基于真⁃伪三连体神经网络的传感器本体匹配方法

doi:10.13229/j.cnki.jdxbgxb.20250233

Abstract

Abstract:

To address the heterogeneity problems in sensor ontology interaction within Internet of Things （IoT） systems， this paper proposes a sensor ontology matching method based on a Genuine-Pseudo Triplet Neural Network（GP-TNN）. Firstly， core entities are extracted by analyzing the in-degree and out-degree of entities to enhance the quality of training samples. Secondly， a deterministic hybrid similarity measurement method is designed to generate high-confidence anchor matching pairs， thereby reducing the dependence on reference alignments during training. Finally， by leveraging the representation capabilities of Siamese and pseudo-Siamese networks for homogeneous and heterogeneous annotation attributes， the GP-TNN is constructed to enhance the extraction of deep semantic features. Evaluation experiments are conducted on the Benchmark dataset from the Ontology Alignment Evaluation Initiative（OAEI） and three real-world sensor ontologies. Experimental results show that the proposed method can more effectively improve the quality of matching results than the comparative methods， and provides reliable theoretical support for the sensor ontology matching task in the Internet of Things.

Key words: computer application, deep learning, genuine-pseudo triplet neural network, sensor ontology matching, ontology alignment evaluation initiative

CLC Number:

TP391

Jia-wei LU,Chang-feng YAN. Sensor ontology matching method based on genuine⁃pseudo triplet neural network[J].Journal of Jilin University(Engineering and Technology Edition), 2026, 56(9): 2456-2466.

Figures/Tables 11

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案例	异质类型	实体数量	对齐数量
总计		8 347	6 586
101	源本体	109	97
201-202	不同的词汇和语言特征	1 090	970
221-247	不同的结构特征	1 538	1 194
248-262	不同的词汇、语言和结构特征	5 610	4 325

案例	AML	LogMap	LogMapLt	LogMapBio	XMap	本文方法	案例	AML	LogMap	LogMapLt	LogMapBio	XMap	本文方法
101	1.00	0.94	0.56	0.50	0.94	1.00	241	1.00	0.85	0.67	0.66	1.00	1.00
201	1.00	0.96	0.51	0.49	0.93	1.00	246	1.00	0.85	0.67	0.66	1.00	0.93
202	1.00	0.95	0.51	0.50	0.93	1.00	247	1.00	0.85	0.67	0.66	1.00	0.91
221	1.00	0.94	0.56	0.50	0.94	1.00	248	1.00	0.94	0.51	0.47	0.94	1.00
222	1.00	0.00	0.57	0.00	0.94	1.00	249	1.00	0.94	0.81	0.50	0.93	1.00
223	1.00	0.94	0.56	0.50	0.94	0.98	250	1.00	0.90	0.27	0.66	1.00	1.00
224	1.00	0.94	0.83	0.50	0.94	1.00	251	1.00	0.00	0.51	0.00	0.93	1.00
225	1.00	0.94	0.56	0.50	0.94	0.99	252	1.00	0.96	0.51	0.51	0.95	1.00
228	1.00	0.85	0.32	0.66	1.00	1.00	253	1.00	0.97	0.80	0.49	0.93	1.00
232	1.00	0.94	0.83	0.50	0.94	1.00	254	1.00	0.86	0.27	0.64	1.00	1.00
233	1.00	0.85	0.32	0.66	1.00	1.00	257	1.00	0.88	0.64	0.64	1.00	1.00
236	1.00	0.85	0.67	0.66	1.00	1.00	258	1.00	0.00	0.82	0.00	0.93	1.00
237	1.00	0.00	0.84	0.00	0.94	1.00	259	1.00	0.94	0.81	0.49	0.94	1.00
238	1.00	0.94	0.83	0.50	0.94	0.98	261	1.00	0.86	0.27	0.64	1.00	0.96
239	1.00	0.85	0.32	0.66	1.00	0.93	262	1.00	0.87	0.63	0.65	1.00	1.00
240	1.00	0.85	0.32	0.66	1.00	0.91	平均	1.00	0.79	0.58	0.50	0.96	0.99

