Ontology-based data integration

About: Ontology-based data integration is a research topic. Over the lifetime, 11065 publications have been published within this topic receiving 216888 citations.

Ontology-based news recommendation

Abstract: Recommending news items is traditionally done by term-based algorithms like TF-IDF. This paper concentrates on the benefits of recommending news items using a domain ontology instead of using a term-based approach. For this purpose, we propose Athena, which is an extension to the existing Hermes framework. Athena employs a user profile to store terms or concepts found in news items browsed by the user. Based on this information, the framework uses a traditional method based on TF-IDF, and several ontology-based methods to recommend new articles to the user. The paper concludes with the evaluation of the different methods, which shows that the new ontology-based method that we propose in this paper performs better (w.r.t. accuracy, precision, and recall) than the traditional method and, with the exception of one measure (recall), also better than the other considered ontology-based approaches.

Ontology research and development. Part 2 - a review of ontology mapping and evolving

Abstract: This is the second of a two-part paper to review ontology research and development, in particular, ontology mapping and evolving. Ontology is defined as a formal explicit specification of a shared conceptualization. Ontology itself is not a static model so that it must have the potential to capture changes of meanings and relations. As such, mapping and evolving ontologies is part of an essential task of ontology learning and development. Ontology mapping is concerned with reusing existing ontologies, expanding and combining them by some means and enabling a larger pool of information and knowledge in different domains to be integrated to support new communication and use. Ontology evolving, likewise, is concerned with maintaining existing ontologies and extending them as appropriate when new information or knowledge is acquired. It is apparent from the reviews that current research into semi-automatic or automatic ontology research in all the three aspects of generation, mapping and evolving have so far ...

Matching unstructured vocabularies using a background ontology

Abstract: Existing ontology matching algorithms use a combination of lexical and structural correspondence between source and target ontologies. We present a realistic case-study where both types of overlap are low: matching two unstructured lists of vocabulary used to describe patients at Intensive Care Units in two different hospitals. We show that indeed existing matchers fail on our data. We then discuss the use of background knowledge in ontology matching problems. In particular, we discuss the case where the source and the target ontology are of poor semantics, such as flat lists, and where the background knowledge is of rich semantics, providing extensive descriptions of the properties of the concepts involved. We evaluate our results against a Gold Standard set of matches that we obtained from human experts.

Ontology construction for information selection

Abstract: Technology in the field of digital media generates huge amounts of non-textual information, audio, video, and images, along with more familiar textual information. The potential for exchange and retrieval of information is vast and daunting. The key problem in achieving efficient and user-friendly retrieval is the development of a search mechanism to guarantee delivery of minimal irrelevant information (high precision) while ensuring relevant information is not overlooked (high recall). The traditional solution employs keyword-based search. The only documents retrieved are those containing user specified keywords. But many documents convey desired semantic information without containing these keywords. One can overcome this problem by indexing documents according to meanings rather than words, although this will entail a way of converting words to meanings and the creation of ontology. We have solved the problem of an index structure through the design and implementation of a concept-based model using domain-dependent ontology. Ontology is a collection of concepts and their interrelationships, which provide an abstract view of an application domain. We propose a new mechanism that can generate ontology automatically in order to make our approach scalable. For this we modify the existing self-organizing tree algorithm (SOTA) that constructs a hierarchy from top to bottom. Furthermore, in order to find an appropriate concept for each node in the hierarchy we propose an automatic concept selection algorithm from WordNet called linguistic ontology. To illustrate the effectiveness of our automatic ontology construction method, we have explored our ontology construction in text documents. The Reuters21578 text document corpus has been used. We have observed that our modified SOTA outperforms hierarchical agglomerative clustering (HAC).

PR-OWL: A Framework for Probabilistic Ontologies

Abstract: Across a wide range of domains, there is an urgent need for a well-founded approach to incorporating uncertain and incomplete knowledge into formal domain ontologies. Although this subject is receiving increasing attention from ontology researchers, there is as yet no broad consensus on the definition of a probabilistic ontology and on the most suitable approach to extending current ontology languages to support uncertainty. This paper presents two contributions to developing a coherent framework for probabilistic ontologies: (1) a formal definition of a probabilistic ontology, and (2) an extension of the OWL Web Ontology Language that is consistent with our formal definition. This extension, PR-OWL, is based on Multi-Entity Bayesian Networks (MEBN), a first-order Bayesian logic that unifies Bayesian probability with First-Order Logic. As such, PR-OWL combines the full representation power of OWL with the flexibility and inferential power of Bayesian logic.