Accepted Papers

Revealing Secrets in SPARQL Session Level
Xinyue Zhang, Meng Wang, Muhammad Saleem, Axel-Cyrille Ngonga Ngomo, Guilin Qi and Haofen Wang.
Detecting Different Forms of Semantic Shifts in Word Embeddings via Paradigmatic and Syntagmatic Association Changes
Anna Wegmann, Florian Lemmerich and Markus Strohmaier.
ExCut: Explainable Embedding-based Clustering over Knowledge Graphs
Mohamed H. Gad-Elrab, Daria Stepanova, Trung-Kien Tran, Heike Adel and Gerhard Weikum.
Fantastic Knowledge Graph Embeddings and How to Find the Right Space for Them
Mojtaba Nayyeri, Chengjin Xu, Sahar Vahdati, Nadezhda Vassilyeva, Emanuel Sallinger, Hamed Shariat Yazdi and Jens Lehmann.
Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition
Chengjin Xu, Nayyeri Mojtaba, Fouad Alkhoury, Hamed Shariat Yazdi and Jens Lehmann.
Generating Expressive Correspondences: An Approach based on User Knowledge Needs and A-box Relation Discovery
Elodie Thiéblin, Ollivier Haemmerlé and Cassia Trojahn.
Learning Short-term Differences and Long-term Dependencies for Entity Alignment
Jia Chen, Zhixu Li, Pengpeng Zhao, An Liu, Lei Zhao, Zhigang Chen and Xiangliang Zhang.
Weakly Supervised Short Text Categorization using World Knowledge
Rima Türker, Lei Zhang, Mehwish Alam and Harald Sack.
NABU – Multilingual Graph-based Neural RDF Verbalizer
Diego Moussallem, Dwaraknath Gnaneshwar, Thiago Castro Ferreira and Axel-Cyrille Ngonga Ngomo.
KnowlyBERT – Hybrid Query Answering over Language Models and Knowledge Graphs
Jan-Christoph Kalo, Leandra Fichtel, Philipp Ehler and Wolf-Tilo Balke.
Contextual Propagation of Properties for Knowledge Graphs: A Sentence Embedding Based Approach
Pierre-Henri Paris, Fayçal Hamdi, Nobal Niraula and Samira Si-Said Cherfi.
GeoSPARQL+: Syntax, Semantics and System for Integrated Querying of Graph, Raster and Vector Data
Timo Homburg, Steffen Staab and Daniel Janke.
LM4KG: Improving Common Sense Knowledge Graphs with Language Models
Janna Omeliyanenko, Albin Zehe, Lena Hettinger and Andreas Hotho.
Generating Referring Expressions from RDF Knowledge Graphs for Data Linking
Armita Khajeh Nassiri, Nathalie Pernelle, Fatiha Saïs and Gianluca Quercini.
Enhancing Online Knowledge Graph Population with Semantic Knowledge
Delia Fernandez-Canellas, Joan Marco Rimmek, Xavier Giro-I-Nieto and Elisenda Bou.
Tentris – A Tensor-Based Triple Store
Alexander Bigerl, Felix Conrads, Charlotte Behning, Mohamed Sherif, Muhammad Saleem and Axel-Cyrille Ngonga Ngomo.
Deciding SHACL Shape Containment through Description Logics Reasoning
Martin Leinberger, Philipp Seifer, Tjitze Rienstra, Ralf Lämmel and Steffen Staab.
Tab2Know: Building a Knowledge Base from Scientifc Tables
Benno Kruit, Hongyu He and Jacopo Urbani.
Explainable Link Prediction for Emerging Entities in Knowledge Graphs
Rajarshi Bhowmik and Gerard de Melo.
FunMap: Efficient Execution of Functional Mappings for Scaled-Up Knowledge Graph Creation
Samaneh Jozashoori, David Chaves-Fraga, Enrique Iglesias, Maria-Esther Vidal and Oscar Corcho.
Leveraging Semantic Parsing for Relation Linking over Knowledge Bases
Nandana Mihindukulasooriya, Gaetano Rossiello, Pavan Kapanipathi, Ibrahim Abdelaziz, Srinivas Ravishankar, Mo Yu, Alfio Gliozzo, Salim Roukos and Alexander Gray.
Prevalence and Effects of Class Hierarchy Precompilation in Biomedical Ontologies
Christian Kindermann, Bijan Parsia and Uli Sattler.
From syntactic structure to semantic relationship: hypernym extraction from definitions by recurrent neural networks using the part of speech information
Yixin Tan, Xiaomeng Wang and Tao Jia.
Generating Compact and Relaxable Answers to Keyword Queries over Knowledge Graphs
Gong Cheng, Shuxin Li, Ke Zhang and Chengkai Li.
Rule-Guided Graph Neural Networks for Recommender Systems
Xinze Lyu, Guangyao Li and Wei Hu.
Privacy-preserving Ontology-based Data Access
Gianluca Cima, Domenico Lembo, Lorenzo Marconi, Riccardo Rosati and Domenico Fabio Savo.
Focused Query Expansion with Entity Cores for Patient-Centric Health Search
Erisa Terolli, Patrick Ernst and Gerhard Weikum.
A Novel Path-based Entity Relatedness Measure for Efficient Collective Entity Linking
