Upper ontology

About: Upper ontology is a research topic. Over the lifetime, 9767 publications have been published within this topic receiving 220721 citations. The topic is also known as: top-level ontology & foundation ontology.

OntoDB: an ontology-based database for data intensive applications

Abstract: Recently, several approaches and systems were proposed to store in the same database data and the ontologies describing their meanings. We call these databases, ontology-based databases (OBDBs). Ontology-based data denotes those data that represent ontology individuals (i.e., instance of ontology classes). To speed up query execution on the top of these OBDBs, efficient representations of ontology-based data become a new challenge. Two main representation schemes have been proposed for ontology-based data: vertical and binary representations with a variant called hybrid. In these schemes, each instance is split into a number of tuples. In this paper, we propose a new representation of ontology-based data, called table per class. It consists in associating a table to each ontology class, where all property values of a class instance are represented in a same row. Columns of this table represent those properties of the ontology class that are associated with a value for at least one instance of this class. We present the architecture of our ontology-based databases and a comparison of the effectiveness of our representation scheme with the existing ones used in Semantic Web applications. Our benchmark involves three categories of queries: (1) targeted class queries, where users know the classes they are querying, (2) no targeted class queries, where users do not know the class(es) they are querying, and (3) update queries.

Ontology-based Information Retrieval

Abstract: In this thesis, we will present methods for introducing ontologies in information retrieval. The main hypothesis is that the inclusion of conceptual knowledge such as ontologies in the information retrieval process can contribute to the solution of major problems currently found in information retrieval. This utilization of ontologies has a number of challenges. Our focus is on the use of similarity measures derived from the knowledge about relations between concepts in ontologies, the recognition of semantic information in texts and the mapping of this knowledge into the ontologies in use, as well as how to fuse together the ideas of ontological similarity and ontological indexing into a realistic information retrieval scenario. To achieve the recognition of semantic knowledge in a text, shallow natural language processing is used during indexing that reveals knowledge to the level of noun phrases. Furthermore, we briefly cover the identification of semantic relations inside and between noun phrases, as well as discuss which kind of problems are caused by an increase in compoundness with respect to the structure of concepts in the evaluation of queries. Measuring similarity between concepts based on distances in the structure of the ontology is discussed. In addition, a shared nodes measure is introduced and, based on a set of intuitive similarity properties, compared to a number of different measures. In this comparison the shared nodes measure appears to be superior, though more computationally complex. Some of the major problems of shared nodes which relate to the way relations differ with respect to the degree they bring the concepts they connect closer are discussed. A generalized measure called weighted shared nodes is introduced to deal with these problems. Finally, the utilization of concept similarity in query evaluation is discussed. A semantic expansion approach that incorporates concept similarity is introduced and a generalized fuzzy set retrieval model that applies expansion during query evaluation is presented. While not commonly used in present information retrieval systems, it appears that the fuzzy set model comprises the flexibility needed when generalizing to an ontology-based retrieval model and, with the introduction of a hierarchical fuzzy aggregation principle, compound concepts can be handled in a straightforward and natural manner.

ON ONTOLOGY MATCHING PROBLEMS - for building a corporate Semantic Web in a multi-communities organization

Abstract: Ontologies are nowadays used in many domains such as Semantic Web, information systems... to represent meaning of data and data sources. In the framework of knowledge management in an heterogeneous organization, the materialization of the organizational memory in a “corporate semantic web” may require to integrate the various ontologies of the different groups of this organization. To be able to build a corporate semantic web in an heterogeneous, multi-communities organization, it is essential to have methods for comparing, aligning, integrating or mapping different ontologies. This paper proposes a new algorithm for matching two ontologies based on all the information available about the given ontologies (e.g. their concepts, relations, information about the structure of each hierarchy of concepts, or of relations), applying TF/IDF scheme (a method widely used in the information retrieval community) and integrating WordNet (an electronic lexical database) in the process of ontology matching.

Methodology for development and employment of ontology based knowledge management applications

Abstract: In this article we illustrate a methodology for introducing and maintaining ontology based knowledge management applications into enterprises with a focus on Knowledge Processes and Knowledge Meta Processes. While the former process circles around the usage of ontologies, the latter process guides their initial set up. We illustrate our methodology by an example from a case study on skills management.

Developing an Ontology for Cyber Security Knowledge Graphs

Abstract: In this paper we describe an ontology developed for a cyber security knowledge graph database. This is intended to provide an organized schema that incorporates information from a large variety of structured and unstructured data sources, and includes all relevant concepts within the domain. We compare the resulting ontology with previous efforts, discuss its strengths and limitations, and describe areas for future work.