# A Query Understanding Framework for Earth Data Discovery
## 1 Introduction  
Effectively discovering Earth science data is challenging, given the data's increased volume, decreased latency, and heterogeneity across a wide variety of domains [1]. One longstanding problem with Earth data discovery is interpreting a user's search intent from the user's input query. While Google has a \"did you mean this... \" feature and Amazon has the \"search instead for... \" capability, most geospatial search engines do not support such kind of search features. Geospatial search engines that rely on keyword-based match search strategy and lack the capability of phrase query. For example, the query \"level 2\" is converted to \"level\" or \"2\" to retrieve data and dataset containing either \"level\" or \"2\" will return to the searcher. However, the real intent of the user is to find the dataset of processing level 2. While humans understand the intent of a query, it is difficult for a machine to capture thedesired meaning, because user queries are just a list of words and they commonly have one or several of the following characteristics: incomplete sentences that are hard for machine's syntax analysis; plethora of acronyms in-lieu of full-names; absence of semantic context; and, descriptions of the same object in different forms. Some geospatial data portals have made great efforts to boost their search capabilities by introducing advanced technologies from computer science domain or customized configurations, e.g., PO.DAAC data portal introduced \"Google-like\" query syntax to support phrase query with input query \"level 2\" [2]; GeoNetwork [3], an open-source, distributed spatial information management system, indexes geospatial data, and supports data discovery upon Lucene [4]. Relevant keywords will not be smashed apart in the indexing and searching process, with advanced configuration of indexed fields and search fields. Although these features optimize the search performance, it requires users to learn related syntax or understand the indexing workflow. Implicit knowledge hidden in metadata and Web usage data are extracted in our research to improve query understanding to meet this gap and reduce the workload of data consumers/publishers. Domain experts generate the metadata and they contain reliable terminologies in the domain. A query understanding framework is proposed to better interpret users' search intents in Earth data search engines by analyzing metadata and user query logs with advanced Natural language processing algorithms to make good use of valuable query-related knowledge hidden in metadata and Web usage data [5; 6; 7]. The query understanding framework consists of four major steps: spatial and temporal parsing; concept recognition; named entity recognition (NER); and, semantic query expansion. With the four components, a user input query is rewritten to a new query, which helps a search engine capture a user's search intents by (1) detecting spatial and temporal range of a query; (2) searching with concepts and their synonyms instead of independent words; and, (3) narrowing search scope with name entity being recognized from the query. An oceanographic data discovery portal is utilized for evaluating the utility of the query understanding framework.