案例	AML	LogMap	LogMapLt	LogMapBio	XMap	本文方法	案例	AML	LogMap	LogMapLt	LogMapBio	XMap	本文方法
101	0.00	0.96	0.99	0.56	1.00	1.00	241	1.00	1.00	1.00	1.00	1.00	1.00
201	0.28	0.76	0.78	0.44	0.64	0.99	246	1.00	1.00	1.00	1.00	1.00	0.97
202	0.28	0.76	0.78	0.44	0.65	0.70	247	1.00	1.00	1.00	1.00	1.00	0.91
221	0.34	0.96	0.99	0.56	1.00	1.00	248	0.29	0.74	0.78	0.42	0.68	0.70
222	0.33	0.00	0.99	0.00	0.67	1.00	249	0.27	0.75	0.79	0.44	0.72	0.70
223	0.34	0.95	0.99	0.56	1.00	0.97	250	0.83	0.79	0.79	0.79	0.79	0.70
224	0.34	0.95	0.99	0.56	1.00	1.00	251	0.27	0.00	0.78	0.00	0.45	0.70
225	0.34	0.96	0.99	0.56	1.00	0.99	252	0.28	0.76	0.79	0.45	0.65	0.70
228	1.00	1.00	1.00	1.00	1.00	1.00	253	0.27	0.76	0.78	0.44	0.62	0.70
232	0.34	0.95	0.99	0.56	1.00	1.00	254	0.83	0.79	0.79	0.79	0.79	0.70
233	1.00	1.00	1.00	1.00	1.00	1.00	257	0.82	0.79	0.79	0.79	0.79	0.70
236	1.00	1.00	1.00	1.00	1.00	1.00	258	0.28	0.00	0.79	0.00	0.46	0.70
237	0.33	0.00	0.99	0.00	0.67	1.00	259	0.28	0.75	0.78	0.44	0.71	0.70
238	0.34	0.96	0.99	0.56	1.00	0.97	261	0.83	0.79	0.79	0.79	0.79	0.70
239	1.00	1.00	1.00	1.00	1.00	0.97	262	0.83	0.79	0.79	0.79	0.79	0.70
240	1.00	1.00	1.00	1.00	1.00	0.91	平均	0.56	0.77	0.90	0.61	0.83	0.86

案例	AML	LogMap	LogMapLt	LogMapBio	XMap	本文方法	案例	AML	LogMap	LogMapLt	LogMapBio	XMap	本文方法
101	0.00	0.95	0.71	0.52	0.97	1.00	241	1.00	0.92	0.80	0.80	1.00	1.00
201	0.44	0.85	0.62	0.47	0.76	0.99	246	1.00	0.92	0.80	0.80	1.00	0.95
202	0.43	0.85	0.62	0.47	0.77	0.82	247	1.00	0.92	0.80	0.80	1.00	0.91
221	0.51	0.95	0.72	0.52	0.97	1.00	248	0.44	0.82	0.62	0.44	0.79	0.82
222	0.50	0.00	0.72	0.00	0.78	1.00	249	0.43	0.84	0.80	0.47	0.81	0.82
223	0.51	0.94	0.72	0.53	0.97	0.97	250	0.91	0.84	0.40	0.71	0.88	0.82
224	0.51	0.94	0.90	0.53	0.97	1.00	251	0.43	0.00	0.62	0.00	0.61	0.82
225	0.51	0.95	0.72	0.52	0.97	0.99	252	0.43	0.85	0.62	0.48	0.77	0.82
228	1.00	0.92	0.48	0.80	1.00	1.00	253	0.43	0.85	0.79	0.47	0.74	0.82
232	0.51	0.94	0.90	0.53	0.97	1.00	254	0.90	0.82	0.40	0.70	0.88	0.82
233	1.00	0.92	0.48	0.80	1.00	1.00	257	0.90	0.83	0.70	0.71	0.88	0.82
236	1.00	0.92	0.80	0.80	1.00	1.00	258	0.44	0.00	0.80	0.00	0.62	0.82
237	0.50	0.00	0.91	0.00	0.78	1.00	259	0.44	0.84	0.80	0.47	0.81	0.82
238	0.51	0.95	0.90	0.52	0.97	0.97	261	0.90	0.82	0.40	0.70	0.88	0.81
239	1.00	0.92	0.48	0.80	1.00	0.95	262	0.90	0.83	0.70	0.71	0.88	0.82
240	1.00	0.92	0.48	0.80	1.00	0.91	平均	0.66	0.77	0.68	0.54	0.88	0.91

Sensor ontology matching method based on genuine⁃pseudo triplet neural network

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