Semantic-based decision support for remote care of dementia patients

This paper investigates the challenges in developing a semantic-based Dementia Care Decision Support System based on the non-intrusive monitoring of the patient's behaviour. Semantic-based approaches are well suited for modelling context-aware scenarios similar to Dementia care systems, where the patient's dynamic behaviour observations (occupants movement, equipment use) need to be analysed against the semantic knowledge about the patient's condition (illness history, medical advice, known symptoms) in an integrated knowledgebase. However, our research findings establish that the ability of semantic technologies to reason upon the complex interrelated events emanating from the behaviour monitoring sensors to infer knowledge assisting medical advice represents a major challenge. We attempt to address this problem by introducing a new approach that relies on propositional calculus modelling to segregate complex events that are amenable for semantic reasoning from events that require pre-processing outside the semantic engine before they can be reasoned upon. The event pre-processing activity also controls the timing of triggering the reasoning process in order to further improve the efficiency of the inference process. Using regression analysis, we evaluate the response-time as the number of monitored patients increases and conclude that the incurred overhead on the response time of the prototype decision support systems remains tolerable

Utilising semantic technologies for decision support in dementia care

The main objective of this work is to discuss our experience in utilising semantic technologies for building decision support in Dementia care systems that are based on the non-intrusive on the non-intrusive monitoring of the patient’s behaviour. Our approach adopts context-aware modelling of the patient’s condition to facilitate the analysis of the patient’s behaviour within the inhabited environment (movement and room occupancy patterns, use of equipment, etc.) with reference to the semantic knowledge about the patient’s condition (history of present of illness, dependable behaviour patterns, etc.). The reported work especially focuses on the critical role of the semantic reasoning engine in inferring medical advice, and by means of practical experimentation and critical analysis suggests important findings related to the methodology of deploying the appropriate semantic rules systems, and the dynamics of the efficient utilisation of complex event processing technology in order to the meet the requirements of decision support for remote healthcare systems

Semantic enabled complex event language for business process monitoring

Efforts are being made to enable business process monitoring and analysis through processing continuously generated events. Several ontologies and tools have been defined and implemented to allow applying general-purpose Business Process Analysis techniques to specific domains. On this basis, a Semantic Enabled Monitoring Event Language (SEMEL) is proposed to facilitate defining complex queries over monitoring data so as to interleave temporal and ontological reasoning. In this paper, the formal semantics of SEMEL is discussed, and the implementation approach of SEMEL interpreter is also briefly described, which encompasses translation into an operational language

Semantic Pivoting Model for Effective Event Detection

Event Detection, which aims to identify and classify mentions of event instances from unstructured articles, is an important task in Natural Language Processing (NLP). Existing techniques for event detection only use homogeneous one-hot vectors to represent the event type classes, ignoring the fact that the semantic meaning of the types is important to the task. Such an approach is inefficient and prone to overfitting. In this paper, we propose a Semantic Pivoting Model for Effective Event Detection (SPEED), which explicitly incorporates prior information during training and captures semantically meaningful correlations between input and events. Experimental results show that our proposed model achieves state-of-the-art performance and outperforms the baselines in multiple settings without using any external resources.Comment: 11 pages, 4 figures; Accepted to ACIIDS 202

Semantic reasoning for intelligent emergency response applications

Emergency response applications require the processing of large amounts of data, generated by a diverse set of sensors and devices, in order to provide for an accurate and concise view of the situation at hand. The adoption of semantic technologies allows for the definition of a formal domain model and intelligent data processing and reasoning on this model based on generated device and sensor measurements. This paper presents a novel approach to emergency response applications, such as fire fighting, integrating a formal semantic domain model into an event-based decision support system, which supports reasoning on this model. The developed model consists of several generic ontologies describing concepts and properties which can be applied to diverse context-aware applications. These are extended with emergency response specific ontologies. Additionally, inference on the model performed by a reasoning engine is dynamically synchronized with the rest of the architectural components. This allows to automatically trigger events based on predefined conditions. The proposed ontology and developed reasoning methodology is validated on two scenarios, i.e. (i) the construction of an emergency response incident and corresponding scenario and (ii) monitoring of the state of a fire fighter during an emergency response

Gesture semantics reconstruction based on motion capturing and complex event processing

A fundamental problem in manual based gesture semantics reconstruction is the specification of preferred semantic concepts for gesture trajectories. This issue is complicated by problems human raters have annotating fast-paced three dimensional trajectories. Based on a detailed example of a gesticulated circular trajectory, we present a data-driven approach that covers parts of the semantic reconstruction by making use of motion capturing (mocap) technology. In our FA3ME framework we use a complex event processing approach to analyse and annotate multi-modal events. This framework provides grounds for a detailed description of how to get at the semantic concept of circularity observed in the data

Semantic chunking

Long sentences pose a challenge for natural language processing (NLP) applications. They are associated with a complex information structure leading to increased requirements for processing resources. Although the issue is present in many areas of research, there is little uniformity in the solutions used by research communities dedicated to individual NLP applications. Different aspects of the problem are addressed by different tasks, such as sentence simplification or shallow chunking. The main contribution of this thesis is the introduction of the task of semantic chunking as a general approach to reducing the cost of processing long sentences. The goal of semantic chunking is to find semantically contained fragments of a sentence representation that can be processed independently and recombined without loss of information. We anchor its principles in established concepts of semantic theory, in particular event and situation semantics. Most of the experiments in this thesis focus on semantic chunking defined on complex semantic representations in Dependency Minimal Recursion Semantics (DMRS), but we also demonstrate that the task can be performed on sentence strings. We present three chunking models: a) rule-based proof-of-concept DMRS chunking system; b) a semi-supervised sequence labelling neural model for surface semantic chunking; c) a system capable of finding semantic chunk boundaries based on the inherent structure of DMRS graphs, generalisable in the form of descriptive templates. We show how semantic chunking can be applied within a divide-and-conquer processing paradigm, using as an example the task of realization from DMRS. The application of semantic chunking yields noticeable efficiency gains without decreasing the quality of results

Beyond gender stereotypes in language comprehension: self sex-role descriptions affect the brain’s potentials associated with agreement processing

We recorded Event-Related Potentials to investigate differences in the use of gender information during the processing of reflexive pronouns. Pronouns either matched the gender provided by role nouns (such as “king” or “engineer”) or did not. We compared two types of gender information, definitional information, which is semantic in nature (a mother is female), or stereotypical (a nurse is likely to be female). When they followed definitional role-nouns, gender-mismatching pronouns elicited a P600 effect reflecting a failure in the agreement process. When instead the gender violation occurred after stereotypical role-nouns the Event Related Potential response was biphasic, being positive in parietal electrodes and negative in anterior left electrodes. The use of a correlational approach showed that those participants with more “feminine” or “expressive” self sex-role descriptions showed a P600 response for stereotype violations, suggesting that they experienced the mismatch as an agreement violation; whereas less “expressive” participants showed an Nref effect, indicating more effort spent in linking the pronouns with the possible, although less likely, counter-stereotypical referent