Social Media Idea Ontology: A Concept for Semantic Search of Product Ideas in Customer Knowledge through User-Centered Metrics and Natural Language Processing

In order to survive on the market, companies must
constantly develop improved and new products. These products are
designed to serve the needs of their customers in the best possible
way. The creation of new products is also called innovation and is
primarily driven by a company’s internal research and development
department. However, a new approach has been taking place for some
years now, involving external knowledge in the innovation process.
This approach is called open innovation and identifies customer
knowledge as the most important source in the innovation process. This paper presents a concept of using social media posts as an external source to support the open innovation approach in its
initial phase, the Ideation phase. For this purpose, the social media
posts are semantically structured with the help of an ontology and
the authors are evaluated using graph-theoretical metrics such as
density. For the structuring and evaluation of relevant social media
posts, we also use the findings of Natural Language Processing, e.
g. Named Entity Recognition, specific dictionaries, Triple Tagger
and Part-of-Speech-Tagger. The selection and evaluation of the tools
used are discussed in this paper. Using our ontology and metrics
to structure social media posts enables users to semantically search
these posts for new product ideas and thus gain an improved insight
into the external sources such as customer needs.




References:
[1] H. Smith, “A CSC white paper, european office of
technology and innovation. what innovation is. how companies
develop operating systems for innovation.” (Online). Available:
http://www.innovationmanagement.se/wp-content/uploads/pdf/
innovation update 2005.pdf, last accessed on 11.11.2017.
[2] J. Xu, R. Houssin, E. Caillaud, and M. Gardoni, “Macro process
of knowledge management for continuous innovation,” in Journal of
Knowledge Management, vol. 14, pp. 573–591. (Online). Available:
http://www.emeraldinsight.com/doi/abs/10.1108/13673271011059536,
last accessed on 03.04.2017.
[3] A.-L. Mention, “Co-operation and co-opetition as open innovation
practices in the service sector: Which influence on innovation
novelty?” in Technovation, ser. Open Innovation - ISPIM
Selected Papers, vol. 31, pp. 44–53. (Online). Available:
http://www.sciencedirect.com/science/article/pii/S0166497210000908,
last accessed on 03.08.2017.
[4] R. Alt and O. Reinhold, Social Customer Relationship
Management. Springer Berlin Heidelberg. (Online). Available:
http://link.springer.com/10.1007/978-3-662-52790-0, last accessed on
22.01.2017.
[5] H. I. Ansoff, “Managing strategic surprise by response to
weak signals,” vol. 18, no. 2, pp. 21–33. (Online). Available:
https://doi.org/10.2307/41164635 last accessed on 07.010.2017.
[6] R. Eckhoff, J. Frank, M. Markus, M. Lassnig, and S. Schoen,
“Detecting innovation signals with technology-enhanced social
media analysis - experiences with a hybrid approach in three
branches,” vol. 17, no. 1, pp. 120–130. (Online). Available:
http://www.ijisr.issr-journals.org/abstract.php?article=IJISR-15-065-09,
last accessed on 07.06.2017.
[7] M. Markus, R. A. Eckhoff, and M. Lassnig, “Innovation
signals in online-communitys ein komplementaerer analytischer
ansatz,” vol. 50, no. 5, pp. 13–21. (Online). Available:
http://link.springer.com/10.1007/BF03340849, last accessed on
17.11.2017.
[8] Apache tomcat (Online). Available: http://tomcat.apache.org/, last
accessed on 17.11.2017.
[9] Apache marmotta. (Online). Available: http://marmotta.apache.org/, last
accessed on 17.11.2017.
[10] Apache marmotta - KiWi triple store. (Online). Available:
http://marmotta.apache.org/kiwi/, last accessed on 17.11.2017.
[11] SPARQL query language for RDF. (Online). Available:
https://www.w3.org/TR/rdf-sparql-query/, last accessed on 17.11.2017.
[12] D. Thorleuchter, D. V. den Poel, and A. Prinzie, “Mining ideas from
textual information,” vol. 37, no. 10, pp. 7182–7188. (Online). Available: http://linkinghub.elsevier.com/retrieve/pii/S0957417410002848, last
accessed on 29.10.2017.
