# A Query Understanding Framework for Earth Data Discovery
## 2 Related Research  
Earth data discovery has been an active research area in the past few years [8]. Many geospatial information data management portals have been deployed across the world to support interactive data access [9; 10], e.g., the European space agency's sentinel online data portal--Copernicus Open Access Hub [11], CIESIN, which serves data for climate, population, soil, etc. [12], the FAO GeoNetwork that contains data, like soil, population, and land use data from global to regional scale [13]. 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]). A few existing libraries are available for spatial and temporal parsing. For example, the Google Maps Geocoding API and CLAVIN [1] convert the location names into geographic coordinates. Stanford Temporal Tagger (SUTime) [5] tags the temporal component of a query. Some Earth data search engines have adopted CLAVIN and SUTime to parse the spatial and temporal range from user queries [7]. [PERSON], e.g., developed a fuzzy grammar and theory-based natural language user interface for spatial queries [21]. However, very few attempted to parse and tag the non-spatial and temporal components of the query syntax, which usually consists of entities, like geophysical variable, instrument name, and processing level. Phrase detection has been applied to large-scale natural language data processing in the search engine and data discovery community [22]. Named entity recognition has been used for learning dictionaries with minimal supervision [23]. Keyword-basedgeospatial data search engines regard input queries as a bag of words without considering the order, and return results, including at least one word in the query. Advanced geospatial search engines that provide customized configurations or \"Google-like\" syntax require data consumers/publishers to learn relevant knowledge. We propose a method of phrase detection to address this challenge, and named entity recognition from user query by leveraging metadata and portal usage data. In addition, a few Earth science researchers have been devoted to building semantic models or ontologies in the Geoscience domain to make domain knowledge computable, organizable, and transparent in standard and formal structure. These semantic models include, but are not limited to, thesauri [24], glossaries [25], and the geospatial ontology Semantic Web for Earth and Environmental Terminology (SWEET) [26]. Taking SWEET, for example, it organizes high-level concepts and relations in the geospatial domain. Concepts and their relations in a knowledge base provide a valuable source for discovering the vocabulary linkages, which can be adopted for reasoning and smart search. However, most existing Earth Science ontologies are not useable in the current research, because the concepts in the ontologies do not cover or are inconsistent with terms in the metadata, since some strong and heavyweight ontologies are very high-level and not detailed enough and others are lightweight or implicit [27]. Developing ontologies from scratch is time-intensive and labor-intensive; an alternative way is to discover latent semantic relationships semi-automatically from user behavior and metadata [19].