Spatial Keywords Research Articles

A personalized query method for spatial keywords in indoor environments

AbstractWith the rapid development of indoor Location Based Services (LBS), a growing volume of textual data is being generated in indoor environments. Consequently, indoor spatial keyword query holds significant potential for development in the coming years. However, existing methods for indoor spatial keyword queries often neglect the personalized needs of users. To solve this problem, we propose an Indoor Spatial Keyword Personalized Query (ISKPQ) method. First, a novel index structure called ISKIR-tree has been designed. This index integrates Hilbert encoding techniques and introduces Bloom filters and distance matrices to enhance the efficiency of processing indoor spatial keyword problems. Subsequently, an efficient pruning algorithm based on the ISKIR-tree index is proposed to refine the dataset effectively. Finally, a comprehensive scoring function that considers text similarity, spatial proximity, and user preferences is introduced to score and rank the pruned data points, thereby filtering out the optimal query results that meet users’ personalized requirements. Theoretical analysis and experimental studies demonstrate the outstanding performance of the proposed method in terms of both efficiency and accuracy.

Islam, Science and Education: Delving into the Progress, Collaboration and Biases

Despite the fact that Islam being one of/counts among the major religions of the world, scientific progress in Muslim majority countries seems to be lagging behind the rest of the world. Therefore, there have been calls by Muslim scholars and clerics to bridge the gap between Islam and science, especially for young Muslims, by harmonising science and Islam in education. This study aims to analyze the documents pertaining to Islam, Science, and Education (ISE) extracted from the historical literature. For the purpose of analysis, firstly documents were retrieved from a scientific database and secondly, a descriptive analysis of temporal trends was conducted. The documents clustered according to as the time-lined categories were and then analysed qualitatively. Later, using VOS viewer, the analysis of spatial distribution, networks, and keywords was conducted. It was found that the evolution of ISE converged into a broader spectrum from earlier in medicine and later to other areas, which encompassed Qur’ānic studies, social studies and economics, and even technology. The volume of publication versus the network links strength provided evidence that there is a need to stimulate collaborative works by/through knowledge dissemination through/from an authoritative platform for the researches concerning ISE. This study also discovered that some severe biases that might contribute towards the results being skewed in the finding or even underrepresentation of scholarly works in ISE. The findings of this study are beneficial for the researchers reviewing this topic and the educators working to harmonize Islam and science in education.

Extracting Entities of Emergent Events from Social Streams Based on a Data-Cluster Slicing Approach for Ontology Engineering

In this paper, we describe our work on developing a model and method for extracting key entities from the online social messages regarding emergent events for enhancing ontology engineering, enabling a sensible solution for prevention of similar disasters. Our work started with the development of an event modelling system using a data-cluster slicing approach, which combines analytics of social data and event lifecycle algorithms, allowing for large-scale emerging novel events to be quickly and accurately analyzed. Subsequently, our system computes the energy of each collected event data sets, and then encapsulates ranked temporal, spatial and topical keywords into a structured node for event-entity extraction, in order to update event ontologies for fast response of emergent events. The preliminary experimental results demonstrate that our developed system is workable, allowing for prediction of possible evolution and early warning of critical incidents with a support of dynamic entity extraction.