International Conference on Semantic Systems最新文献

Towards a Knowledge-Aware AI - SEMANTiCS 2022 - Proceedings of the 18th International Conference on Semantic Systems, 13-15 September 2022, Vienna, Austria 迈向知识感知AI - SEMANTiCS 2022 -第18届语义系统国际会议论文集，2022年9月13-15日，奥地利维也纳

International Conference on Semantic Systems Pub Date : 2022-09-06 DOI: 10.3233/ssw55

引用次数: 0

Automatically Drafting Ontologies from Competency Questions with FrODO 用FrODO自动从能力问题中起草本体

International Conference on Semantic Systems Pub Date : 2022-06-06 DOI: 10.3233/SSW220014

Aldo Gangemi, Anna Sofia Lippolis, Giorgia Lodi, Andrea Giovanni Nuzzolese

引用次数: 0

Further with Knowledge Graphs - Proceedings of the 17th International Conference on Semantic Systems, SEMANTiCS 2017, Amsterdam, The Netherlands, September 6-9, 2021 第十七届国际语义系统会议论文集，语义学2017，荷兰阿姆斯特丹，2021年9月6-9日

International Conference on Semantic Systems Pub Date : 2021-08-31 DOI: 10.3233/ssw53

引用次数: 0

Embedding Taxonomical, Situational or Sequential Knowledge Graph Context for Recommendation Tasks 为推荐任务嵌入分类、情景或顺序知识图上下文

International Conference on Semantic Systems Pub Date : 2021-08-31 DOI: 10.3233/ssw210046

Simon Werner, Achim Rettinger, Lavdim Halilaj, Jürgen Lüttin

{"title":"Embedding Taxonomical, Situational or Sequential Knowledge Graph Context for Recommendation Tasks","authors":"Simon Werner, Achim Rettinger, Lavdim Halilaj, Jürgen Lüttin","doi":"10.3233/ssw210046","DOIUrl":"https://doi.org/10.3233/ssw210046","url":null,"abstract":"Learned latent vector representations are key to the success of many recommender systems in recent years. However, traditional approaches like matrix factorization produce vector representations that capture global distributions of a static recommendation scenario only. Such latent user or item representations do not capture background knowledge and are not customized to a concrete situational context and the sequential history of events leading up to it. This is a fundamentally limiting restriction for many tasks and applications, since the latent state can depend on a) abstract background information, b) the current situational context and c) the history of related observations. An illustrating example is a restaurant recommendation scenario, where a user’s assessment of the situation depends a) on taxonomical information regarding the type of cuisine, b) on situational factors like time of day, weather or location and c) on the subjective individual history and experience of this user in preceding situations. This situation-specific internal state of the user is not captured when using a traditional collaborative filtering approach, since background knowledge, the situational context and the sequential nature of an individual’s history cannot easily be represented in the matrix. In this paper, we investigate how well state-of-the-art approaches do exploit those different dimensions relevant to POI recommendation tasks. Naturally, we represent such a scenario as a temporal knowledge graph and compare plain knowledge graph, a taxonomy and a hypergraph embedding approach, as well as a recurrent neural network architecture to exploit the different context-dimensions of such rich information. Our empirical evidence indicates that the situational context is most crucial to the prediction performance, while the taxonomical and sequential information are harder to exploit. However, they still have their specific merits depending on the situation.","PeriodicalId":275036,"journal":{"name":"International Conference on Semantic Systems","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122582268","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

Literal2Feature: An Automatic Scalable RDF Graph Feature Extractor 一个自动可伸缩的RDF图特征提取器

International Conference on Semantic Systems Pub Date : 2021-08-31 DOI: 10.3233/ssw210036

Farshad Bakhshandegan Moghaddam, C. Draschner, Jens Lehmann, Hajira Jabeen

{"title":"Literal2Feature: An Automatic Scalable RDF Graph Feature Extractor","authors":"Farshad Bakhshandegan Moghaddam, C. Draschner, Jens Lehmann, Hajira Jabeen","doi":"10.3233/ssw210036","DOIUrl":"https://doi.org/10.3233/ssw210036","url":null,"abstract":"The last decades have witnessed significant advancements in terms of data generation, management, and maintenance. This has resulted in vast amounts of data becoming available in a variety of forms and formats including RDF. As RDF data is represented as a graph structure, applying machine learning algorithms to extract valuable knowledge and insights from them is not straightforward, especially when the size of the data is enormous. Although Knowledge Graph Embedding models (KGEs) convert the RDF graphs to low-dimensional vector spaces, these vectors often lack the explainability. On the contrary, in this paper, we introduce a generic, distributed, and scalable software framework that is capable of transforming large RDF data into an explainable feature matrix. This matrix can be exploited in many standard machine learning algorithms. Our approach, by exploiting semantic web and big data technologies, is able to extract a variety of existing features by deep traversing a given large RDF graph. The proposed framework is open-source, well-documented, and fully integrated into the active community project Semantic Analytics Stack (SANSA). The experiments on real-world use cases disclose that the extracted features can be successfully used in machine learning tasks like classification and clustering.","PeriodicalId":275036,"journal":{"name":"International Conference on Semantic Systems","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126419064","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

