Original: D OCUMENT S TRUCTURE AWARE R ELATIONAL G RAPH C ONVOLUTIONAL N ETWORKS FOR O NTOLOGY P OPULATION
Revised: D OCUMENT S TRUCTURE AWARE R ELATIONAL G RAPH C ONVOLUTIONAL N ETWORKS FOR O NTOLOGY P OPULATION

Original: Anonymous authors

Original: Paper under double-blind review

Original: A BSTRACT
Revised: A BSTRACT

Original: Ontologies comprising of concepts, their attributes, and relationships are used in many knowledge based AI systems.
Revised: Ontologies comprising of concepts, their attributes, and relationships are used in many knowledge based AI systems.

Original: While there have been efforts towards populating domain specific ontologies, we examine the role of document structure in learning ontological relationships between concepts in any document corpus.
Revised: While there have been efforts towards populating domain specific ontologies, we examine the role of document structure in learning ontological relationships between concepts in any document corpus.

Original: Inspired by ideas from hypernym discovery and explainability, our method performs about 15 points more accurate than a stand-alone R-GCN model for this task.
Revised: Inspired by ideas from hypernym discovery and explainability, our method performs about 15 points more accurate than a stand-alone R-GCN model for this task.

Original:  I NTRODUCTION
Revised:  I NTRODUCTION

Original: Ontology induction (creating an ontology) and ontology population (populating ontology with instances of concepts and relations) are important tasks in knowledge based AI systems.
Revised: Ontology induction (creating an ontology) and ontology population (populating ontology with instances of concepts and relations) are important tasks in knowledge based AI systems.

Original: While the focus in recent years has shifted towards automatic knowledge base population and individual tasks like entity recognition, entity classification, relation extraction among other things, there are infrequent advances in ontology related tasks.
Revised: While the focus in recent years has shifted towards automatic knowledge base population and individual tasks like entity recognition, entity classification, relation extraction among other things, there are infrequent advances in ontology related tasks.

Original: Ontologies are usually created manually and tend to be domain specific i.e. meant for a particular industry.
Revised: Ontologies are usually created manually and tend to be domain specific i.e. meant for a particular industry.

Original: For example, there are large standard ontologies like Snomed for healthcare, and FIBO for finance.
Revised: For example, there are large standard ontologies like Snomed for healthcare, and FIBO for finance.

Original: However there are also requirements for cross domain ontologies for applications in data protection, meta-data management and data discovery in Data Fabric.
Revised: However there are also requirements for cross domain ontologies for applications in data protection, meta-data management and data discovery in Data Fabric.

Original: However, creating and maintaining ontologies and related knowledge graphs is a laborious process.
Revised: However, creating and maintaining ontologies and related knowledge graphs is a laborious process.

Original: There have been few efforts in recent years to automate ontology population.
Revised: There have been few efforts in recent years to automate ontology population.

Original: Chen et al.
Revised: Chen et al.

Original: (2018) introduced constraints on relations that should be part of ontologies.
Revised: (2018) introduced constraints on relations that should be part of ontologies.

Original: Guan et al.
Revised: Guan et al.

Original: (2019) et al proposed a method for Link Prediction in n-ary relational data.
Revised: (2019) et al proposed a method for Link Prediction in n-ary relational data.

Original: Shen et al.
Revised: Shen et al.

Original: (2020) used a R-GCN model for the related task of taxonomy expansion.
Revised: (2020) used a R-GCN model for the related task of taxonomy expansion.

Original: We continue with the recent trend to use R-GCN to predict relation type (link type prediction) between entities.
Revised: We continue with the recent trend to use R-GCN to predict relation type (link type prediction) between entities.

Original: In this work, we focus on relation extraction using relational graph neural networks (RGCN) for ontology population, and introduce ideas from symbolic systems that can improve neural model performance.
Revised: In this work, we focus on relation extraction using relational graph neural networks (RGCN) for ontology population.

Original: These ideas stem from observations ofhow people tend to use ontologies and knowledge based systems in industrial and academic datasets.
Revised: These ideas stem from observations of knowledge based systems in industrial applications.

Original: Many of these datasets have been derived from formatted documents, but the document structure information is often discarded by converting to triples and  other dataset formats.
Revised: Many of these datasets used for this task are usually derived from formatted documents, but the document structure information is often discarded by converting to triples and ∗ Equal Contribution other dataset formats.

Original: We conducted a case study in the related domain of information retrieval which showed promising results when the index is optimised with document structure and ontological concepts.

Original: So for the relation extraction task, as shown in Figure 1 instead of starting from plain text sentences, we explore incorporating the document structure information to improve accuracy in R-GCN models.
Revised: So for the relation extraction task, as shown in Figure 1 instead of starting from plain text sentences, we explore incorporating the document structure information to improve accuracy in R-GCN models.

