Matches in Nanopublications for { ?s <https://github.com/LaraHack/linkflows_model/blob/master/Linkflows.ttl#hasCommentText> ?o ?g. }
- comment-14 hasCommentText "* Figure 3 could be additionally provided in a translated version, such that non-italien readers could understand it." assertion.
- comment-15 hasCommentText "* Captions on Figures (e.g. 8) are too small." assertion.
- comment-16 hasCommentText "* Figure 8 is in general hard to interpret, as lines are hard to distinguish." assertion.
- comment-1 hasCommentText "This article presents work on "N-ary Relation Extraction for Joint T-Box and A-Box Knowledge Base Augmentation". The authors propose the FactExtractor system, i.e., a workflow that runs unstructured natural language text through an NLP pipeline in order to generate machine-readable statements that can be used to extend an existing knowledge base. Their approach capitalizes on Frame Semantics as a theoretical backbone from linguistic theory that serves as an interface between an ontology or data model and natural language. The authors demonstrate the capabilities of FactExtractor in a use case based on Italian Wikipedia text (snapshot of 52.000 articles about soccer players) and DBpedia as the target knowledge base to be enriched. The mapping between the DBPO data model and the natural language extractions is achieved by manually defined frames, which provide event classes and expressive roles partipating in these events, both of which can be readily transformed into RDF statements in order to populate the KB. For the given use case, the authors had to define a total of six frames and 15 roles which are particularly tailored to the domain at hand. As such, the proposed method provides an interesting complement to KB population from semi-structured sources such as Wikipedia infoboxes that is commonly used approach in the DBpedia community. Therefore, and due to its novel linguistic underpinnings, I consider this work highly original." assertion.
- comment-2 hasCommentText "The paper is generally well structured, the line of argumentation mostly clear and comprehensible, with some qualifications however:" assertion.
- comment-3 hasCommentText "* From my perspective, the aspect of joint T-Box and A-Box population is somewhat overstated. Certainly, FactExtractor is capable of populating both T-Box and A-Box _simultaneously_, i.e., relying on one and the same pipeline of analyis. However, I cannot see any aspect in the system that indicates a genuinely _joint_ approach in the sense that T-Box and A-Box knowledge acquisition is closely intertwined in order to exploit mutual dependencies between the two (which would correspond to the common use of the term in the machine learning or NLP literature). I would suggest to change the terminology here." assertion.
- comment-4 hasCommentText "* The approach is claimed to be based on "supervised, yet reasonably priced" machine learning methods. However, this comes at the cost of a highly demanding crowdsourcing step that somehow questions the generalizability of the approach: I can barely imagine a crowd of laymen annotating natural language text according to a large-scale, generalized frame inventory. From a more long-term perspective, such a generalization step to (a) less restricted domains and (b) beyond Wikipedia text would be clearly necessary at some point, if the authors take their own argument seriously that KB content should be validated against third-party (i.e., non Wikimedia) resources." assertion.
- comment-5 hasCommentText "*I also do not completely understand the "anatomy" (weird term) of the crowdsourcing task: The description in Section 7.2.1 and Figure 3 suggest that the sentence to be annotated is presented to the workers together with the frame label. How can this be determined in advance? I suspect that this is done by assuming a fixed mapping between lexical units and a frame, which obviously neglects potential lexical ambiguity at the level of lexical units. This aspect needs clarification, and it should be quantified to what extent such ambiguities really occur and pose a problem to the system." assertion.
- comment-6 hasCommentText "Given the considerable amount of substantial work that underlies the paper, it is a bit unfortunate that the significance of the results suffers from issues in the experimental settings and the evaluation:" assertion.
- comment-7 hasCommentText "* The evaluation of classification performance (Section 11.1) is conducted in a rather lenient fashion only, as full credit is given to partial overlap of predicted and correct chunks of text. At least for comparison, I would like to see a more strict setting relying on complete overlap (or a discussion why this is not feasible). What is more, it seems to me that chunks that are labeled with "0" in the gold standard (i.e., should not be labeled by the system) are excluded from the evaluation in the first place." assertion.
- comment-8 hasCommentText "Figure 4 suggests that there is a considerable proportion of cases where "0" chunks are erroneously assigned an FE label by the system. This clearly leads to an illegitimate boost of precision. The final version must at least include an additional setting where these cases are correctly evaluated as false positives." assertion.
- comment-9 hasCommentText "* In purely quantiative terms, the relative gains obtained from A-Box and T-Box augmentation as reported in Tables 5 and 6 are very impressive. However, it would also be interesting to assess the correctness of the additional statements. Given the reservations mentioned in the previous point, I could imagine that there might be a considerable proportion of noise in the extractions. Please provide a snapshot evaluation, e.g., by manually annotating a random sample of extracted assertions." assertion.
