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Semantic distances for technology landscape visualization

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Abstract

This paper presents a novel approach to the visualization of research domains in science and technology. The proposed methodology is based on the use of bibliometrics; i.e., analysis is conducted using information regarding trends and patterns of publication rather than the actual content. In particular, we explore the use of term co-occurrence frequencies as an indicator of semantic closeness between pairs of terms. To demonstrate the utility of this approach, a number of visualizations are generated for a collection of renewable energy related keywords. As these keywords are regarded as manifestations of the associated research topics, we contend that the proposed visualizations can be interpreted as representations of the underlying technology landscape.

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Notes

  1. Year 2005. Source: Energy Information Administration, DOE, US Government.

  2. http://scholar.google.com

  3. http://www.scirus.org

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Acknowledgements

We would like to thank the Masdar Institute of Science and Technology (MIST) and the Masdar Initiative for their support of this work.

Author information

Authors and Affiliations

  1. Computing and Information Science Program, Masdar Institute of Science and Technology, P.O. Box 54224, Abu Dhabi, United Arab Emirates

    Wei Lee Woon

  2. Sloan School of Management, Massachusetts Institute of Technology, E62-422, Cambridge, MA, 02139, USA

    Stuart Madnick

Authors
  1. Wei Lee Woon
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  2. Stuart Madnick
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Corresponding author

Correspondence to Wei Lee Woon.

Appendices

Appendix A: Renewable energy related keywords

1.1 A.1 Keywords from Kajikawa et al.

Combustion, coal, battery, petroleum, fuel cell, wastewater, heat pump, engine, solar cell, power system.

1.2 A.2 Author keywords

Biomass, CDS, CDTE, energy efficiency, gasification, global warming, least-cost energy policies, power generation, populus, qtl, renewable energy, review, sustainable farming and forestry, adsorption, alternative fuel, arabidopsis, ash deposits, bio-fuels, biodiesel, biomass, biomass-fired power boilers, carbon nanotubes, chemicals, co-firing, coal, corn stover, electricity, emissions, energy balance, energy conversion, energy economy and management, energy policy, energy sources, enzymatic digestion, fast pyrolysis, fuels, gas engines, gas storage, gasification, genome sequence, genomics, high efficiency, hydrolysis, inorganic material, investment, landfill, model plant, natural gas, poplar, pretreatment, pyrolysis, renewable energy, renewables, sugars, sunflower oil, thermal conversion, thermal processing, thin films, transesterification.

1.3 A.3 Keyword plus

16s ribosomal-rna, activation, active-site, adsorption, agrobacterium-mediated transformation, anabaena-variabilis, anacystis-nidulans, aqueous ammonia, bidirectional hydrogenase, biomass conversion processes, briquettes, canopy structure, catalysts, cds, cdte, cells, cellulases, cellulose, ch4, chalcopyrite, charge-transfer dynamics, chemical heat pipe, co2, cocombustion, combustion, composites, conversion, corn stover, coupled electron-transfer, devolatilization, diesel-power-plant, differentiation, dye, efficiency, electrocatalytic hydrogen evolution, electrochemical reduction, electrodes, electron-transfer, elemental sulfur, energy, enzymatic-hydrolysis, families, fermi-level equilibration, films, fimi, flash pyrolysis, fluidized-bed, fuel, fuel-cell vehicles, fuels, functionalized gold nanoparticles, gasification, gasoline, gel electrolyte, gene-transfer, genetic-linkage maps, glycosyl hydrolases, grain morphology, graphite nanofibers, herbaceous biomass, homogeneous catalysis, hybrid poplar, hydrogen, hydrogen-peroxide, hydrogen-production, hydrolysis, ignition, infrastructure, kinetics, light interception, lignin removal, lignocellulosic materials, lime pretreatment, liquefaction, liquid, liquids, mechanisms, metal-complexes, metals, molecular-genetics, monte-carlo simulations, mutagenesis, nanocrystalline semiconductor-films, nickel, nitrogen-fixation, open-top chambers, oxidative addition, partial oxidation, particles, photoelectrochemical cells, photoelectrochemical properties, photoinduced electron-transfer, photonic crystals, photoproduction, photosystem-ii, physisorption, place-exchange-reactions, pores, pressure cooking, products, proton reduction, pulverized coal, pyrolysis, rapd markers, recombination, recycled percolation process, ruthenium polypyridyl complex, seawater, sediment, sensitized nanocrystalline TiO2, sensitizers, short-rotation, solar furnace, solar-cells, sp strain atcc-29133, sputtering deposition method, step gene replacement, surface-plasmon resonance, synergism, synthesis gas, system, TiO2 films, TiO2 thin-films, titanium-dioxide films, transgenic poplar, transport, trichoderma-reesei qm-9414, values, walled carbon nanotubes, waste paper, water-oxidation, wheat-straw mixtures, wood.