Cheikh Brahim El Vaigh, Francois Goasdoue, Guillaume Gravier and Pascale Sébillot.
BCRL: Long Text Friendly Knowledge Graph Representation Learning
Gang Wu, Wenfang Wu, Guodong Zhao, Donghong Han and Baiyou Qiao.
Extending SPARQL with Similarity Joins
Sebastián Ferrada, Benjamin Bustos and Aidan Hogan.
Cost- and Robustness-based Query Optimization for Triple Pattern Fragment Clients
Lars Heling and Maribel Acosta.
Computing Compliant Anonymisations of Quantified ABoxes w.r.t. EL Policies
Franz Baader, Francesco Kriegel, Adrian Nuradiansyah and Rafael Peñaloza.
Linked Credibility Reviews for Explainable Misinformation Detection
Ronald Denaux and Jose Manuel Gomez-Perez.
Refining Node Embeddings via Semantic Proximity
Melisachew Wudage Chekol and Giuseppe Pirrò.
SHACL Satisfiability and Containment
Paolo Pareti, George Konstantinidis, Fabio Mogavero and Timothy Norman.
PNEL: Pointer Network based End-To-End Entity Linking over Knowledge Graphs
Debayan Banerjee, Debanjan Chaudhuri, Mohnish Dubey and Jens Lehmann.
In-database Analytics with Recursive SPARQL
Adrián Soto Suárez, Juan Reutter and Aidan Hogan.
PreFace: Faceted Retrieval of Prerequisites using domain-specific Knowledge Bases
Prajna Upadhyay and Maya Ramanath.
CASQAD – A New Dataset For Context-aware Spatial Question Answering
Jewgeni Rose and Jens Lehmann
HDTCat: let’s make HDT scale
Dennis Diefenbach and Jose M. Gimenez-Garcia. 
Squirrel – Crawling RDF Knowledge Graphs on the Web
Michael Röder, Geraldo de Souza and Axel-Cyrille Ngonga Ngomo
OBA: An Ontology-Based Framework for Creating REST APIs for Knowledge Graphs
Daniel Garijo and Maximiliano Osorio. 
Schímatos: a SHACL-based Web-Form Generator for Knowledge Graph Editing
Sergio José Rodríguez Méndez, Jesse Wright, Armin Haller, Kerry Taylor and Pouya Ghiasnezhad Omran. 
OWL2Bench: A Benchmark for OWL 2 Ontologies
Gunjan Singh, Sumit Bhatia and Raghava Mutharaju
RuBQ: A Russian Dataset for Question Answering over Wikidata
Vladislav Korablinov and Pavel Braslavski
Ontology to formalize gesture-controlled interfaces in Internet of Things (IoT) systems
Madhawa PereraArmin HallerMatt Adcock and Sergio J. Rodríguez Méndez
AI-KG: an Automatically Generated Knowledge Graph of Artificial Intelligence
Danilo Dessì, Francesco Osborne, Diego Reforgiato Recupero, Davide Buscaldi, Enrico Motta and Harald Sack
Nanomine: A Knowledge Graph for Nanocomposite Materials Science
James McCusker, Neha Keshan, Sabbir Rashid, Michael Deagen, Cate Brinson and Deborah McGuinness
G2GML: Graph to Graph Mapping Language for Bridging RDF and Property Graphs
Hirokazu Chiba, Ryota Yamanaka and Shota Matsumoto. 
The International Data Spaces Information Model
Sebastian Bader, Jaroslav Pullmann, Christian Mader, Sebastian Tramp, Christoph Quix, Andreas Mueller, Haydar Akyürek, Matthias Böckmann, Benedikt Imbusch, Johannes Lipp, Sandra Geisler and Christoph Lange
LDflex: a Read/Write Linked Data Abstraction for Front-End Web Developers
Ruben Verborgh and Ruben Taelman
Huanyu Li, Rickard Armiento and Patrick Lambrix. An Ontology for the Materials Design Domain
Explanation Ontology: A Model of Explanations for User-Centered AI
Shruthi Chari, Oshani Seneviratne, Daniel Gruen, Morgan Foreman, Amar Das and Deborah McGuinness
An SKOS-based Vocabulary on the Swift Programming Language
Christian Grévisse and Steffen Rothkugel. 
The Virtual Knowledge Graph System Ontop
Guohui XiaoDavide LantiRoman Kontchakov, Sarah Komla Ebri, Elem Guzel Kalayci, Linfang Ding, Julien Corman, Benjamin CogrelDiego Calvanese and Elena Botoeva
KGTK: A Toolkit for Large Knowledge Graph Manipulation and Analysis
Filip Ilievski, Daniel Garijo, Hans Chalupsky, Naren Teja, Yixiang Yao, Craig Rogers, Ronpeng Li, Jun Liu, Amandeep Singh, Daniel Schawbe and Pedro Szekely. 
Covid-on-the-Web: Knowledge Graph and Services to Advance Covid-19 Research
Franck MichelFabien Gandon, Valentin Ah-Kane, Anna Bobasheva, Elena Cabrio, Olivier Corby, Raphael Gazzotti, Alain Giboin, Santiago Marro, Tobias Mayer, Mathieu Simon, Serena Villata and Marco Winkler. 
ODArchive – Creating an archive for structured data from Open Data Portals
Thomas Weber, Johann Mitlöhner, Sebastian Neumaier and Axel Polleres