- comment-10 hasCommentText "* The experimental settings include a rather simple strategy for seed selection (for both training the frame/FE classifiers and selecting the sentences to be used for extracting assertions in the first place), viz., sentence filtering according to a maximum length of words. First, for the sake of exactness and replicability of the results, this threshold should be explicitly stated. Second, I am a bit concerned that this strategy might introduce a bias towards shorter sentence with a relatively simple syntactic structure, which might explain why Named Entity Linking serves well as a surrogate of syntactic parsing. If so, this clearly questions the scalability of the approach. In any case, I would like to see a more comprehensive discussion of these aspects." assertion.
- comment-11 hasCommentText "* The article should be rendered more self-contained by making less extensive use of references to the authors' own previous work (Fossati et al., 2013) without giving any substantial details about the approach taken there." assertion.
- comment-12 hasCommentText "* While it is certainly fair to say that the workflow as proposed in the paper makes use of a "lightweight NLP machinery" only, the NLP pipeline still requires a lot of manual effort due to the construction of domain-specific FrameNets and the manual annotation work that is needed in order to train classifiers for frame and frame element detection. These modules being core parts of the pipeline, it is certainly not adequate to claim that there be "no need for ... semantic role labeling" in FactExtractor." assertion.
- comment-13 hasCommentText "* Section 5.2: Why is lexical units selection framed as a ranking problem (rather than a filtering/classification problem), and how are the two scores (TF/IDF and standard deviation) combined?" assertion.
- comment-14 hasCommentText "* In Section 9, the formulation "...which we call reification" is misleading, as reification is certainly not a new term that is introduced here." assertion.
- comment-15 hasCommentText "* Table 2: What are "gold units", what are "untrusted judgments"? Please explain." assertion.
- comment-16 hasCommentText "* In Section 8, I was surprised to see that low agreement among the annotators on numerical FEs can be recovered from by using rule-based heuristics. What was the source of the low agreement then?" assertion.
- comment-17 hasCommentText "* Section 11.1.1: "Due to an error in the training set crowdsourcing step, we lack of VITTORIA and PARTITA samples": This issue should be corrected in the final version." assertion.
- comment-18 hasCommentText "* Table 4 mentions "frequency %" in the heading of column 1; the corresponding description in Section 11.2 talks about "absolute occurence frequencies". Please harmonize." assertion.
- comment-19 hasCommentText "* Figure 8 definitely needs a better resolution. In the current version, the curves are barely distinguishable, the legend hardly readable." assertion.
- comment-20 hasCommentText "* Section 13.1: RE (in the authors' use of the term) and OIE are certainly not "two principal fields in Information Extraction", but rather refer two different paradigms in relation extraction (which is in itself a subtask of information extraction)." assertion.
- comment-21 hasCommentText "* p. 13: "lack of ontology property usage in 4 out of 7 classes" --> 3 out of 6?" assertion.
- comment-1 hasCommentText "The paper presents Fact Extractor, a NLP pipeline that allows to generate machine-readable statements based on the extraction of n-ary relations from a textual corpus." assertion.
- comment-2 hasCommentText "The pipeline shows its potentialities when applied to KB enrichment by exploiting textual corpora (e.g., Wikipedia articles for DBpedia)." assertion.
- comment-3 hasCommentText "The overall quality of writing is good. However, some issues may need clarification, furthermore, not too much, but some minor issues like typos exist that require some changes." assertion.
- comment-4 hasCommentText "The structure of the paper is very clear and consistent with the hypotheses and contributions listed in the abstract/introduction." assertion.
- comment-5 hasCommentText "Some sections could be merged together (e.g., Section 2 -> 1, Section 8 -> 7, etc.)." assertion.
- comment-6 hasCommentText "Some claims should be toned down. For example, in Section 3 (Use case) when the authors motivate the reason behind their choice of adopting the Soccer domain for the use case, they argue that 5% of the whole English Wikipedia is a significant portion of the whole chapter. However, 5% can be hardly considered a significant portion of a dataset/sample. Moreover, this ratio is provided with respect to the English Wikipedia, while in the rest of the paper and in the evaluation the authors use the Italian Wikipedia. Hence, the ratio should be provided with respect to the Italian Wikipedia or at least the authors should motivate this incoherence." assertion.
- comment-7 hasCommentText "In Section 5 (Corpus Analysis) the authors "argue that the loss of information is not significant and can be neglected despite the recall costs". Again, the term significant should be used with more accuracy or ir should be better justified by providing supporting data and proper analysis." assertion.
