A knowledge graph approach for recommending patents to companies
Tóm tắt
Online platforms have emerged to facilitate patent transfer between academia and industry, but a recommendation method that matches patents with company needs is missing in the literature. Previous patent recommendation methods were designed mainly for query-driven patent search contexts, where user needs are given. However, company needs are implicit in the patent transfer context. The problem of profiling the needs and recommending patents accordingly remains unsolved. This research proposes a knowledge graph approach to address the problem. The proposed approach defines and constructs a patent knowledge graph to capture the semantic information between keywords in the patent domain. Then, it profiles patents and companies as weighted graphs based on the patent knowledge graph. Finally, it generates recommendations by comparing the weighted graphs based on the graph edit distance measure. During the recommendation process, three recommendation strategies (i.e., supplementary, complementary, and hybrid recommendation strategies) are proposed to profile different company needs and make recommendations accordingly. The proposed approach has been implemented and tested on a knowledge transfer platform in Jiangxi province, R.P. China. A pretest experiment shows that the proposed approach outperforms several baseline methods in terms of precision, recall, F-score, and mean average precision. User feedback from an online experiment further demonstrates the usability and the effectiveness of the proposed approach for recommending patents to companies.
Tài liệu tham khảo
Lee, J.-S., Park, J.-H., & Bae, Z.-T. (2017). The effects of licensing-in on innovative performance in different technological regimes. Research Policy., 46, 485–496. https://doi.org/10.1016/j.respol.2016.12.002
Parker, D. D., & Zilberman, D. (1993). University technology transfers: Impacts on local and US economies, Contemporary Policy Issues. Huntington Beach, 11, 87. https://doi.org/10.1111/j.1465-7287.1993.tb00382.x
McDevitt, V. L., Mendez-Hinds, J., Winwood, D., Nijhawan, V., Sherer, T., Ritter, J. F., & Sanberg, P. R. (2014). More than money: The exponential impact of academic technology transfer. Technology & Innovation., 16, 75–84. https://doi.org/10.3727/194982414X13971392823479
Roessner, D., Bond, J., Okubo, S., & Planting, M. (2013). The economic impact of licensed commercialized inventions originating in university research. Research Policy, 42, 23–34. https://doi.org/10.1016/j.respol.2012.04.015
Gambardella, A., Giuri, P., & Luzzi, A. (2007). The market for patents in Europe. Research Policy., 36, 1163–1183. https://doi.org/10.1016/j.respol.2007.07.006
Caviggioli, F., & Ughetto, E. (2013). The drivers of patent transactions: Corporate views on the market for patents. R&D Management, 43, 318–332. https://doi.org/10.1111/radm.12016
Kani, M., & Motohashi, K. (2012). Understanding the technology market for patents: New insights from a licensing survey of Japanese firms. Research Policy, 41, 226–235. https://doi.org/10.1016/j.respol.2011.08.002
Muscio, A. (2010). What drives the university use of technology transfer offices? Evidence from Italy. The Journal of Technology Transfer, 35, 181–202. https://doi.org/10.1007/s10961-009-9121-7
Trappey, A. J. C., Trappey, C. V., Wu, C.-Y., Fan, C. Y., & Lin, Y.-L. (2013). Intelligent patent recommendation system for innovative design collaboration. Journal of Network and Computer Applications., 36, 1441–1450. https://doi.org/10.1016/j.jnca.2013.02.035
Lu, J., Wu, D., Mao, M., Wang, W., & Zhang, G. (2015). Recommender system application developments: A survey. Decision Support Systems., 74, 12–32. https://doi.org/10.1016/j.dss.2015.03.008
Ji, X., Gu, X., Dai, F., Chen, J., & Le, C. (2011). Patent collaborative filtering recommendation approach based on patent similarity. In 2011 eighth international conference on fuzzy systems and knowledge discovery (FSKD), pp. 1699–1703. https://doi.org/https://doi.org/10.1109/FSKD.2011.6019821.
Krestel, R., & Smyth, P. (2013). Recommending patents based on latent topics. In Proceedings of the 7th ACM conference on recommender systems, ACM, New York, NY, USA, pp. 395–398. https://doi.org/https://doi.org/10.1145/2507157.2507232.