Original: We summarize our contributions in this work as follows:
Revised: We summarize our contributions in this work as follows:

Original: • We propose a document structure measure (DSM) similar to other measures in hypernmy discovery to model ontological relationships between entities in documents.
Revised: • We propose a document structure measure (DSM) similar to other measures in hypernmy discovery to model ontological relationships between entities in documents.

Original: • We experiment with different methods to incorporate the DSM vectors in state of the art relational graph convolutional networks and share our results.
Revised: • We experiment with different methods to incorporate the DSM vectors in state of the art relational graph convolutional networks and share our results.

Original: All the code and data used in this work are available at this anonymous url.

Original:  R ELATED W ORK
Revised:  R ELATED W ORK

Original: Chiticariu et al.

Original: (2010) present a rule based system that can be used to extract document structure as well annotate personal data entities in unstructured data.

Original: Chen et al.
Revised: Chen et al.

Original: (2018) incorporated hierarchy for relation extraction in Ontology Population.
Revised: (2018) incorporated hierarchy for relation extraction in Ontology Population.

Original: Our document structure measures differs from such works in that these don’t just capture hierarchy but are very general and encompass document summary, section titles, bulleted lists, highlighted text, info box etc.
Revised: Our document structure measures differs from such works in that these don’t just capture hierarchy but are very general and encompass document summary, section titles, bulleted lists, highlighted text, infobox etc.

Original: We also extend this concept of document structure to personal data (binary).
Revised: We also extend this concept of document structure to personal data (binary).

Original: Nagpal et al.
Revised: Nagpal et al.

Original: (2022) described a method to use rule based systems along with a neural model to improve fine grained entity classification.
Revised: (2022) described a method to use rule based systems along with a neural model to improve fine grained entity classification.

Original: Vannur et al.
Revised: Vannur et al.

Original: (2020) described a method to augment the training data for better personal knowledge base population.
Revised: (2020) described a method to augment the training data for better personal knowledge base population.

Original: Ganesan et al.
Revised: Ganesan et al.

Original: (2020) uses graph neural networks to predict missing links between people in a property graph.
Revised: (2020) uses graph neural networks to predict missing links between people in a property graph.

Original: Chiticariu et al.
Revised: Chiticariu et al.

Original: (2010) presented a System T method of rule based information extraction.
Revised: (2010) presented a System T method of rule based information extraction.

Original: They have also shown a way to determine if the extraction information is relevant to a domain or otherwise Wang et al.
Revised: They have also shown a way to determine if the extraction information is relevant to a domain or otherwise Wang et al.

Original: Recent work Madaan et al.
Revised: Madaan et al.

Original: (2017) has specifically dealt with extracting titles, section and subsection headings, paragraphs, and sentences from large documents.
Revised: (2017) has specifically dealt with extracting titles, section and subsection headings, paragraphs, and sentences from large documents.

Original: Additionally Agarwal et al.
Revised: Additionally Agarwal et al.

Original: (2017) extracts structure and concepts from html documents in compliance specific applications.
Revised: (2017) extracts structure and concepts from html documents in compliance specific applications.

Original: We deploy bases from Agarwal et al.

Original: ; Madaan et al.

Original: (2017) to automate our process of document structure discovery and annotations.

Original: Models making use of dependency parses of the input sentences, or dependency-based models, have proven to be very effective in relation extraction, as they can easily capture long-range syntactic relations.

Original: Zhang et al.
Revised: Zhang et al.

Original: (2018) proposed an extension of graph convolutional network that is tailored for relation extraction.
Revised: (2018) proposed an extension of graph convolutional network that is tailored for relation extraction.

Original: Their model encodes the dependency structure over the input sentence with efficient graph convolution operations, then extracts entity-centric representations to make robust relation predictions.
Revised: Their model encodes the dependency structure over the input sentence with efficient graph convolution operations, then extracts entity-centric representations to make robust relation predictions.

Original: Hierarchical relation embedding (HRE) focuses on the latent hierarchical structure from the data. Chen et al.
Revised: We derive some of our inspiration from the TaxoExpan work Shen et al.

Original: (2018) introduced neighbourhood constraints in node-proximitybased or translational methods.
Revised: (2020) that also uses RGCN to find hyponyms for taxonym expansion.

Original: Finally, ontologies enable a number of applications in Business Analytics and Master Data Management by enabling Natural Language Querying Saha et al. (2016) and Reasoning solutions on top of large data Karanam et al.
Revised: We note that our work is however significantly more comprehensive in both the types of rules and number of such relationship extractions (not restricted to hypernym discovery).

Original:  L EARNING D OCUMENT S TRUCTURE
Revised:  L EARNING D OCUMENT S TRUCTURE

Original: Chen et al.
Revised: Chen et al.