- comment-8 hasCommentText "The authors should provide more details for the following parts:" assertion.
- comment-9 hasCommentText "* Section 6: provide ranks and more context about the selection of the LUs esordire, giocare, perdere, rimanere and vincere." assertion.
- comment-10 hasCommentText "* Section 8: how many rules were defined for the normalisation of numerical expressions? Are these rules available for consultation?" assertion.
- comment-11 hasCommentText "* Section 11.1: no information about the inter-rater reliability is provided. This information is needed in order to demonstrate the value of the goldstandard built for the evaluation." assertion.
- comment-12 hasCommentText "* Section 11.2: can the data about property statistics made be available?" assertion.
- comment-13 hasCommentText "The representation of generated statements should be better clarified. In fact, in Section 2 they authors provide an example that does not reflect the semantics provided by the example in Section 9 (page 10). Namely, in the first example the authors state that, from the sentence "In Euro 1992, Germany reached the final, but lost 0-2 to Denmark", the pipeline generates: * a new statement "Germany defeat Denmark" where defeat is the frame * a set of facts about the new property by using FEs. Instead, in the second example, for the same sentence, the pipeline generates * a new statement ":Germany defeat :Defeat01" * a set of facts about the object :Defeat01 The semantics of the first example leds to extensional issues when adding new facts to a property/frame. This issues seem to be resolved by the second example. However, the statement is completely different as no explicit binary relation is generated between Germany and Denmark. Rather, a more coherent (with respect to first example) and correct formalisation could be the following: :Germany :defeat_01 :Denmark . :defeat_01 rdfs:subPropertyOf :defeat . :defeat_01 :winner :Denmark ; :loser :Germany ; :competition :Euro_1992 ; :score "0-2" ." assertion.
- comment-14 hasCommentText "The state of the art is focused on (open) information extraction and semantification. Nevertheless, it misses the comparison with a large slice of relevant works in the areas of machine reading, n-ary relations extraction, existing frame related KBs and theories in the Semantic Web. These works include FRED [1] (see also the paper submitted to the SWJ [2]), FrameBase (see also the paper submitted to the SWJ [4]), [5], [6] and [7]. [1] "Knowledge Extraction Based on Discourse Representation Theory and Linguistic Frames". Valentina Presutti, Francesco Draicchio, Aldo Gangemi. EKAW 2012: 114-129 [2] http://semantic-web-journal.org/system/files/swj1297.pdf [3] "FrameBase: Representing N-Ary Relations Using Semantic Frames". Jacobo Rouces, Gerard de Melo, Katja Hose. ESWC 2015: 505-521 [4] http://semantic-web-journal.org/system/files/swj1239.pdf [5] "Gathering lexical linked data and knowledge patterns from FrameNet". Andrea Giovanni Nuzzolese, Aldo Gangemi, Valentina Presutti. K-Cap 2011: 41-48 [6] "Frame Detection over the Semantic Web". Bonaventura Coppola, Aldo Gangemi, Alfio Gliozzo, Davide Picca, Valentina Presutti. ESWC 2009: 126-142 [7] "Towards a Pattern Science for the Semantic Web". Aldo Gangemi and Valentina Presutti. Semantic Web Journal 1.1, 2 (2010): 61-68." assertion.
- comment-15 hasCommentText "The comparison with Legalo is unfair (cf. [8] for more details) In fact, Legalo: * relies on FRED (and its frame-based representation of natural language sentences) for generating binary relations; * works either on Wikipedia articles or generic free text inputs; * can be used for KB enrichment (cf. property matcher in the architecture of Legalo). [8] "From hyperlinks to Semantic Web properties using Open Knowledge Extraction". Valentina Presutti, Andrea Giovanni Nuzzolese, Sergio Consoli, Aldo Gangemi, and Diego Reforgiato. Accepted for publication on Semantic Web Journal (http://semantic-web-journal.org/system/files/swj1195.pdf)." assertion.
- comment-16 hasCommentText "* page 1: "DECIPHERING its meaning". If you do not use any cryptographic techniques the term understanding would be more appropriate." assertion.
- comment-17 hasCommentText "* page 2: to "to CREDIBLE (thus high-quality)" -> "to RELIABLE (thus high-quality)" " assertion.
- comment-18 hasCommentText "* page 2: "from raw text and produces e.g, " -> "from raw text and produces, e.g, " " assertion.
- comment-19 hasCommentText "* page 6: "The selected LUs comply to" -> "The selected LUs comply WITH" " assertion.
