Kunal Jha

Kunal Jha
Depiction of Kunal Jha
Address Work
Goerdelerring 9, Room P905, 04109 Leipzig
Email Office
Phone Work
+49-341-97-32260
Fax Work
+49-341-229037-99

Bio

Kunal is a Masters Student (Computer Science) at University of Bonn and works as a Research Assistant at AKSW in Leipzig. Before joining AKSW, Kunal has completed Bachelors in Information and Communication Technology from DA-IICT, India.

Kunal's recent works have been in the areas of Benchmarking and Semantic Search systems but in due course of his activities he has developed interests in Software-Container Virtualisation (Docker), Machine Learning, Knowledge Graphs, NLP and Functional Programming Approaches.

He completed his Bachelor's Thesis with AKSW in May 2016 on a tool that helps in improving NER/ NED gold standard Datasets. He also completed his Google Summer of Code 2016 with DBpedia and is currently a co-maintainer for the DBpedia Lookup Service.

Current Projects

  • DIESELDistributed Search in Large Enterprise Data
  • GERBILGeneral Entity Annotation Benchmark Framework

Past Projects

  • DBpediaQuerying Wikipedia like a Semantic Database

Publications

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News

SANSA 0.7.1 (Semantic Analytics Stack) Released ( 2020-01-17T09:52:41+01:00 by Prof. Dr. Jens Lehmann)

2020-01-17T09:52:41+01:00 by Prof. Dr. Jens Lehmann

We are happy to announce SANSA 0.7.1 – the seventh release of the Scalable Semantic Analytics Stack. SANSA employs distributed computing via Apache Spark and Flink in order to allow scalable machine learning, inference and querying capabilities for large knowledge graphs. Read more about "SANSA 0.7.1 (Semantic Analytics Stack) Released"

More Complete Resultset Retrieval from Large Heterogeneous RDF Sources ( 2019-12-05T15:46:09+01:00 Andre Valdestilhas)

2019-12-05T15:46:09+01:00 Andre Valdestilhas

Over recent years, the Web of Data has grown significantly. Various interfaces such as LOD Stats, LOD Laundromat and SPARQL endpoints provide access to hundreds of thousands of RDF datasets, representing billions of facts. Read more about "More Complete Resultset Retrieval from Large Heterogeneous RDF Sources"

DL-Learner 1.4 (Supervised Structured Machine Learning Framework) Released ( 2019-09-24T22:41:46+02:00 by Simon Bin)

2019-09-24T22:41:46+02:00 by Simon Bin

Dear all, The Smart Data Analytics group [1] and the E.T.-db-MOLE sub-group located at the InfAI Leipzig [2] is happy to announce DL-Learner 1.4. DL-Learner is a framework containing algorithms for supervised machine learning in RDF and OWL. Read more about "DL-Learner 1.4 (Supervised Structured Machine Learning Framework) Released"

DBpedia Day @ SEMANTiCS 2019 ( 2019-08-01T10:35:05+02:00 Sandra Bartsch)

2019-08-01T10:35:05+02:00 Sandra Bartsch

 We are happy to announce that SEMANTiCS 2019 will host the 14th DBpedia Community Meeting at the last day of the conference on September 12, 2019. Read more about "DBpedia Day @ SEMANTiCS 2019"

LDK conference @ University of Leipzig ( 2019-03-22T09:21:41+01:00 by Julia Holze)

2019-03-22T09:21:41+01:00 by Julia Holze

With the advent of digital technologies, an ever-increasing amount of language data is now available across various application areas and industry sectors, thus making language data more and more valuable. Read more about "LDK conference @ University of Leipzig"