Original: (2018) showed that some relations prove to be rich enough to go into the T-box (relations between Concepts), while other relations need only be present in the A-box.
Revised: (2018) showed that some relations prove to be rich enough to go into the T-box (relations between Concepts), while other relations need only be present in the A-box.

Original: A corollary to the above classification of relations is the use of different measures to solve the hypernym discovery problem.
Revised: A corollary to the above classification of relations is the use of different measures to solve the hypernym discovery problem.

Original: These could be broadly classified as Similarity Measures (Cosine, Lin, ApSyn), Inclusion Measures (ClarkeDE, balAPinc, invCL), Informativeness Measures (SLQS), and Reversed Inclusion Measures (Reversed ClarkeDE).
Revised: These could be broadly classified as Similarity Measures (Cosine, Lin, ApSyn), Inclusion Measures (ClarkeDE, balAPinc, invCL), Informativeness Measures (SLQS), and Reversed Inclusion Measures (Reversed ClarkeDE).

Original: Inspired by these measures, we introduce a new measure based on document features and structure that helps identifying relationships for knowledge base population.
Revised: Inspired by these measures, we introduce a new measure based on document features and structure that helps identifying relationships for knowledge base population.

Original: We broadly term these under the umbrella of Document Structure Measures (DSM) and further deploy a Relational Graph Convolutional Network (RGCN) to learn the attributions of the same towards our larger goal.
Revised: We broadly term these under the umbrella of Document Structure Measures (DSM) and further deploy a Relational Graph Convolutional Network (RGCN) to learn the attributions of the same towards our larger goal.

Original: We derive some of our inspiration from the TaxoExpan work Shen et al.

Original: (2020) that also uses RGCN to find hyponyms for taxonym expansion.

Original: We note that our work is however significantly more comprehensive in both the types of rules and number of such relationship extractions (not restricted to hypernym discovery).

Original: To validate the effectiveness of using DSM, welater perform experiments ona Wikipedia dataset.
Revised: To validate the effectiveness of using DSM, we perform experiments on Wikipedia pages of entities in our datasets.

Original: Our focus on unstructured text and personal data as with the Wikipedia example is primarily due to restrictions on using emails or social media posts for research.
Revised: Our focus on unstructured text and personal data in Wikipedia pages is primarily due to restrictions on using emails or social media posts for research.

Original: However, the approach is general to any type of document with a template.
Revised: However, the approach is general to any type of document with a template.

Original: Some easy examples include corporate documents, emails, tweets, logs, and chat transcripts.
Revised: Some easy examples include corporate documents, emails, tweets, logs, and chat transcripts.

Original: 3. D OCUMENT S TRUCTURE M EASURE (DSM)
Revised: 3. D OCUMENT S TRUCTURE M EASURE (DSM)

Original: To motivate the notational aspects, even before defining our model, we shed light on the broad term called “document features”.
Revised: To motivate the notational aspects, even before defining our model, we shed light on the broad term called “document features”.

Original: These can correspond to positioning of words, their hierarchy in a document, and even their formatting.
Revised: These can correspond to positioning of words, their hierarchy in a document, and even their formatting.

Original: We note that some of the features from embedding can also be cast into this framework, but we precisely mention that our structural features are generic and not tied to the aspects of any specific language.
Revised: We note that some of the features from embedding can also be cast into this framework, but we precisely mention that our structural features are generic and not tied to the aspects of any specific language.

Original: As an example, text within bullets can represent important data such as sensitive data, hypernyms, or even a short excerpt.
Revised: As an example, text within bullets can represent important data such as sensitive data, hypernyms, or even a short excerpt.

Original: As another isometric viewpoint, personal data like anonymized or pseudonymized credit card numbers can be presented in brackets, found in footnotes, and even given by fine worded text (small text) to mask readability.
Revised: As another isometric viewpoint, personal data like anonymized or pseudonymized credit card numbers can be presented in brackets, found in footnotes, and even given by fine worded text (small text) to mask readability.

Original: In our scope of work corresponding to ontologies, we choose “personal information” as our running example.
Revised: In our scope of work corresponding to ontologies, we choose “personal information” as our running example.

Original: These can say point to names, addresses, biographical details etc. and covers a very broad range of labels.
Revised: These can say point to names, addresses, biographical details etc. and covers a very broad range of labels.

Original: As the most crucial aspect, we define the metric called importance probability, ρ .
Revised: As the most crucial aspect, we define the metric called importance probability, ρ .

Original: This measure signifies the factor by which a specific occurrence of text corresponds to a classification label in the space defined by document structure measure.
Revised: This measure signifies the factor by which a specific occurrence of text corresponds to a classification label in the space defined by document structure measure.

