A comprehensive methodology for discovering semantic relationships among geospatial vocabularies using oceanographic data discovery as an example

Jiang, Yongyao; Li, Yun; Yang, Chaowei; Liu, Kai; Armstrong, E. M.; Huang, Thomas S.; Moroni, David; Finch, Christopher J.

doi:10.1080/13658816.2017.1357819

Cited by 14 publications

(21 citation statements)

References 21 publications

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“…Some of the data portals support data discovery through utilizing distributed search and analytical engine, such as Lucene [4] and Elasticsearch [14], some are built upon comprehensive data portal platforms, such as GeoNetwork [3], GeoNode [15], and CKAN [16]. Various researches have focused on optimizing the search process, such as optimizing the search ranking of retrieved datasets [17], supporting reasoning with semantic ontologies [18,19], and providing query understanding [5], to further improve data search capability. Query understanding serves as a communication channel between users and the search engine before the search engine retrieves and ranks results, and in so doing tries to understand search queries and interpret the intent of a query through multiple methods (e.g., extraction of the semantic meaning of the searcher's keywords [5,20]).…”

Section: Related Researchmentioning

confidence: 99%

A Query Understanding Framework for Earth Data Discovery

Jiang

Goldstein

et al. 2020

Applied Sciences

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One longstanding complication with Earth data discovery involves understanding a user’s search intent from the input query. Most of the geospatial data portals use keyword-based match to search data. Little attention has focused on the spatial and temporal information from a query or understanding the query with ontology. No research in the geospatial domain has investigated user queries in a systematic way. Here, we propose a query understanding framework and apply it to fill the gap by better interpreting a user’s search intent for Earth data search engines and adopting knowledge that was mined from metadata and user query logs. The proposed query understanding tool contains four components: spatial and temporal parsing; concept recognition; Named Entity Recognition (NER); and, semantic query expansion. Spatial and temporal parsing detects the spatial bounding box and temporal range from a query. Concept recognition isolates clauses from free text and provides the search engine phrases instead of a list of words. Name entity recognition detects entities from the query, which inform the search engine to query the entities detected. The semantic query expansion module expands the original query by adding synonyms and acronyms to phrases in the query that was discovered from Web usage data and metadata. The four modules interact to parse a user’s query from multiple perspectives, with the goal of understanding the consumer’s quest intent for data. As a proof-of-concept, the framework is applied to oceanographic data discovery. It is demonstrated that the proposed framework accurately captures a user’s intent.

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Section: Related Researchmentioning

confidence: 99%

A Query Understanding Framework for Earth Data Discovery

Jiang

Goldstein

et al. 2020

Applied Sciences

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“…As introduced above, a domain ontology has been created for the PD platform, containing 146 PD concepts and relationships among these concepts, i.e., "SubClassOf" (Hyponymy) as shown in Figure 3. Jiang et al [23] proposed two methodologies to measure concept similarity in the ontology with the following two equations:…”

Section: Smart Searchmentioning

confidence: 99%

“…The distance between X and Y is measured by accumulating the value of Edge function (Equation (1)). Jiang et al [23] proposed two methodologies to measure concept similarity in the ontology with the following two equations: where Edge (Type) indicates the relationship between two concepts. If the relation is "SubClassOf," the function returns 1, and otherwise returns infinity.…”

Section: Smart Searchmentioning

confidence: 99%

Planetary Defense Mitigation Gateway: A One-Stop Gateway for Pertinent PD-Related Contents