- comment-20 hasCommentText "* page 7: "We alleviate this through EL techniques". What does EL stand for?" assertion.
- comment-21 hasCommentText "page 9: "which we call reification". I would say "called reification" " assertion.
- comment-22 hasCommentText "* page 13/14: "In average, most raw properties" -> "ON average, most raw properties" " assertion.
- comment-1 hasCommentText "The authors proposed the WiseNet ontology for combining, analyzing and re-purposing the information from a smart camera network. The described process is interesting and combines different existing ontologies, such as, IFC, event, time. Furthermore, a demonstrator is built to show the procedure of the framework." assertion.
- comment-2 hasCommentText "The paper is work in progress as stated by the authors, but some preliminary evaluation with existing frameworks should be included in this paper before publishing." assertion.
- comment-3 hasCommentText "The paper is lacking a clear structure and different sections are mixed and hard to follow." assertion.
- comment-4 hasCommentText "I would recommend breaking the long paragraphs into smaller ones to ease readability, including itemizations and enumerations whenever possible." assertion.
- comment-5 hasCommentText "A strong reduction, or clarification on specific ontology terms in Section 3 is necessary to increase the readability of the text." assertion.
- comment-6 hasCommentText "It should be made more clear in Section 3 and 4 that this is the new proposed framework." assertion.
- comment-7 hasCommentText "There are suggestions to integrate some WiseNet elements in the IFC standard, but no comparison is done with the recent IFC4 ontology. It should be made more clear what are the elements that could be integrated. Furthermore, a smaller ontology is proven to be more successful this should also been taken into account." assertion.
- comment-8 hasCommentText "The authors use computer vision mechanisms to exploit the camera network. It is stated that this is outside the scope of this paper, but they should be discussed (shortly) in this paper." assertion.
- comment-9 hasCommentText "There should be more explanation on how the WiseNet architecture would overcome the computer vision limitations (false detections) before publishing this paper. Currently, there are only detections included and there is from my point of view no possibility to solve these false detections or occlusions." assertion.
- comment-10 hasCommentText "In the paper there is a short description on the visual descriptors, for a person, focusing only on RGB values. This is not useful for people tracking or re-identification. I would suggest the authors to extend descriptors with other person semantic classes (i.e., age, gender, facial expression)." assertion.
- comment-11 hasCommentText "In the future work section I would append some extra applications( i.e., smoke or fire detection, door or window status (open-closed)) to make a more smarter network." assertion.
- comment-1 hasCommentText "The paper addresses a problem that is relevant both from scientific and industrial perspective." assertion.
- comment-2 hasCommentText "The proposed approach is well fitting in the scope of the SWJ journal, even though more experiments would be beneficial to show how the WiseNET system can be actually applied and give added value compared to alternative and already existing solutions. If some modules of the architecture are still under development, then the authors could at least define some scenarios and KPIs to be evaluated in the future." assertion.
- comment-3 hasCommentText "The authors should further highlight which are the actual novel contributions beyond the state of the art with respect of the various modules of the proposed approach." assertion.
- comment-4 hasCommentText "Some concepts and goals are repeated along the article. It would make sense to formalize them at the beginning of the paper and then reference them." assertion.
- comment-5 hasCommentText "An overall graphical representation of the architecture to show how the ontology modules are integrated is missing (cf. table 2)." assertion.
- comment-6 hasCommentText "Are the classes and properties in Tables 3 and 4 novel definitions of the WiseNET ontology? It is suggested to include the prefix to avoid misunderstanding." assertion.
- comment-7 hasCommentText "It should be better explained why the WiseNET ontology redefines part of the content of ifcowl (e.g. properties “aggregates”, “spaceContains”). Why isn’t it enough to extract a fragment of ifcowl and use it in integration with other ontology modules? This would allow to skip a few query+update steps in the proposed methodology." assertion.
- comment-8 hasCommentText "The query update examples for sect.6.2 are missing. This part is probably even more interesting and relevant than what is shown in listings 4 and 5" assertion.
- comment-9 hasCommentText "- It is not appropriate to include a link in the abstract" assertion.
- comment-10 hasCommentText "- A reference could be placed when WiseNET is mentioned the first time in Sect.1" assertion.
- comment-11 hasCommentText "- In page 3, [10] is not the most proper reference about IFC EXPRESS to OWL. In addition to [30], also the paper Pauwels et al. 2017 (“Enhancing the ifcOWL ontology with an alternative representation for geometric data”) can be considered, since it includes the final version of ifcowl approved by bSI." assertion.
