API recommendation for Mashup creation based on neural graph collaborative filtering

Lian, Sixian; Tang, Mingdong

doi:10.1080/09540091.2021.1974819

Cited by 29 publications

(9 citation statements)

References 28 publications

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“…Most works use the dataset from Pro-grammableWeb, and they exploit different kinds of auxiliary information, including functional descriptions, tags, categories, providers, architectural styles, etc., as the mashupservice composition record is extremely sparse. Based on the modeling of the mashup-service composition record, existing research can be roughly divided into three categories: neighbor-based collaborative filtering (CF) methods [11][12][13][14][15], latent factor-based CF methods [6,[16][17][18][19][20][21] and deep learning-based methods [8,9,[22][23][24][25][26][27].…”

Section: Related Workmentioning

confidence: 99%

“…In [25], Zhang et al propose a Semantic Variational Graph Auto-Encoder model, where they construct the service graph using the composition relations in the mashup, and use a variational graph autoencoder model as a link prediction task for service recommendation. In [26], Lian and Tang propose a service recommendation method that exploits the higher-order connectivity between mashups and services based on the neural graph collaborative filtering technique. In [27], He et al propose a service link prediction method based on a heterogeneous graph attention network, where they select five types of neighbors associated with service links, and two levels of attention are applied to learn the importance of different nodes and their associations.…”

Section: Related Workmentioning

confidence: 99%

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Multigraph Convolutional Network Enhanced Neural Factorization Machine for Service Recommendation

Gao

2022

Mathematical Problems in Engineering

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With an increasing number of web services on the Web, selecting appropriate services to meet the developer’s needs for mashup development has become a difficult task. To tackle the problem, various service recommendation methods have been proposed. However, there are still challenges, including the sparsity and imbalance of features, as well as the cold-start of mashups and services. To tackle these challenges, in this paper, we propose a Multigraph Convolutional Network enhanced Neural Factorization Machine model (MGCN-NFM) for service recommendation. It first constructs three graphs, namely, the collaborative graph, the description graph, and the tag graph. Each graph represents a different type of relation between mashups and services. Next, graph convolution is performed on the three graphs to learn the feature embeddings of mashups, services, and tags. Each node iteratively aggregates the information from its higher-order neighbors through message passing in each graph. Finally, the feature embeddings as well as the description features learned by Doc2vec are modeled by the neural factorization machine model, which captures the nonlinear and higher-order feature interaction relations between them. We conduct extensive experiments on the ProgrammableWeb dataset, and demonstrate that our proposed method outperforms state-of-the-art factorization machine-based methods in service recommendation.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Multigraph Convolutional Network Enhanced Neural Factorization Machine for Service Recommendation

Gao

2022

Mathematical Problems in Engineering

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“…JWASA facilitates the task of generating a formal API semantic artifact, however, deciding the vocabulary to use for the annotations remains a pure manual task, and requires business domain knowledge to accurately map schema elements to ontology languages [9]. To overcome the need for this manual task, noticeable emerging Mashups recommendation tools [6], [11], [15], [23] proposed by researchers, employ Machine Learning and Deep Learning models exploiting datasets of known Mashups.…”

Section: Related Workmentioning

confidence: 99%

How Composable is the Web? An Empirical Study on OpenAPI Data model Compatibility

Serbout

Pautasso

Zdun

2022

2022 IEEE International Conference on Web Services (ICWS)

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Composing Web APIs is a widely adopted practice by developers to speed up the development process of complex Web applications, mashups, and data processing pipelines. However, since most publicly available APIs are built independently of each other, developers often need to invest their efforts in solving incompatibility issues by writing ad-hoc glue code, adapters and message translation mappings. How likely are Web APIs to be directly composable?The paper presents an empirical study to determine the potential composability of a large collection of 20,587 public Web APIs by verifying their schemas' compatibility. We define three levels of data model elements compatibility -considering matches between property names and/or data types -which can be determined statically based on API descriptions conforming to the OpenAPI specification. The study research questions address: to which extent are Web APIs compatible; the average number of compatible endpoints within each API; the likelihood of finding two APIs with at least one pair of compatible endpoints.To perform the analysis we developed a compatibility checker tool which can statically determine API schema compatibility on the three levels and find matching pairs of API responses which can be directly forwarded as requests to the same or other APIs. We run the tool on a dataset of 751,390 request and response message schemas extracted from publicly available OpenAPI descriptions.The results indicate a relatively high number of compatible APIs when matching their data models only on the level of their elements' data type. However, this number gets lower narrowing the scope to only the ones handling data objects having identical properties name. The average likelihood of finding two compatible APIs with both matching property names and data types reaches 21%. Also, the number of compatible endpoints within the same API is very low.

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“…Different from other works, our method naturally merges the different types of information as interactions between mashups and services on a graph, and uses GCN to further exploit the high-order relatedness between mashups and services. Previous works [27,28] also adopt GCN-related techniques for service recommendation. In [27], Zhang et al propose an end-to-end approach based on GCN, where they utilize the service composition relationship in the mashup to construct the service graph, with the textual description as side information, and use a variational graph auto-encoder model as a link prediction task in the graph.…”

Section: Related Workmentioning

confidence: 99%

“…Our method constructs a multi-relational graph with both mashups and services, and can address the task when only mashup requirements are available without any composing service. In [28], Lian and Tang propose a neural graph collaborative filtering technique for API recommendation. However, their method only exploits high-order connections between users and APIs, and neglects other potentially relevant side information.…”

Section: Related Workmentioning

confidence: 99%

Multi-Relational Graph Convolution Network for Service Recommendation in Mashup Development

Gao¹,

Wu²

2022

Applied Sciences

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With the rapid development of service-oriented computing, an overwhelming number of web services have been published online. Developers can create mashups that combine one or multiple services to meet complex business requirements. To speed up the mashup development process, recommending suitable services for developers is a vital problem. In this paper, we address the data sparsity and cold-start problems faced in service recommendation, and propose a novel multi-relational graph convolutional network framework (MRGCN) for service recommendation. Specifically, we first construct a multi-relational mashup-service graph with three types of relations, namely composition relation, functional relation, and tagging relation. These three relations are indispensable and complement each other for capturing multi-view information. Then, the three relations in the graph are seamlessly fused with various strategies. Next, graph convolution is performed on the fused multi-relational graph to capture the high-order relational information between mashups and services. Finally, the relevance between mashup requirements and services is predicted based on the learned features on the graph. We conduct extensive experiments on the ProgrammableWeb dataset and demonstrate that our proposed method can outperform state-of-the-art methods in recommending services when only mashup requirements are available.

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API recommendation for Mashup creation based on neural graph collaborative filtering

Cited by 29 publications

References 28 publications

Multigraph Convolutional Network Enhanced Neural Factorization Machine for Service Recommendation

Multigraph Convolutional Network Enhanced Neural Factorization Machine for Service Recommendation

How Composable is the Web? An Empirical Study on OpenAPI Data model Compatibility

Multi-Relational Graph Convolution Network for Service Recommendation in Mashup Development

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