Appendix B: Additional visualizations

1.1 B.1 Author keywords

In this appendix, alternative visualizations/clusterings generated using the author-generated keywords are presented. As mentioned in Section 4.1, two alternative forms are presented here. The first, which is shown in Fig. 8, uses Sammon Mapping to generate a topographic representation of the inter-keyword distances. Secondly, k-means clusters were generated using the Jaccard distance, which was described in Section 3.1. This is presented in Table 3. We note that:

  1. (1)

    As discussed in Section 4.1, as well as by the labeling of highly similar clusters found in Fig. 8, the different visualizations were broadly in agreement with the earlier results as to the overall structure of the research landscape.

  2. (2)

    However, at the same time, the representations are not identical; this is not surprising since most of these methods are non-linear and would result in distortion and “stretching” of the visualization space.

  3. (3)

    While the results were consistent with the earlier results, we felt that the visualizations obtained using the Google distance followed by hierarchical clustering or k-means tended to be clearer.

  4. (4)

    For e.g., the Sammon map shown in Fig. 8 is quite difficult to read except under very high magnification. This is a result of the Sammon mapping technique which, by definition, places similar nodes very close to each other in the visualization space; in many cases this resulted in overlapping terms, while on the other hand large sections of the visualization space remained relatively sparse.

  5. (5)

    Also, when using the Jaccard distance the clusters tended to be more unbalanced. For e.g. in Table 3, we see that many of the keywords have been lumped together in cluster JK1. This implies that the mapping induced by the Jaccard distance was not able to achieve a good uniform “spread” of the keywords.

Fig. 8
figure 8

Sammon map of author keywords data. Thematic clusters have been highlighted, and where possible have been linked to clusters found in the hierarchical maps

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Table 3 Clusters generated automatically by applying the k-means algorithm to the author keywords data (Jaccard distances)
Full size table

1.2 B.2 Keyword plus

Similar trends were observed here as with the previous sub-section. Again, the broad structure of the research landscape seems to have been preserved. As before, Sammon Mapping (shown in Figs. 9 and 10) resulted in a somewhat cluttered representation of the landscape, while application of the k-means algorithm to the Jaccard distances again resulted in a less uniform distribution of keywords amongst clusters, where it can be seen that there are a significant number of clusters with only one keyword or phrase (nine such clusters were found in Table 4, as compared to only one such cluster in Table 2).

Fig. 9
figure 9

Sammon map of keyword plus terms, set 1. Thematic clusters have been highlighted, and where possible have been linked to clusters found in the hierarchical maps

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Fig. 10
figure 10

Sammon map of author keyword plus terms, set 2. Thematic clusters have been highlighted, and where possible have been linked to clusters found in the hierarchical maps

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Table 4 Clusters generated using K-means: keyword plus data (Jaccard distances)
Full size table

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Woon, W.L., Madnick, S. Semantic distances for technology landscape visualization. J Intell Inf Syst 39, 29–58 (2012). https://doi.org/10.1007/s10844-011-0182-3

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