Entity search (ES) is a problem finding relevant entities given a query as an entity label. This problem is challenging because many entities could have the same label names, and entities could have many names. Moreover, the query values are noisy, such as abbreviations or misspellings. It is a more challenging problem when the query is expressed in multilingual. (1) Objectives: We introduce an entity search tool called MTabES focused on dealing with noisy queries. In particular, we introduce a reranking function as a weighted fusion of fuzzy search with edit distance, keyword search with BM25 algorithm, and entities' popularities with PageRank scores. MTabES key advantage is the ability to boost the hit rate performance with the fuzzy search. (2) Conclusions: Entity search experimental results on SemTab 2020 and Tough Table datasets show that our toolkit could achieve a higher hit rate than knowledge graphs standard lookups i.g. Wikidata, and Wikipedia. Moreover, MTabES also work efficiently with about five queries/second in MTabES efficiency mode. MTabES toolkit is available at https://github.com/phucty/mtab_tool.
