data in the context of home range use,
and the third is the exploitation of domain knowledge encoded as a collection of rules that help the system
imagine possible foraging and social
behaviors from environmental and
geographic parameters. Much of what
is creative and interesting about the
blogs derives from the latter domain-specific types of data analyses. Although the developed principles apply more broadly, new applications
would require construction of knowl-edgebases pertinent to the domain of
use. While this is a clear limitation of
our approach, note our ecological interpretation of movement data in particular would be applicable to several
other species. For example, we have
already developed a version of Blogging Birds for golden eagles (Aquila
chrysaetos) for use by RSPB conservation officers, successfully reusing the
second, as well as the first, type of
During the course of the project, we
also discovered ecologists had limited
knowledge of the foraging behavior of
red kites in Scotland, as they had not
been studied extensively following
their relatively recent reintroduction.
We could thus encode only a limited
number of rules per habitat type. The
absence of any large-scale corpus of
texts in this domain also meant we
could not apply the deep learning
methods that are rapidly gaining popularity for generating linguistic variation in computer-generated texts.27 In
future work, we plan to invite Blogging
Birds’ users to contribute behavioral
observations from across the U.K., enabling us to simultaneously curate a
larger set of rules and further public
Finally, our ideas demonstrated
here are applicable more generally.
Telemetric data is ubiquitous, captured by smartphones and other mobile devices, as well as through GPS
sensors embedded in vehicles used by
the transportation industry and others.
Even albums of time-stamped and geo-tagged photos provide data similar to
what we used here. The nature of the
blogs, along with the information
sources used for data enrichment,
would depend on the application, to
blog about a holiday or reveal the provenance and journey of a food item in a
supermarket. In effect, we have demonstrated it is possible to blog about
such data through a process of data enrichment and natural language generation, opening up new avenues for using
AI to engage people through data.
This research was supported by an
award from the RCUK Digital Economy
Programme to the dot.rural Digital
Economy Hub, award reference EP/
1. Binsted, K. and Ritchie, G. Computational rules for
generating punning riddles. International Journal of
Humor Research 10, 1 (July 1997), 25–76.
2. Calenge, C. The package ‘adehabitat’ for the R
software: A tool for the analysis of space and habitat
use by animals. Ecological modelling 197, 3 (Apr.
3. Callaway, C.B. and Lester, J.C. Narrative prose
generation. Artificial Intelligence 139, 2 (Aug. 2002),
4. Carter, I. The Red Kite. Arlequin Press, Chelmsford,
Essex, U.K., 2007.
5. Gatt, A., Portet, F., Reiter, E., Hunter, J., Mahamood,
S., Moncur, W., and Sripada, S. From data to text in the
neonatal intensive care unit: Using NLG technology
for decision support and information management. AI
Communications 22, 3 (third quarter 2009), 153–186.
6. Gatt, A. and Reiter, E. SimpleNLG: A realisation
engine for practical applications. In Proceedings of
the 12th European Workshop on Natural Language
Generation (Athens, Greece, Mar. 30–31). Association
for Computational Linguistics, Stroudsburg, PA, 2009,
7. Gervás, P. Computational approaches to storytelling
and creativity. AI Magazine 30, 3 (Fall 2009), 49–62.
8. Ghazvininejad, M., Shi, X., Choi, Y., and Knight, K.
Generating topical poetry. In Proceedings of Empirical
Methods in Natural Language Processing (Austin, TX,
Nov. 1–5). Association for Computational Linguistics,
Stroudsburg, PA, 2016, 1183–1191.
9. Goldberg, E., Driedger, N., and Kittredge, R.I. Using
natural language processing to produce weather
forecasts. IEEE Expert 9, 2 (Apr. 1994), 45–53.
10. Hebblewhite, M. and Haydon, D. T. Distinguishing
technology from biology: A critical review of the
use of GPS telemetry data in ecology. Philosophical
Transactions of the Royal Society of London B:
Biological Sciences 365, 1550 (July 2010), 2303–2312.
11. Panetta, K. Neural Networks and Modern BI Platforms
Will Evolve Data and Analytics. Gartner, Inc.,
Stamford, CT, Jan. 16, 2017; http://www.gartner.com/
12. Ponnamperuma, K., Siddharthan, A., Zeng, C., Mellish,
C., and Wal, R. Tag2Blog: Narrative generation
from satellite tag data. In Proceedings of the 51st
Annual Meeting of the Association for Computational
Linguistics: System Demonstrations (Sofia, Bulgaria,
Aug. 4–9). Association for Computational Linguistics,
Stroudsburg, PA, 2013, 169–174.
13. Portet, F., Reiter, E., Gatt, A., Hunter, J., Sripada,
S., Freer, Y., and Sykes, C. Automatic generation of
textual summaries from neonatal intensive care data.
Artificial Intelligence 173, 7–8 (May 2009), 789–816.
14. Pschera, A. Animal Internet: Nature and the Digital
Revolution. New Vessel Press, New York, 2016.