Original: Say, in our running example, if the text “xxxx–56” is found within brackets, we attribute a higher probability score ρ to its classification as personal data.
Revised: Say, in our running example, if the text “xxxx–56” is found within brackets, we attribute a higher probability score ρ to its classification as personal data.

Original: As another example in a slightly different context, say if the word “red” appears in bullets, and the word “color” appears in the main text, we attribute a higher probability score to the hypernym-hyponym relationship between the two words.
Revised: As another example in a slightly different context, say if the word “red” appears in bullets, and the word “color” appears in the main text, we attribute a higher probability score to the hypernym-hyponym relationship between the two words.

Original: Next, we define vectors, v a ( x ) , v b ( x ) , and v c ( x ) to define the document structure measure corresponding to a word x .
Revised: Next, we define vectors, v a ( x ) , v b ( x ) , and v c ( x ) to define the document structure measure corresponding to a word x .

Original: Here, v a ( x ) and v b ( x ) refer to relational measures and v c ( x ) refers to an absolute measure.
Revised: Here, v a ( x ) and v b ( x ) refer to relational measures and v c ( x ) refers to an absolute measure.

Original: More importantly, v a refers to a higher hierarchy and v b refers to a lower hierarchy.
Revised: More importantly, v a refers to a higher hierarchy and v b refers to a lower hierarchy.

Original: Say, in the example of “red/color” relationship above, let “ x ” denote “red”.
Revised: Say, in the example of “red/color” relationship above, let “ x ” denote “red”.

Original: Then, v a ( x ) takes a lower value (say 0) and v b takes a higher value (say 1).
Revised: Then, v a ( x ) takes a lower value (say 0) and v b takes a higher value (say 1).

Original: Without loss of generality (this can take non-integral values also), we assume that the values taken by v a , v b , and v c are 0/1 (similar to an indicator function).
Revised: Without loss of generality (this can take non-integral values also), we assume that the values taken by v a , v b , and v c are 0/1 (similar to an indicator function).

Original: Next, we further specify the fine details on the quantification of these vectors based on the logical document structure rules.
Revised: Next, we further specify the fine details on the quantification of these vectors based on the logical document structure rules.

Original: Noting space considerations, we discuss in detail in about relational DSM vectors in the next subsection.
Revised: Noting space considerations, we discuss in detail in about relational DSM vectors in the next subsection.

Original: Details regarding absolute DSM vectors can be found in the supplementary material.
Revised: Details regarding absolute DSM vectors can be found in the supplementary material.

Original: 3. R ELATIONAL DSM V ECTORS
Revised: 3. R ELATIONAL DSM V ECTORS

Original: We start with an example of bulleted text.
Revised: We start with an example of bulleted text.

Original: Without loss of generality, we split the entire document into multiple paragraphs ensuring that each paragraph at the most contains only one set of bulleted text.
Revised: Without loss of generality, we split the entire document into multiple paragraphs ensuring that each paragraph at the most contains only one set of bulleted text.

Original: Bulleted text are more probable to be hyponyms (Lists / Enumerations also included) as with the the instance below.
Revised: Bulleted text are more probable to be hyponyms (Lists / Enumerations also included) as with the the instance below.

Original: “X contains the following:
Revised: “X contains the following:

Original: • X • X2”
Revised: • X • X2”

Original: Here, X1 and X2 are hyponyms of X. Note that hypernym-hyponym exactly corresponds to one relationship where v a denotes the former and v b denotes the latter.
Revised: Here, X1 and X2 are hyponyms of X. Note that hypernym-hyponym exactly corresponds to one relationship where v a denotes the former and v b denotes the latter.

Original: This can be easily generalized to other relationships and document structure rules.
Revised: This can be easily generalized to other relationships and document structure rules.

Original: Given two words x and y , their probability of their hierarchical (hypernym-hyponym in this case) relationship specific to a document feature i (in this case bulleted list) can be stated as follows:
Revised: Given two words x and y , their probability of their hierarchical (hypernym-hyponym in this case) relationship specific to a document feature i (in this case bulleted list) can be stated as follows:

Original: [Equation]
Revised: [Equation]

Original: Here, j refers to an entity mention, which in this case is the presence of the word x in paragraph j that contains a document feature, for example, a bulleted list.
Revised: Here, j refers to an entity mention, which in this case is the presence of the word x in paragraph j that contains a document feature, for example, a bulleted list.

Original: Note that the suffix k corresponds to the index of the document structure measure.
Revised: Note that the suffix k corresponds to the index of the document structure measure.

Original: Say for the three measures bulleted text, footnotes, and title, the indexes are respectively k
Revised: Say for the three measures bulleted text, footnotes, and title, the indexes are respectively k

Original: = 1, 2, and 3.
Revised: = 1, 2, and 3.

