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13 results for “semantic knowledge”
SemTab 2024: Semantic Web Challenge on Tabular Data to Knowledge Graph Matching Data Sets - WikidataTables2024R1 and WikidataTables2024R2
<p>Data Sets from the ISWC 2024 Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, Round 1, Wikidata Tables. Links to other datasets can be found on the challenge website: https://sem-tab-challenge.github.io/2024/ as well as the proceedings of the challenge published on CEUR.</p> <p>For details about the challenge, see: http://www.cs.ox.ac.uk/isg/challenges/sem-tab/</p> <p>For 2024 edition, see: https://sem-tab-challenge.github.io/2024/</p> <p>Note on License: This data includes data from the following sources. Refer to each source for license details:<br>- Wikidata https://www.wikidata.org/</p> <p>THIS DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.</p>
Semantics 2023 Knowledge Graph
<p>Input data sources, mappings and RDF results used to generate the Semantics 2023 KG.</p> <p>We follow a simple data model, reusing the [Schema.org](https://schema.org/) vocabulary as much as possible. All papers are schema:ScholarlyArticles, that are part of tracks (schema:Event) and are authored by schema:Persons. Each article has either datasets (schema:Dataset), code repositories (schema:SoftwareSourceCode), ontologies (owl:Ontology) or Demos (schema:SoftwareApplication).</p> <p>As a result, our KG has 2521 triples, which describe 50 papers, 22 code repositories, 11 datasets, 7 demos and 5 ontologies.</p>
Semantic links between selected CSV datasets harvested by the European Data Portal and the DBpedia knowledge graph
<p>These dataset contains the results of the interlinking process between selected csv datasets harvested by the European DAta Portal and the DBpedia knowledge graph. </p> <p>We aim at answering the following questions:<br> What are the more popular column types? This will provide hindsight about what the datasets hold and how they can be joined. It will also provide hindsight on what specific linking schemes could be applied in future elements.<br> What datasets have columns of the same type? This will suggest datasets that may be similar or related.<br> What entities appear in most datasets (co-referent entities)? This will suggest entities for which more data is published.<br> What datasets share a particular entity? This will suggest datasets that may be joined, or are related through that particular entity</p> <p>Results are provided as augmented tables, that contain the columns of the original csv, plus a metadata file in JSON-LD format. The metadata files can be loaded in an RDF-store and queried.</p> <p>Refer to the accompanying report of activities for more details on the methodolog and how to query the dataset.</p> <p><br> </p>
Learning Unsupervised Knowledge-Enhanced Representations to Reduce the Semantic Gap in Information Retrieval (Evaluation datasets)
<p>This dataset contains all the runs, pools, plots and analyses to reproduce the results presented in the paper: "Learning Unsupervised Knowledge-Enhanced Representations to Reduce the Semantic Gap in Information Retrieval ", 2020.</p>
Semantic Web resources and Machine Learning systems - Knowledge Graph (SWeMLS-KG)
<p>This resource is part of our submission to ESWC 2023 resource track, which includes:</p> <p>Datasets:<br> - Folder "pattern" - a set of SWeMLS patterns represented based on OPMW and P-Plan ontology,<br> - Folder "shapes" - a set of SHACL constraints to check the conformance of SWeML Systems against SWeMLS patterns as well as a set of SHACL-AF rules to generate links between system components,<br> - File "swemls-ontology.ttl" - an ontology to represent Semantic Web resources and Machine Learning systems (SWeMLS),<br> - File "swemls-instances.ttl" - a set of triples representing the extracted metadata from 476 SWeML systems and papers,<br> - File "swemls-kg.ttl" - an integrated and validated KG containing all above files, including enrichment from SHACL-AF rules using "swemls-toolkit" [2].</p> <p>These resources are produced based on the result of the Systematic Mapping Study (SMS) reported in [1]. The latest SNAPSHOT-version of the resource can be accessed through our resource landing page: <a href="https://w3id.org/semsys/sites/swemls-kg/">https://w3id.org/semsys/sites/swemls-kg/</a></p> <p>[1] Breit, A., Waltersdorfer, L., Ekaputra, J.F., Sabou, M., Ekelhart, A., Iana, A., Paulheim, H., Portisch, J., Revenko, A., Ten Teije, A., van Harmelen, F.: Combining Machine Learning and Semantic Web -A Systematic Mapping Study (under review). ACM CSUR (2022)<br> [2] Source code of swemls-toolkit is available at: https://github.com/semanticsystems/swemls-toolkit</p>
Decoding Knowledge Claims: the Evaluation of Scientific Publication Contributions through Semantic Analysis
<p>This data were used to compute the RWMD distance as described in the study submitted for the STI 2024 conference, Berlin.</p>
CONCEPT AS A FORM OF EXPRESSION OF KNOWLEDGE ABOUT THE WORLD OF COGNITIVE SEMANTICS
