# A Query Understanding Framework for Earth Data Discovery
## 7 Conclusion and Future Work  
Query understanding focuses on the beginning of the search process and it is significant for capturing a user's search intent. This research reports our findings on a comprehensive query understanding solution to fill the intent gap in Earth data discovery and using oceanographic data query as an example.  
* Qualitative and quantitative evaluation indicates a query understanding framework consisting of spatial and temporal parsing, concept recognition, name entity recognition, and query expansion can capture a user's intent.
* N-gram models trained from metadata can detect reasonable phrases from a query.
* Metadata attributes and descriptions serve as valuable sources to train a customized NER model for user-defined entities. The NER model can recognize the name entities from a query for filters correction or supplement.  
Figure 10: Evaluation of valid query understanding results of 169 synthetic queries.  
Figure 9: Evaluation of query understanding results of 169 synthetic queries.  
* A vocabulary similarity knowledge base built from metadata and Web usage logs provide acronyms and synonyms to phrases detected from the original query.  
Although we used ocean science data as example here, other domain data can be handled similarly with easily replacing of metadata, synonyms, and domain phrases, name entities in a flexible fashion, as noted in the architecture (Figure 6) section. Each component in the framework can be improved or replaced with better solutions, respectively, in the future research. One limitation of the proposed framework is that phrases containing more than three words are undetected. The state-of-the-art methods (e.g., CNN or convolutional neural network) model could solve this problem when enough train data are available, instead of simply increasing the number of n in the n-gram model [43]. Another limitation concerns the complexity of the user input query. The proposed tool concentrates on the phrases and entities in the query and it is unable to understand more complicated queries (e.g., \"what is the temperature data at 700 m higher than the sea surface?\"). In the current research, the spatial and temporal parsing module simply relies on open source APIs. In future research, more spatial and temporal parsing tools can be integrated to provide comprehensive search capability with customized configuration, e.g., converting ocean names to polygons for spatial matching. In addition, customized spatial and temporal parsing tools can be developed to improve the accuracy of the spatiotemporal parsing results. Future research will also integrate the query understanding tool into Big Earth data analytics frameworks [44; 45] and Earth science data centers to make the search function of the existing systems operational.  
The contributions of the respective authors are as follows: [PERSON] and [PERSON] came up with the original idea; [PERSON] and [PERSON] designed and developed the study; [PERSON], [PERSON], [PERSON] and [PERSON] conducted the experiments; [PERSON], [PERSON], and [PERSON] performed the analysis; [PERSON] supervised the research, revised the paper and obtained research funding; and [PERSON] wrote a draft of the manuscript, which was iteratively improved by all other authors of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research is supported by Earth Science Information Partners (ESIP) Lab as an incubator project and based on the NASA AIST MUDROD project.  
The authors declare no conflict of interest.