A Fully Decentralized Triplestore Managed via the Ethereum Blockchain 通过以太坊区块链管理的完全分散的三重存储

International Conference on Semantic Systems Pub Date : 2021-08-31 DOI: 10.3233/ssw210044

Sina Mahmoodi

引用次数: 0

Annotating Entities with Fine-Grained Types in Austrian Court Decisions 在奥地利法院判决中用细粒度类型注释实体

International Conference on Semantic Systems Pub Date : 2021-08-31 DOI: 10.3233/ssw210041

Artem Revenko, Anna Breit, V. Mireles, J. Moreno-Schneider, C. Sageder, Sotirios Karampatakis

{"title":"Annotating Entities with Fine-Grained Types in Austrian Court Decisions","authors":"Artem Revenko, Anna Breit, V. Mireles, J. Moreno-Schneider, C. Sageder, Sotirios Karampatakis","doi":"10.3233/ssw210041","DOIUrl":"https://doi.org/10.3233/ssw210041","url":null,"abstract":"The usage of Named Entity Recognition tools on domain-specific corpora is often hampered by insufficient training data. We investigate an approach to produce fine-grained named entity annotations of a large corpus of Austrian court decisions from a small manually annotated training data set. We apply a general purpose Named Entity Recognition model to produce annotations of common coarse-grained types. Next, a small sample of these annotations are manually inspected by domain experts to produce an initial fine-grained training data set. To efficiently use the small manually annotated data set we formulate the task of named entity typing as a binary classification task – for each originally annotated occurrence of an entity, and for each fine-grained type we verify if the entity belongs to it. For this purpose we train a transformer-based classifier. We randomly sample 547 predictions and evaluate them manually. The incorrect predictions are used to improve the performance of the classifier – the corrected annotations are added to the training set. The experiments show that re-training with even a very small number (5 or 10) of originally incorrect predictions can significantly improve the classifier performance. We finally train the classifier on all available data and re-annotate the whole data set.","PeriodicalId":275036,"journal":{"name":"International Conference on Semantic Systems","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130834288","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: 1

LLOD-Driven Bilingual Word Embeddings Rivaling Cross-Lingual Transformers in Quality of Life Concept Detection from French Online Health Communities llod驱动的双语词嵌入在法国在线健康社区的生活质量概念检测中与跨语言转换器相抗衡

International Conference on Semantic Systems Pub Date : 2021-08-31 DOI: 10.3233/ssw210037

Katharina Allgaier, S. Veríssimo, Sherry Tan, Matthias Orlikowski, Matthias Hartung

{"title":"LLOD-Driven Bilingual Word Embeddings Rivaling Cross-Lingual Transformers in Quality of Life Concept Detection from French Online Health Communities","authors":"Katharina Allgaier, S. Veríssimo, Sherry Tan, Matthias Orlikowski, Matthias Hartung","doi":"10.3233/ssw210037","DOIUrl":"https://doi.org/10.3233/ssw210037","url":null,"abstract":"We describe the use of Linguistic Linked Open Data (LLOD) to support a cross-lingual transfer framework for concept detection in online health communities. Our goal is to develop multilingual text analytics as an enabler for analyzing health-related quality of life (HRQoL) from self-reported patient narratives. The framework capitalizes on supervised cross-lingual projection methods, so that labeled training data for a source language are sufficient and are not needed for target languages. Cross-lingual supervision is provided by LLOD lexical resources to learn bilingual word embeddings that are simultaneously tuned to represent an inventory of HRQoL concepts based on the World Health Organization’s quality of life surveys (WHOQOL). We demonstrate that lexicon induction from LLOD resources is a powerful method that yields rich and informative lexical resources for the cross-lingual concept detection task which can outperform existing domain-specific lexica. Furthermore, in a comparative evaluation we find that our models based on bilingual word embeddings exhibit a high degree of complementarity with an approach that integrates machine translation and rule-based extraction algorithms. In a combined configuration, our models rival the performance of state-of-the-art cross-lingual transformers, despite being of considerably lower model complexity.","PeriodicalId":275036,"journal":{"name":"International Conference on Semantic Systems","volume":"166 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115165166","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: 1

Object-Action Association Extraction from Knowledge Graphs 从知识图谱中提取对象-动作关联

International Conference on Semantic Systems Pub Date : 2021-08-31 DOI: 10.3233/ssw210048

Alexandros Vassiliades, T. Patkos, Vasilis Efthymiou, Antonis Bikakis, Nick Bassiliades, D. Plexousakis

引用次数: 2

Adding Structure and Removing Duplicates in SPARQL Results with Nested Tables 在嵌套表的SPARQL结果中添加结构和删除重复项

International Conference on Semantic Systems Pub Date : 2021-08-31 DOI: 10.3233/ssw210047

S. Ferré

引用次数: 1