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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>
Data for The "Effect of Semantic Knowledge Graph Richness on Embedding Based Recommender Systems"
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Semantic interoperability in an international comprehensive knowledge organisation system
<p>In this paper, the functional and relational characteristics and requirements for various<br> types of semantic interoperability in a comprehensive international knowledge organisation<br> system are being discussed with regard to an analysis of the underlying retrieval paradigms.<br> Furthermore, this paper analyses the potential benefits and perspectives of the selective transfer<br> of modelling strategies from the field of semantic technologies for the refinement of relational<br> structures of inter-system and inter-concept relations as a requirement for expressive and<br> functional indexing languages supporting advanced types of semantic interoperability.</p>
SMWCloud: A Corpus of Domain-Specific Knowledge Graphs from Semantic MediaWikis
<p>Semantic wikis have become an increasingly popular means of collaboratively managing Knowledge Graphs. They are powered by platforms such as Semantic MediaWiki and Wikibase, both of which enable MediaWiki to store and publish structured data. While there are many semantic wikis currently in use, there has been little effort to collect and analyse their structured data, nor to make it available for the research community. This resource seeks to address this gap by systematically collecting structured data from an extensive corpus of Semantic-MediaWiki-powered portals and providing an in-depth analysis of the ontological diversity (and re-use) amongst these wikis using a variety of ontological metrics. Our resource aims to demonstrate that semantic wikis are a valuable and extensive part of Linked Open Data (LOD), and in fact may be considered an own active ``sub-cloud'' within the LOD ecosystem, which can provide useful insights into the evolution of small and medium-sized domain-specific Knowledge Graphs.</p>
ARENA_Hierarchical Organization of Distributed Semantic Knowledge in the Human Language System_Language Study Pt. 1: stimulus set
<p>P4_WP1_01 Language Study Pt. 1: stimulus set</p> <p> </p> <p>Folder structure: </p> <p>Raw_input<br>The raw_input contains the text for every chapter of the book to be used in the experiment (Moonwalk mit Einstein: Wie aus einem vergeßlichen Mann ein Gedächtnis-Champion wurde, by Joshua Foer, translated by Ulla Rahn-Huber. Published by Riemann Verlag (28 Mar. 2011)). <br>Additionally, in the folder there is the pretrained vector model for German words (model comes from https://fasttext.cc/docs/en/crawl-vectors.html), ratings for concreteness and word frequency (all references are included in the scripts). Moreover, there is a translated version of the Things labels for intersecting the single words with the THINGS dataset (Hebart MN, Dickter AH, Kidder A, Kwok WY, Corriveau A, et al. (2019) THINGS: A database of 1,854 object concepts and more than 26,000 naturalistic object images. PLOS ONE 14(10): e0223792. https://doi.org/10.1371/journal.pone.0223792)</p> <p>Scripts<br>In this folder, there are 6 scripts to sample single words from the raw text. The scripts are numbered according to the intended order of use. <br>- 1_from_text_to_df.py: from raw text only nouns and verbs are extracted with their relative word frequency, concreteness, lemma form, and number of characters. </p> <p>- 2_cluster_words.py: cluster analysis of word vectors to sample the semantic space as broadly as possible. Loosely based on Pereira, F., Lou, B., Pritchett, B. et al. Toward a universal decoder of linguistic meaning from brain activation. Nat Commun 9, 963 (2018). https://doi.org/10.1038/s41467-018-03068-4. </p> <p>- 3_compute_orthographic_density.py: add information for OND20 for both word forms and lemma forms. </p> <p>- 4_syntactic_valency.py: this applies only to verbs. It counts the number of arguments necessary for a verb to saturate its syntactic valency (e.g., subject + object). </p> <p>- 5_sampling_nouns.py: it samples nouns by preserving the distributions of all variables. The number of characters for every word is kept under 10. Additionally, the extreme quantiles of concreteness are matched by all other variables to ensure that more concrete and more abstract words in the set are still match along the other ratings. </p> <p>- 6_sampling_verbs.py: same as above but for verbs. </p> <p>Stimuli <br>In this folder, the pool of words before sampling is included. Note that some words have been manually excluded for several reasons: e.g., parsed wrongly in their lemma form; offensive words; words coming from other languages. </p> <p> </p>
Interview Fragenkatalog: Unlocking AI-based Knowledge Management Potential for SMEs: Exploring Semantic Search Adoption
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