Proceedings of the 11th on Knowledge Capture Conference最新文献_第2页

Tensor Decomposition for Link Prediction in Temporal Knowledge Graphs 时间知识图中链接预测的张量分解

Proceedings of the 11th on Knowledge Capture Conference Pub Date : 2021-12-02 DOI: 10.1145/3460210.3493558

M. Chekol

引用次数: 3

Characterising the Gap Between Theory and Practice of Ontology Reuse 本体重用的理论与实践差距表征

Proceedings of the 11th on Knowledge Capture Conference Pub Date : 2021-12-02 DOI: 10.1145/3460210.3493568

Reham Alharbi, V. Tamma, F. Grasso

{"title":"Characterising the Gap Between Theory and Practice of Ontology Reuse","authors":"Reham Alharbi, V. Tamma, F. Grasso","doi":"10.1145/3460210.3493568","DOIUrl":"https://doi.org/10.1145/3460210.3493568","url":null,"abstract":"Ontology reuse is a complex process that requires the support of methodologies and tools to minimise errors and to keep the ontologies consistent and coherent. Although the vast majority of ontology engineering methodologies include a reuse phase, and reuse has been investigated for different tasks and purposes (e.g.ontology integration), this body of work does not seem to translate into practice, neither in the form of strict criteria for reuse, nor as a set of community proposed guidelines. In this paper, we report the salient results from a study aimed at ontology developers and practitioners, whose objective is to gain an insight into the gap between the theory and the practice of ontology reuse. Thefocus of our study is to gain practitioners' views on i) their preferred reuse approaches; ii) the types of ontologies they tend to reuse (e.g. specific domain ontologies or upper-level ontologies)iii) what reporting information they deem useful when deciding which ontology to reuse; iv) what are the main reasons deterring them from reusing an ontology. Our findings confirm and extend established results from the literature, but in addition, the study provides a fresh view on the practice of reuse with an explicit focus on highly experienced developers and moderately experienced ones. The study corroborates the need for a comprehensive set of recommendations, that are widely accepted by the community, and are possibly implemented in development tools.","PeriodicalId":377331,"journal":{"name":"Proceedings of the 11th on Knowledge Capture Conference","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122027351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Upwardly Abstracted Definition-Based Subontologies 向上抽象的基于定义的子本体

Proceedings of the 11th on Knowledge Capture Conference Pub Date : 2021-12-02 DOI: 10.1145/3460210.3493564

Ghadah Alghamdi, R. Schmidt, Warren Del-Pinto, Yongsheng Gao

引用次数: 1

Spread2RML Spread2RML

Proceedings of the 11th on Knowledge Capture Conference Pub Date : 2021-12-02 DOI: 10.1145/3460210.3493544

M. Schröder, Christian Jilek, A. Dengel

引用次数: 1

User Scored Evaluation of Non-Unique Explanations for Relational Graph Convolutional Network Link Prediction on Knowledge Graphs 知识图上关系图卷积网络链接预测的非唯一解释的用户评分评价

Proceedings of the 11th on Knowledge Capture Conference Pub Date : 2021-12-02 DOI: 10.1145/3460210.3493557

Nicholas F Halliwell, Fabien L. Gandon, F. Lécué

{"title":"User Scored Evaluation of Non-Unique Explanations for Relational Graph Convolutional Network Link Prediction on Knowledge Graphs","authors":"Nicholas F Halliwell, Fabien L. Gandon, F. Lécué","doi":"10.1145/3460210.3493557","DOIUrl":"https://doi.org/10.1145/3460210.3493557","url":null,"abstract":"Relational Graph Convolutional Networks (RGCNs) are commonly used on Knowledge Graphs (KGs) to perform black box link prediction. Several algorithms, or explanation methods, have been proposed to explain their predictions. Evaluating performance of explanation methods for link prediction is difficult without ground truth explanations. Furthermore, there can be multiple explanations for a given prediction in a KG. No dataset exists where observations have multiple ground truth explanations to compare against. Additionally, no standard scoring metrics exist to compare predicted explanations against multiple ground truth explanations. In this paper, we introduce a method, including a dataset (FrenchRoyalty-200k), to benchmark explanation methods on the task of link prediction on KGs, when there are multiple explanations to consider. We conduct a user experiment, where users score each possible ground truth explanation based on their understanding of the explanation. We propose the use of several scoring metrics, using relevance weights derived from user scores for each predicted explanation. Lastly, we benchmark this dataset on state-of-the-art explanation methods for link prediction using the proposed scoring metrics.","PeriodicalId":377331,"journal":{"name":"Proceedings of the 11th on Knowledge Capture Conference","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117207087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Capturing Expert Knowledge for Building Enterprise SME Knowledge Graphs 获取专家知识构建中小企业知识图谱

Proceedings of the 11th on Knowledge Capture Conference Pub Date : 2021-12-02 DOI: 10.1145/3460210.3493569