Tough Tables: Carefully Benchmarking Semantic Table Annotators
Vincenzo Cutrona, Federico Bianchi, Ernesto Jiménez-Ruiz and Matteo Palmonari. 
Facilitating COVID-19 Meta-analysis Through a Literature Knowledge Graph
Bram SteenwinckelGilles VandewieleIlja RauschPieter HeyvaertRuben TaelmanPieter ColpaertPieter SimoensAnastasia DimouFilip De Turck and Femke Ongenae
Crime event localization and deduplication
Federica Rollo and Laura Po. 
Transparent Integration and Sharing of Life Cycle Sustainability Data with Provenance
Emil Riis Hansen, Matteo Lissandrini, Agneta Ghose, Søren Løkke, Christian Thomsen and Katja Hose. 
A Knowledge Graph for Assessing Agressive Tax Planning Strategies
Niklas Lüdemann, Ageda Shiba, Nikolaos Thymianis, Nicolas Heist, Christopher Ludwig and Heiko Paulheim. 
Turning Transport Data into EU Compliance while Enabling a Multimodal Transport Knowledge Graph
Mario Scrocca, Marco Comerio, Alessio Carenini and Irene Celino. 
Enhancing Public Procurement in the European Union through Constructing and Exploiting an Integrated Knowledge Graph
Ahmet Soylu, Oscar Corcho, Brian Elvesæter, Carlos Badenes-Olmedo, Francisco Yedro, Matej Kovacic, Matej Posinkovic, Ian Makgill, Chris Taggart, Elena Simperl, Till C. Lech and Dumitru Roman. 
The OpenCitations Data Model
Marilena Daquino, Silvio Peroni, David Shotton, Giovanni Colavizza, Behnam Ghavimi, Anne Lauscher, Philipp Mayr, Matteo Romanello and Philipp Zumstein. 
Semantic Integration of Bosch Manufacturing Data Using Virtual Knowledge Graphs
Elem Güzel Kalayci, Irlan Grangel Gonzalez, Felix Loesch, Guohui Xiao, Anees Ul Mehdi, Evgeny Kharlamov and Diego Calvanese. 
A Semantic Framework for Enabling Radio Spectrum Policy Management and Evaluation
Henrique Santos, Alice Mulvehill, John Erickson, James McCusker, Minor Gordon, Owen Xie, Samuel Stouffer, Gerard Capraro, Alex Pidwerbetsky, Allan Berlinsky, John Burgess, Kurt Turck, Jonathan Ashdown and Deborah McGuinness. 
Leveraging Linguistic Linked Data for Cross-lingual Model Transfer in the Pharmaceutical Domain
Jorge Gracia, Christian Fäth, Matthias Hartung, Max Ionov, Julia Bosque-Gil, Susana Verìssimo, Christian Chiarcos and Matthias Orlikowski. 
Reasoning Engine for Prescriptive Maintenance
Rana Farah, Simon Halle, Jiye Li, Freddy Lecue, Baptiste Abeloos, Dominique Perron, Juliette Mattioli, Pierre-Luc Gregoire, Sebastien Laroche, Michel Mercier and Paul Cocaud. 
Ontology-Enhanced Machine Learning: a Bosch Use Case of Welding Quality Monitoring
Yulia Svetashova, Baifan Zhou, Tim Pychynski, Stefan Schmid, York Sure-Vetter, Ralf Mikut and Evgeny Kharlamov. 
Revisiting Ontologies of Units of Measure for Harmonising Quantity Values – a Use Case
Francisco Martin-Recuerda, Dirk Walther, Siegfried Eisinger, Graham David Moore, Petter Andersen, Per-Olav Opdahl and Lillian Hella. 
NEO: A Tool for Taxonomy Enrichment with New Emerging Occupations
Anna Giabelli, Lorenzo Malandri, Fabio Mercorio, Mario Mezzanzanica and Andrea Seveso. 
Domain-specific customization of schema.org based on SHACL
Umutcan Şimşek, Kevin Angele, Elias Kärle, Oleksandra Panasiuk and Dieter Fensel. 
Understanding Data Centers from Logs: Leveraging External Knowledge for Distant Supervision
Chad DeLuca, Anna Lisa Gentile, Petar Ristoski and Steve Welch. 
Assisting the RDF Annotation of a Digital Humanities Corpus using Case-Based Reasoning
Nicolas Lasolle, Olivier Bruneau, Jean Lieber, Emmanuel Nauer and Siyana Pavlova. 
A First Step Towards A Streaming Linked Data Life-Cycle
Riccardo Tommasini, Mohamed Ragab, Alessandro Falcetta and Emanuele Della Valle. 
AWARE: A Situational Awareness Framework for Facilitating Adaptive Behavior of Autonomous Vehicles in Manufacturing
Boulos El Asmar, Syrine Chelly, Nour Azzi, Lynn Nassif, Jana El Asmar and Michael Faerber. 
Google Dataset Search by the Numbers
Omar Benjelloun, Shiyu Chen and Natasha Noy. 
Dynamic Faceted Search for Technical Support exploiting Induced Knowledge
Nandana Mihindukulasooriya, Ruchi Mahindru, Md. Faisal Mahbub Chowdhury, Yu Deng, Nicolas Rodolfo Fauceglia, Gaetano Rossiello, Sarthak Dash and Alfio Gliozzo. 
Linking ontological classes and archaeological forms
Vincenzo Lombardo. 

