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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.

Coding conventions are a set of coding guidelines used by software developers to improve the readability of source code, increase software maintainability, and promote the reuse of coding patterns. In this paper, we introduce CCBase, a knowledge base of coding conventions, that was constructed from online resources. Specifically, CCBase was constructed as follows. We designed the ontology of the coding convention domain, crawled data related to coding conventions from a variety of online resources, and then extracted entities and relations using an NLP-enabled rule matching method. To uncover the latent relations, we further proposed a similarity metric to reveal the similar-to and relate-to relations, and developed a RCE algorithm to establish a unified type hierarchy of coding conventions. The resulting knowledge base contains 3139 coding conventions for Java and C++, with 3761 entities and 767 relations. Furthermore, we have extended the usability of CCBase by developing a question answering system on the base. We have conducted experiments to evaluate CCBase. The experimental results show that CCBase has a wide coverage on entities and relations in coding conventions domain, and the QA system achieves an F1 score of 84.5% on 214 questions raised in StackOverflow.

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Trigger-action (TA) programming is a programming paradigm that allows end-users to automate and connect IoT devices and online services using if-trigger-then-action rules. Early studies have demonstrated this paradigms usability, but more recent work has also highlighted complexities that arise in realistic scenarios. To facilitate end-users in TA programming, we propose AutoTAR, a context-aware conversational recommendation technique for recommending TA rules. AutoTAR leverages a TA knowledge graph to encode semantic features and abstract functionalities of rules, and then takes a two-phase method to recommend TA rules to end-users: during the context-aware recommendation phase, it elicits user preferences from programming context and recommends the top-N rules using a mixed content and collaborative technique; during the conversational recommendation phase, it justifies recommendations by iteratively raising questions and collecting feedback from end-users. We evaluate AutoTAR on Mturk and real data collected from the IFTTT community. The results show that our method outperforms state-of-the-arts significantly — its context-aware recommendation outperforms RecRules by 26% on R@5 and 21% on NDCG@5; its conversational recommendation outperforms LARecommender (a conversational recommender with the LA model) by 67.64% on accuracy. In addition, AutoTAR is effective in solving three problems frequently occurring in TA rule recommendations, i.e., the cold-start problem, the repeat-consumption problem, and the incomplete-intent problem.