Uncertainty handling in the context of ontology mapping for question answering

This paper describes a framework for integrating similarity measures and Dempster-Shafer belief functions for data integration in the context of multi agent ontology mapping. In order to incorporate uncertainty inherent to the ontology mapping process, we propose utilizing the Dempster-Shafer model for dealing with incomplete and uncertain information produced during the mapping. A novel approach is presented how assessing belief can influence the similarities originally created by both syntactic and semantic similarity algorithms. Our approach is an alternative to the classical Bayesian reasoning which has been investigated for improving the efficiency of creating ontology mappings

DSSim-ontology mapping with uncertainty

This paper introduces an ontology mapping system that is used with a multi agent ontology mapping framework in the context of question answering. Our mapping algorithm incorporates the Dempster Shafer theory of evidence into the mapping process in order to improve the correctness of the mapping. Our main objective was to assess how applying the belief function can improve correctness of the ontology mapping through combining the similarities which were originally created by both syntactic and semantic similarity algorithms. We carried out experiments with the data sets of the Ontology Alignment Evaluation Initiative 2006 which served as a test bed to assess both the strong and weak points of our system. The experiments confirm that our algorithm performs well with both concept and property names

Ontology Mapping with domain specific agents in the AQUA Question Answering System

This paper describes a domain specific multi-agent ontology-mapping solution in the AQUA query answering system. In order to incorporate uncertainty inherent to the mapping process, the system uses the Dempster-Shafer model for dealing with incomplete and uncertain information produced during the mapping. A novel approach is presented how specialized agents with partial local knowledge of the particular domain achieve ontology mapping without creating global or reference ontology. Our approach is particularly fit for a query-answering scenario, where answer needs to be created in real time to satisfy a query posed by the user

Neogeography: The Challenge of Channelling Large and Ill-Behaved Data Streams

Neogeography is the combination of user generated data and experiences with mapping technologies. In this article we present a research project to extract valuable structured information with a geographic component from unstructured user generated text in wikis, forums, or SMSes. The extracted information should be integrated together to form a collective knowledge about certain domain. This structured information can be used further to help users from the same domain who want to get information using simple question answering system. The project intends to help workers communities in developing countries to share their knowledge, providing a simple and cheap way to contribute and get benefit using the available communication technology

DSSim - managing uncertainty on the Semantic Web

Managing uncertainty on the Semantic Web can potentially improve the ontology mapping precision which can lead to better acceptance of systems that operate in this environment. Further ontology mapping in the context of Question Answering can provide more correct results if the mapping process can deal with uncertainty effectively that is caused by the incomplete and inconsistent information used and produced by the mapping process. In this paper we introduce our algorithm called “DSSim” and describe the improvements that we have made compared to OAEI 2006

Viewpoints on emergent semantics

Authors include:Philippe Cudr´e-Mauroux, and Karl Aberer (editors), Alia I. Abdelmoty, Tiziana Catarci, Ernesto Damiani, Arantxa Illaramendi, Robert Meersman, Erich J. Neuhold, Christine Parent, Kai-Uwe Sattler, Monica Scannapieco, Stefano Spaccapietra, Peter Spyns, and Guy De Tr´eWe introduce a novel view on how to deal with the problems of semantic interoperability in distributed systems. This view is based on the concept of emergent semantics, which sees both the representation of semantics and the discovery of the proper interpretation of symbols as the result of a self-organizing process performed by distributed agents exchanging symbols and having utilities dependent on the proper interpretation of the symbols. This is a complex systems perspective on the problem of dealing with semantics. We highlight some of the distinctive features of our vision and point out preliminary examples of its applicatio

Querying Probabilistic Ontologies with SPARQL

In recent years a lot of efforts was put into the field of Semantic Web research to specify knowledge as precisely as possible. However, optimizing for precision alone is not sufficient. The handling of uncertain or incomplete information is getting more and more important and it promises to significantly improve the quality of query answering in Semantic Web applications. My plan is to develop a framework that extends the rich semantics offered by ontologies with probabilistic information, stores this in a probabilistic database and provides query answering with the help of query rewriting. In this proposal I describe how these three aspects can be combined. Especially, I am focusing on how uncertainty is incorporated into the ABox and how it is handled by the database and the rewriter during query answering

Building high-quality merged ontologies from multiple sources with requirements customization

Ontologies are the prime way of organizing data in the Semantic Web. Often, it is necessary to combine several, independently developed ontologies to obtain a knowledge graph fully representing a domain of interest. Existing approaches scale rather poorly to the merging of multiple ontologies due to using a binary merge strategy. Thus, we aim to investigate the extent to which the n-ary strategy can solve the scalability problem. This thesis contributes to the following important aspects: 1. Our n-ary merge strategy takes as input a set of source ontologies and their mappings and generates a merged ontology. For efficient processing, rather than successively merging complete ontologies pairwise, we group related concepts across ontologies into partitions and merge first within and then across those partitions. 2. We take a step towards parameterizable merge methods. We have identified a set of Generic Merge Requirements (GMRs) that merged ontologies might be expected to meet. We have investigated and developed compatibilities of the GMRs by a graph-based method. 3. When multiple ontologies are merged, inconsistencies can occur due to different world views encoded in the source ontologies To this end, we propose a novel Subjective Logic-based method to handling the inconsistency occurring while merging ontologies. We apply this logic to rank and estimate the trustworthiness of conflicting axioms that cause inconsistencies within a merged ontology. 4. To assess the quality of the merged ontologies systematically, we provide a comprehensive set of criteria in an evaluation framework. The proposed criteria cover a variety of characteristics of each individual aspect of the merged ontology in structural, functional, and usability dimensions. 5. The final contribution of this research is the development of the CoMerger tool that implements all aforementioned aspects accessible via a unified interface