Understanding user’s search intent in vertical websites like IT service crowdsourcing platform relies heavily on domain knowledge. Meanwhile, searching for services accurately on crowdsourcing platforms is still difficult, because these platforms do not contain enough information to support high-performance search. To solve these problems, we build and leverage a knowledge graph named ITServiceKG to enhance search performance of crowdsourcing IT services. The main ideas are to (1) build an IT service knowledge graph from Wikipedia, Baidupedia, CN-DBpedia, StuQ and data in IT service crowdsourcing platforms, (2) use properties and relations of entities in the knowledge graph to expand user query and service information, and (3) apply a listwise approach with relevance features and topic features to re-rank the search results. The results of our experiments indicate that our approach outperforms the traditional search approaches.

Mining search intents in vertical websites like IT service crowdsourcing platform relies heavily on domain knowledge. Meanwhile, it still remains a difficulty of searching services in crowdsourcing platforms, as these platforms do contain much insufficient information, for example, users tend to use images describing IT services for the purpose of advertisements. To solve these problems, we build and leverage a knowledge graph to enhance searching of crowdsourcing IT services. The key idea is to (1) build an IT service knowledge graph from StackOverflow tag synonym system, Wikipedia, StuQ and data in IT service crowdsourcing platforms, (2) plug two activities into the basic search processterm expansion and service re-ranking, (3) use superordinates, hypernyms, synonyms, descriptions and relations of entities in the knowledge graph to expand user query and service information, and (4) apply a learning-to-rank model with four features to re-rank the search results, enforcing those more relevant services have the higher-ranking position. We have conducted several experiments to evaluate our approach. The results show that our approach achieves an MRR 34.9% higher and a Recall@15 11% higher than those of a basic search approach.