Proceedings of the 11th on Knowledge Capture Conference最新文献

Cat2Type: Wikipedia Category Embeddings for Entity Typing in Knowledge Graphs Cat2Type:维基百科知识图中实体类型的类别嵌入

Proceedings of the 11th on Knowledge Capture Conference Pub Date : 2021-12-02 DOI: 10.1145/3460210.3493575

Russa Biswas, Radina Sofronova, Harald Sack, Mehwish Alam

{"title":"Cat2Type: Wikipedia Category Embeddings for Entity Typing in Knowledge Graphs","authors":"Russa Biswas, Radina Sofronova, Harald Sack, Mehwish Alam","doi":"10.1145/3460210.3493575","DOIUrl":"https://doi.org/10.1145/3460210.3493575","url":null,"abstract":"The entity type information in Knowledge Graphs (KGs) such as DBpedia, Freebase, etc. is often incomplete due to automated generation. Entity Typing is the task of assigning or inferring the semantic type of an entity in a KG. This paper introduces an approach named Cat2Type which exploits the Wikipedia Categories to predict the missing entity types in a KG. This work extracts information from Wikipedia Category names and the Wikipedia Category graph which are the sources of rich semantic information about the entities. In Cat2Type, the characteristic features of the entities encapsulated in Wikipedia Category names are exploited using Neural Language Models. On the other hand, a Wikipedia Category graph is constructed to capture the connection between the categories. The Node level representations are learned by optimizing the neighbourhood information on the Wikipedia category graph. These representations are then used for entity type prediction via classification. The performance of Cat2Type is assessed on two real-world benchmark datasets DBpedia630k and FIGER. The experiments depict that Cat2Type obtained a significant improvement over state-of-the-art approaches.","PeriodicalId":377331,"journal":{"name":"Proceedings of the 11th on Knowledge Capture Conference","volume":"46 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":"114840730","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

Cutting Events: Towards Autonomous Plan Adaption by Robotic Agents through Image-Schematic Event Segmentation 切割事件:通过图像-示意图事件分割实现机器人代理的自主计划自适应

Proceedings of the 11th on Knowledge Capture Conference Pub Date : 2021-12-02 DOI: 10.1145/3460210.3493585

Kaviya Dhanabalachandran, Vanessa Hassouna, Maria M. Hedblom, Michaela Küempel, Nils Leusmann, M. Beetz

引用次数: 7

KG-ZESHEL: Knowledge Graph-Enhanced Zero-Shot Entity Linking 知识图增强零射击实体链接

Proceedings of the 11th on Knowledge Capture Conference Pub Date : 2021-12-02 DOI: 10.1145/3460210.3493549

Petar Ristoski, Zhizhong Lin, Qunzhi Zhou

{"title":"KG-ZESHEL: Knowledge Graph-Enhanced Zero-Shot Entity Linking","authors":"Petar Ristoski, Zhizhong Lin, Qunzhi Zhou","doi":"10.1145/3460210.3493549","DOIUrl":"https://doi.org/10.1145/3460210.3493549","url":null,"abstract":"Entity linking is a fundamental task for a successful use of knowledge graphs in many information systems. It maps textual mentions to their corresponding entities in a given knowledge graph. However, with the rapid evolution of knowledge graphs, a large number of entities is continuously added over time. Performing entity linking on new, or unseen, entities poses a great challenge, as standard entity linking approaches require large amounts of labeled data for all new entities, and the underlying model must be regularly updated. To address this challenge, several zero-shot entity linking approaches have been proposed, which don't require additional labeled data to perform entity linking over unseen entities and new domains. Most of these approaches use large language models, such as BERT, to encode the textual description of the mentions and entities in a common embedding space, which allows linking mentions to unseen entities. While such approaches have shown good performance, one big drawback is that they are not able to exploit the entity symbolic information from the knowledge graph, such as entity types, relations, popularity scores and graph embeddings. In this paper, we present KG-ZESHEL, a knowledge graph-enhanced zero-shot entity linking approach, which extends an existing BERT-based zero-shot entity linking approach with mention and entity auxiliary information. Experiments on two benchmark entity linking datasets, show that our proposed approach outperforms the related BERT-based state-of-the-art entity linking models.","PeriodicalId":377331,"journal":{"name":"Proceedings of the 11th on Knowledge Capture Conference","volume":"1 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":"129412675","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: 8