Original: The notation for the vectors v a , v b , and v c follow similar suite.
Revised: The notation for the vectors v a , v b , and v c follow similar suite.

Original: In case a paragraph j does not contain the document feature, the corresponding entries are 0 for both the numerator and denominator sub-portions.
Revised: In case a paragraph j does not contain the document feature, the corresponding entries are 0 for both the numerator and denominator sub-portions.

Original: In some cases a document feature may contain more than one occurrence of a hypernym / word.
Revised: In some cases a document feature may contain more than one occurrence of a hypernym / word.

Original: We merely consider the above expression to have an indicator function and do not pursue on the track of multiple occurrences.
Revised: We merely consider the above expression to have an indicator function and do not pursue on the track of multiple occurrences.

Original: Usually, the vectors v a and v b are specified in an opposite sense, where say v i,ja ( x ) = 1 , v i,jb = 0 and viceversa.
Revised: Usually, the vectors v a and v b are specified in an opposite sense, where say v i,j a ( x ) = 1 , v i,j b = 0 and viceversa.

Original: Say, if a word occurs at the text preceding the bullets, they are directed towards the indicator function in v a
Revised: Say, if a word occurs at the text preceding the bullets, they are directed towards the indicator function in v a

Original: and if they occur within the sub-bullets, those are accounted towards the indicator function in v b .
Revised: and if they occur within the sub-bullets, those are accounted towards the indicator function in v b .

Original: However, it can also be true that some words can be present in both the preceding text or sub-bullets, leading to both v a and v b taking the value of 1.
Revised: However, it can also be true that some words can be present in both the preceding text or sub-bullets, leading to both v a and v b taking the value of 1.

Original: Generalizing the above to all possible document features, we have the following.
Revised: Generalizing the above to all possible document features, we have the following.

Original: [Equation]
Revised: [Equation]

Original: where w k refers to the weight assigned to each document feature (pre-set by the user depending on the application) and f k ( . )
Revised: where w k refers to the weight assigned to each document feature (pre-set by the user depending on the application) and f k ( . )

Original: denotes an importance function corresponding to the occurrence of the entities both in the presence of the context and overall (presence and absence included).
Revised: denotes an importance function corresponding to the occurrence of the entities both in the presence of the context and overall (presence and absence included).

Original: More specifically, n k,x refers to the number of times the word x has occurred in text preceding the document feature in the document and n x denotes the overall number of times the word x has appeared in the document.
Revised: More specifically, n k,x refers to the number of times the word x has occurred in text preceding the document feature in the document and n x denotes the overall number of times the word x has appeared in the document.

Original: For a complete set of rules and document features, please refer to the supplementary material.
Revised: For a complete set of rules and document features, please refer to the supplementary material.

Original: Alternatively, instead of a summation, ρ can be kept as a vector and given by ρ ( x, y ) =
Revised: Alternatively, instead of a summation, ρ can be kept as a vector and given by ρ ( x, y ) =

Original: (cid:8) ρ k ( x, y ) (cid:9)
Revised: (cid:8) ρ k ( x, y ) (cid:9)

Original: Kr k
Revised: Kr k

Original: =1 , where Kr denotes the total number of relative features.
Revised: =1 , where Kr denotes the total number of relative features.

Original: 3. G ENERATING DSM VECTORS
Revised: 3. G ENERATING DSM VECTORS

Original: This in section we’ll describe our information retrieval based approach to generate document structure measure vectors for each pair of entities in the Wikipeople and TACRED datasets.
Revised: This in section we’ll describe our information retrieval based approach to generate document structure measure vectors for each pair of entities in the Wikipeople and TACRED datasets.

Original: We use
Revised: We use

Original: Wikipedia as the document to produce the document structure, but any corpus of documents with a templated format like electronic health records, customer invoices, legislation, regulatory documents could be used to generate these DSM vectors.
Revised: Wikipedia pages to produce the document structure, but any corpus of documents with a templated format like electronic health records, customer invoices, legislation, regulatory documents could be used to generate these DSM vectors.

Original: Additionally, as discussed in Section 3, data sources like tweets, chat conversations, emails also display templated document structure and hence can be processed for generating DSM vectors using the method described in this section.
Revised: Additionally, as discussed in Section 3, data sources like tweets, chat conversations, emails also display templated document structure and hence can be processed for generating DSM vectors using the method described in this section.

Original: As discussed in Section 4.1 and shown on Table 1, Wikipeople and TACRED are relational datasets and unstructured datasets respectively, which we have converted into graph formats for this work.
Revised: As discussed in Section 4.1 and shown on Table 1, Wikipeople and TACRED are relational datasets and unstructured datasets respectively, which we have converted into graph formats for this work.

