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5 results for “Open Research Knowledge Graph”
Evaluation Set - Contributions Similarity in the Open Research Knowledge Graph
<p>This evaluation set has been created for evaluating a content-based recommender system in the context of the Open Research Knowledge Graph (ORKG). The recommender system accepts structured ORKG contribution as input and recommends existing contributions in the ORKG semantically relevant to the given one.</p> <p> </p> <p>The evaluation set is manually annotated based on the <a href="https://www.orkg.org/orkg/featured-comparisons">featured comparisons</a> in the ORKG. In the course of this, it has been distinguished between homogeneous (those who are dissimilar in 2-3 properties) and heterogeneous (otherwise) instances. Multiple annotations have been obtained for the former and exactly one for the latter.</p> <p> </p> <p>It has been also distinguished between "with_response" and "without_response" instances (50 instances for each). The former are those contributions for them the initial version of the contributions similarity service has found similarities and the latter are the opposite case.</p> <p> </p> <p>This evaluation set has been created and applied on a modified version of the contributions similarity service in the context of <a href="https://doi.org/10.15488/11834">this master's thesis</a>. The modified version of the service has simplified the document representation of contributions that are stored in an ElasticSearch index by omitting redundant terms.</p> <p>The evaluation set has the following schema:</p> <pre><code class="language-json">{ "with_response": [ { "contribution_id": "some_id", "comparison_id": "some_id", "comparison_label": "some_label", "contribution_label": "some_label", "paper": "some_id", "research_field": "some_id", "research_problems": [ "some_id" ], "annotations": [ "some_id of a similar contribution", ... ] }, ... ], "without_response": [ ... ] }</code></pre> <p> </p>
Description of the Features of the Open Research Knowledge Graph as a Crowdsourcing Platform based on the 4 Pillars of Crowdsourcing
<p>This dataset provides the description of the features of the <a href="https://www.orkg.org/orkg/">Open Research Knowledge Graph</a> (ORKG) based on the 4 pillars of crowdsourcing according to the reference model for crowdsourcing by Hosseini et al. [1]. This overview represents the features of the current implementation status of ORKG as a crowdsourcing platform.</p> <p>[1] M. Hosseini, K. Phalp, J. Taylor, and R. Ali, "<a href="https://ieeexplore.ieee.org/abstract/document/6861072?casa_token=zWTHNBHeH6kAAAAA:1n3EJjajqgSkSk154g4DlNFAmJs_7o3KY7LnobvP_W7AUIr-FvM5OyGP1FaRn68zUX-2oYHh7A">The Four Pillars of Crowdsourcing: A Reference Model</a>", in 2014 IEEE 8th International Conference on Research Challenges in Information Science (RCIS). IEEE, 2014, pp. 1–12.</p>
Workflow for structured literature reviews using the Open Research Knowledge Graph (ORKG)
<p>Figure showing a workflow of making a structured literature review using the core features of the Open Research Knowledge Graph (ORKG). </p>
Dataset - Templates Recommendation in the Open Research Knowledge Graph
<p>This dataset has been created for implementing a content-based recommender system in the context of the Open Research Knowledge Graph (ORKG). The recommender system accepts research paper's title and abstracts as input and recommends existing templates in the ORKG semantically relevant to the given paper.</p> <p> </p> <p>Two approaches have been trained on this dataset in the context of <a href="https://doi.org/10.15488/11834">this master's thesis</a>, namely a Natural Language Inference (NLI) approach based on SciBERT embeddings and an unsupervised approach based on ElasticSearch.</p> <p> </p> <p>This publication consists therefore of one general dataset, two training sets for each approach, validation set for the supervised approach and a test set for both approaches.</p> <p> </p> <p><strong>dataset.json</strong></p> <p>The main JSON object consists of a list of templates and a list of neutral papers.</p> <p>Each template object has an ID, label, list of research fields, list of properties and list of papers using that template, whereas each paper object has ID, label, DOI, research field and abstract.</p> <p>Each neutral paper object has the same schema of a paper object using that template.</p> <p>See an example instance below.</p> <p> </p> <pre><code class="language-json">{ "templates": [ { "id": "R138668", "label": "Psychiatric Disorders AI Overview", "research_fields": [ { "id": "http://orkg.org/orkg/resource/R133", "label": "Artificial Intelligence" } ... ], "properties": [ "Study cohort", ... ], "papers": [ { "id": "R138698", "label": "Application of Autoencoder in Depression Diagnosis", "doi": "10.12783/dtcse/csma2017/17335", "research_field": { "id": "R104", "label": "Bioinformatics" }, "abstract": "Major depressive disorder (MDD) is a mental disorder characterized by at least two weeks of low mood which is present across most situations. Diagnosis of MDD using rest-state functional magnetic resonance imaging (fMRI) data faces many challenges due to the high dimensionality, small samples, noisy and individual variability. No method can automatically extract discriminative features from the origin time series in fMRI images for MDD diagnosis. In this study, we