A Morphology-Driven Method for Measuring Technology Complementarity: Empirical Study Involving Alzheimer's Disease

doi:10.2478/jdis-2022-0017

Abstract

Abstract:

Purpose: Measuring the exact technology complementarity between different institutions is necessary to obtain complementary technology resources for R&D cooperation.
Design/methodology/approach: This study constructs a morphology-driven method for measuring technology complementarity, taking medical field as an example. First, we calculate semantic similarities between subjects (S and S) and action-objects (AO and AO) based on the Metathesaurus, forming clusters of S and AO based on a semantic similarity matrix. Second, we identify key technology issues and methods based on clusters of S and AO. Third, a technology morphology matrix of several dimensions is constructed using morphology analysis, and the matrix is filled with subjects -action-objects (SAO) structures according to corresponding key technology issues and methods for different institutions. Finally, the technology morphology matrix is used to measure the technology complementarity between different institutions based on SAO.
Findings: The improved technology complementarity method based on SAO is more of a supplementary and refined framework for the traditional IPC method.
Research limitations: In future studies we will reprocess and identify the SAO structures which were not in the technology morphology matrix, and find other methods to characterize key technical issues and methods. Furthermore, we will add the comparison between proposed method and traditional and mostly used complementarity measurement method based on industry chain and industry code.
Practical implications: This study takes medical field as an example. The morphology-driven method for measuring technology complementarity can be migrated and applied for any given field.
Originality/value: From the perspective of complementary technology resources, this study develops and tests a more accurate morphology-driven method for technology complementarity measurement.

Key words: Technology complementarity, SAO structure, Technology morphology analysis, Alzheimer's disease

Xuefeng Wang , Rongrong Li , Yuqin Liu , and Ming Lei .(2022). A Morphology-Driven Method for Measuring Technology Complementarity: Empirical Study Involving Alzheimer's Disease. Journal of Data and Information Science, 7(3), 20-48. DOI: 10.2478/jdis-2022-0017

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Figures/Tables 18

Figure 1.

Figure 2.

Table 1.

Table 2.

Table 3.

Table 4.

Table 5.

Table 6.

The semantic similarity calculation results between AO based on UMLS.

S1	S2	similarity	S1	S2	similarity	S1	S2	similarity	S1	S2	similarity	S1	S2	similarity	S1	S2	similarity	…..
5,474	5,518	1	1,673	1,807	0.9978	4,588	4,769	0.8387	713	4,074	0.7931	36	1,324	0.75	387	388	0.55	…..
1,989	2,047	1	2,510	5,622	0.997	5,647	6,284	0.8364	723	2,367	0.7895	5,650	5,651	0.75	215	3,310	0.549	…..
749	1,410	1	704	3,003	0.9921	132	3,738	0.8364	3,835	5,584	0.7895	752	2,602	0.7442	1,913	2,778	0.5455	…..
2,971	5,534	1	1,702	2,235	0.992	2,794	2,795	0.8333	1,760	4,323	0.7878	6	3,894	0.7436	24	161	0.5455	…..
1,176	3,239	1	2,462	2,555	0.9906	640	644	0.8333	1,474	2,939	0.7872	667	691	0.7394	2,782	4,319	0.5455	…..
4,198	4,210	1	2,677	4,598	0.9903	1,830	3,717	0.8333	524	5,140	0.7872	1,152	2,685	0.7342	2,214	2,855	0.5455	…..
1,601	1,623	1	107	3,317	0.9822	2,967	3,144	0.8315	28	3,003	0.7865	5,921	6,045	0.7303	2,433	2,434	0.5432	…..
2,432	4,948	1	3,553	5,534	0.9808	1,606	2,144	0.8315	4,542	4,560	0.7826	869	3,299	0.7297	579	835	0.5405	…..
2,695	2,899	1	662	1,567	0.9785	3,313	3,574	0.8312	44	2,217	0.7826	13	3,990	0.7296	1,651	4,657	0.5405	…..
478	2,504	1	6	1,626	0.9766	304	1,974	0.8308	2,629	3,511	0.7805	450	3,582	0.7273	3,691	5,537	0.5399	…..
5,460	6,173	1	9	5,136	0.9714	1,809	3,239	0.8293	27	2,265	0.7805	1,446	5,312	0.7222	1,340	4,978	0.5385	…..
90	1,526	1	3,055	4,685	0.9697	1,748	3,630	0.8286	1,796	2,584	0.7805	61	3,519	0.7215	793	827	0.5385	…..
3,919	5,935	1	3,724	5,492	0.9688	1,174	3,704	0.8266	637	3,321	0.7778	552	2,545	0.7179	569	676	0.5342	…..
1,750	1,993	1	207	3,006	0.9655	4,145	5,770	0.8235	2,441	2,443	0.7778	906	2,565	0.7179	1,798	3,144	0.5279	…..
3,824	5,534	1	939	5,556	0.9651	2,654	2,828	0.8235	106	734	0.7713	9	4,969	0.7158	1,684	2,565	0.5263	…..
4,288	5,974	1	250	313	0.9619	648	739	0.8234	215	1,639	0.7708	3,605	5,681	0.7143	2,359	5,253	0.5263	…..
3,245	5,595	1	1,370	5,153	0.9616	648	739	0.8234	954	1,606	0.7704	106	5,450	0.7131	1,767	5,829	0.5256	…..
5,524	5,915	1	4,435	4,531	0.96	1,731	1,733	0.8205	659	767	0.7692	472	2,258	0.7097	1,546	4,570	0.5246	…..
936	1,012	1	791	3,555	0.96	390	3,633	0.8205	834	2,514	0.7692	5,649	5,651	0.7083	407	3,474	0.5238	…..
1,985	2,098	1	24	2,221	0.96	600	1,279	0.8205	739	823	0.7691	226	1,418	0.7059	186	455	0.5217	…..
…..	…..	…..	…..	…..	…..	…..	…..	…..	…..	…..	…..	…..	…..	…..	…..	…..	…..	…..

