Review Comment:
Paper Summary:
In this vision paper the author presents her ideas on how Semantic Web technologies could be applied to further exploit and progress computational creativity. The author follows the categorization of Boden and accordingly differentiates (i) exploratory creativity, (ii) computational creativity, as well as (iii) transformational creativity in combination with Wiggins’ unifying formalization of creativity as search. Thereby exploratory creativity refers to search within a predefined search space (i.e. a knowledge graph) to mine new (hidden) concepts of potential value. Combinational creativity refers to the combination of already known concepts within a search space to form new and potentially useful concepts. To achieve this, conceptual blending as well as bisociation are introduced, the application of which are suggested to be extended for ontologies. At last, transformational creativity is referred to by modification of the search space itself by “modifying rules and constraints”. However, transformational creativity is further exemplified on the example of word embeddings including recent approaches to improve expressivity ba using Non-Euclidian Spaces. In the end, the trivial conclusion is drawn that “to achieve its full potential the Semantic Web must be accompanied with valuable applications”, and potential applications for future research are pointed out.
General Comments:
In general, the topic of computational creativity for the Semantic Web is - from the reviewer’s perspective - new and rather interesting. To apply the potential of Knowledge Graphs, ontologies, and rules to the different types of creativity pointed out in the paper seem to be rather promising. However, in the paper only research questions have been phrased and some already existing approaches have been used to further illustrate the underlying concepts, whereas ideas regarding the solution on how to address or even solve the raised research questions are missing. Especially on transformational creativity the relation of latent knowledge representations as e.g. word embeddings to explicit knowledge representations and how to leverage latent representations to achieve/evoke transformational creativity could have been illustrated in more detail.
Detailed Comments:
p.1, c.2, l.36-51:Starting with the first groundbreaking Semantic Web article of Tim Berners-Lee the connection to knowledge graphs is directly drawn via the earlier approach of semantic networks. Here the role of RDF (esp. its graph interpretation) & Linked Data for the popularity of knowledge graphs could have been emphasized more.
P.2, c.1, l.27-44: The author claims a development in Semantic Web from deductive reasoning over inductive reasoning (both analytical approaches) towards abductive reasoning (synthetic and thus generative approaches). This hypothesis should be supported by further evidence in terms of references. In addition the paragraph would be much more helpful, if it could be endorsed with examples and further explanations.(Word) Embeddings are not directly related to Semantic Web technology. They suffer from the same polysemic problems as natural language does and are a latent (implicit) representation of knowledge. However, there are approaches to embed different word senses into separate vectors, as e.g. polysemic aware word embeddings or distributed multi sense embeddings, which would serve better for the task.
P.2, c.2, l.15-38. In this paragraph you stress the example of Ada Lovelace (1815-1852), who could not imagine the computer to be creative. This is a nice anecdote. However, I don’t think that the experiences of one of the first programmers in history are sufficient to motivate contemporary reservations against the possibility of “computational creativity”. Please also note that Ada Lovelace being referred to as “the first programmer” still is a subject of dispute [1].
P.2, c.2, l.44-51: Please maintain the same sequence for the three types of creativity all over the paper.
P.3,c.2,l.1-9. An example of the mentioned refinement operators would be helpful. Also the question of quality measures for creativity could have been further discussed (wrt. Usefulness, novelty, serendipity, etc.)
P. 4, c.2, l.20-35. How can generic spaces be created (also manually). Please give an example. Concerning the “large number of possible combinations to create blends”, here also a quality assessment to come up with some kind of ranking for the most promising candidates should be discussed.
P.5, c.1, Fig.4 This figure is not rather helpful. Please provide node labels to better understand the concept of BisoNet.
P.5, c.1, Fig.45-46. How are bisociations helpful to discover analogies and to create metaphors. Please elaborate on that and give a justification.
P.5,c.2,l.17-22 For transformational creativity no example is given as for the other creativity types.
P.5,c.2,l.32ff Again plain word embeddings are used this time to illustrate(?) transformational creativity, while an improvement in performance is pointed out for non-Euclidian vector spaces. To better transform the (explicit) search space of a knowledge graph, probably the concept of graph embeddings would be better suited. However, as already pointed out, an improvement of plain word embeddings would simply be to use polysemic aware word embeddings instead that take one vector per meaning. Overall the entire paragraph is not clear to me. How can a potential “transformation” to embedding space leverate transformational creativity for knowledge graphs? Plase be more explicit.
P.6, c.2, l.21-23. The paper only raises research questions (which are helpful). However, it does not really “explore under-explored and rising opportunities”. For that I would have expected some ideas and directions in a vision paper.
P.6, c.2, l.27. I missed the aspect on reasoning in general (besides the hypothesis in the beginning that there has been a shift from deductive over inductive to abductive reasoning)..
Minor Issues:
P.2, c.2, l.10: after “etc.” there are overall 2 periods. This should only be one period.
P.4, c.1, Fig.2: “housebot” -> “houseboat”
[1] Simonite, Tom (24 March 2009). "Short Sharp Science: Celebrating Ada Lovelace: the 'world's first programmer'". New Scientist. Archived from the original on 27 March 2009.
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