Do you want to compute dataset statistics for large-scale OWL datasets? Then check out OWLStats, a distributed in-memory approach for computing 50 statistical criteria for OWL datasets utilizing Apache Spark. More details at: https://t.co/dr9wz1EXhC #semanticweb

Efficient computation of comprehensive statistical information of large OWL datasets: a scalable approach: . https://t.co/CR7SI5FWSJ #enterprise #informationsystems #EIS

I am extremely excited to join @amazon @alexa99 AI today and start a new chapter in my scientific career working in an excellent environment on question answering, knowledge graphs, reasoning, natural language understanding and more generally the future of conversational AI.

Do you want to evaluate the #linkprediction capabilities of your #knowledgegraphembeddingmodel on a diverse set of datasets? Then check out our recent #NeurIPS paper in which we present a novel set of datasets: https://t.co/GyWQxW8hLz #machinelearning #knowledegegraphs

Do you need to perform reasoning over large-scale OWL datasets? Then check out our recent #KEOD21 paper. More details at: https://t.co/dNx9BGwFky #bigdata #semanticweb #webontologylanguage #distributedcomputing

Do you require embeddings for a KG with many N to M relations? Then check out our model Trans4E, which is described in our paper published in #Neurocomputing. More details at: https://t.co/bQStfcyQzs #knowledgegraphembeddings #knowledgegraphs #linkprediction

In our four accepted #ESWC21 papers, we describe our recent works covering #conversationalai, #questionanswering, and a knowledge graph for enabling defect traceability and customer service analytics. Interested? More details at:

#linkprediction based on local and global graph features of entities? Answer verbalization in #questionanswering through multi-task learning? Check out our #ECML21 papers:

We are thrilled that our paper describing our #knowledgegraphembeddingmodel LogicENN has been published in #TPAMI (IF: 16.389). LogicENN can learn every ground truth of encoded rules in a knowledge graph. More details at: https://t.co/eY3dhsW4tQ #knowledgegraphs #linkprediction

Do you want to compute explainable feature matrices for large-scale #RDF graphs? Then check out our generic, distributed, and scalable software framework described in our #SEMANTICS21 paper. More details at: https://t.co/x3xpv8qdWw #knowledgegraphs #machinelearning #semanticweb

We are happy that we got two papers accepted at #KEOD2020. We present a distributed approach for parsing large-scale OWL datasets and the Physics #Ontology (PhySci) to represent physics-related scholarly data. More details at https://t.co/qbwNNnv7XO #semanticweb

With OWLStats, you can compute statistics for large-scale OWL datasets. For further information, check out our #WIIAT20 paper: https://t.co/kP5sS92fQh #semanticweb #apachespark

We are happy to announce "call for paper" at https://t.co/KPLDtVg7YS @ECAI2020 Looking forward to have your research contribution submitted and presented at #KR4L #RepresentationLearning #KnowledgeGraph

We are very happy to announce the 0.7.1 release of the SANSA (Semantic ANalytics StAck) framework - for large-scale analysis, inference, and querying of knowledge graphs! https://t.co/QPSNHa50Tr #AI #machinelearning #semanticweb #apachespark #apacheflink #knowledgegraphs

Shimaa Ibrahim presenting Cross-lingual Ontology Enrichment @SemanticsConf and generating great interest #SEMANTiCS2019 @SDA_Research @shmkhaled

@sm_fathalla is presenting the work about "EVENTSKG: A 5-Star Dataset of Top-ranked Events in Eight Computer Science Communities" @eswc_conf #eswc2019 https://t.co/bF88h1gNN1

The SDA team wishes you a Merry Christmas!

We are very happy to announce the 0.1 release of the AskNow Question Answering components and tools! https://t.co/4o7VRfbbU7 https://t.co/ZaZk1gh5TQ #QuestionAnswering #AI #MachineLearning #SemanticWeb #KnowledgeGraphs #LinkedData @SDA_Research @eis_bonn

A very interesting analysis of hubs and authorities in the #Ethereum transaction network by @AlethioEthstats powered by SANSA: https://t.co/tDBkIY9SFt #semantics #rdf #blockchain #ethereum #SPARK #KnowledgeGraph

More efficient #RDF N-Triples #Parser introduced in #SANSA: Parsing Improvements of up to an order of Magnitude, e.g. #DBpedia can be read in <100 seconds on a 7 node cluster! More details at https://t.co/NZs2Je7l3K #ApacheSpark #semantics #BigData