@inproceedings{DBLP:conf/aaai/NayyeriVA021,

title = {5* Knowledge Graph Embeddings with Projective Transformations},

author = {Mojtaba Nayyeri and Sahar Vahdati and Can Aykul and Jens Lehmann},

url = {https://ojs.aaai.org/index.php/AAAI/article/view/17095

https://infai.org/wp-content/uploads/2020/11/5-star-Knowledge-Graph-Embeddings-with-Projective-Transformations.pdf},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 

 2021, Thirty-Third Conference on Innovative Applications of Artificial 

 Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances 

 in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 

 2021},

pages = {9064--9072},

publisher = {AAAI Press},

abstract = {Performing link prediction using knowledge graph embedding models has become a popular approach for knowledge graph completion. Such models employ a transformation function that maps nodes via edges into a vector space in order to measure the likelihood of the links. While mapping the individual nodes, the structure of subgraphs is also transformed. Most of the embedding models designed in Euclidean geometry usually support a single transformation type - often translation or rotation, which is suitable for learning on graphs with small differences in neighboring subgraphs. However, multi-relational knowledge graphs often include multiple sub-graph structures in a neighborhood (e.g. combinations of path and loop structures), which current embedding models do not capture well. To tackle this problem, we propose a novel KGE model (5*E) in projective geometry, which supports multiple simultaneous transformations - specifically inversion, reflection, translation, rotation, and homothety. The model has several favorable theoretical properties and subsumes the existing approaches. It outperforms them on the most widely used link prediction benchmarks.},

keywords = {Selected Publication},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/ercimdl/VahdatiPNLAV18,

title = {Unveiling Scholarly Communities over Knowledge Graphs},

author = {Sahar Vahdati and Guillermo Palma and Rahul Jyoti Nath and Christoph Lange and S\"{o}ren Auer and Maria-Esther Vidal},

editor = {Eva M\'{e}ndez and Fabio Crestani and Cristina Ribeiro and Gabriel David and Jo\~{a}o Correia Lopes},

url = {https://doi.org/10.1007/978-3-030-00066-0_9

https://infai.org/wp-content/uploads/2020/11/Unveiling-scholarly-communities-over-knowledge-graphs.pdf},

doi = {10.1007/978-3-030-00066-0_9},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

booktitle = {Digital Libraries for Open Knowledge, 22nd International Conference 

 on Theory and Practice of Digital Libraries, TPDL 2018, Porto, Portugal, 

 September 10-13, 2018, Proceedings},

volume = {11057},

pages = {103--115},

publisher = {Springer},

series = {Lecture Notes in Computer Science},

abstract = {Knowledge graphs represent the meaning of properties of real-world entities and relationships among them in a natural way. Exploiting semantics encoded in knowledge graphs enables the implementation of knowledge-driven tasks such as semantic retrieval, query processing, and question answering, as well as solutions to knowledge discovery tasks including pattern discovery and link prediction. In this paper, we tackle the problem of knowledge discovery in scholarly knowledge graphs, i.e., graphs that integrate scholarly data, and present Korona, a knowledge-driven framework able to unveil scholarly communities for the prediction of scholarly networks. Korona implements a graph partition approach and relies on semantic similarity measures to determine relatedness between scholarly entities. As a proof of concept, we built a scholarly knowledge graph with data from researchers, conferences, and …},

keywords = {Selected Publication},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/ekaw/VahdatiAA016,

title = {OpenResearch: Collaborative Management of Scholarly Communication  Metadata},

author = {Sahar Vahdati and Natanael Arndt and S\"{o}ren Auer and Christoph Lange},

editor = {Eva Blomqvist and Paolo Ciancarini and Francesco Poggi and Fabio Vitali},

url = {https://doi.org/10.1007/978-3-319-49004-5_50

https://infai.org/wp-content/uploads/2020/11/OpenResearch-Collaborative-Management-of-Scholarly-Communication-Metadata.pdf},

doi = {10.1007/978-3-319-49004-5_50},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

booktitle = {Knowledge Engineering and Knowledge Management - 20th International 

 Conference, EKAW 2016, Bologna, Italy, November 19-23, 2016, Proceedings},

volume = {10024},

pages = {778--793},

series = {Lecture Notes in Computer Science},

abstract = {Scholars often need to search for matching, high-profile scientific events to publish their research results. Information about topical focus and quality of events is not made sufficiently explicit in the existing communication channels where events are announced. Therefore, scholars have to spend a lot of time on reading and assessing calls for papers but might still not find the right event. Additionally, events might be overlooked because of the large number of events announced every day. We introduce OpenResearch, a crowd sourcing platform that supports researchers in collecting, organizing, sharing and disseminating information about scientific events in a structured way. It enables quality-related queries over a multidisciplinary collection of events according to a broad range of criteria such as acceptance rate, sustainability of event series, and reputation of people and organizations. Events are …},

keywords = {Selected Publication},

pubstate = {published},

tppubtype = {inproceedings}

}