1. Introduction：

In this paper, we present a mobile application knowledge graph, namely MAKG, which aims to collect the apps from various resources (e.g., application markets, encyclopedias, news). We design a lightweight ontology of apps. It can bring a well-defined schema of collected apps so that these apps could share more linkage with each other. Moreover, we also evaluate the algorithms of information extraction and knowledge alignment during the process of construction. Experimental results show that several algorithms can be competent to above tasks to some extend and enrich the structured triples in MAKG. Finally, we list three use-cases about MAKG that are helpful to provide better services for security analysts and users.

2. Usage:

MAKG consists of five important resources, including Ontology (Knowledge Schema), AppMarket-Triples, Encyclopedias, AppMarket-Alignments, Extraction-Triples.

A technical report with details of these resources and related evaluations can be downloaded in the same address.

Ontology:

We design one lightweight ontology of apps. It can bring a well-defined schema of collected apps so that these apps could share more linkage with each other. It contains 26 basic classes, 11 relations and 45 properties.

We provide two files (appOntology_en5.owl and appSchema-1.2.xlsx) for researchers to use it. For the former file, it needs to install protege to open it.

AppMarket-Triples:

This dataset contains raw triples crawlled from Huawei App Market, Xiaomi App Store, Google Play, App Store.

All of the files of these triples from application markets are provided in the format of .nt.

Encyclopedias:

This dataset contains the triples of apps crawled from Baidu Baike, Toutiao Baike, Wikipedia.

As the number of Wikipedia is few, we only provide the files of Baidu Baike, Toutiao Baike.

AppMarket-Alignments:

This dataset contains the alignments of apps, which can share and reuse the description information of apps so as to provide better services based on MAKG for security analysts and users. We utilize two kinds of entity alignment techniques (i.e., Rule miner method, Knowledge graph embedding-based platform) to obtain the best results of them.

As the number of App Store is fewer than other application markets, we only obtain the alignments among Huawei App Market, Xiaomi App Store, Google Play.

Extraction-Triples:

This dataset contains the triples extracted from textual descriptions and news related to apps. We utilize three strategies (i.e., Infobox-based Method, Named entity recognition, Relation extraction platforms including OpenNRE, DeepKE, FewRel) and select the best models to extract basic triples.

Laukas	Reikšmė
Data last updated	2021 m. gegužės 23 d.
Metadata last updated	2021 m. gegužės 23 d.
Sukurtas	2021 m. gegužės 23 d.
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移动应用知识图谱 (Mobile Application Knowledge Graph) v1.0