Knowledge Graph and Deep Learning-based Text-to-GQL Model for Intelligent Medical Consultation Chatbot

Text-to-GQL (Text2GQL) is a task that converts the user's questions into GQL (Graph Query Language) when a graph database is given. That is a task of semantic parsing that transforms natural language problems into logical expressions, which will bring more efficient direct communication between humans and machines. The existing related work mainly focuses on Text-to-SQL tasks, and there is no available semantic parsing method and data set for the graph database. In order to fill the gaps in this field to serve the medical Human–Robot Interactions (HRI) better, we propose this task and a pipeline solution for the Text2GQL task. This solution uses the Adapter pre-trained by “the linking of GQL schemas and the corresponding utterances" as an external knowledge introduction plug-in. By inserting the Adapter into the language model, the mapping between logical language and natural language can be introduced faster and more directly to better realize the end-to-end human–machine language translation task. In the study, the proposed Text2GQL task model is mainly constructed based on an improved pipeline composed of a Language Model, Pre-trained Adapter plug-in, and Pointer Network. This enables the model to copy objects' tokens from utterances, generate corresponding GQL statements for graph database retrieval, and builds an adjustment mechanism to improve the final output. And the experiments have proved that our proposed method has certain competitiveness on the counterpart datasets (Spider, ATIS, GeoQuery, and 39.net) converted from the Text2SQL task, and the proposed method is also practical in medical scenarios

Migrating to GraphQL: A Practical Assessment

GraphQL is a novel query language proposed by Facebook to implement Web-based APIs. In this paper, we present a practical study on migrating API clients to this new technology. First, we conduct a grey literature review to gain an in-depth understanding on the benefits and key characteristics normally associated to GraphQL by practitioners. After that, we assess such benefits in practice, by migrating seven systems to use GraphQL, instead of standard REST-based APIs. As our key result, we show that GraphQL can reduce the size of the JSON documents returned by REST APIs in 94% (in number of fields) and in 99% (in number of bytes), both median results.Comment: 11 pages. Accepted at 26th International Conference on Software Analysis, Evolution and Reengineerin

Knowledge Graph and Deep Learning-based Text-to-GQL Model for Intelligent Medical Consultation Chatbot

Text-to-GQL (Text2GQL) is a task that converts the user's questions into GQL (Graph Query Language) when a graph database is given. That is a task of semantic parsing that transforms natural language problems into logical expressions, which will bring more efficient direct communication between humans and machines. The existing related work mainly focuses on Text-to-SQL tasks, and there is no available semantic parsing method and data set for the graph database. In order to fill the gaps in this field to serve the medical Human–Robot Interactions (HRI) better, we propose this task and a pipeline solution for the Text2GQL task. This solution uses the Adapter pre-trained by “the linking of GQL schemas and the corresponding utterances" as an external knowledge introduction plug-in. By inserting the Adapter into the language model, the mapping between logical language and natural language can be introduced faster and more directly to better realize the end-to-end human–machine language translation task. In the study, the proposed Text2GQL task model is mainly constructed based on an improved pipeline composed of a Language Model, Pre-trained Adapter plug-in, and Pointer Network. This enables the model to copy objects' tokens from utterances, generate corresponding GQL statements for graph database retrieval, and builds an adjustment mechanism to improve the final output. And the experiments have proved that our proposed method has certain competitiveness on the counterpart datasets (Spider, ATIS, GeoQuery, and 39.net) converted from the Text2SQL task, and the proposed method is also practical in medical scenarios

A Mechanized Formalization of GraphQL

International audienceGraphQL is a novel language for specifying and querying web APIs, allowing clients to flexibly and efficiently retrieve data of interest. The GraphQL language specification is unfortunately only available in prose, making it hard to develop robust formal results for this language. Recently, Har-tig and Pérez proposed a formal semantics for GraphQL in order to study the complexity of GraphQL queries. The semantics is however not mechanized and leaves certain key aspects unverified. We present GraphCoQL, the first mechanized formalization of GraphQL, developed in the Coq proof assistant. GraphCoQL covers the schema definition DSL, query definitions, validation of both schema and queries, as well as the semantics of queries over a graph data model. We illustrate the application of GraphCoQL by formalizing the key query transformation and interpretation techniques of Hartig and Pérez, and proving them correct, after addressing some imprecisions and minor issues. We hope that GraphCoQL can serve as a solid formal baseline for both language design and verification efforts for GraphQL

Configurable Per-Query Data Minimization for Privacy-Compliant Web APIs

The purpose of regulatory data minimization obligations is to limit personal data to the absolute minimum necessary for a given context. Beyond the initial data collection, storage, and processing, data minimization is also required for subsequent data releases, as it is the case when data are provided using query-capable Web APIs. Data-providing Web APIs, however, typically lack sophisticated data minimization features, leaving the task open to manual and all too often missing implementations. In this paper, we address the problem of data minimization for data-providing, query-capable Web APIs. Based on a careful analysis of functional and non-functional requirements, we introduce Janus, an easy-to-use, highly configurable solution for implementing legally compliant data minimization in GraphQL Web APIs. Janus provides a rich set of information reduction functionalities that can be configured for different client roles accessing the API. We present a technical proof-of-concept along with experimental measurements that indicate reasonable overheads. Janus is thus a practical solution for implementing GraphQL APIs in line with the regulatory principle of data minimization.Comment: Preprint version (2022-03-18) This version of the contribution has been accepted for publication at the 22nd International Conference on Web Engineering (ICWE 2022), Bari, Ital

A Principled Approach to GraphQL Query Cost Analysis

International audienceThe landscape of web APIs is evolving to meet new client requirements and to facilitate how providers fulfill them. A recent web API model is GraphQL, which is both a query language and a runtime. Using GraphQL, client queries express the data they want to retrieve or mutate, and servers respond with exactly those data or changes. GraphQL's expressiveness is risky for service providers because clients can succinctly request stupendous amounts of data, and responding to overly complex queries can be costly or disrupt service availability. Recent empirical work has shown that many service providers are at risk. Using traditional API management methods is not sufficient, and practitioners lack principled means of estimating and measuring the cost of the GraphQL queries they receive. In this work, we present a linear-time GraphQL query analysis that can measure the cost of a query without executing it. Our approach can be applied in a separate API management layer and used with arbitrary GraphQL backends. In contrast to existing static approaches, our analysis supports common GraphQL conventions that affect query cost, and our analysis is provably correct based on our formal specification of GraphQL semantics. We demonstrate the potential of our approach using a novel GraphQL query-response corpus for two commercial GraphQL APIs. Our query analysis consistently obtains upper cost bounds, tight enough relative to the true response sizes to be actionable for service providers. In contrast, existing static GraphQL query analyses exhibit over-estimates and under-estimates because they fail to support GraphQL conventions

JavaScript SBST Heuristics to Enable Effective Fuzzing of NodeJS Web APIs

JavaScript is one of the most popular programming languages. However, its dynamic nature poses several challenges to automated testing techniques. In this paper, we propose an approach and open-source tool support to enable white-box testing of JavaScript applications using Search-Based Software Testing (SBST) techniques. We provide an automated approach to collect search-based heuristics like the common Branch Distance and to enable Testability Transformations. To empirically evaluate our results, we integrated our technique into the EvoMaster test generation tool, and carried out analyses on the automated system testing of RESTful and GraphQL APIs. Experiments on eight Web APIs running on NodeJS show that our technique leads to significantly better results than existing black-box and grey-box testing tools, in terms of code coverage and fault detection.publishedVersio