Multi-view Knowledge Graph Embedding for Entity Alignment


Qingheng Zhang (Nanjing University), Zequn Sun (Nanjing University), Wei Hu (Nanjing University), Muhao Chen (University of Pennsylvania), Lingbing Guo (Nanjing University) and Yuzhong Qu (Nanjing University).

Proc. 28th International Joint Conference on Artificial Intelligence (IJCAI), Macao (CN), pp5429-5435, 2019

Abstract. We study the problem of embedding-based entity alignment between knowledge graphs (KGs). Previous works mainly focus on the relational structure of entities. Some further incorporate another type of features, such as attributes, for refinement. However, a vast of entity features are still unexplored or not equally treated together, which impairs the accuracy and robustness of embedding-based entity alignment. In this paper, we propose a novel framework that unifies multiple views of entities to learn embeddings for entity alignment. Specifically, we embed entities based on the views of entity names, relations and attributes, with several combination strategies. Furthermore, we design some cross-KG inference methods to enhance the alignment between two KGs. Our experiments on real-world datasets show that the proposed framework significantly outperforms the state-of-the-art embedding-based entity alignment methods. The selected views, cross-KG inference and combination strategies all contribute to the performance improvement.

Url:  https://doi.org/10.24963/ijcai.2019/754
No:  472

 

Enriching Knowledge Bases with Interesting Negative Statements

Hiba Arnaout (Max Planck Institute for Informatics), Simon Razniewski (Max Planck Institute for Informatics) and Gerhard Weikum (Max Planck Institute for Informatics).