Original: Hence in the case of Wikipeople, we’re only concerned with Person to Person relations and in
Revised: Hence in the case of Wikipeople, we’re only concerned with Person to Person relations and in

Original: TACRED, only on relationships between people and organizations.
Revised: TACRED, only on relationships between people and organizations.

Original: In order to generate document structure measure for the relationship between any of the above pairs, we begin by computing the frequency of the occurrence of pairs of entities in the corresponding
Revised: In order to generate document structure measure for the relationship between any of the above pairs, we begin by computing the frequency of the occurrence of pairs of entities in the corresponding

Original: Wikipedia pages of these entities.
Revised: Wikipedia pages of these entities.

Original: We index different parts of the Wikipedia pages in a search index and retrieve frequencies of occurrences using the surface forms of the entities (names) as queries.
Revised: We index different parts of the Wikipedia pages in a search index and retrieve frequencies of occurrences using the surface forms of the entities (names) as queries.

Original: We observe that an indexing system that leverages document structure and ontological information to enrich the raw documents, has higher performance than regular document indexing.
Revised: We observe that an indexing system that leverages document structure and ontological information to enrich the raw documents, has higher performance than regular document indexing.

Original: We treat the search index as a black box (which is usually the case in many cloud based implementations) and make all our improvements to the documents being indexed.
Revised: We treat the search index as a black box (which is usually the case in many cloud based implementations) and make all our improvements to the documents being indexed.

Original: We leave further improvements to the search index as future work.
Revised: We leave further improvements to the search index as future work.

Original:  E XPERIMENTS 4. D ATASETS
Revised:  E XPERIMENTS 4. D ATASETS

Original: We have created our datasets from the Wikipedia pages for the entities in Wikipeople and TACRED datasets.
Revised: We have created our datasets from the Wikipedia pages for the entities in Wikipeople and TACRED datasets.

Original: We used NLTK to split the text into sentences and tokenize the sentences.

Original: The  relations in the datasets are as shown in Figures 4 and 3.
Revised: The original relations in the datasets are as shown in Figures 4 and 3.

Revised: We first used NLTKLoper & Bird (2002) to split the text into sentences and tokenize the sentences.

Original: We used the method described in Chiticariu et al.
Revised: We used the method described in Chiticariu et al.

Original: (2010) to identify the span of entity mentions in
Revised: (2010) to identify the span of entity mentions in

Original: Wikipedia pages.
Revised: Wikipedia pages.

Original: We call these scripts as Personal Data Annotators.
Revised: We call these scripts as Personal Data Annotators.

Original: This method requires creation of dictionaries each named after the entity type, and populated with entity mentions.
Revised: This method requires creation of dictionaries each named after the entity type, and populated with entity mentions.

Original: We ran the
Revised: We ran the

Original: Personal Data Annotators on these sentences, providing the bulk of the annotations that are reported in Table 1.
Revised: Personal Data Annotators on these sentences, providing the bulk of the annotations that are reported in Table 1.

Original: [Table]
Revised: [Table]

Original: We consider the following document structures in this experiment.
Revised: We consider the following document structures in this experiment.

Original: Title, introduction section, infobox, section (and subsection) headings and text.
Revised: Title, introduction section, infobox, section (and subsection) headings and text.

Original: We could include other structures like category, in and outlinks in the case of Wikipedia.
Revised: We could include other structures like category, in and outlinks in the case of Wikipedia.

Original: We leave that for future work.
Revised: We leave that for future work.

Original: To calculate the document structure measure for a relation, { subject, relation, object } , we use a weighted average of the frequency of their occurrences in the corpus.
Revised: To calculate the document structure measure for a relation, { subject, relation, object } , we use a weighted average of the frequency of their occurrences in the corpus.

Original: The weight of each document structure is manually assigned by us, but it could be learned from the corpus as well.
Revised: The weight of each document structure is manually assigned by us, but it could be learned from the corpus as well.

Original: We leave this also to future work.
Revised: We leave this also to future work.

Original: This approach does not take the context of the entity mentions while assigning labels and hence the data is somewhat noisy.
Revised: This approach does not take the context of the entity mentions while assigning labels and hence the data is somewhat noisy.

Original: However, labels for name, email address, location, website do not suffer much from the lack of context and hence were annotated using this tool.
Revised: However, labels for name, email address, location, website do not suffer much from the lack of context and hence were annotated using this tool.

Original: 4. E XPERIMENTAL SETUP
Revised: 4. E XPERIMENTAL SETUP

Original: We experiment on the R-GCN models proposed by Schlichtkrull et al.
Revised: We experiment on the R-GCN models proposed by Schlichtkrull et al.

Original: ; Shen et al.
Revised: ; Shen et al.