Mahdabi, P., & Crestani, F. (2014). Query-driven mining of citation networks for patent citation retrieval and recommendation. In Proceedings of the 23rd ACM international conference on conference on information and knowledge management, ACM, New York, NY, USA, pp. 1659–1668. https://doi.org/https://doi.org/10.1145/2661829.2661899.
Oh, S., Lei, Z., Lee, W., & Yen, J. (2014). Recommending missing citations for newly granted patents. In 2014 international conference on data science and advanced analytics (DSAA), pp. 442–448. https://doi.org/https://doi.org/10.1109/DSAA.2014.7058110.
Oh, S., Lei, Z., Lee, W.-C., Mitra, P., & Yen, J. (2013). CV-PCR: a context-guided value-driven framework for patent citation recommendation. In Proceedings of the 22nd ACM international conference on conference on information and knowledge management, ACM, New York, NY, USA, pp. 2291–2296. https://doi.org/https://doi.org/10.1145/2505515.2505659.
Friesl, M. (2012). Knowledge acquisition strategies and company performance in young high technology companies. British Journal of Management., 23, 325–343. https://doi.org/10.1111/j.1467-8551.2011.00742.x
Knudsen, M. P. (2007). The relative importance of interfirm relationships and knowledge transfer for new product development success*. Journal of Product Innovation Management, 24, 117–138. https://doi.org/10.1111/j.1540-5885.2007.00238.x
Yang, M. C., Su, F., Chang, Y.-H., Lai, K. K., Lin, C. Y., & Chang, H. Y. (2016). “Expand/offense” and “deepen/defense” strategy of patent acquisition for leader and follower: Evidence from drug-eluting stent. In 2016 Portland international conference on management of engineering and technology (PICMET), pp. 1560–1566. https://doi.org/https://doi.org/10.1109/PICMET.2016.7806630.
Chang, P., Chang, Y., Su, F., Chen, S., & Lai, K. K. (2014). The study on patent acquisition from complementarity and supplementarity: Evidence from Smartphones of Apple and Samsung. In Proceedings of PICMET ’14 conference: Portland international center for management of engineering and technology; Infrastructure and Service Integration, pp. 2996–3003.
Buckley, P. J., Glaister, K. W., Klijn, E., & Tan, H. (2009). Knowledge accession and knowledge acquisition in strategic alliances: The impact of supplementary and complementary dimensions. British Journal of Management, 20, 598–609. https://doi.org/10.1111/j.1467-8551.2008.00607.x
Makri, M., Hitt, M. A., & Lane, P. J. (2010). Complementary technologies, knowledge relatedness, and invention outcomes in high technology mergers and acquisitions. Strategic Management Journal, 31, 602–628.
Wang, Q., Yu, J., & Deng, W. (2019). An adjustable re-ranking approach for improving the individual and aggregate diversities of product recommendations. Electronic Commerce Research, 19, 59–79. https://doi.org/10.1007/s10660-018-09325-4
Jing, N., Jiang, T., Du, J., & Sugumaran, V. (2018). Personalized recommendation based on customer preference mining and sentiment assessment from a Chinese e-commerce website. Electronic Commerce Research, 18, 159–179. https://doi.org/10.1007/s10660-017-9275-6
Wang, Q., Ma, J., Liao, X., & Du, W. (2017). A context-aware researcher recommendation system for university-industry collaboration on R&D projects. Decision Support Systems, 103, 46–57. https://doi.org/10.1016/j.dss.2017.09.001
Xu, Y., Zhou, D., & Ma, J. (2019). Scholar-friend recommendation in online academic communities: An approach based on heterogeneous network. Decision Support Systems, 119, 1–13. https://doi.org/10.1016/j.dss.2019.01.004
Jeong, H. J., & Kim, M. H. (2019). HGGC: A hybrid group recommendation model considering group cohesion. Expert Systems with Applications, 136, 73–82. https://doi.org/10.1016/j.eswa.2019.05.054
Feng, S., Zhang, H., Wang, L., Liu, L., & Xu, Y. (2019). Detecting the latent associations hidden in multi-source information for better group recommendation. Knowledge-Based Systems, 171, 56–68. https://doi.org/10.1016/j.knosys.2019.02.002
Trappey, A. J. C., Trappey, C. V., Wu, C., Fan, C. Y., & Lin, Y. (2012). Intelligent recommendation methodology and system for patent search. In Proceedings of the 2012 IEEE 16th international conference on computer supported cooperative work in design (CSCWD), pp. 172–178. https://doi.org/https://doi.org/10.1109/CSCWD.2012.6221815.