Table 6.

Table 7.

The semantic similarity calculation results between AO based on UMLS.

AO1	AO2	similarity	AO1	AO2	similarity	AO1	AO2	similarity	AO1	AO2	similarity	AO1	AO2	similarity	AO1	AO2	similarity	…..
7	661	1	3	4	0.5	15	2,715	0.1861	15	2,285	0.079	15	6,404	0.0278	107	3,714	0.0085	…..
28	728	1	3	5	0.5	15	7,638	0.1861	15	3,700	0.079	15	7,392	0.0278	107	3,925	0.0085	…..
100	320	1	9	2,835	0.48	40	1,630	0.1828	15	3,791	0.079	15	7,497	0.0278	107	6,199	0.0085	…..
9	19	0.98	9	3,872	0.48	40	2,578	0.1828	15	4,099	0.079	15	7,982	0.0278	107	7,552	0.0085	…..
86	210	0.9728	9	5,795	0.48	40	3,761	0.1828	15	4,295	0.079	15	8,291	0.0278	27	7,813	0.0077	…..
46	125	0.9706	90	3,053	0.4706	40	3,983	0.1828	15	5,800	0.079	22	1,452	0.0278	71	1,540	0.0075	…..
90	282	0.9706	90	8,578	0.4706	40	6,052	0.1828	15	5,913	0.079	22	2,325	0.0278	71	3,655	0.0075	…..
25	835	0.96	25	2,509	0.46	40	7,427	0.1828	15	5,953	0.079	22	3,738	0.0278	71	3,737	0.0075	…..
59	810	0.9445	25	8,307	0.46	1	3,954	0.1667	15	5,955	0.079	22	3,879	0.0278	71	6,001	0.0075	…..
60	1,207	0.9	59	2,358	0.4445	1	7,175	0.1667	15	6,056	0.079	22	5,939	0.0278	71	6,129	0.0075	…..
87	566	0.8936	59	5,968	0.4445	41	1,408	0.1667	15	7,359	0.079	22	6,051	0.0278	71	7,454	0.0075	…..
26	859	0.8846	59	6,323	0.4445	41	2,662	0.1667	15	7,572	0.079	22	7,369	0.0278	46	1,645	0.0073	…..
13	41	0.875	59	7,905	0.4445	41	7,606	0.1667	109	3,226	0.0715	37	1,497	0.0266	46	4,009	0.0073	…..
27	1,137	0.8334	61	3,502	0.4385	23	1,450	0.1539	69	2,962	0.0709	37	3,544	0.0266	30	1,761	0.0067	…..
76	896	0.7756	73	1,625	0.4	23	2,567	0.1539	69	7,469	0.0709	37	3,736	0.0266	30	2,752	0.0067	…..
69	258	0.775	73	2,488	0.4	23	2,851	0.1539	97	5,964	0.0648	37	5,920	0.0266	30	4,207	0.0067	…..
19	66	0.7728	73	6,196	0.4	23	6,274	0.1539	26	1,761	0.0633	37	6,059	0.0266	30	7,587	0.0067	…..
74	775	0.7586	73	7,854	0.4	23	8,562	0.1539	26	2,752	0.0633	37	7,592	0.0266	48	1,653	0.0062	…..
87	498	0.74	87	1,592	0.3936	15	1,496	0.1464	26	4,207	0.0633	41	1,340	0.0266	86	4,115	0.006	…..
31	540	0.7364	87	2,976	0.3936	15	2,353	0.1464	26	7,587	0.0633	41	2,286	0.0266	23	1,536	0.0058	…..
…..	…..	…..	…..	…..	…..	…..	…..	…..	…..	…..	…..	…..	…..	…..	…..	…..	…..	…..