Original: These have been used in the cases of link prediction and hyponym based taxonomy expansion respectively.
Revised: These have been used in the cases of link prediction and hyponym based taxonomy expansion respectively.

Original: For training R-GCN and our improvements, we use a single V100 GPU with 16GB memory.
Revised: For training R-GCN and our improvements, we use a single V100 GPU with 16GB memory.

Original: We use the RGCN implementation in DGL Wang et al.
Revised: We use the RGCN implementation in DGL Wang et al.

Original: and the elastic search instance from a cloud provider.
Revised: and the elastic search instance from a cloud provider.

Original: 4. L INK T YPE PREDICTION WITH DSM
Revised: 4. L INK T YPE PREDICTION WITH DSM

Original: Assuming that the network is defined appropriately, the hidden representation for each node i at (l+1) th layer can be written as follows.
Revised: Assuming that the network is defined appropriately, the hidden representation for each node i at (l+1) th layer can be written as follows.

Original: [Equation]
Revised: [Equation]

Original: B ASELINE
Revised: B ASELINE

Original: We use the heterograph version of R-GCN from DGL.
Revised: We use the heterograph version of R-GCN from DGL.

Original: One of the limitations of using the heterograph version is the requirement to add a self-loop for all the singleton nodes in the graph.
Revised: One of the limitations of using the heterograph version is the requirement to add a self-loop for all the singleton nodes in the graph.

Original: As discussed in Section 4.1, we train all our R-GCN models by consuming the datasets as attributed graphs.
Revised: As discussed in Section 4.1, we train all our R-GCN models by consuming the datasets as attributed graphs.

Original: In the case of Wikipeople, the relations are of the form (Person, relation type, Person), and in the case of TACRED, we have relations of the form (Person, relation type, Person), (Person, relation type, Org), (Org, relation type, Person) and (Org, relation type, Org).
Revised: In the case of Wikipeople, the relations are of the form (Person, relation type, Person), and in the case of TACRED, we have relations of the form (Person, relation type, Person), (Person, relation type, Org), (Org, relation type, Person) and (Org, relation type, Org).

Original: The baseline
Revised: The baseline

Original: RGCN results seem comparable to results on attributed multiplex heterogenous networks in Cen et al.
Revised: RGCN results seem comparable to results on attributed multiplex heterogenous networks in Cen et al.

Original: , Vannur et al.
Revised: , Vannur et al.

Original: R EGULARIZATION
Revised: R EGULARIZATION

Original: We began by incorporating the DSM scores for each pair of relationship in the cross entropy loss function similar to any regularization parameter.
Revised: We began by incorporating the DSM scores for each pair of relationship in the cross entropy loss function similar to any regularization parameter.

Original: As shown in Table 2, this did not yield any improvement in performance.
Revised: As shown in Table 2, this did not yield any improvement in performance.

Original: H IDDEN LAYER
Revised: H IDDEN LAYER

Original: We then tried incorporating our DSM vectors by adding another hidden layer to the network.
Revised: We then tried incorporating our DSM vectors by adding another hidden layer to the network.

Original: We pass the output of the first hidden layer in RGCN to this new layer and update the representation of entities in the (l+1)th layer with the DSM vectors.
Revised: We pass the output of the first hidden layer in RGCN to this new layer and update the representation of entities in the (l+1)th layer with the DSM vectors.

Original: Recall that the hidden representation of entities in (l+1)th layer in R-GCN can be formulated as shown in Equation 1.
Revised: Recall that the hidden representation of entities in (l+1)th layer in R-GCN can be formulated as shown in Equation 1.

Original: We updated the first hidden layer output for an entity my multiplying with the corresponding aggregate DSM score of all the edges incident on the entity.
Revised: We updated the first hidden layer output for an entity my multiplying with the corresponding aggregate DSM score of all the edges incident on the entity.

Original: This method gave marginal improvement in some relationship types, but hurts the performance in general.
Revised: This method gave marginal improvement in some relationship types, but hurts the performance in general.

Original: We observe that using the aggregate scores from DSM vectors for all edges does not help to capture the correlation between the document structure and the edge.
Revised: We observe that using the aggregate scores from DSM vectors for all edges does not help to capture the correlation between the document structure and the edge.

Original: Hence we tried incorporating the DSM vectors in the message passing layer, which we describe in the next section.
Revised: Hence we tried incorporating the DSM vectors in the message passing layer, which we describe in the next section.

Original: E DGE W EIGHTS
Revised: E DGE W EIGHTS

Original: Here, we describe a way to incorporate document structure measure in the node representation.
Revised: Here, we describe a way to incorporate document structure measure in the node representation.

Original: We add a key embodiment to Equation 1 in the form of ρ , corresponding to document structure measures.
Revised: We add a key embodiment to Equation 1 in the form of ρ , corresponding to document structure measures.

