SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Lifting

Learning knowledge graph (KG) embeddings is an emerging technique for a variety of downstream tasks such as summarization, link prediction, information retrieval, and question answering. However, most existing KG embedding models neglect space and, therefore, do not perform well when applied to (geo)spatial data and tasks. For those models that consider space, most of them primarily rely on some notions of distance. These models suffer from higher computational complexity during training while still losing information beyond the relative distance between entities. In this work, we propose a location-aware KG embedding model called SE-KGE. It directly encodes spatial information such as point coordinates or bounding boxes of geographic entities into the KG embedding space. The resulting model is capable of handling different types of spatial reasoning. We also construct a geographic knowledge graph as well as a set of geographic query-answer pairs called DBGeo to evaluate the performance of SE-KGE in comparison to multiple baselines. Evaluation results show that SE-KGE outperforms these baselines on the DBGeo dataset for geographic logic query answering task. This demonstrates the effectiveness of our spatially-explicit model and the importance of considering the scale of different geographic entities. Finally, we introduce a novel downstream task called spatial semantic lifting which links an arbitrary location in the study area to entities in the KG via some relations. Evaluation on DBGeo shows that our model outperforms the baseline by a substantial margin.Comment: Accepted to Transactions in GI

Semantically-Enriched Search Engine for Geoportals: A Case Study with ArcGIS Online

Many geoportals such as ArcGIS Online are established with the goal of improving geospatial data reusability and achieving intelligent knowledge discovery. However, according to previous research, most of the existing geoportals adopt Lucene-based techniques to achieve their core search functionality, which has a limited ability to capture the user's search intentions. To better understand a user's search intention, query expansion can be used to enrich the user's query by adding semantically similar terms. In the context of geoportals and geographic information retrieval, we advocate the idea of semantically enriching a user's query from both geospatial and thematic perspectives. In the geospatial aspect, we propose to enrich a query by using both place partonomy and distance decay. In terms of the thematic aspect, concept expansion and embedding-based document similarity are used to infer the implicit information hidden in a user's query. This semantic query expansion 1 2 G. Mai et al. framework is implemented as a semantically-enriched search engine using ArcGIS Online as a case study. A benchmark dataset is constructed to evaluate the proposed framework. Our evaluation results show that the proposed semantic query expansion framework is very effective in capturing a user's search intention and significantly outperforms a well-established baseline-Lucene's practical scoring function-with more than 3.0 increments in DCG@K (K=3,5,10).Comment: 18 pages; Accepted to AGILE 2020 as a full paper GitHub Code Repository: https://github.com/gengchenmai/arcgis-online-search-engin

Computational Time and Space Tradeoffs in Geo Knowledge Graphs

Over the past several years, the Web of Linked Data has continued to grow in size, both in terms of the breadth of domains covered as well as the depth and precision of knowledge. As a consequence to this growth, the community has been led to confront challenges that arise from incorporating large-scale geographic information into knowledge graphs. These challenges include data quality, data storage, data transmission, and the scaling of geospatial query processing. A crucial concern in real-time computing is about striking a balance between the time complexity of algorithms and memory consumption or data storage (i.e., space). Given a computational problem and the domain of its inputs, there are several decisions that researchers, engineers, and practitioners must make based on the constraints of available computational resources, as well as the desired program's `reaction' time for the sake of human-computer interaction. Understanding how to strike such a balance requires a thorough understanding of the data structures and algorithms used to solve a problem. Geospatial data and geospatial queries in particular require innovators to possess deep background knowledge in order to research and develop viable solutions. As a geographic information scientist working with Linked Data, I attempt to improve the quality, accessibility, reliability, and query performance of geographic data in knowledge graphs. In this dissertation, I study three specific trade-offs: (i) whether certain geographic properties and relations should be computed on-demand or materialized beforehand; (ii) whether carefully precomputing topological relations is more useful than providing users with geometries to compute topological relations on-demand; and finally, (iii) whether the challenges of hosting public geographic knowledge graph services on the Web can be mitigated, and at what cost, by a peer-to-peer architecture in which the clients possess more intelligence

Computational Time and Space Tradeoffs in Geo Knowledge Graphs

Over the past several years, the Web of Linked Data has continued to grow in size, both in terms of the breadth of domains covered as well as the depth and precision of knowledge. As a consequence to this growth, the community has been led to confront challenges that arise from incorporating large-scale geographic information into knowledge graphs. These challenges include data quality, data storage, data transmission, and the scaling of geospatial query processing. A crucial concern in real-time computing is about striking a balance between the time complexity of algorithms and memory consumption or data storage (i.e., space). Given a computational problem and the domain of its inputs, there are several decisions that researchers, engineers, and practitioners must make based on the constraints of available computational resources, as well as the desired program's `reaction' time for the sake of human-computer interaction. Understanding how to strike such a balance requires a thorough understanding of the data structures and algorithms used to solve a problem. Geospatial data and geospatial queries in particular require innovators to possess deep background knowledge in order to research and develop viable solutions. As a geographic information scientist working with Linked Data, I attempt to improve the quality, accessibility, reliability, and query performance of geographic data in knowledge graphs. In this dissertation, I study three specific trade-offs: (i) whether certain geographic properties and relations should be computed on-demand or materialized beforehand; (ii) whether carefully precomputing topological relations is more useful than providing users with geometries to compute topological relations on-demand; and finally, (iii) whether the challenges of hosting public geographic knowledge graph services on the Web can be mitigated, and at what cost, by a peer-to-peer architecture in which the clients possess more intelligence

Understanding the Spatial, Platial, and Temporal Properties of Cryptocurrency Ecosystems

Cryptocurrencies and their underlying technologies such as blockchains and smart contracts are rapidly gaining traction in sectors such as banking, identity management, supply chain management, cloud-computing, voting, forecasting, and so forth. With this change in visibility and first signs of mainstream adoption, there is a growing interest in understanding the cryptocurrency ecosystem, e.g., regarding market trends or inherent risks. Interestingly, however, spatial and platial aspects have not yet received much attention. One possible reason for this lack of analysis may be due to the perception of cryptocurrencies being global and living outside of legal frameworks. We will show that this is a misconception and that understanding the cryptocurrency ecosystem requires looking at the spaces and places involved in their creation, consumption, and regulation

Genomewide Clonal Analysis of Lethal Mutations in the Drosophila melanogaster Eye: Comparison of the X Chromosome and Autosomes

Using a large consortium of undergraduate students in an organized program at the University of California, Los Angeles (UCLA), we have undertaken a functional genomic screen in the Drosophila eye. In addition to the educational value of discovery-based learning, this article presents the first comprehensive genomewide analysis of essential genes involved in eye development. The data reveal the surprising result that the X chromosome has almost twice the frequency of essential genes involved in eye development as that found on the autosomes