Ontology revision on the semantic web: integration of belief revision theory

The vision of the Semantic Web is to enable content of web resources to be interpreted and processed by software agents. Ontology provides a means to share and reuse data associated with web resources in a manner that can be autonomously performed by software agents. In the context of knowledge representation, ontology represents the abstract world of web resources in the Semantic Web. The Semantic Web will comprise of small, simple ontologies constructed by individual users. It is unlikely that ontology will be built from scratch each time. On the other hand, it is more likely that ontology will be adopted and modified from existing ontology. Why is ontology revision important? Very often, ontology exists in a particular period of timeline is designed based on the purpose of a specific domain of interest at that instance of time. Over time, ontology needs to be revised due to changes in domain, content, requirements, or structural representation. In this regard, ontology is the beliefs that the agents need to reference to in order to perform task in an autonomous way. As ontology evolves, beliefs in agents also evolve and knowledge gained by agents must be reflected in the ontology. This research investigates issues of ontology revision from the theoretical foundation of the belief revision theory. The AGM model of the coherence theory in belief revision is of particular relevant in this research. The AGM model uses three operations of expansion, contraction and revision in conjunction with the concept of epistemic entrenchment to revise the belief set. This research develops an ontology revision framework to manage the ontology revision process. The research will also illustrate a vision in which the practicability of this approach can be applied in e-commerce

Developing ontology revision framework: A case study on the use of the coherence theory for semantic shopping mall

Why is ontology revision important? Very often, ontology exists in a particular period of timeline is often designed based on the purpose of a domain of interest at that instance of time. However over time, ontology needs to be revised due to changes in content, environment, requirements, or even structural representation. As a result, revision and updating of necessary components in the pre-defined ontology is unavoidable. When this happens, it is important to ensure that revision is conducted in a consistent manner so that it does not result in unforseen redundancies and inconsistencies. Any revision performed must be accompanied by a rational change to be dealt with from the consistency perspective. This paper presents an ontology revision approach to achieve this aim based on the coherence theory model of belief revision theory. An application scenario of semantic shopping mall is used to demonstrate the approach

Discovering Beaten Paths in Collaborative Ontology-Engineering Projects using Markov Chains

Biomedical taxonomies, thesauri and ontologies in the form of the International Classification of Diseases (ICD) as a taxonomy or the National Cancer Institute Thesaurus as an OWL-based ontology, play a critical role in acquiring, representing and processing information about human health. With increasing adoption and relevance, biomedical ontologies have also significantly increased in size. For example, the 11th revision of the ICD, which is currently under active development by the WHO contains nearly 50,000 classes representing a vast variety of different diseases and causes of death. This evolution in terms of size was accompanied by an evolution in the way ontologies are engineered. Because no single individual has the expertise to develop such large-scale ontologies, ontology-engineering projects have evolved from small-scale efforts involving just a few domain experts to large-scale projects that require effective collaboration between dozens or even hundreds of experts, practitioners and other stakeholders. Understanding how these stakeholders collaborate will enable us to improve editing environments that support such collaborations. We uncover how large ontology-engineering projects, such as the ICD in its 11th revision, unfold by analyzing usage logs of five different biomedical ontology-engineering projects of varying sizes and scopes using Markov chains. We discover intriguing interaction patterns (e.g., which properties users subsequently change) that suggest that large collaborative ontology-engineering projects are governed by a few general principles that determine and drive development. From our analysis, we identify commonalities and differences between different projects that have implications for project managers, ontology editors, developers and contributors working on collaborative ontology-engineering projects and tools in the biomedical domain.Comment: Published in the Journal of Biomedical Informatic

An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise

Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.Comment: 25 pages, 6 figures, 5 tables, minor revision

Embedding defeasible argumentation in the semantic web: an ontology-based approach

The SemanticWeb is a project intended to create a universal medium for information exchange by giving semantics to the content of documents on the Web by means of ontology definitions. Ontologies intended for knowledge representation in intelligent agents rely on common-sense reasoning formalizations. Defeasible argumentation has emerged as a successful approach to model common-sense reasoning. Recent research has linked argumentation with belief revision in order to model the dynamics of knowledge. This paper outlines an approach which combines ontologies, argumentation and belief revision by defining an ontology algebra. We suggest how different aspects of ontology integration can be defined in terms of defeasible argumentation and belief revision.Eje: Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

Ontology Revision on the Semantic Web: Integration of belief revision theory

Ontology is used to define terms and relations on the Semantic Web to form well-structured semantics of Web resources. Ontology revision refers to the process of updating ontology to ensure changes are made in a consistent manner. Belief revision theory deals with approaches to ensure consistency in the belief sets is maintained when beliefs need to be revised. This paper discusses the integration of belief revision theory to the ontology reengineering method as a means to ensure consistency in ontology revision

Hematopoietic Cell Types: Prototype for a Revised Cell Ontology

The Cell Ontology (CL) is an OBO Foundry candidate ontology intended for the representation of cell types from all of biology. A recent workshop sponsored by NIAID on hematopoietic cell types in the CL addressed issues of both the content and structure of the CL. The section of the ontology dealing with hematopoietic cells was extensively revised, and plans were made for restructuring these cell type terms as cross-products with logical definitions based on relationships to external ontologies, such as the Protein Ontology and the Gene Ontology. The improvements to the CL in this area represent a paradigm for the future revision of the whole of the CL

Revision in networks of ontologies

euzenat2015aInternational audienceNetworks of ontologies are made of a collection of logic theories, called ontologies, related by alignments. They arise naturally in distributed contexts in which theories are developed and maintained independently, such as the semantic web. In networks of ontologies, inconsistency can come from two different sources: local inconsistency in a particular ontology or alignment, and global inconsistency between them. Belief revision is well-defined for dealing with ontologies; we investigate how it can apply to networks of ontologies. We formulate revision postulates for alignments and networks of ontologies based on an abstraction of existing semantics of networks of ontologies. We show that revision operators cannot be simply based on local revision operators on both ontologies and alignments. We adapt the partial meet revision framework to networks of ontologies and show that it indeed satisfies the revision postulates. Finally, we consider strategies based on network characteristics for designing concrete revision operators