Wang, Q., Du, W., Ma, J., & Liao, X. (2019). Recommendation mechanism for patent trading empowered by heterogeneous information networks. International Journal of Electronic Commerce, 23, 147–178. https://doi.org/10.1080/10864415.2018.1564549
He, Q., Spangler, W. S., He, B., Chen, Y., & Kato, L. (2012). Prospective client driven technology recommendation. In 2012 annual SRII global conference, pp. 110–119. https://doi.org/https://doi.org/10.1109/SRII.2012.23.
Fu, T., Lei, Z., & Lee, W. (2015). Patent citation recommendation for examiners. In 2015 IEEE international conference on data mining, pp. 751–756. https://doi.org/https://doi.org/10.1109/ICDM.2015.151.
Deng, N., Chen, X., & Li, D. (2017). Intelligent recommendation of chinese traditional medicine patents supporting new medicine’s R&D. Journal of Computational and Theoretical Nanoscience, 13(9), 5907–5913. https://doi.org/10.1166/jctn.2016.5505
Chen, Y.-L., & Chiu, Y.-T. (2011). An IPC-based vector space model for patent retrieval. Information Processing & Management, 47, 309–322. https://doi.org/10.1016/j.ipm.2010.06.001
Helmers, L., Horn, F., Biegler, F., Oppermann, T., & Müller, K.-R. (2019). Automating the search for a patent’s prior art with a full text similarity search. PLoS ONE, 14, e0212103. https://doi.org/10.1371/journal.pone.0212103
Liu, S.-H., Liao, H.-L., Pi, S.-M., & Hu, J.-W. (2011). Development of a patent retrieval and analysis platform—A hybrid approach. Expert Systems with Applications, 38, 7864–7868. https://doi.org/10.1016/j.eswa.2010.12.114
Kim, D., Seo, D., Cho, S., & Kang, P. (2019). Multi-co-training for document classification using various document representations: TF–IDF, LDA, and Doc2Vec. Information Sciences, 477, 15–29. https://doi.org/10.1016/j.ins.2018.10.006
Schuhmacher, M., & Ponzetto, S. P. (2014). Knowledge-based graph document modeling. In Proceedings of the 7th ACM international conference on web search and data mining, association for computing machinery, New York, NY, USA, pp. 543–552. https://doi.org/https://doi.org/10.1145/2556195.2556250.
Manrique, R., & Mariño, O. (2018). Knowledge graph-based weighting strategies for a scholarly paper recommendation scenario. In KaRS@RecSys.
Watford, S. M., Grashow, R. G., De La Rosa, V. Y., Rudel, R. A., Friedman, K. P., & Martin, M. T. (2018). Novel application of normalized pointwise mutual information (NPMI) to mine biomedical literature for gene sets associated with disease: Use case in breast carcinogenesis. Computational Toxicology, 7, 46–57. https://doi.org/10.1016/j.comtox.2018.06.003
Manrique, R., Cueto-Ramirez, F., & Mariño, O. (2018). Comparing graph similarity measures for semantic representations of documents. https://doi.org/https://doi.org/10.1007/978-3-319-98998-3_13.
