Personal named entity linking based on simple partial tree matching and context free grammar

Personal name disambiguation is the task of linking a personal name to a unique comparable entry in the real world, also known as named entity linking (NEL). Algorithms for NEL consist of three main components: extractor, searcher, and disambiguator. Existing approaches for NEL use exact-matched look-up over the surface form to generate a set of candidate entities in each of the mentioned names. The exact-matched look-up is wholly inadequate to generate a candidate entity due to the fact that the personal names within a web page lack uniform representation. In addition, the performance of a disambiguator in ranking candidate entities is limited by context similarity. Context similarity is an inflexible feature for personal disambiguation because natural language is highly variable. We propose a new approach that can be used to both identify and disambiguate personal names mentioned on a web page. Our NEL algorithm uses: as an extractor: a control flow graph; AlchemyAPI, as a searcher: Personal Name Transformation Modules (PNTM) based on Context Free Grammar and the Jaro-Winkler text similarity metric and as a disambiguator: the entity coherence method: the Occupation Architecture for Personal Name Disambiguation (OAPnDis), personal name concepts and Simple Partial Tree Matching (SPTM). Experimental results, evaluated on real-world data sets, show that the accuracy of our NEL is 92%, which is higher than the accuracy of previously used methods

Integrating Spatial-Temporal Risk Factors for an Ambulance Allocation Strategy: A Case Study in Bangkok

Dedicated emergency medical services (EMS) are important to patients’ chances of survival. In particular, the quicker such services arrive at the scene of an incident, the higher the survival rate. Therefore, the management of ambulance bases is an essential aspect of emergency medical services. Further, the locations of ambulance bases are determined based on patient demand. However, in practice, many elements should be taken into account in a risk assessment of given areas within a locale. Specifically, each area should be assessed for the number and severity of accidents that ordinarily take place there, the number and size of the public events it hosts, its population density, and the number of elderly people resident. In this study, we use a spatial-temporal approach to integrate those factors into a risk assessment of areas relative to each other in a locale. Based on this risk assessment, we determine the optimal locations for ambulance bases in order to minimize response time. We validate our approach using Bangkok as a case study

Allocation strategy for an ambulance base under traffic congestion

One of crucial issues for emergency medical service (EMS) is to reduce response time. However, in metropolis city, a traffic congestion is an obstacle for an ambulance to responsively reach at the scene, then patient mortality and disability rates increase. Traffic congestion is considered as a complex spatial–temporal situation. It is often triggered by repeating factors, such as car lane capacity, weather, and unexpected events. Therefore, a real-time traffic condition is required to effectively determine the location of an ambulance. The current ambulance base allocation strategy model considers only demand point, resulting inability to handle high traffic congestion. This paper proposed a covering model based on traffic congestion (using Google map API) to allocate ambulance bases that covering all demand point, while minimizing the number of the ambulance. In addition, our model was applied to the case study of Bangkok EMS

Integrating Spatial-Temporal Risk Factors for an Ambulance Allocation Strategy: A Case Study in Bangkok

Dedicated emergency medical services (EMS) are important to patients’ chances of survival. In particular, the quicker such services arrive at the scene of an incident, the higher the survival rate. Therefore, the management of ambulance bases is an essential aspect of emergency medical services. Further, the locations of ambulance bases are determined based on patient demand. However, in practice, many elements should be taken into account in a risk assessment of given areas within a locale. Specifically, each area should be assessed for the number and severity of accidents that ordinarily take place there, the number and size of the public events it hosts, its population density, and the number of elderly people resident. In this study, we use a spatial-temporal approach to integrate those factors into a risk assessment of areas relative to each other in a locale. Based on this risk assessment, we determine the optimal locations for ambulance bases in order to minimize response time. We validate our approach using Bangkok as a case study