Original: Note that we pre-compute ρ based on samples from the training data.
Revised: Note that we pre-compute ρ based on samples from the training data.

Original: For the expression below, we use the general vector version of ρ , with individual ρ k ’s contributing towards the activation θ .
Revised: For the expression below, we use the general vector version of ρ , with individual ρ k ’s contributing towards the activation θ .

Original: This can be replaced by the scalar ρ k , where the activation can be changed accordingly.
Revised: This can be replaced by the scalar ρ k , where the activation can be changed accordingly.

Original: Note that the dimension of inputs and outputs corresponding to the layer can be re-defined accordingly with respect to the dimensions of x and y.
Revised: Note that the dimension of inputs and outputs corresponding to the layer can be re-defined accordingly with respect to the dimensions of x and y.

Original: [Equation]
Revised: [Equation]

Original: We implemented the above by updating the edge weights in the forward pass of RGCN.
Revised: We implemented the above by updating the edge weights in the forward pass of RGCN.

Original: A similar approach had been taken in Qin et al.
Revised: A similar approach had been taken in Qin et al.

Original: but the initial edge weights there were based on the node type and were updated with self-adversarial training.
Revised: but the initial edge weights there were based on the node type and were updated with self-adversarial training.

Original: We instead incorporate the DSM vector during message passing by adding DSM scores to each corresponding edge weight.
Revised: We instead incorporate the DSM vector during message passing by adding DSM scores to each corresponding edge weight.

Original: As discussed in the next section, this improves RGCN accuracy in both the datasets, with the Wikipeople increasing by 15 points.
Revised: As discussed in the next section, this improves RGCN accuracy in both the datasets, with the Wikipeople increasing by 15 points.

Original: 4. R ESULTS
Revised: 4. R ESULTS

Original: We report the performance of RGCN models as shown in Table 2.
Revised: We report the performance of RGCN models as shown in Table 2.

Original: Our RGCN model with DSM edge weights performs about 15% better than the baseline model.
Revised: Our RGCN model with DSM edge weights performs about 15% better than the baseline model.

Original: We also observe that using the DSM vectors for regularization and as a hidden layer do not seem to help and those models performed worse than the baseline.
Revised: We also observe that using the DSM vectors for regularization and as a hidden layer do not seem to help and those models performed worse than the baseline.

Original: [Table]
Revised: [Table]

Original: In Figures 5a and 5b, we overlay the accuracy of the models on the class distribution.
Revised: In Figures 5a and 5b, we overlay the accuracy of the models on the class distribution.

Original: Sibling relationship type dominates the Wikipeople dataset and the use of document structure helps this type too.
Revised: Sibling relationship type dominates the Wikipeople dataset and the use of document structure helps this type too.

Original: We believe that sibling relationship (which are symmetric) benefits from document structure
Revised: We believe that sibling relationship (which are symmetric) benefits from document structure

Original: [Figure]
Revised: [Figure]

Original: because it gets the boost from the way we index sentences and associate the document and section title with the sentences.
Revised: because it gets the boost from the way we index sentences and associate the document and section title with the sentences.

Original: We also observe that no relation class which might rarely occur other than as text (and not in the infobox of a wikipedia page for example), does not get much improvement in accuracy.
Revised: We also observe that no relation class which might rarely occur other than as text (and not in the infobox of a wikipedia page for example), does not get much improvement in accuracy.

Original: We observe similar distribution in accuracy improvement for tacred, though the improvement is relatively less compared to Wikipeople.
Revised: We observe similar distribution in accuracy improvement for tacred, though the improvement is relatively less compared to Wikipeople.

Original: This seems to be because of the number of no relation examples in this dataset.
Revised: This seems to be because of the number of no relation examples in this dataset.

Original: C ONCLUSION
Revised: C ONCLUSION

Original: In this work, we proposed using the document structure to improve ontological relation extraction from unstructured documents.
Revised: In this work, we proposed using the document structure to improve ontological relation extraction from unstructured documents.

Original: In particular, we described a document structure measure (DSM) vector that can be incorporated while training a relational graph convolutional network (RGCN).
Revised: In particular, we described a document structure measure (DSM) vector that can be incorporated while training a relational graph convolutional network (RGCN).

Original: We theoretically explained the need for such a measure in ontology population, and conducted experiments on different ways to incorporate the document structure measure in RGCNs.
Revised: We theoretically explained the need for such a measure in ontology population, and conducted experiments on different ways to incorporate the document structure measure in RGCNs.

Original: Our experiments show good improvement in RGCN performance while using our approach on the Wikipeople and
Revised: Our experiments show good improvement in RGCN performance while using our approach on the Wikipeople and

Original: TACRED datasets.
Revised: TACRED datasets.