Table 7.

Table 8.

Table 9.

Results of technology complementarity between institutions based on SAO.

	AAKS-C	ABBI-C	ABBO-C	ABLY-C	ACAD-C	ACET-C	ACIM-C	ACOR-C	ACVE-C	ADCE-C	ADDE-C	ADIR-C	AFFI-C	…..
AAKS-C	0	0.002	0.0016	0.0023	0.0021	0.0023	0.0023	0.0023	0.0023	0.0023	0.0023	0.0023	0.0023	…..
ABBI-C	0.0535	0	0.0201	0.0529	0.0509	0.0509	0.0504	0.0513	0.0527	0.0521	0.0511	0.0534	0.0528	…..
ABBO-C	0.0656	0.0332	0	0.0653	0.0634	0.0626	0.063	0.0642	0.0654	0.0647	0.0637	0.066	0.0652	…..
ABLY-C	0.005	0.0041	0.0041	0	0.0051	0.0049	0.005	0.005	0.0048	0.005	0.0049	0.0048	0.005	…..
ACAD-C	0.0096	0.0068	0.0068	0.0098	0	0.0086	0.0087	0.0088	0.0096	0.0093	0.0093	0.0096	0.0093	…..
ACET-C	0.0125	0.0095	0.0087	0.0124	0.0113	0	0.0122	0.0116	0.0123	0.0119	0.0121	0.0125	0.0121	…..
ACIM-C	0.0085	0.0049	0.005	0.0085	0.0074	0.0082	0	0.0076	0.0079	0.0077	0.008	0.0084	0.0075	…..
ACOR-C	0.0071	0.0046	0.005	0.0071	0.0061	0.0062	0.0063	0	0.0066	0.0062	0.0066	0.0071	0.007	…..
ACVE-C	0.0023	0.0012	0.0014	0.0021	0.0021	0.0021	0.0018	0.0017	0	0.0019	0.0021	0.0019	0.0021	…..
ADCE-C	0.004	0.0023	0.0025	0.0041	0.0035	0.0035	0.0033	0.0031	0.0037	0	0.0039	0.004	0.0039	…..
ADDE-C	0.0091	0.0063	0.0062	0.0089	0.0085	0.0086	0.0085	0.0085	0.0089	0.0089	0	0.009	0.0089	…..
ADIR-C	0.0016	0.0012	0.0014	0.0014	0.0014	0.0015	0.0015	0.0015	0.0012	0.0015	0.0015	0	0.0015	…..
AFFI-C	0.0023	0.0013	0.0013	0.0023	0.0018	0.002	0.0014	0.0022	0.0021	0.0021	0.0021	0.0023	0	…..
AGEN-C	0.0017	0.0008	0.0013	0.0017	0.0016	0.0018	0.0012	0.0014	0.0015	0.0017	0.0014	0.0017	0.0014	…..
AICU-C	0.0025	0.0021	0.0013	0.0025	0.0022	0.0023	0.0022	0.0021	0.0021	0.0021	0.0025	0.0025	0.0023	…..
AISS-C	0.006	0.0046	0.0043	0.006	0.0051	0.0055	0.0053	0.0056	0.0058	0.0056	0.006	0.0058	0.0058	…..
AJIN-C	0.0029	0.0023	0.0027	0.0029	0.0025	0.0026	0.0027	0.0027	0.0027	0.0027	0.0027	0.0029	0.0027	…..
ALKP-C	0.0027	0.0018	0.0016	0.0027	0.0028	0.0025	0.0027	0.0025	0.0025	0.0025	0.0021	0.0027	0.0027	…..
ALKU-C	0.0042	0.0028	0.0025	0.0044	0.0035	0.0039	0.0044	0.0044	0.0044	0.0044	0.0043	0.0042	0.0042	…..
ALLR-C	0.0327	0.0297	0.0284	0.033	0.0316	0.0311	0.0323	0.0321	0.0327	0.0322	0.0314	0.033	0.0327	…..
ALLX-C	0.0004	0.0002	0.0002	0.0004	0.0004	0.0004	0.0004	0.0004	0.0004	0.0004	0.0004	0.0004	0.0004	…..
…..	…..	…..	…..	…..	…..	…..	…..	…..	…..	…..	…..	…..	…..	…..

