Matches in ESWC 2020 for { ?s ?p This work introduces a new method to induce a class taxonomy from knowledge graphs. The problem is interesting and relevant to the community. The main contribution is the idea of re-forming triples into document-tag tuples thus the commonly used word-frequency and co-occurrence techniques from NLP can be applied on knowledge graphs. Experimental results also demonstrate the merits of the proposed technique over traditional methods. Pros: (1). Comparing with popular machine learning techniques, this proposed work is straightforward and easy to implement, without sacrificing the performance. (2). The experiments are very well designed. For example, the experiment and following discussions on selecting the decay factor are not limited to the dataset used in the paper, but provide an insightful guidance to future users applying the approach. Also, experiments on dataset size would guide future user to make decisions using the approach as well. Cons: There are still some issues that could be improved in order to better demonstrate the work: (1). The methods seems to be a deterministic approach based on the description of the algorithm. Then why "we ran the methods five times on each dataset...."? (2). Any explanation on why 'Heymann and Garcia-Molina was not able to terminate sufficiently fast enough for us to obtain results on the Life dataset'? Also, it would be great if the author could discuss why the proposed method is faster to terminate than traditional methods. (3). I would suggest to also discuss about the validity of the assumption "subclasses will co-occur in document with their superclasses more often than with classes they are not logical descendants of", especially in the context of knowledge graph. (4). There are also many typos across the paper, the authors have to proofread it. Here are just some examples: (a). "high distribution across many topics" --> I guess you mean "high frequency" (b). "lower the more distant ..." --> "lower than the more distant..." (c). "It is preferred evaluation" --> 'it is a preferred evaluation' (d). "to derive the the harmonic mean between ..." --> duplicated "the"". }
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- Paper.193_Review.1 hasContent "This work introduces a new method to induce a class taxonomy from knowledge graphs. The problem is interesting and relevant to the community. The main contribution is the idea of re-forming triples into document-tag tuples thus the commonly used word-frequency and co-occurrence techniques from NLP can be applied on knowledge graphs. Experimental results also demonstrate the merits of the proposed technique over traditional methods. Pros: (1). Comparing with popular machine learning techniques, this proposed work is straightforward and easy to implement, without sacrificing the performance. (2). The experiments are very well designed. For example, the experiment and following discussions on selecting the decay factor are not limited to the dataset used in the paper, but provide an insightful guidance to future users applying the approach. Also, experiments on dataset size would guide future user to make decisions using the approach as well. Cons: There are still some issues that could be improved in order to better demonstrate the work: (1). The methods seems to be a deterministic approach based on the description of the algorithm. Then why "we ran the methods five times on each dataset...."? (2). Any explanation on why 'Heymann and Garcia-Molina was not able to terminate sufficiently fast enough for us to obtain results on the Life dataset'? Also, it would be great if the author could discuss why the proposed method is faster to terminate than traditional methods. (3). I would suggest to also discuss about the validity of the assumption "subclasses will co-occur in document with their superclasses more often than with classes they are not logical descendants of", especially in the context of knowledge graph. (4). There are also many typos across the paper, the authors have to proofread it. Here are just some examples: (a). "high distribution across many topics" --> I guess you mean "high frequency" (b). "lower the more distant ..." --> "lower than the more distant..." (c). "It is preferred evaluation" --> 'it is a preferred evaluation' (d). "to derive the the harmonic mean between ..." --> duplicated "the""".