Uncertainty in Ontologies: Dempster-Shafer Theory for Data Fusion Applications

Nowadays ontologies present a growing interest in Data Fusion applications. As a matter of fact, the ontologies are seen as a semantic tool for describing and reasoning about sensor data, objects, relations and general domain theories. In addition, uncertainty is perhaps one of the most important characteristics of the data and information handled by Data Fusion. However, the fundamental nature of ontologies implies that ontologies describe only asserted and veracious facts of the world. Different probabilistic, fuzzy and evidential approaches already exist to fill this gap; this paper recaps the most popular tools. However none of the tools meets exactly our purposes. Therefore, we constructed a Dempster-Shafer ontology that can be imported into any specific domain ontology and that enables us to instantiate it in an uncertain manner. We also developed a Java application that enables reasoning about these uncertain ontological instances.Comment: Workshop on Theory of Belief Functions, Brest: France (2010

Towards Log-Linear Logics with Concrete Domains

We present $\mathcal{MEL}^{++}$ (M denotes Markov logic networks) an extension of the log-linear description logics $\mathcal{EL}^{++}$-LL with concrete domains, nominals, and instances. We use Markov logic networks (MLNs) in order to find the most probable, classified and coherent $\mathcal{EL}^{++}$ ontology from an $\mathcal{MEL}^{++}$ knowledge base. In particular, we develop a novel way to deal with concrete domains (also known as datatypes) by extending MLN's cutting plane inference (CPI) algorithm.Comment: StarAI201

An approach to description logic with support for propositional attitudes and belief fusion

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-89765-1_8Revised Selected and Invited Papers of ISWC International Workshops, URSW 2005-2007.In the (Semantic) Web, the existence or producibility of certain, consensually agreed or authoritative knowledge cannot be assumed, and criteria to judge the trustability and reputation of knowledge sources may not be given. These issues give rise to formalizations of web information which factor in heterogeneous and possibly inconsistent assertions and intentions, and make such heterogeneity explicit and manageable for reasoning mechanisms. Such approaches can provide valuable metaknowledge in contemporary application fields, like open or distributed ontologies, social software, ranking and recommender systems, and domains with a high amount of controversies, such as politics and culture. As an approach to this, we introduce a lean formalism for the Semantic Web which allows for the explicit representation of controversial individual and group opinions and goals by means of so-called social contexts, and optionally for the probabilistic belief merging of uncertain or conflicting statements. Doing so, our approach generalizes concepts such as provenance annotation and voting in the context of ontologies and other kinds of Semantic Web knowledgeThis work was partially funded by the German National Research Foundation DFG (Br609/13-1, research project “Open Ontologies and Open Knowledge Bases”) and by the Spanish National Plan of R+D, project no. TSI2005-08225-C07-0

An extended HD Fluent Analysis of Temporal knowledge in OWL-based clinical Guideline System

The Web Ontology Language (OWL) based clinical guideline system is a kind of clinical decision support system which is often used to assist health professionals to find clinical recommendations from the guidelines and check clinical compliance issues in terms of the guideline recommendations. However, due to some limitations of the current OWL language constructs, temporal knowledge contained in various knowledge domains cannot be directly represented in OWL. As a result, the representation, query and reasoning of temporal knowledge are largely ignored in many OWL-based clinical guideline ontology systems. The aim of this research is to investigate a temporal knowledge modelling method namely “4D fluent” and extend it to represent the temporal constraints contained in clinical guideline recommendations within OWL language constructs. The extended 4D fluent method can model temporal constraints including valid calendar time, interval, duration, repetitive or cyclical temporal constraints and temporal relations such that it can enable reasoning over these temporal constraints in the OWL-based clinical guideline ontology system and overcome the shortcoming of the traditional OWL-based clinical guideline system to an extent

Fusing Automatically Extracted Annotations for the Semantic Web

This research focuses on the problem of semantic data fusion. Although various solutions have been developed in the research communities focusing on databases and formal logic, the choice of an appropriate algorithm is non-trivial because the performance of each algorithm and its optimal configuration parameters depend on the type of data, to which the algorithm is applied. In order to be reusable, the fusion system must be able to select appropriate techniques and use them in combination. Moreover, because of the varying reliability of data sources and algorithms performing fusion subtasks, uncertainty is an inherent feature of semantically annotated data and has to be taken into account by the fusion system. Finally, the issue of schema heterogeneity can have a negative impact on the fusion performance. To address these issues, we propose KnoFuss: an architecture for Semantic Web data integration based on the principles of problem-solving methods. Algorithms dealing with different fusion subtasks are represented as components of a modular architecture, and their capabilities are described formally. This allows the architecture to select appropriate methods and configure them depending on the processed data. In order to handle uncertainty, we propose a novel algorithm based on the Dempster-Shafer belief propagation. KnoFuss employs this algorithm to reason about uncertain data and method results in order to refine the fused knowledge base. Tests show that these solutions lead to improved fusion performance. Finally, we addressed the problem of data fusion in the presence of schema heterogeneity. We extended the KnoFuss framework to exploit results of automatic schema alignment tools and proposed our own schema matching algorithm aimed at facilitating data fusion in the Linked Data environment. We conducted experiments with this approach and obtained a substantial improvement in performance in comparison with public data repositories