proposed a new method for feature extraction and a workflow which can make an automatic feature extraction and classification without a prior knowledge. An autoencoder was used to learn pre-training parameters of a dimensionality reduction process using 3-D convolution network. Through comparison with the other three feature extraction methods, our method achieved the best classification performance. This method can be used not only in MDD diagnosis, but also other similar disorders." }, ... }, ... ] "neutral_papers": [ { "id": "R109377", "label": "Structural basis of SARS-CoV-2 3CLpro and anti-COVID-19 drug discovery from medicinal plants", "doi": "10.1016/j.jpha.2020.03.009", "research_field": { "id": "R104", "label": "Bioinformatics" }, "abstract": "Abstract The recent outbreak of coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 in December 2019 raised global health concerns. The viral 3-chymotrypsin-like cysteine protease (3CLpro) enzyme controls coronavirus replication and is essential for its life cycle. 3CLpro is a proven drug discovery target in the case of severe acute respiratory syndrome coronavirus (SARS-CoV) and middle east respiratory syndrome coronavirus (MERS-CoV). Recent studies revealed that the genome sequence of SARS-CoV-2 is very similar to that of SARS-CoV. Therefore, herein, we analysed the 3CLpro sequence, constructed its 3D homology model, and screened it against a medicinal plant library containing 32,297 potential anti-viral phytochemicals/traditional Chinese medicinal compounds. Our analyses revealed that the top nine hits might serve as potential anti- SARS-CoV-2 lead molecules for further optimisation and drug development process to combat COVID-19." }, ... ] }</code></pre> <p> </p> <p><strong>All other files</strong></p> <p>The main JSON object consists of a list of entailments, a list of contradiction and a list of neutrals.</p> <p>Each object of the above mentioned lists has the same schema. An instance_id created by concatenating the template_id (when exists) with the paper_id, a template_id, a paper_id, premise (representing the paper's title), hypthesis (representing the paper's abstract), their concatenation in sequence and the target class.</p> <p>See an example instance below.</p> <p> </p> <pre><code class="language-json">{ "entailments": [ { "instance_id": "R138668xR138698", "template_id": "R138668", "paper_id": "R138698", "premise": "psychiatric disorders ai overview study cohort outcome assessment aims performance findings used models data", "hypothesis": "application of autoencoder in depression diagnosis major depressive disorder (mdd) is a mental disorder characterized by at least two weeks of low mood which is present across most situations diagnosis of mdd using rest state functional magnetic resonance imaging (fmri) data faces many challenges due to the high dimensionality, small samples, noisy and individual variability no method can automatically extract discriminative features from the origin time series in fmri images for mdd diagnosis in this study, we proposed a new method for feature extraction and a workflow which can make an automatic feature extraction and classification without a prior knowledge an autoencoder was used to learn pre training parameters of a dimensionality reduction process using 3 d convolution network through comparison with the other three feature extraction methods, our method achieved the best classification performance this method can be used not only in mdd diagnosis, but also other similar disorders", "sequence": "[CLS] psychiatric disorders ai overview study cohort outcome assessment aims performance findings used models data [SEP] application of autoencoder in depression diagnosis major depressive disorder (mdd) is a mental disorder characterized by at least two weeks of low mood which is present across most situations diagnosis of mdd using rest state functional magnetic resonance imaging (fmri) data faces many challenges due to the high dimensionality, small samples, noisy and individual variability no method can automatically extract discriminative features from the origin time series in fmri images for mdd diagnosis in this study, we proposed a new method for feature extraction and a workflow which can make an automatic feature extraction and classification without a prior knowledge an autoencoder was used to learn pre training parameters of a dimensionality reduction process using 3 d convolution network through comparison with the other three feature extraction methods, our method achieved the best classification performance this method can be used not only in mdd diagnosis, but also other similar disorders [SEP]", "target": "entailment" }, ... ], "contradictions": [ ... ], "neutrals": [ ... ] } </code></pre> <p> </p> <p><strong>Statistics</strong></p> <table align="center"> <tbody> <tr> <td>-</td> <td><strong>Training (supervised)</strong></td> <td><strong>Validation (supervised)</strong></td> <td><strong>Training (unsupervised)</strong></td> <td><strong>Test</strong></td> </tr> <tr> <td>Entailment</td> <td>180</td> <td>20</td> <td>200</td> <td>52</td> </tr> <tr> <td>Neutral</td> <td>180</td> <td>20</td> <td>200</td> <td>64</td> </tr> <tr> <td>Contradictrion</td> <td>736</td> <td>84</td> <td>0</td> <td>0</td> </tr> <tr> <td>Total</td> <td>1096</td> <td>124</td> <td>400</td> <td>116</td> </tr> </tbody> </table> <p> </p>