- comment-12 hasCommentText "- In page 3, the sentence “Currently, ontologies are represented using OWL-2 language…” is not correct since OWL-2 is not the only ontology language available." assertion.
- comment-13 hasCommentText "- Page 5, [28] is not the appropriate reference to ifcowl. Consider [30] and Pauwels et al. 2017." assertion.
- comment-14 hasCommentText "- Section 3.2 can be made shorted since its contribution to the paper is quite limited." assertion.
- comment-15 hasCommentText "- Fig.4 is included before Fig.3" assertion.
- comment-16 hasCommentText "- Sect.5. Actually, ifcowl is just the ontology T-box and it does not include proper instances. It is suggested to use IFC-RDF graph (or something else) when referring to the instances. This means also that querying the ifcowl instances are not returned (cf. sect.6.1.)" assertion.
- comment-17 hasCommentText "- Sect.5. The use of the term “compliance check” while just looking at which classes are instantiated is a bit misleading." assertion.
- comment-18 hasCommentText "- Sect.5.2. The prefix “inst” is a pure convention related to the IFC-to-RDF conversion and is not defined in the ifcowl ontology." assertion.
- comment-19 hasCommentText "- Listing 3. For completeness, also the inverse properties of ifcowl:relatingObject_IfcRelDecomposes ifcowl:relatedObjects_IfcRelDecomposes should be considered in the query." assertion.
- comment-20 hasCommentText "- Listing 3. The query may return bindings with ?elementType=owl:NamedIndividual, therefore it would be better to add a FILTER like in Question2 of Listing 6." assertion.
- comment-21 hasCommentText "- Fig.5. It’s a bit strange that a property of the a door (key system) is entered in the Camera Setup GUI. It is indeed an extension of what is converted from the IFC file." assertion.
- comment-22 hasCommentText "- Listing 5. The query can be made more compact by exploiting “,”, avoiding to unnecessarily repeat rdf:type." assertion.
- comment-23 hasCommentText "- Listing 5. Why is DUL:hasLocation used instead of what can be already found in ifcowl?" assertion.
- comment-24 hasCommentText "- Listing 6. Question 2. A semi-colon should go after ?x instead of a dot." assertion.
- comment-25 hasCommentText "- Listing 6. Question 3. No need to specify a time stamp?" assertion.
- comment-26 hasCommentText "The paper is well structured and the use of English language is generally good, but it must be improved. There are errors and typos throughout the paper, including grammatical and wrong lexical choices. Here are some examples" assertion.
- comment-27 hasCommentText "- Page 4, column 1, line 10, “unify” in place of “unified”" assertion.
- comment-28 hasCommentText "- Page 4, column 1, line 14, “where” in place of “were”" assertion.
- comment-29 hasCommentText "- Several times the word “fusion” is used as a verb, but it is a noun (cf. page 4, 17)" assertion.
- comment-30 hasCommentText "- Page 4, column 1, “warrants” in place of “warranties” " assertion.
- comment-31 hasCommentText "- Page 5, col 1, “interoperability between” in place of “interoperability among” " assertion.
- comment-32 hasCommentText "- Page 8, col 2, “focus on” in place of “focus in” " assertion.
- comment-33 hasCommentText "- Page 9, col 1, “inserting it into the” in place of “insert it to” " assertion.
- comment-34 hasCommentText "- Page 9, col 2, “people are” in place of “people is” " assertion.
- comment-35 hasCommentText "- Page 9, col 2, “may have occurred” in place of “may occurred” " assertion.
- comment-36 hasCommentText "- “consist on” is wrong, either use “consist of” or “consist in”, depending on the meaning (cf. pages 10, 13, 15)" assertion.
- comment-37 hasCommentText "- Page 10, col 2, “contained in” in place of “contained on” " assertion.
- comment-38 hasCommentText "- Page 15, 17, “especially” in place of “specially” " assertion.
- comment-39 hasCommentText "- Page 15, “it satisfies” in place of “it satisfy” " assertion.
- comment-40 hasCommentText "- Page 15, to clarify “the devices utilize”, maybe it should be “devices utilized”" assertion.
- comment-41 hasCommentText "- Page 15, “does not need” in place of “does not needs” " assertion.
- comment-42 hasCommentText "- Several errors in the final paragraphs of sect.7." assertion.
- comment-1 hasCommentText "In this paper, the authors present an ontology to serve as the backbone of an indoor CCTV system. The ontology integrates a number of well-established vocabularies and ontology models including the OWL representation of the widely-used Industry Foundation Classes model ifcOWL, in order to extend existing Building Information Models with dynamic surveillance data as a precursor to an 'intelligent' building." assertion.