Combining Knowledge Graph Embedding and Network Embedding for Detecting Similar Mobile Applications

Abstract

With the popularity of mobile devices, large amounts of mobile applications (a.k.a.“app”) have been developed and published. Detecting similar apps from a large pool of apps is a fundamental and important task because it has many benefits for various purposes. There exist several works that try to combine different metadata of apps for measuring the similarity between apps. However, few of them pay attention to the roles of this service. Besides, existing methods do not distinguish the characters of contents in the metadata. Therefore, it is hard to obtain accurate semantic representations of apps and capture their fine-grained correlations. In this paper, we propose a novel framework by knowledge graph (KG) techniques and a hybrid embedding strategy to fill above gaps. For the construction of KG, we design a lightweight ontology tailored for the service of cybersecurity analysts. Benefited from a defined schema, more linkages can be shared among apps. To detect similar apps, we divide the relations in KG into structured and unstructured ones according to their related content. Then, TextRank algorithm is employed to extract important tokens from unstructured texts and transform them into structured triples. In this way, the representations of apps in our framework can be iteratively learned by combining KG embedding methods and network embedding models for improving the performance of similar apps detection. Preliminary results indicate the effectiveness of our method comparing to existing models in terms of reciprocal ranking and minimum ranking.