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of a current portion of text is related to assembly or
not.
6.2 Extraction of Necessary Knowledge
After segregating the relevant portions from a text, di-
agnostic knowledge must be extracted from these por-
tions. For this, enough information that can be used as
knowledge for diagnosis must be acquired. The extent
of information necessary for performing diagnosis is a
research question in itself, for which we already have
some basis (Madhusudanan and Chakrabarti, 2011a).
When enough information is available for con-
structing diagnostic knowledge, a knowledge base
must then be constructed from it. Translating the
acquired knowledge to a knowledge base must also
be done carefully, since it influences how the knowl-
edge would be used. The choice of different types
of knowledge based systems, such as rule-based sys-
tems, frames, semantic nets, or logic systems, plays a
crucial role here, subject to practical constraints such
as implementation.
6.3 Applying the Acquired Knowledge
Once the knowledge base is constructed, the knowl-
edge has to be applied on assemblies to diagnose is-
sues. This is where another research question is asked
- how does one represent assembly to the knowledge
base ? A good clue can be found in Section 6.1, where
the various information related to assembly were pre-
sented. A model of assembly that can cover these as-
pects would be ideal to represent assembly situations
for applying knowledge. The progress that has been
made in this respect is presented in the Appendix.
7 EXPECTED OUTCOMES
The expected outcomes of this research are some of
the following:
• A method of identifying context of text using on-
tologies and similar structures as reference
• A means of extracting assembly diagnostic
knowledge from documents
• A knowledge base to diagnose assemblies using
acquired knowledge
• A method of modeling assembly situations that
covers various practical facets of assembly both
as a product and a process
REFERENCES
Aydin, S., Kahraman, C., and Kaya,
˙
I. (2012). A new
fuzzy multicriteria decision making approach: An
application for european quality award assessment.
Knowledge-Based Systems, 32:37–46.
Baena-Garcıa, M. and Morales-Bueno, R. (2012). Min-
ing interestingness measures for string pattern mining.
Knowledge-Based Systems, 25(1):45–50.
Dawari, A., B, S., Venkatayogi, C., Chakrabarti, A., Sen,
D., Gurumoorthy, B., and Appelman, H. (2011).
Developing a virtual environment for aiding assess-
ment and improvement of assemblability of aerospace
structures. In Research into Design Supporting Sus-
tainable Product Development.
Dehuri, S. and Cho, S. (2010). Theoretical foundations of
knowledge mining and intelligent agent. Knowledge
Mining Using Intelligent Agents, 6:1.
Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P.
(1996). From data mining to knowledge discovery in
databases. AI magazine, 17(3):37.
Frawley, W. J., Piatetsky-Shapiro, G., and Matheus, C. J.
(1992). Knowledge discovery in databases: An
overview. AI magazine, 13(3):57.
Gomez, F. and Segami, C. (2007). Semantic interpretation
and knowledge extraction. Knowledge-Based Sys-
tems, 20(1):51–60.
Han, X. and Sun, L. (2012). An entity-topic model for entity
linking. In Proceedings of the 2012 Joint Conference
on Empirical Methods in Natural Language Process-
ing and Computational Natural Language Learning,
pages 105–115. Association for Computational Lin-
guistics.
Iwanska, L., Mata, N., and Kruger, K. (2000). Fully auto-
matic acquisition of taxonomic knowledge from large
corpora of texts: Limited-syntax knowledge represen-
tation system based on natural language. In In LM
Iwanksa and SC Shapiro, editors, Natural Language
Processing and Knowledge Processing. Citeseer.
Jun, Y., Liu, J., Ning, R., and Zhang, Y. (2005). Assembly
process modeling for virtual assembly process plan-
ning. International Journal of Computer Integrated
Manufacturing, 18(6):442–451.
Khashei, M., Zeinal Hamadani, A., and Bijari, M. (2012).
A fuzzy intelligent approach to the classification prob-
lem in gene expression data analysis. Knowledge-
Based Systems, 27:465–474.
Madhusudanan, N. and Chakrabarti, A. (2011a). An in-
teractive questioning based method to acquire knowl-
edge for knowledge-based systems. In International
conference on trends in product life cycle, modeling,
simulation and synthesis- PLMSS.
Madhusudanan, N. and Chakrabarti, A. (2011b). A model
for visualizing mechanical assembly situations. Re-
search into Design-Supporting Sustainable Product
Development (ICoRD’11), pages 238–246.
Madhusudanan, N. and Chakrabarti, A. (2013). Implemen-
tation and initial validation of a knowledge acquisi-
tion system for mechanical assembly. In CIRP Design
2012, pages 267–277. Springer.
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