Knowledge Engineering of PhD Stories: A Preliminary Study 博士故事的知识工程:初步研究

Proceedings of the 11th on Knowledge Capture Conference Pub Date : 2021-12-02 DOI: 10.1145/3460210.3493579

Viet Bach Nguyen, V. Svátek, M. Dudás, Óscar Corcho

{"title":"Knowledge Engineering of PhD Stories: A Preliminary Study","authors":"Viet Bach Nguyen, V. Svátek, M. Dudás, Óscar Corcho","doi":"10.1145/3460210.3493579","DOIUrl":"https://doi.org/10.1145/3460210.3493579","url":null,"abstract":"Support for PhD students and their advisors in decision-making before and along their PhD journeys requires providing them with a deep understanding and knowledge of the life-cycle of a PhD. This means giving them access to a thorough understanding of causal relations between events, decisions, and the possible outcome. This knowledge can be attained primarily from insider stories, study reports, communications threads with advisors and colleagues, interviews, and scholarly databases. However, it is unclear how to give this knowledge a reasonable structure (due to the heterogeneity of concepts and data sources) so that we can use it for decision-making during the PhD journey. In this paper, we explore how to analyze and model PhD stories to uncover and extract causal relationships found within each story to get insights into the co-occurrences and causalities. We analyze these stories with thematic analysis to understand their main points and we use concept maps to create semi-formal graphs of connected events and objects where the relationships are being emphasized from the perspective of cause and effect. Our results at this point are a collection of PhD stories in the form of concept maps, thematic codes, a proposed approach for goal-directed PhD story modeling which we describe in this paper.","PeriodicalId":377331,"journal":{"name":"Proceedings of the 11th on Knowledge Capture Conference","volume":"22 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":"122356838","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

Geospatial Knowledge in Housing Advertisements: Capturing and Extracting Spatial Information from Text 住房广告中的地理空间知识:从文本中获取和提取空间信息

Proceedings of the 11th on Knowledge Capture Conference Pub Date : 2021-12-02 DOI: 10.1145/3460210.3493547

L. Cadorel, Alicia Blanchi, A. Tettamanzi

{"title":"Geospatial Knowledge in Housing Advertisements: Capturing and Extracting Spatial Information from Text","authors":"L. Cadorel, Alicia Blanchi, A. Tettamanzi","doi":"10.1145/3460210.3493547","DOIUrl":"https://doi.org/10.1145/3460210.3493547","url":null,"abstract":"Information of the geographical and spatial type is found in numerous text documents and constitutes a very challenging target for extraction. Geoparsing applications have been developed to extract geographic terms. However, off-the-shelf Named Entity Recognition (NER) models are mainly designed for Toponym recognition and are very sensitive to language specificity. In this paper, we propose a workflow to first extract geographic and spatial entities based on a BiLSTM-CRF architecture with a concatenation of several text representations. We also propose a Relation Extraction module, particularly aimed at spatial relationships extraction, to build a structured Geospatial knowledge base. We demonstrate our pipeline by applying it to the case of French housing advertisements, which generally provide information about a property's location and neighbourhood. Our results show that the workflow tackles French language and the variability and irregularity of housing advertisements, generalizes Geoparsing to all geographic and spatial terms, and successfully retrieves most of the relationships between entities from the text.","PeriodicalId":377331,"journal":{"name":"Proceedings of the 11th on Knowledge Capture Conference","volume":"22 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":"126895581","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: 10

Capturing Contentiousness: Constructing the Contentious Terms in Context Corpus 捕获争议性:构建上下文语料库中的争议术语

Proceedings of the 11th on Knowledge Capture Conference Pub Date : 2021-12-02 DOI: 10.1145/3460210.3493553

Ryan Brate, A. Nesterov, Valentin Vogelmann, J. V. Ossenbruggen, L. Hollink, M. Erp

引用次数: 6

Discovering Interpretable Topics by Leveraging Common Sense Knowledge 利用常识知识发现可解释的主题

Proceedings of the 11th on Knowledge Capture Conference Pub Date : 2021-12-02 DOI: 10.1145/3460210.3493586