M. Mansfield, V. Tamma, Phil Goddard, Frans Coenen

{"title":"Capturing Expert Knowledge for Building Enterprise SME Knowledge Graphs","authors":"M. Mansfield, V. Tamma, Phil Goddard, Frans Coenen","doi":"10.1145/3460210.3493569","DOIUrl":"https://doi.org/10.1145/3460210.3493569","url":null,"abstract":"Whilst Knowledge Graphs (KGs) are increasingly used in business scenarios, the construction of enterprise ontologies and the population of KGs from existing relational data remains a significant challenge. In this paper we report our experience in supporting CSols (an SME operating in the analytical laboratory domain) in transitioning their data from legacy databases to a bespoke KG. We modelled the KG using a streamlined approach based on state of the art ontology engineering methodologies, that addresses the challenges faced by SMEs when transitioning to new technologies: lack of resources to devote to the transition, paucity of comprehensive data governance policies, and resistance within the organisation to accepting new practices and knowledge. Our approach uses a combination of UML diagrams and a controlled language glossary to support stakeholders in reaching consensus during the knowledge capture phase, thus reducing the intervention of the ontology engineer only to cases where no agreement can be found. We present a case study illustrating the generation of the KG from a UML specification of part of the analytical domain and from legacy relational data, and we discuss the benefits and challenges of the approach.","PeriodicalId":377331,"journal":{"name":"Proceedings of the 11th on Knowledge Capture Conference","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124007057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Proceedings of the 11th on Knowledge Capture Conference 第十一届知识捕获会议论文集

Proceedings of the 11th on Knowledge Capture Conference Pub Date : 2021-12-02 DOI: 10.1145/3460210

引用次数: 1

CTrO-Editor: A Web-based Tool to Capture Clinical Trial Data for Aggregation and Pooling ctro编辑器:一个基于网络的工具，以获取临床试验数据汇总和汇集

Proceedings of the 11th on Knowledge Capture Conference Pub Date : 2021-12-02 DOI: 10.1145/3460210.3493576

Olivia Sanchez-Graillet, Arne Kramer-Sunderbrink, P. Cimiano

{"title":"CTrO-Editor: A Web-based Tool to Capture Clinical Trial Data for Aggregation and Pooling","authors":"Olivia Sanchez-Graillet, Arne Kramer-Sunderbrink, P. Cimiano","doi":"10.1145/3460210.3493576","DOIUrl":"https://doi.org/10.1145/3460210.3493576","url":null,"abstract":"As the number of clinical trials carried out and published worldwide keeps growing, better tools for synthesizing the available knowledge become increasingly important. It still requires a significant effort and expertise to aggregate the evidence and results from different clinical trials, a task that is at the core of secondary or comparative studies, meta-analyses, and (living) systematic reviews. Our hypothesis is that the practical challenges involved in synthesizing evidence can be alleviated if the results of clinical trials would be published in a machine-readable format using a well-defined (semantic) vocabulary. Building on the C-TrO ontology that we developed in earlier work to support the aggregation of evidence from clinical trials as the main use case, in this paper we examine the question whether it is feasible for clinical researchers and medical practitioners to describe the results of clinical trials using the C-TrO ontology. For this purpose, we implemented a Web-based tool called CTrO-Editor that uses a form-based interaction paradigm to allow users to enter all the details regarding study population, arms, endpoints, observations and results of a clinical trial, and that exports the data in an RDF format. We describe the results of the evaluation of the CTrO-Editor with five medical students. Our preliminary results suggest that our paradigm for semantifying clinical trials is feasible, as the students could all successfully model a publication of their choice using our tool within a couple of hours.","PeriodicalId":377331,"journal":{"name":"Proceedings of the 11th on Knowledge Capture Conference","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125208673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

GATES: Using Graph Attention Networks for Entity Summarization 盖茨:使用图注意网络进行实体摘要

Proceedings of the 11th on Knowledge Capture Conference Pub Date : 2021-12-02 DOI: 10.1145/3460210.3493574

A. Firmansyah, Diego Moussallem, A. N. Ngomo

{"title":"GATES: Using Graph Attention Networks for Entity Summarization","authors":"A. Firmansyah, Diego Moussallem, A. N. Ngomo","doi":"10.1145/3460210.3493574","DOIUrl":"https://doi.org/10.1145/3460210.3493574","url":null,"abstract":"The sheer size of modern knowledge graphs has led to increased attention being paid to the entity summarization task. Given a knowledge graph T and an entity e found therein, solutions to entity summarization select a subset of the triples from T which summarize e's concise bound description. Presently, the best performing approaches rely on sequence-to-sequence models to generate entity summaries and use little to none of the structure information of T during the summarization process. We hypothesize that this structure information can be exploited to compute better summaries. To verify our hypothesis, we propose GATES, a new entity summarization approach that combines topological information and knowledge graph embeddings to encode triples. The topological information is encoded by means of a Graph Attention Network. Furthermore, ensemble learning is applied to boost the performance of triple scoring. We evaluate GATES on the DBpedia and LMDB datasets from ESBM (version 1.2), as well as on the FACES datasets. Our results show that GATES outperforms the state-of-the-art approaches on 4 of 6 configuration settings and reaches up to 0.574 F-measure. Pertaining to resulted summaries quality, GATES still underperforms the state of the arts as it obtains the highest score only on 1 of 6 configuration settings at 0.697 NDCG score. An open-source implementation of our approach and of the code necessary to rerun our experiments are available at https://github.com/dice-group/GATES.","PeriodicalId":377331,"journal":{"name":"Proceedings of the 11th on Knowledge Capture Conference","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124678375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generating Explainable Abstractions for Wikidata Entities 为维基数据实体生成可解释的抽象

Proceedings of the 11th on Knowledge Capture Conference Pub Date : 2021-12-02 DOI: 10.1145/3460210.3493580

Nicholas Klein, F. Ilievski, Pedro A. Szekely

引用次数: 2