Dataset - Clustering Semantic Predicates in the Open Research Knowledge Graph
<p>This dataset has been created for implementing a content-based recommender system in the context of the Open Research Knowledge Graph (ORKG). The recommender system accepts research paper's title and abstracts as input and recommends existing predicates in the ORKG semantically relevant to the given paper.</p> <p> </p> <p>The paper instances in the dataset are grouped by ORKG comparisons and therefore the <strong><em>data.json</em></strong> file is more comprehensive than <strong><em>training_set.json</em></strong> and <strong><em>test_set.json.</em></strong></p> <p> </p> <p><strong><em>data.json</em></strong></p> <p>The main JSON object consists of a list of comparisons. Each comparisons object has an ID, label, list of papers and list of predicates, whereas each paper object has ID, label, DOI, research field, research problems and abstract. Each predicate object has an ID and a label. See an example instance below.</p> <pre><code class="language-json">{ "comparisons": [ { "id": "R108331", "label": "Analysis of approaches based on required elements in way of modeling", "papers": [ { "id": "R108312", "label": "Rapid knowledge work visualization for organizations", "doi": "10.1108/13673270710762747", "research_field": { "id": "R134", "label": "Computer and Systems Architecture" }, "research_problems": [ { "id": "R108294", "label": "Enterprise engineering" } ], "abstract": "Purpose \u2013 The purpose of this contribution is to motivate a new, rapid approach to modeling knowledge work in organizational settings and to introduce a software tool that demonstrates the viability of the envisioned concept.Design/methodology/approach \u2013 Based on existing modeling structures, the KnowFlow toolset that aids knowledge analysts in rapidly conducting interviews and in conducting multi\u2010perspective analysis of organizational knowledge work is introduced.Findings \u2013 This article demonstrates how rapid knowledge work visualization can be conducted largely without human modelers by developing an interview structure that allows for self\u2010service interviews. Two application scenarios illustrate the pressing need for and the potentials of rapid knowledge work visualizations in organizational settings.Research limitations/implications \u2013 The efforts necessary for traditional modeling approaches in the area of knowledge management are often prohibitive. This contribution argues that future research needs ..." }, .... ], "predicates": [ { "id": "P37126", "label": "activities, behaviours, means [for knowledge development and/or for knowledge conveyance and transformation" }, { "id": "P36081", "label": "approach name" }, .... ] }, .... ] }</code></pre> <p> </p> <p><strong><em>training_set.json </em></strong>and<strong><em> test_set.json</em></strong></p> <p>The main JSON object consists of a list of training/test instances. Each instance has an instance_id with the format (comparison_id X paper_id) and a text. The text is a concatenation of the paper's label (title) and abstract. See an example instance below.</p> <p>Note that test instances are not duplicated and do not occur in the training set. Training instances are also not duplicated, BUT training papers can be duplicated in a concatenation with different comparisons.</p> <pre><code class="language-json">{ "instances": [ { "instance_id": "R108331xR108301", "comparison_id": "R108331", "paper_id": "R108301", "text": "A notation for Knowledge-Intensive Processes Business process modeling has become essential for managing organizational knowledge artifacts. However, this is not an easy task, especially when it comes to the so-called Knowledge-Intensive Processes (KIPs). A KIP comprises activities based on acquisition, sharing, storage, and (re)use of knowledge, as well as collaboration among participants, so that the amount of value added to the organization depends on process agents' knowledge. The previously developed Knowledge Intensive Process Ontology (KIPO) structures all the concepts (and relationships among them) to make a KIP explicit. Nevertheless, KIPO does not include a graphical notation, which is crucial for KIP stakeholders to reach a common understanding about it. This paper proposes the Knowledge Intensive Process Notation (KIPN), a notation for building knowledge-intensive processes graphical models." }, ... ] }</code></pre> <p> </p> <p><strong>Dataset Statistics:</strong></p> <table align="center"> <thead> <tr> <th scope="col">-</th> <th scope="col">Papers</th> <th scope="col">Predicates</th> <th scope="col">Research Fields</th> <th scope="col">Research Problems</th> </tr> </thead> <tbody> <tr> <td>Min/Comparison</td> <td>2</td> <td>2</td> <td>1</td> <td>0</td> </tr> <tr> <td>Max/Comparison</td> <td>202</td> <td>112</td> <td>5</td> <td>23</td> </tr> <tr> <td>Avg./Comparison</td> <td>21,54</td> <td>12,79</td> <td>1,20</td> <td>1,09</td> </tr> <tr> <td>Total</td> <td>4060</td> <td>1816</td> <td>46</td> <td>178</td> </tr> </tbody> </table> <p><strong>Dataset Splits:</strong></p> <table align="center"> <thead> <tr> <th scope="col">-</th> <th scope="col">Papers</th> <th scope="col">Comparisons</th> </tr> </thead> <tbody> <tr> <td>Training Set</td> <td>2857</td> <td>214</td> </tr> <tr> <td>Test Set</td> <td>1203</td> <td>180</td> </tr> </tbody> </table> <p> </p>
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