Proc. 1st Automated Knowledge Base Construction conference (AKBC) 2020

Abstract. Knowledge bases (KBs), pragmatic collections of knowledge about notable entities, are an important asset in applications such as search, question answering and dialogue. Rooted in a long tradition in knowledge representation, all popular KBs only store positive information, but abstain from taking any stance towards statements not contained in them. In this paper, we make the case for explicitly stating interesting statements which are not true. Negative statements would be important to overcome current limitations of question answering, yet due to their potential abundance, any effort towards compiling them needs a tight coupling with ranking. We introduce two approaches towards automatically compiling negative statements. (i) In peer-based statistical inferences, we compare entities with highly related entities in order to derive potential negative statements, which we then rank using supervised and unsupervised features. (ii) In pattern-based query log extraction, we use a pattern-based approach for harvesting search engine query logs. Experimental results show that both approaches hold promising and complementary potential. Along with this paper, we publish the first datasets on interesting negative information, containing over 1.4M statements for 130K popular Wikidata entities.

URL:  https://akbc.ws/2020/papers/pSLmyZKaS
No:  473

 

Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs

Yuting Wu (Peking University), Xiao Liu (Peking University), Yansong Feng (Peking University), Zheng Wang (University of Leeds), Rui Yan (Peking University) and Dongyan Zhao (Peking University).


Proc. 28th International Joint Conference on Artificial Intelligence (IJCAI), Macao (CN), pp5278-5284, 2019

Abstract. Entity alignment is the task of linking entities with the same real-world identity from different knowledge graphs (KGs), which has been recently dominated by embedding-based methods. Such approaches work by learning KG representations so that entity alignment can be performed by measuring the similarities between entity embeddings. While promising, prior works in the field often fail to properly capture complex relation information that commonly exists in multi-relational KGs, leaving much room for improvement. In this paper, we propose a novel Relation-aware Dual-Graph Convolutional Network (RDGCN) to incorporate relation information via attentive interactions between the knowledge graph and its dual relation counterpart, and further capture neighboring structures to learn better entity representations. Experiments on three real-world cross-lingual datasets show that our approach delivers better and more robust results over the state-of-the-art alignment methods by learning better KG representations.

URL:  https://doi.org/10.24963/ijcai.2019/733
No:  475

 

Representation Learning for Attributed Multiplex Heterogeneous Network

Yukuo Cen (Tsinghua University), Xu Zou (Tsinghua University), Jianwei Zhang (Alibaba Group), Hongxia Yang (Alibaba Group), Jingren Zhou (Alibaba Group) and Jie Tang (Tsinghua University).


Proc. 25th ACM SIGKDD international conference on knowledge discovery and data mining (KDD), Anchorage (US), pp1358-1368, 2019

Abstract. Network embedding (or graph embedding) has been widely used in many real-world applications. However, existing methods mainly focus on networks with single-typed nodes/edges and cannot scale well to handle large networks. Many real-world networks consist of billions of nodes and edges of multiple types and each node is associated with different attributes. In this paper, we formalize the problem of embedding learning for the Attributed Multiplex Heterogeneous Network and propose a unified framework to address this problem. The framework supports both transductive and inductive learning. We also give the theoretical analysis of the proposed framework, showing its connection with previous works and proving its better generalization ability. We conduct systematical evaluations for the proposed framework on four different genres of challenging datasets: Amazon, YouTube, Twitter, and Alibaba. Experimental results demonstrate that with the learned embeddings from the proposed framework, we can achieve statistically significant improvements (e.g., 5.99-28.23% lift by F1 scores; p<<0.01, t-test) over previous state-of-the-arts for link prediction. The framework has also been successfully deployed on the recommendation system of a worldwide leading E-Commerce company, Alibaba Group. Results of the online A/B tests on product recommendation further confirm the effectiveness and efficiency of the framework in practice.

URL:  https://dl.acm.org/doi/10.1145/3292500.3330964 / https://arxiv.org/abs/1905.01669
No:  479

 

Dividing the Ontology Alignment Task with Semantic Embeddings and Logic-based Modules

Ernesto Jimenez-Ruiz (City, University of London), Asan Agibetov (Medical University of Vienna), Jiaoyan Chen (University of Oxford), Matthias Samwald (Medical University of Vienna, Austria) and Valerie Cross (Miami University).


