Jens Lehmann (scientist)

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Jens Lehmann
Jens Lehmann (Computer Scientist).jpg
Born (1982-03-29) 29 March 1982 (age 41)
Meissen, Saxony, West Germany
NationalityGerman
Scientific career
FieldsKnowledge Graphs, Conversational AI, Large-scale Data Analytics (Big Data), Relational Learning
Institutions University of Bonn, University of Leipzig, University of Oxford, Fraunhofer IAIS, Institute for Applied Informatics (InfAI)
Website http://jens-lehmann.org

Jens Lehmann is a computer scientist, who works with knowledge graphs and artificial intelligence. He is a principal scientist at Amazon, an honorary professor at TU Dresden [1] and a fellow of European Laboratory for Learning and Intelligent Systems. [2] Formerly, he was a full professor at the University of Bonn, Germany and lead scientist for Conversational AI and Knowledge Graphs at Fraunhofer IAIS.

Contents

Research

In 2007, Lehmann co-founded the DBpedia knowledge graph project. [3] [4] [5]

Lehmann also works on Symbolic Artificial Intelligence, in particular for learning concepts in Description Logics (DLs) [6] as well as Web Ontology Language class expressions. [7] [8]

Within the field of Question Answering, he developed approaches to transform natural language questions into queries against a knowledge graph. [9] He investigates representation learning approaches for knowledge graphs and their application in downstream machine learning tasks. [10] [11]

Within the scope of the Center for Explainable and Efficient AI Technologies CEE AI, [12] [13] a collaboration between the Fraunhofer Society and Technische Universität Dresden, Jens Lehmann was coordinating the Fraunhofer IAIS Dresden lab with a main focus on Conversational Artificial Intelligence. [14]

Recent projects include a demonstrator presented at Hannover fair 2019 [15] in a collaboration between the Fraunhofer Cluster of Excellence Cognitive Internet Technologies, Volkswagen and the Fraunhofer Institute for Integrated Circuits IIS, [16] and the SPEAKER project towards an AI platform for business-to-business speech assistants. [17]

Education and Career

Lehmann graduated with a master's degree in Computer Science from the Technical University of Dresden and the University of Bristol in 2006. He then obtained a doctoral degree (Dr. rer. nat) with grade summa cum laude at the Leipzig University in 2010 [18] and was a research visitor at the University of Oxford. In 2013, he became a leader of the Agile Knowledge Engineering and Semantic Web research group (AKSW) at the Leipzig University. Subsequently, he was appointed as professor at University of Bonn and Fraunhofer IAIS in 2015. Since 2016, he is leading the Smart Data Analytics Research Group involving researchers from the University of Bonn, Fraunhofer IAIS, the Institute of Applied Informatics co-affiliation with the University of Leipzig and TU Dresden. In 2019, he started the Fraunhofer IAIS Dresden lab.

In 2022, he moved to Amazon as principal scientist and was awarded honorary professor at TU Dresden.

Awards

The impact of his research has been awarded in different ways by the community. He received the 10 Year SWSA Award [19] for his work on DBpedia together with other co-founders that was published at the International Semantic Web Conference, the Semantic Web Journal outstanding paper Award, [20] ESWC 7-Year Most Influential Paper Award, Outstanding Paper Award Winner at the Literati Network Awards for Excellence 2013, the ISWC 2011 Best Research Paper Award, and the Journal of Web Semantics Most Cited Paper Award 2006–2010. He also received a 10-year award [21] for his work on LinkedGeoData.

For his early work on learning concepts in description logics, he received the Best Student Paper Award [22] at the International Conference on Inductive Logic Programming (ILP) 2007.

Related Research Articles

Knowledge representation and reasoning is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge in order to design formalisms that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of reasoning, such as the application of rules or the relations of sets and subsets.

<span class="mw-page-title-main">Semantic network</span> Knowledge base that represents semantic relations between concepts in a network

A semantic network, or frame network is a knowledge base that represents semantic relations between concepts in a network. This is often used as a form of knowledge representation. It is a directed or undirected graph consisting of vertices, which represent concepts, and edges, which represent semantic relations between concepts, mapping or connecting semantic fields. A semantic network may be instantiated as, for example, a graph database or a concept map. Typical standardized semantic networks are expressed as semantic triples.

<span class="mw-page-title-main">Semantic Web</span> Extension of the Web to facilitate data exchange

The Semantic Web, sometimes known as Web 3.0, is an extension of the World Wide Web through standards set by the World Wide Web Consortium (W3C). The goal of the Semantic Web is to make Internet data machine-readable.

An annotation is extra information associated with a particular point in a document or other piece of information. It can be a note that includes a comment or explanation. Annotations are sometimes presented in the margin of book pages. For annotations of different digital media, see web annotation and text annotation.