Table 9.

Table 10.

Results of technology complementarity between institutions based on IPC classification numbers.

	AAKS-C	ABBI-C	ABBO-C	ABLY-C	ACAD-C	ACET-C	ACIM-C	ACOR-C	ACVE-C	ADCE-C	ADDE-C	ADIR-C	AFFI-C	…..
AAKS-C	0	0.0897	0.1091	0.0769	0.0684	0.1047	0.0576	0.0538	0.0544	0.0692	0.0682	0.0333	0.0612	…..
ABBI-C	0.0712	0	0.124	0.0687	0.0681	0.126	0.0619	0.0561	0.0511	0.0727	0.0803	0.03	0.0686	…..
ABBO-C	0.0818	0.1135	0	0.0745	0.0741	0.1288	0.0594	0.059	0.0719	0.0867	0.0696	0.0478	0.0548	…..
ABLY-C	0.0983	0.1077	0.1242	0	0.0783	0.1336	0.0725	0.0749	0.0765	0.0941	0.074	0.0436	0.0715	…..
ACAD-C	0.1367	0.153	0.1695	0.1251	0	0.1458	0.1009	0.0924	0.1132	0.1395	0.1232	0.0777	0.1038	…..
ACET-C	0.1058	0.1456	0.1636	0.1174	0.0859	0	0.0954	0.0695	0.0996	0.1131	0.0936	0.0764	0.1006	…..
ACIM-C	0.1005	0.1222	0.1306	0.0932	0.0765	0.1318	0	0.0808	0.0812	0.1006	0.0662	0.0408	0.0576	…..
ACOR-C	0.1093	0.1296	0.1449	0.1077	0.0781	0.1174	0.0927	0	0.09	0.1061	0.0905	0.0708	0.0902	…..
ACVE-C	0.0864	0.1028	0.1326	0.088	0.0775	0.1312	0.0714	0.0682	0	0.0805	0.0771	0.0425	0.0662	…..
ADCE-C	0.0831	0.1041	0.1292	0.0857	0.0857	0.1246	0.071	0.0636	0.0605	0	0.0685	0.0518	0.0723	…..
ADDE-C	0.1183	0.1464	0.1478	0.1027	0.105	0.1391	0.0745	0.0863	0.0941	0.1067	0	0.063	0.0788	…..
ADIR-C	0.1149	0.1274	0.1561	0.1039	0.0929	0.1556	0.0798	0.0969	0.0908	0.1183	0.0918	0	0.0904	…..
AFFI-C	0.1107	0.1342	0.133	0.0997	0.0898	0.1463	0.0653	0.0871	0.0828	0.1088	0.0763	0.0583	0	…..
AGEN-C	0.1147	0.1321	0.1695	0.1249	0.1017	0.1295	0.0966	0.0826	0.0896	0.1013	0.1174	0.0812	0.1081	…..
AICU-C	0.0916	0.1114	0.1296	0.0934	0.0862	0.1333	0.0652	0.0741	0.0585	0.0768	0.0689	0.0425	0.0641	…..
AISS-C	0.1211	0.1379	0.1665	0.1327	0.0944	0.159	0.1033	0.1076	0.1065	0.1188	0.1129	0.0764	0.0965	…..
AJIN-C	0.1206	0.1338	0.1498	0.108	0.1054	0.1572	0.0947	0.097	0.0859	0.102	0.0913	0.0578	0.0849	…..
ALKP-C	0.1511	0.1663	0.1826	0.1467	0.1155	0.1668	0.129	0.1101	0.1203	0.1473	0.1285	0.0936	0.1178	…..
ALKU-C	0.1144	0.1455	0.1328	0.1083	0.0915	0.1319	0.0975	0.0833	0.106	0.1266	0.1094	0.0681	0.1024	…..
ALLR-C	0.1419	0.1666	0.1766	0.1337	0.0935	0.1461	0.1179	0.0996	0.1217	0.1443	0.1116	0.0944	0.1089	…..
…..	…..	…..	…..	…..	…..	…..	…..	…..	…..	…..	…..	…..	…..	…..