Proc. 24th European conference on artificial intelligence (ECAI), Santiago de Compostella (ES), pp784-791, 2020

Abstract. Large ontologies still pose serious challenges to state-of-the-art ontology alignment systems. In this paper we present an approach that combines a neural embedding model and logic-based modules to accurately divide an input  ontology matching task into smaller and more tractable matching (sub)tasks. We have conducted a comprehensive evaluation using the datasets of the Ontology Alignment Evaluation Initiative. The results are encouraging and suggest that the proposed method is adequate in practice and can be integrated within the workflow of systems unable to cope with very large ontologies.

URL:  http://ecai2020.eu/papers/792_paper.pdf / https://arxiv.org/abs/2003.05370
No:  483

 

Learning Triple Embeddings from Knowledge Graphs

Valeria Fionda (University of Calabria) and Giuseppe Pirrò (Sapienza University of Rome).


Proc. Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), New-York city (US), pp3874-3881, 2020

Abstract. Graph embedding techniques allow to learn high-quality feature vectors from graph structures and are useful in a variety of tasks, from node classification to clustering. Existing approaches have only focused on learning feature vectors for the nodes and predicates in a knowledge graph. To the best of our knowledge, none of them has tackled the problem of directly learning triple embeddings. The approaches that are closer to this task have focused on homogeneous graphs involving only one type of edge and obtain edge embeddings by applying some operation (e.g., average) on the embeddings of the endpoint nodes. The goal of this paper is to introduce Triple2Vec, a new technique to directly embed knowledge graph triples. We leverage the idea of line graph of a graph and extend it to the context of knowledge graphs. We introduce an edge weighting mechanism for the line graph based on semantic proximity. Embeddings are finally generated by adopting the SkipGram model, where sentences are replaced with graph walks. We evaluate our approach on different real-world knowledge graphs and compared it with related work. We also show an application of triple embeddings in the context of user-item recommendations.

URL:  https://doi.org/10.1609/aaai.v34i04.5800  / https://arxiv.org/abs/1905.11691v1
No:  491

 

Adaptive Low-level Storage of Very Large Knowledge Graphs

Jacopo Urbani (Vrije Universiteit Amsterdam) and Ceriel Jacobs (Vrije Universiteit Amsterdam).


Proceedings of The Web Conference (29th International World Wide Web Conference). Taipei (TW), pp. 1761-1772, 2020

Abstract. The increasing availability and usage of Knowledge Graphs (KGs) on the Web calls for scalable and general-purpose solutions to store this type of data structures. We propose Trident, a novel storage architecture for very large KGs on centralized systems. Trident uses several interlinked data structures to provide fast access to nodes and edges, with the physical storage changing depending on the topology of the graph to reduce the memory footprint. In contrast to single architectures designed for single tasks, our approach offers an interface with few low-level and general-purpose primitives that can be used to implement tasks like SPARQL query answering, reasoning, or graph analytics. Our experiments show that Trident can handle graphs with 10^11 edges using inexpensive hardware, delivering competitive performance on multiple workloads.

URL:  https://dl.acm.org/doi/10.1145/3366423.3380246 / https://arxiv.org/abs/2001.09078
No:  503

 

Stream Reasoning Agents

Riccardo Tommasini (University of Tartu), Davide Calvaresi (University of Applied Sciences Western Switzerland) and Jean-Paul Calbimonte (University of Applied Sciences and Arts Western Switzerland HES-SO).


Proc. 18th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), Montréal (CA), pp. 1664-1680. 2019

Abstract. Data streams are increasingly needed for different types of applications and domains, where dynamicity and data velocity are of foremost importance. In this context, research challenges raise regarding the generation, publication, processing, and discovery of these streams, especially in distributed, heterogeneous and collaborative environments such as the Web. Stream reasoning has addressed some of these challenges in the last decade, presenting a novel data processing paradigm that lays at the intersection among semantic data modeling, stream processing, and inference techniques. However, stream reasoning works have focused almost exclusively on architectures and approaches that assume an isolated processing environment. Therefore, they lack, in general, the means for discovering, collaborating, negotiating, sharing, or validating data streams on a highly heterogeneous ecosystem as the Web. Agents and multi-agent systems research has long developed principles and foundations for enabling some of these features, although usually under assumptions that require to be revised in order to comply with the characteristics of data streams. This paper presents a vision for a Web of stream reasoning agents, capable of sharing not only streaming data, but also processing duties, using collaboration and negotiation protocols, while relying on common vocabularies and protocols that take into account the high dynamicity of their knowledge, goals, and behavioral patterns.

URL:   https://dl.acm.org/doi/abs/10.5555/3306127.3331894 / http://publications.hevs.ch/index.php/publications/show/2522

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