15. Reiter, E. and Dale, R. Building Natural Language
Generation Systems. Cambridge University Press,
Cambridge, U.K., 2000.
16. Reiter, E., Sripada, S., Hunter, J., Yu, J., and Davy, I.
Choosing words in computer-generated weather
forecasts. Artificial Intelligence 167, 1–2 (Sept. 2005),
17. Rishes, E., Lukin, S.M., Elson, D. K., and Walker, M. A.
Generating different story tellings from semantic
representations of narrative. In Proceedings of the
International Conference on Interactive Digital
Storytelling (Istanbul, Turkey, Nov. 6–9) Springer, New
York, 2013, 192–204.
18. Sharples, M. An account of writing as creative design.
In The Science of Writing. Lawrence Erlbaum,
Hillsdale, NJ, 1996.
19. Sternberg, R. J. Handbook of Creativity. Cambridge
University Press, Cambridge, U. K., 1999.
20. Theune, M., Faas, S., Heylen, D.K. J., and Nijholt, A. The
virtual storyteller: Story creation by intelligent agents.
In Proceedings of the Conference on Technologies for
Interactive Digital Storytelling and Entertainment, S.
Göbel et al., Eds. (Darmstadt, Germany, Mar. 24–26).
Fraunhofer IRB Verlag, Stuttgart, Germany, 2003,
21. Theune, M., Klabbers, E., de Pijper, J.-R., Krahmer, E.,
and Odijk, J. From data to speech: A general approach.
Natural Language Engineering 7, 1 (Mar. 2001), 47–86.
22. Tintarev, N., Reiter, E., Black, R., Waller, A., and
Reddington, J. Personal storytelling: Using natural
language generation for children with complex
communication needs, in the wild. International Journal
of Human-Computer Studies 92 (Aug. 2016), 1–16.
23. Tomkiewicz, S.M., Fuller, M.R., Kie, J.G., and Bates, K.K.
Global positioning system and associated technologies in
animal behaviour and ecological research. Philosophical
Transactions of the Royal Society of London B: Biological
Sciences 365, 1550 (July 2010), 2163–2176.
24. van der Wal, R., Zeng, C., Heptinstall, D.,
Ponnamperuma, K., Mellish, C., Ben, S., and
Siddharthan, A. Automated data analysis to rapidly
derive and communicate ecological insights from
satellite-tag data: A case study of reintroduced red
kites. Ambio 44, 4 (Oct. 2015), 612–623.
25. Verma, A., van der Wal, R., and Fischer, A. Microscope
and spectacle: On the complexities of using new
visual technologies to communicate about wildlife
conservation. Ambio 44, 4 (Oct. 2015), 648–660.
26. Wall, J., Wittemyer, G., Klinkenberg, B., and
Douglas-Hamilton, I. Novel opportunities for wildlife
conservation and research with real-time monitoring.
Ecological Applications 24, 4 (June 2014), 593–601.
27. Wen,, T.-H., Gašić, M., Mrkšić, N., Su, P.-H., Vandyke, D.,
and Young, S. Semantically conditioned LSTM-based
natural language generation for spoken dialogue
systems. In Proceedings of the Conference on
Empirical Methods in Natural Language Processing
(Lisbon, Portugal, Sept. 17–21). Association for
Computational Linguistics, Stroudsburg, PA, 2015.
28. Yan, R. I, Poet: Automatic poetry composition through
recurrent neural networks with iterative polishing
schema. In Proceedings of the International Joint
Conference on Artificial Intelligence. New York, July
9–15). AAAI Press, Palo Alto, CA, 2016, 2238–2244.
29. Zhang, X. and Lapata, M. Chinese poetry generation
with recurrent neural networks. In Proceedings of the
Conference on Empirical Methods in Natural Language
Processing (Doha, Qatar. Oct. 25–29). Association for
Computational Linguistics, Stroudsburg, PA, 2014,
Advaith Siddharthan (firstname.lastname@example.org)
is a reader in the Knowledge Media Institute at The Open
University, Milton Keynes, U.K.
Kapila Ponnamperuma (kapila.ponnamperuma@arria.
com) is the lead natural language engineer at Arria NLG
plc, Aberdeen, Scotland, U.K.
Chris Mellish (email@example.com), now retired, was
a professor of computer science at the University of
Aberdeen, Scotland, U.K., at the time this research was
Chen Zeng (firstname.lastname@example.org) was a research
assistant on the Blogging Birds Project at the time this
research was conducted.
Daniel Heptinstall (email@example.com) is a senior
international biodiversity adviser on the U.K. government’s
Joint Nature Conservation Committee.
Annie Robinson (firstname.lastname@example.org) was a
research fellow on the Blogging Birds Project at the time
this research was conducted.
Stuart Benn (email@example.com) is a communications
officer for the Royal Society for the Protection of Birds in
René van der Wal (r.vander firstname.lastname@example.org) is a
professor of ecology at the University of Aberdeen,
Copyright held by authors.