Semantic Event Model and Its Implication on Situation Detection

Events are at the core of reactive applications, which have become popular in many domains. Contemporary modeling tools lack the capability express the event semantics and relationships to other entities. This research is aimed at providing the system designer a tool to define and describe events and their relationships to other events, object and tasks. It follows the semantic data modeling approach, and applies it to events, by using the classification, aggregation, generalization and association abstractions in the event world. The model employs conditional generalizations that are specific to the event domain, and determine conditions in which an event that is classified to lower level class, is considered as a member of a higher-level event class, for the sake of reaction to the event. The paper describes the event model, its knowledge representation scheme and its properties, and demonstrates these properties through a comprehensive example

Semantic Event Model and its Implication on Situation Detection

Abstract -Events are at the core of reactive applications, which have become popular in many domains. Contemporary modeling tools lack the capability express the event semantics and relationships to other entities. This research is aimed at providing the system designer a tool to define and describe events and their relationships to other events, object and tasks. It follows the semantic data modeling approach, and applies it to events, by using the classification, aggregation, generalization and association abstractions in the event world. The model employs conditional generalizations that are specific to the event domain, and determine conditions in which an event that is classified to lower level class, is considered as a member of a higher-level event class, for the sake of reaction to the event. The paper describes the event model, its knowledge representation scheme and its properties, and demonstrates these properties through a comprehensive example

Quantum Non-Objectivity from Performativity of Quantum Phenomena

We analyze the logical foundations of quantum mechanics (QM) by stressing non-objectivity of quantum observables which is a consequence of the absence of logical atoms in QM. We argue that the matter of quantum non-objectivity is that, on the one hand, the formalism of QM constructed as a mathematical theory is self-consistent, but, on the other hand, quantum phenomena as results of experimenter's performances are not self-consistent. This self-inconsistency is an effect of that the language of QM differs much from the language of human performances. The first is the language of a mathematical theory which uses some Aristotelian and Russellian assumptions (e.g., the assumption that there are logical atoms). The second language consists of performative propositions which are self-inconsistent only from the viewpoint of conventional mathematical theory, but they satisfy another logic which is non-Aristotelian. Hence, the representation of quantum reality in linguistic terms may be different: from a mathematical theory to a logic of performative propositions. To solve quantum self-inconsistency, we apply the formalism of non-classical self-referent logics

Using Description Logics for Recognising Textual Entailment

The aim of this paper is to show how we can handle the Recognising Textual Entailment (RTE) task by using Description Logics (DLs). To do this, we propose a representation of natural language semantics in DLs inspired by existing representations in first-order logic. But our most significant contribution is the definition of two novel inference tasks: A-Box saturation and subgraph detection which are crucial for our approach to RTE

Semantics-driven event clustering in Twitter feeds

Detecting events using social media such as Twitter has many useful applications in real-life situations. Many algorithms which all use different information sources - either textual, temporal, geographic or community features - have been developed to achieve this task. Semantic information is often added at the end of the event detection to classify events into semantic topics. But semantic information can also be used to drive the actual event detection, which is less covered by academic research. We therefore supplemented an existing baseline event clustering algorithm with semantic information about the tweets in order to improve its performance. This paper lays out the details of the semantics-driven event clustering algorithms developed, discusses a novel method to aid in the creation of a ground truth for event detection purposes, and analyses how well the algorithms improve over baseline. We find that assigning semantic information to every individual tweet results in just a worse performance in F1 measure compared to baseline. If however semantics are assigned on a coarser, hashtag level the improvement over baseline is substantial and significant in both precision and recall

State-of-the-art on evolution and reactivity

This report starts by, in Chapter 1, outlining aspects of querying and updating resources on the Web and on the Semantic Web, including the development of query and update languages to be carried out within the Rewerse project. From this outline, it becomes clear that several existing research areas and topics are of interest for this work in Rewerse. In the remainder of this report we further present state of the art surveys in a selection of such areas and topics. More precisely: in Chapter 2 we give an overview of logics for reasoning about state change and updates; Chapter 3 is devoted to briefly describing existing update languages for the Web, and also for updating logic programs; in Chapter 4 event-condition-action rules, both in the context of active database systems and in the context of semistructured data, are surveyed; in Chapter 5 we give an overview of some relevant rule-based agents frameworks

Logic and reasoning in jokes

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