Combining uncertainty and vagueness in time intervals

Database systems contain data representing properties of real-life objects or concepts. Many of these data represent time indications and such time indications are often subject to imperfections. Although several existing proposals deal with the modeling of uncertainty or vagueness in time indications in database systems, only a few of them summarily examine the interpretation and semantics of such imperfections. The work presented in this paper starts at a more thorough examination of the semantics and modeling of uncertainty or vagueness in time intervals in database systems and presents methods to model combinations of uncertainty and vagueness in time intervals in database systems, based on examinations of their requisite interpretations

Representing Imprecise Time Intervals in OWL 2

International audienceRepresenting and reasoning on imprecise temporal information is a common requirement in the field of Semantic Web. Many works exist to represent and reason on precise temporal information in OWL; however, to the best of our knowledge, none of these works is devoted to imprecise temporal time intervals. To address this problem, we propose two approaches: a crisp-based approach and a fuzzy-based approach. (1) The first approach uses only crisp standards and tools and is modelled in OWL 2. We extend the 4D-fluents model, with new crisp components, to represent imprecise time intervals and qualitative crisp interval relations. Then, we extend the Allen’s interval algebra to compare imprecise time intervals in a crisp way and inferences are done via a set of SWRL rules. (2) The second approach is based on fuzzy sets theory and fuzzy tools and is modelled in Fuzzy-OWL 2. The 4D-fluents approach is extended, with new fuzzy components, in order to represent imprecise time intervals and qualitative fuzzy interval relations. The Allen’s interval algebra is extended in order to compare imprecise time intervals in a fuzzy gradual personalized way. Inferences are done via a set of Mamdani IF-THEN rules

Interval Logic Tensor Networks

In this paper, we introduce Interval Real Logic (IRL), a two-sorted logic that interprets knowledge such as sequential properties (traces) and event properties using sequences of real-featured data. We interpret connectives using fuzzy logic, event durations using trapezoidal fuzzy intervals, and fuzzy temporal relations using relationships between the intervals' areas. We propose Interval Logic Tensor Networks (ILTN), a neuro-symbolic system that learns by propagating gradients through IRL. In order to support effective learning, ILTN defines smoothened versions of the fuzzy intervals and temporal relations of IRL using softplus activations. We show that ILTN can successfully leverage knowledge expressed in IRL in synthetic tasks that require reasoning about events to predict their fuzzy durations. Our results show that the system is capable of making events compliant with background temporal knowledge

Dissemination and geovisualization of territorial entities\u27 history

This paper describes an innovative solution for geovisualization of the demographic and administrative history of French municipalities named communes in French. This solution allows for the open dissemination of such data. The challenge is to provide a web interface for unskilled users in order to help them understand complex information about the demographic evolution of French territories. Our approach combines interactive thematic spatial and temporal views. We describe our architecture based on open-source technologies and the organization of this imperfect geo-historical information in our spatiotemporal database. Our second contribution concerns the concept of an acquaintance graph that has been used to obtain an efficient design with good performance in our geovisualization website

Interval Algebra - an effective means of scheduling surveillance radar networks

Interval Algebra provides an effective means to schedule surveillance radar networks, as it is a temporal ordering constraint language. Thus it provides a solution to a part of resource management, which is included in the revised Data Fusion Information Group model of information fusion. In this paper, the use of Interval Algebra to schedule mechanically steered radars to make multistatic measurements for selected targets of importance is shown. Interval Algebra provides a framework for incorporating a richer set of requirements, without requiring modi cations to the underlying algorithms. The performance of Interval Algebra was compared to that of the Greedy Randomised Adaptive Search Procedure and the applicability of Interval Algebra to nimble scheduling was investigated using Monte-Carlo simulations of a binary radar system. The comparison was done in terms of actual performance as well as in terms of computation time required. The performance of the algorithms was quanti ed by keeping track of the number of targets that could be measured simultaneously. It was found that nimble scheduling is important where the targets are moving fast enough to rapidly change the recognised surveillance picture during a scan. Two novel approaches for implementing Interval Algebra for scheduling surveillance radars are presented. It was found that adding targets on the y and improving performance by incrementally growing the network is more e cient than pre-creating the full network. The second approach stemmed from constraint ordering. It was found that for simple constraint sets, the Interval Algebra relationship matrix reduces to a single vector of interval sets. The simulations revealed that an Interval Algebra algorithm that utilises both approaches can perform as well as the Greedy Randomised Adaptive Search Procedure with similar processing time requirements. Finally, it was found that nimble scheduling is not required for surveillance radar networks where ballistic and supersonic targets can be ignored. Nevertheless, Interval Algebra can easily be used to perform nimble scheduling with little modi - cation and may be useful in scheduling the scans of multifunction radars.Council for Scientific and Industrial Research, the University of Cape Town and the University of Pretoria.http://www.elsevier.com/locate/inffushb201

Temporal Information Extraction and Knowledge Base Population

Temporal Information Extraction (TIE) from text plays an important role in many Natural Language Processing and Database applications. Many features of the world are time-dependent, and rich temporal knowledge is required for a more complete and precise understanding of the world. In this thesis we address aspects of two core tasks in TIE. First, we provide a new corpus of labeled temporal relations between events and temporal expressions, dense enough to facilitate a change in research directions from relation classification to identification, and present a system designed to address corresponding new challenges. Second, we implement a novel approach for the discovery and aggregation of temporal information about entity-centric fluent relations

Temporal reasoning about fuzzy intervals

Traditional approaches to temporal reasoning assume that time periods and time spans of events can be accurately represented as intervals. Real-world time periods and events, on the other hand, are often characterized by vague temporal boundaries, requiring appropriate generalizations of existing formalisms. This paper presents a framework for reasoning about qualitative and metric temporal relations between vague time periods. In particular, we show how several interesting problems, like consistency and entailment checking, can be reduced to reasoning tasks in existing temporal reasoning frameworks. We furthermore demonstrate that all reasoning tasks of interest are NP-complete, which reveals that adding vagueness to temporal reasoning does not increase its computational complexity. To support efficient reasoning, a large tractable subfragment is identified, among others, generalizing the well-known ORD Horn subfragment of the Interval Algebra (extended with metric constraints)