Lin, C.-J. (2007). Projected gradient methods for nonnegative matrix factorization. Neural Computation, 19, 2756–2779. https://doi.org/10.1162/neco.2007.19.10.2756
Wang, D., Liang, Y., Xu, D., Feng, X., & Guan, R. (2018). A content-based recommender system for computer science publications. Knowledge-Based Systems, 157, 1–9. https://doi.org/10.1016/j.knosys.2018.05.001
Zheng, N., Song, S., & Bao, H. (2015). A temporal-topic model for friend recommendations in Chinese microblogging systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45, 1245–1253. https://doi.org/10.1109/TSMC.2015.2391262
Kong, X., Mao, M., Wang, W., Liu, J., & Xu, B. (2019). VOPRec: Vector representation learning of papers with text information and structural identity for recommendation. IEEE Transactions on Emerging Topics in Computing. https://doi.org/10.1109/TETC.2018.2830698
Shani, G., & Gunawardana, A. (2011). Evaluating recommendation systems. In F. Ricci, L. Rokach, B. Shapira, & P. B. Kantor (Eds.), Recommender systems handbook (pp. 257–297). Boston, MA: Springer. https://doi.org/10.1007/978-0-387-85820-3_8
Satta, G., Parola, F., Penco, L., & de Falco, S. E. (2016). Insights to technological alliances and financial resources as antecedents of high-tech firms’ innovative performance. R&D Management, 46, 127–144. https://doi.org/10.1111/radm.12117
Caviggioli, F., De Marco, A., Scellato, G., & Ughetto, E. (2017). Corporate strategies for technology acquisition: Evidence from patent transactions. Management Decision, 55, 1163–1181. https://doi.org/10.1108/MD-04-2016-0220
Liu, D., Lian, J., Wang, S., Qiao, Y., Chen, J.-H., Sun, G., & Xie, X. (2020) KRED: Knowledge-aware document representation for news recommendations. In Fourteenth ACM conference on recommender systems, association for computing machinery, New York, NY, USA, pp. 200–209. https://doi.org/https://doi.org/10.1145/3383313.3412237.
Gao, W., Peng, M., Wang, H., Zhang, Y., Xie, Q., & Tian, G. (2019). Incorporating word embeddings into topic modeling of short text. Knowledge and Information Systems, 61, 1123–1145. https://doi.org/10.1007/s10115-018-1314-7
Dong, C., Jia, H., & Wang, C. (2018). Unsupervised leraning for sematic representation of short text. In 2018 5th IEEE international conference on cloud computing and intelligence systems (CCIS), pp. 475–478. https://doi.org/https://doi.org/10.1109/CCIS.2018.8691363.
Jipeng, Q., Zhenyu, Q., Yun, L., Yunhao, Y., & Xindong, W. (2019) Short text topic modeling techniques, applications, and performance: A survey, ArXiv:1904.07695 [Cs]. http://arxiv.org/abs/1904.07695. Accessed October 9, 2019.
Huang, L., Wang, C., Chao, H., Lai, J., & Yu, P. S. (2019). A score prediction approach for optional course recommendation via cross-user-domain collaborative filtering. IEEE Access, 7, 19550–19563. https://doi.org/10.1109/ACCESS.2019.2897979
Maksai, A., Garcin, F., & Faltings, B. (2015). Predicting online performance of news recommender systems through richer evaluation metrics. In Proceedings of the 9th ACM conference on recommender systems, ACM, New York, NY, USA, pp. 179–186. https://doi.org/https://doi.org/10.1145/2792838.2800184.
Sauro, J., & Lewis, J. R. (2016). Chapter 9—Six enduring controversies in measurement and statistics. In J. Sauro & J. R. Lewis (Eds.), Quantifying the user experience (2nd edn) (pp. 249–276). Boston: Morgan Kaufmann. https://doi.org/10.1016/B978-0-12-802308-2.00009-6
Nonparametric statistics: An introduction. In Nonparametric statistics for non-statisticians. Wiley, 2009: pp. 1–11. https://doi.org/https://doi.org/10.1002/9781118165881.ch1.
Porteous, I., Newman, D., Ihler, A., Asuncion, A., Smyth, P., & Welling, M. (2008). Fast collapsed Gibbs sampling for latent Dirichlet allocation. In Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, New York, NY, USA, pp. 569–577. https://doi.org/https://doi.org/10.1145/1401890.1401960.
Yuan, H., Lau, R. Y. K., & Xu, W. (2016). The determinants of crowdfunding success: A semantic text analytics approach. Decision Support Systems, 91, 67–76. https://doi.org/10.1016/j.dss.2016.08.001