Table 10.

Figure 3.

Table 11.

Appendix 1.

Appendix 2.

The number of AO and the concept of AO.

NO	A	O	NO	A	O	NO	A	O	…..
1	Affects	Myelination	21	Affects	Hair Growth	41	Affects	Tauopathies	…..
2	Affects	Septicemia	22	Affects	AIDS Dementia Complex	42	Affects	Graves' Disease	…..
3	Affects	Chronic Pain	23	Affects	Rheumatoid Arthritis	43	Affects	Orchitis	…..
4	Affects	Bacteria	24	Affects	Diabetes Mellitus	44	Affects	Hashimoto Disease	…..
5	Affects	Helper-Inducer T-Lymphocyte	25	Affects	Glomerulonephritis	45	Affects	Autoimmune Oophoritis	…..
6	Affects	Proteins	26	Affects	Spinal Muscular Atrophy	46	Affects	Oxidation-Reduction	…..
7	Affects	Gngm	27	Affects	Parkinson Disease	47	Affects	Prostate	…..
8	Affects	Response	28	Affects	Psoriasis	48	Affects	Gluconeogenesis	…..
9	Affects	Aging-Related Process	29	Affects	Cerebellar Degeneration	49	Affects	Drug Interactions	…..
10	Affects	Entire Nervous System	30	Affects	Amyotrophic Lateral Sclerosis	50	Affects	Acidification	…..
11	Affects	Neuraxis	31	Affects	Lupus Erythematosus, Systemic	51	Affects	Energy Balance	…..
12	Affects	Hydrolysis	32	Affects	Retinitis Pigmentosa	52	Affects	Graft Rejection	…..
13	Affects	Alzheimer's Disease	33	Affects	Retroviridae Infections	53	Affects	Facilitation	…..
14	Affects	Disease	34	Affects	Inflammatory Disorder	54	Affects	Receptor Function	…..
15	Affects	Dementia	35	Affects	Patients	55	Affects	Autoimmune Diseases	…..
16	Affects	Signal Transduction Inhibition	36	Affects	Immune System Diseases	56	Affects	Sex Behavior	…..
17	Affects	Folliculitis	37	Affects	Neurodegenerative Disorders	57	Affects	Growth	…..
18	Affects	Diabetes	38	Affects	Neoplasm	58	Affects	Organism	…..
19	Affects	Aging	39	Affects	Lamellipodium Biogenesis	59	Affects	Memory Disorders	…..
20	Affects	Apoptosis	40	Affects	Intestinal Diseases	60	Affects	Insulin Resistance	…..
…..	…..	…..	…..	…..	…..	…..	…..	…..	…..

