  Search is one of the key functionalities in digital platforms and
applications such as an electronic dictionary, a search engine, and an
e-commerce platform. While the search function in some languages is trivial,
Khmer word search is challenging given its complex writing system. Multiple
orders of characters and different spelling realizations of words impose a
constraint on Khmer word search functionality. Additionally, spelling mistakes
are common since robust spellcheckers are not commonly available across the
input device platforms. These challenges hinder the use of Khmer language in
search-embedded applications. Moreover, due to the absence of WordNet-like
lexical databases for Khmer language, it is impossible to establish semantic
relation between words, enabling semantic search. In this paper, we propose a
set of robust solutions to the above challenges associated with Khmer word
search. The proposed solutions include character order normalization, grapheme
and phoneme-based spellcheckers, and Khmer word semantic model. The semantic
model is based on the word embedding model that is trained on a 30-million-word
corpus and is used to capture the semantic similarities between words.