Ismail Harrando, Raphael Troncy

引用次数: 1

GATES 盖茨

Proceedings of the 11th on Knowledge Capture Conference Pub Date : 2021-12-02 DOI: 10.1163/1574-9347_dnp_e419450

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

引用次数: 0

Capturing Knowledge about Drug-Drug Interactions to Enhance Treatment Effectiveness 获取药物-药物相互作用知识以提高治疗效果

Proceedings of the 11th on Knowledge Capture Conference Pub Date : 2021-12-02 DOI: 10.1145/3460210.3493560

Ariam Rivas, Maria-Esther Vidal

{"title":"Capturing Knowledge about Drug-Drug Interactions to Enhance Treatment Effectiveness","authors":"Ariam Rivas, Maria-Esther Vidal","doi":"10.1145/3460210.3493560","DOIUrl":"https://doi.org/10.1145/3460210.3493560","url":null,"abstract":"Capturing knowledge about Drug-Drug Interactions (DDI) is a crucial factor to support clinicians in better treatments. Nowadays, public drug databases provide a wealth of information on drugs that can be exploited to enhance tasks, e.g., data mining, ranking, and query answering. However, all the interactions in the public database are focused on pairs of drugs. Since current treatments are composed of multi-drugs, it is extremely challenging to know which potential drugs affect the effectiveness of the treatment. In this work, we tackle the problem of discovering DDIs and reduce this problem to link prediction over a property graph represented in RDF-star. A deductive system captures knowledge about the conditions that define when a group of drugs interacts as Datalog rules. Extensional statements represent the property graph. Lastly, the intensional rules guide the deduction process to discover relationships in the graph and their properties. As a proof concept, we have implemented a graph traversal method on top of the property graph and the deduced edges. The technique aims to identify the combination of drugs whose interactions may reduce the effectiveness of a treatment or increase the number of toxicities. This traversal method relies on the computation of wedges in the property graph. Albeit illustrated in the context of DDI, this method could be generalized to other link traversal tasks. We conduct an experimental study on a DDIs property graph for different treatments. The results suggest that by capturing knowledge about DDIs, our approach can discover the drugs that decrease the effectiveness of the treatment. Our results are promising and suggest that clinicians can better understand the DDIs in treatment and prescribe improved treatments through the knowledge captured by our approach.","PeriodicalId":377331,"journal":{"name":"Proceedings of the 11th on Knowledge Capture Conference","volume":"1 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":"130379982","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

Multi-domain and Explainable Prediction of Changes in Web Vocabularies 网络词汇变化的多领域可解释预测

Proceedings of the 11th on Knowledge Capture Conference Pub Date : 2021-12-02 DOI: 10.1145/3460210.3493583

Albert Meroño-Peñuela, Romana Pernisch, Christophe Guéret, S. Schlobach

{"title":"Multi-domain and Explainable Prediction of Changes in Web Vocabularies","authors":"Albert Meroño-Peñuela, Romana Pernisch, Christophe Guéret, S. Schlobach","doi":"10.1145/3460210.3493583","DOIUrl":"https://doi.org/10.1145/3460210.3493583","url":null,"abstract":"Web vocabularies (WV) have become a fundamental tool for structuring Web data: over 10 million sites use structured data formats and ontologies to markup content. Maintaining these vocabularies and keeping up with their changes are manual tasks with very limited automated support, impacting both publishers and users. Existing work shows that machine learning can be used to reliably predict vocabulary changes, but on specific domains (e.g. biomedicine) and with limited explanations on the impact of changes (e.g. their type, frequency, etc.). In this paper, we describe a framework that uses various supervised learning models to learn and predict changes in versioned vocabularies, independent of their domain. Using well-established results in ontology evolution we extract domain-agnostic and human-interpretable features and explain their influence on change predictability. Applying our method on 139 WV from 9 different domains, we find that ontology structural and instance data, the number of versions, and the release frequency highly correlate with predictability of change. These results can pave the way towards integrating predictive models into knowledge engineering practices and methods.","PeriodicalId":377331,"journal":{"name":"Proceedings of the 11th on Knowledge Capture Conference","volume":"97 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":"131988445","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