontology concept retention and ontology concept placement. This comprehensive evaluation in a single framework is novel and we show that our approach yields promising results.

2005

Large information repositories as digital libraries, online shops, etc. rely on a taxonomy of the objects under consideration to structure the vast contents and facilitate browsing and searching (e.g., ACM topic classification for computer science literature, Amazon product taxonomy, etc.). As in heterogeneous communities users typically will use different parts of such an ontology with varying intensity, customization and personalization of the ontologies is desirable. In this paper we adapt a collaborative filtering recommender system to assist users in the management and evolution of their personal ontology by providing detailed suggestions of ontology changes. Such a system has been implemented in the context of Bibster, a peer-to-peer based personal bibliography management tool. Finally, we report on an in-situ experiment with the Bibster community that shows the performance improvements over nonpersonalized recommendations.

Manuscript, Vrije Universiteit Brussel, 2008

Ontologies are becoming nowadays a recurrent topic in many research areas of computer science. Among other things, they are used as best model for representing knowledge of a given field of expertise. More specifically, the evolution of Semantic Web mostly relies on the use of ontologies in describing various web resources. However, the world is changing, so do resources and knowledge. Consequently, ontologies on which are based knowledge and resources are also called to evolve. One of the solutions helping to ensure this evolution is to manage the development process in order to guarantee supported services as well as some quality requirements. Many tools have been designed to ensure designing/editing of ontologies, but none of them has enough functions to ensure their evolution. Evolution of ontology considered as its adaptation to changes in the field is a key issue in the ontology engineering field. Several studies have been focused on this problem, but proposed solutions did not manage all the side effects that may derive from the evolution. This article proposes an extension of MS-ONTO [1] called POOE. POOE not only supports the evolution effects on a double plan, externally and internally (verification of integrity constraints, quality analysis of the evolved ontology) but it constitutes a true validation tool of a change log.

… : Ontologies and the …, 2002

With rising importance of knowledge interchange, many industrial and academic applications have adopted ontologies as their conceptual backbone. However, industrial and academic environments are very dynamic, thus inducing changes to application requirements. To fulfill these changes, often the underlying ontology must be evolved as well. As ontologies grow in size, the complexity of change management increases, thus requiring a wellstructured ontology evolution process. In this paper we identify a possible sixphase evolution process and focus on providing the user with capabilities to control and customize it. We introduce the concept of an evolution strategy encapsulating policy for evolution with respect to user's requirements.

2008

One of the current bottlenecks for automating ontology evolution is resolving the right links between newly arising information and the existing knowledge in the ontology. Most of existing approaches mainly rely on the user when it comes to capturing and representing new knowledge. Our ontology evolution framework intends to reduce or even eliminate user input through the use of background knowledge. In this paper, we show how various sources of background knowledge could be exploited for relation discovery.

International Journal of Computer Applications, 2018

Ontologies are formal artifacts that are designed to represent the knowledge related to a specific or generic domain in terms of the relevant concepts, relationships between these concepts and the instances of these concepts. Ontology evolution can be defined as the process to adapt and change the ontology in a timely and consistent manner. In this paper, we present a brief description of ontology evolution process of recent research.