Shams

Yang

et al. 2019

Data

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Planetary Defense (PD) has become a critical effort of protecting our home planet by discovering potentially hazardous objects (PHOs), simulating the potential impact, and mitigating the threats. Due to the lack of structured architecture and framework, pertinent information about detecting and mitigating near earth object (NEO) threats are still dispersed throughout numerous organizations. Scattered and unorganized information can have a significant impact at the time of crisis, resulting in inefficient processes, and decisions made on incomplete data. This PD Mitigation Gateway (pd.cloud.gmu.edu) is developed and embedded within a framework to integrate the dispersed, diverse information residing at different organizations across the world. The gateway offers a home to pertinent PD-related contents and knowledge produced by the NEO mitigation team and the community through (1) a state-of-the-art smart-search discovery engine based on PD knowledge base; (2) a document archiving and understanding mechanism for managing and utilizing the results produced by the PD science community; (3) an evolving PD knowledge base accumulated from existing literature, using natural language processing and machine learning; and (4) a 4D visualization tool that allows the viewers to analyze near-Earth approaches in a three-dimensional environment using dynamic, adjustable PHO parameters to mimic point-of-impact asteroid deflections via space vehicles and particle system simulations. Along with the benefit of accessing dispersed data from a single port, this framework is built to advance discovery, collaboration, innovation, and education across the PD field-of-study, and ultimately decision support.Data 2019, 4, 47 2 of 15 for NEOs, both for asteroids and comets (such as albedo, brightness, shape, and phase). The scientific interest in comets and asteroids is mainly due to their possible collision with our planet, representing a hazard to life on Earth [1]. As many scientific types of research highlight, impacts of NEOs have contributed to mass extinctions and evolution. Moreover, it is a proven fact that NEOs will continue to hit the Earth at irregular intervals in the future [2]. The impactors range from benign fireballs, through the largest airbursts, to globally disastrous destruction on the ground, which are very unlikely to occur in any given lifetime but are probably randomly distributed in time. For events of harmless fireballs, the methods of civil defense are sufficient for saving human lives. For more massive airbursts, changing the path of the near-Earth objects reaching the Earth's vicinity is the appropriate solution. For the global catastrophic events that cause mass extinctions, there is no current technology capable of avoiding disaster [3].Several studies and explorations have been established in global organizations to mitigate the potential impact of near-Earth objects. Programs like NASA's NEO Survey share necessary information with the public that can be utilized to support decision-making for impact mitigati...

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“…In fact, if more sub-surface/deep datasets are made available on PO.DAAC, the proposed method can automatically update the similarity according to the user access pattern. The search recall and precision can be improved by query expansion based on these synonymous queries, which has been systematically evaluated at Jiang, Li [26]. The third query is "ghrsst" with the similarity value of 0.83.…”

Section: Use Casesmentioning

confidence: 99%

“…The similarity results are stored in the knowledge base and updated periodically. More details can be found at Jiang, Li [26]. The highly-related terms along with their associated similarity values can be used for query expansion and suggestion.…”

Section: Semantic Similarity Calculatormentioning

confidence: 99%

A Smart Web-Based Geospatial Data Discovery System with Oceanographic Data as an Example

Jiang

Yang

et al. 2018

IJGI

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Discovering and accessing geospatial data presents a significant challenge for the Earth sciences community as massive amounts of data are being produced on a daily basis. In this article, we report a smart web-based geospatial data discovery system that mines and utilizes data relevancy from metadata user behavior. Specifically, (1) the system enables semantic query expansion and suggestion to assist users in finding more relevant data; (2) machine-learned ranking is utilized to provide the optimal search ranking based on a number of identified ranking features that can reflect users' search preferences; (3) a hybrid recommendation module is designed to allow users to discover related data considering metadata attributes and user behavior; (4) an integrated graphic user interface design is developed to quickly and intuitively guide data consumers to the appropriate data resources. As a proof of concept, we focus on a well-defined domain-oceanography and use oceanographic data discovery as an example. Experiments and a search example show that the proposed system can improve the scientific community's data search experience by providing query expansion, suggestion, better search ranking, and data recommendation via a user-friendly interface.

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A comprehensive methodology for discovering semantic relationships among geospatial vocabularies using oceanographic data discovery as an example

Cited by 14 publications

References 21 publications

A Query Understanding Framework for Earth Data Discovery

A Query Understanding Framework for Earth Data Discovery

Planetary Defense Mitigation Gateway: A One-Stop Gateway for Pertinent PD-Related Contents

A Smart Web-Based Geospatial Data Discovery System with Oceanographic Data as an Example

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