Appendix 2.

Appendix 3.

Appendix 4.

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		Attribute 1		Attribute 2		Attribute 3
		Level 1	Level 2	Level 1	Level 2	Level 1	Level 2
Attribute 1	Level 1
Attribute 1	Level 2
Attribute 2	Level 1
Attribute 2	Level 2
Attribute 3	Level 1
Attribute 3	Level 2

	TQ₁	TQ₂	………	TQ_k
TM₁
TM₂
………			SAO_ij
TM_f

Number	Cleaning process	Results
#1	Separation of primitive natural semantic relations.	306,957
#2	Delete 684 records with missing relationships.	305,913
	Delete 2231 records with missing subject.	303,682
	Delete 2072 records with missing predicate.	301,610
	Delete 362 records with missing PMID number.	301,248
#3	Delete the remarks and analysis information of subject, predicate and object.	301,248
#4	Delete records where Subject and Object are meaningless numbers and mathematical formulas.	301,242
#5	Remove the SAO semantic structure that is not related to technology and only retain SAO semantic structure whose semantic relationship are the functionally related and conceptually related.	122,769

Patent Number	Patentee Code	S(Subject)	A(Action)	O(Object)
JP2006199666	AAKS-C; NAGS-C NAGS-C	Agent	Treats	Amnesia
JP2008214245	NAGS-C	Inhibitors	Treats	Alzheimer's Disease
		Inhibitors	Treats	Arteriosclerosis
		Inhibitors	Treats	Diabetic Nephropathy
		Inhibitors	Treats	Diabetic Neuropathies
		Inhibitors	Treats	Diabetic Retinopathy
		Inhibitors	Treats	Inflammation
		Inhibitors	Treats	Cerebrovascular accident
		Inhibitors	Treats	Myocardial Ischemia
WO200294259; EP1387678-A1; AU2002314036; US2004235813	PLAC-C	Peptides	Disrupts	Adenosine triphosphatase activity
		Amyloid	Interacts_With	aapp
		HSP90 Heat-Shock Proteins	Interacts_With	aapp
		Pharmaceutical Preparations	Treats	Disease
		HSP90 Heat-Shock Proteins	Treats	Disease
		HSP90 Heat-Shock Proteins	Treats	Creutzfeldt-Jakob Syndrome
WO200288108; US2003013712; EP1383759; US6727364; AU2002305226; JP2004528351; CN1505625; AU2002305226	PROC-C	Macular degeneration	Affects	Hair growth
		Pterygium	Affects	Hair growth
		Disease	Associated_With	cytokine activity
		Acquired Immunodeficiency Syndrome	Causes	Cachexia
		Prophylactic treatment	Causes	skin disorder
		Prophylactic treatment	Causes	Dermatitis, Atopic
		Prophylactic treatment	Causes	Scleroderma
		Prophylactic treatment	Causes	Epidermolysis Bullosa
		Prophylactic treatment	Causes	Psoriasis
		Macular degeneration	Affects	Hair growth
……	……	……	……	……

Institutions	rankings based on SAO	rankings based on expert	absolute value	Institutions	rankings based on IPC	rankings based on expert	absolute value
HOFF-C	1	6	5	UYMI-C	1	7	6
PFIZ-C	2	1	1	UYXM-C	2	9	7
TAKE-C	3	8	5	UCNT-C	3	8	5
FARB-C	4	5	1	SYTO-C	4	5	1
GLAX-C	5	2	3	UJIN-C	5	10	5
BRIM-C	6	4	2	ZYMO-C	6	2	4
ASTR-C	7	3	4	UYSY-C	7	4	3
AMHP-C	8	9	1	ZHJA-C	8	6	2
SMIK-C	9	10	1	TAKI-C	9	1	8
NOVS-C	10	7	3	THRE-C	10	3	7
The sum of absolute value of difference between the rankings based on technology similarity method with rankings based on expert			26				48
The average value of absolute value of difference between the rankings based on technology similarity method with rankings based on expert			2.6				4.8