[13] D. Thorleuchter and D. Van den Poel, “Web
mining based extraction of problem solution ideas,”
vol. 40, no. 10, pp. 3961–3969. (Online). Available:
http://linkinghub.elsevier.com/retrieve/pii/S095741741300016X, last
accessed on 28.10.2017.
[14] A. Westerski, C. A. Iglesias, and F. T. Rico, “A model for integration and
interlinking of idea management systems,” in Metadata and Semantic
Research, ser. Communications in Computer and Information Science.
Springer, Berlin, Heidelberg, pp. 183–194. (Online). Available:
https://link.springer.com/chapter/10.1007/978-3-642-16552-8 18, last
accessed on 17.10.2017.
[15] Idea storm. (Online). Available: http://www.ideastorm.com/, last
accessed on 20.10.2017.
[16] M. Haeusl, J. Forster, and D. Kailer, “An approach to identify SPAM
tweets based on metadata.” IEEE, pp. 48–51. (Online). Available:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7397420,
last accessed on 17.03.2017.
[17] Eccleston and Griseri, “How does web 2.0 stretch traditional influencing
patterns? international journal of market research,” no. 50, pp. 591–661.
[18] NameAPI - intelligence in names. (Online). Available:
https://www.nameapi.org/, last accessed on 22.10.2017.
[19] D. Rao, D. Yarowsky, A. Shreevats, and M. Gupta, “Classifying latent
user attributes in twitter.” ACM Press, p. 37. (Online). Available:
http://portal.acm.org/citation.cfm?doid=1871985.1871993, last accessed
on 20.10.2017.
[20] D. Klein and C. D. Manning, “Accurate unlexicalized parsing,”
in Proceedings of the 41st Annual Meeting on Association for
Computational Linguistics - Volume 1, ser. ACL ’03. Association
for Computational Linguistics, pp. 423–430. (Online). Available:
https://doi.org/10.3115/1075096.1075150, last accessed on 08.11.2017.
[21] G. Angeli, M. Jose Johnson Premkumar, and C. D. Manning,
“Leveraging linguistic structure for open domain information
extraction,” vol. 1, pp. 344–354.
[22] L. Derczynski, A. Ritter, S. Clark, and K. Bontcheva, “Twitter part-of-speech tagging for all: Overcoming sparse and noisy data,”
in International Conference Recent Advances in Natural Language
Processing, RANLP.
[23] M. P. Marcus, M. A. Marcinkiewicz, and B. Santorini,
“Building a large annotated corpus of english: The penn
treebank,” vol. 19, no. 2, pp. 313–330. (Online). Available:
http://dl.acm.org/citation.cfm?id=972470.972475, last accessed on
12.10.2017.
[24] K. Toutanova, D. Klein, C. D. Manning, and Y. Singer, “Feature-rich
part-of-speech tagging with a cyclic dependency network,” in
Proceedings of the 2003 Conference of the North American
Chapter of the Association for Computational Linguistics on Human
Language Technology - Volume 1, ser. NAACL ’03. Association
for Computational Linguistics, pp. 173–180. (Online). Available:
https://doi.org/10.3115/1073445.1073478, last accessed on 01.11.2017.
[25] P. P.-S. Chen, “The entity-relationship model toward a unified
view of data,” vol. 1, no. 1, pp. 9–36. (Online). Available:
http://doi.acm.org/10.1145/320434.320440, last accessed on 05.11.2017.
[26] B. T. Todorovic, S. R. Rancic, I. M. Markovic, E. H. Mulalic, and V. M.
Ilic, “Named entity recognition and classification using context hidden
markov model,” in 2008 9th Symposium on Neural Network Applications
in Electrical Engineering, pp. 43–46.
[27] P. D. Turney, “Learning algorithms for keyphrase extraction,”
vol. 2, no. 4, pp. 303–336. (Online). Available:
http://link.springer.com/article/10.1023/A:1009976227802, last accessed
on 01.11.2017.
[28] R. A. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval.
Addison-Wesley Longman Publishing Co., Inc., 1999.