<span class="mw-page-title-main">Deborah McGuinness</span>

Deborah Louise McGuinness is an American computer scientist and researcher at Rensselaer Polytechnic Institute (RPI). She is a professor of Computer, Cognitive and Web Sciences, Industrial and Systems Engineering, and an endowed chair in the Tetherless World Constellation, a multidisciplinary research institution within RPI that focuses on the study of theories, methods and applications of the World Wide Web. Her fields of expertise include interdisciplinary data integration, artificial intelligence, specifically in knowledge representation and reasoning, description logics, the semantic web, explanation, and trust.

Ontotext is a software company with offices in Europe and USA. It is the semantic technology branch of Sirma Group. Its main domain of activity is the development of software based on the Semantic Web languages and standards, in particular RDF, OWL and SPARQL. Ontotext is best known for the Ontotext GraphDB semantic graph database engine. Another major business line is the development of enterprise knowledge management and analytics systems that involve big knowledge graphs. Those systems are developed on top of the Ontotext Platform that builds on top of GraphDB capabilities for text mining using big knowledge graphs.

<span class="mw-page-title-main">Ian Horrocks</span> British academic (b.1958)

Ian Robert Horrocks is a professor of computer science at the University of Oxford in the UK and a Fellow of Oriel College, Oxford. His research focuses on knowledge representation and reasoning, particularly ontology languages, description logic and optimised tableaux decision procedures.

<span class="mw-page-title-main">Ontology learning</span> Automatic creation of ontologies

Ontology learning is the automatic or semi-automatic creation of ontologies, including extracting the corresponding domain's terms and the relationships between the concepts that these terms represent from a corpus of natural language text, and encoding them with an ontology language for easy retrieval. As building ontologies manually is extremely labor-intensive and time-consuming, there is great motivation to automate the process.

<span class="mw-page-title-main">Linked data</span> Structured data and method for its publication

In computing, linked data is structured data which is interlinked with other data so it becomes more useful through semantic queries. It builds upon standard Web technologies such as HTTP, RDF and URIs, but rather than using them to serve web pages only for human readers, it extends them to share information in a way that can be read automatically by computers. Part of the vision of linked data is for the Internet to become a global database.

<span class="mw-page-title-main">DBpedia</span> Online database project

DBpedia is a project aiming to extract structured content from the information created in the Wikipedia project. This structured information is made available on the World Wide Web. DBpedia allows users to semantically query relationships and properties of Wikipedia resources, including links to other related datasets.

The International Semantic Web Conference (ISWC) is a series of academic conferences and the premier international forum for the Semantic Web, Linked Data and Knowledge Graph Community. Here, scientists, industry specialists, and practitioners meet to discuss the future of practical, scalable, user-friendly, and game changing solutions. Its proceedings are published in the Lecture Notes in Computer Science by Springer-Verlag.

Knowledge extraction is the creation of knowledge from structured and unstructured sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing. Although it is methodically similar to information extraction (NLP) and ETL, the main criterion is that the extraction result goes beyond the creation of structured information or the transformation into a relational schema. It requires either the reuse of existing formal knowledge or the generation of a schema based on the source data.

<span class="mw-page-title-main">Ulrike Sattler</span>

Ulrike M. Sattler is a professor of computer science in the information management group of the Department of Computer Science at the University of Manchester and a visiting professor at the University of Oslo.

Inductive programming (IP) is a special area of automatic programming, covering research from artificial intelligence and programming, which addresses learning of typically declarative and often recursive programs from incomplete specifications, such as input/output examples or constraints.

Semantic Scholar is a research tool powered by artificial intelligence for scientific literature. It was developed at the Allen Institute for AI and publicly released in November 2015. It uses advances in natural language processing to provide summaries for scholarly papers. The Semantic Scholar team is actively researching the use of artificial intelligence in natural language processing, machine learning, human–computer interaction, and information retrieval.

<span class="mw-page-title-main">Pascal Hitzler</span> German-American computer scientist

Pascal Hitzler is a German American computer scientist specializing in Semantic Web and Artificial Intelligence. He is endowed Lloyd T. Smith Creativity in Engineering Chair, Co-Director of the Institute for Digital Agriculture and Advanced Analytics (ID3A) and Director of the Center for Artificial Intelligence and Data Science (CAIDS) at Kansas State University, and the founding Editor-in-Chief of the Semantic Web journal and the IOS Press book series Studies on the Semantic Web.

Kristian Kersting is a German computer scientist. He is Professor of Artificial intelligence and Machine Learning at the Department of Computer Science at the Technische Universität Darmstadt, Head of the Artificial Intelligence and Machine Learning Lab (AIML) and Co-Director of hessian.AI, the Hessian Center of Artificial Intelligence.

Sheila McIlraith is a Canadian computer scientist specializing in Artificial Intelligence (AI). She is a Professor in the Department of Computer Science, University of Toronto, Canada CIFAR AI Chair, and Associate Director and Research Lead of the Schwartz Reisman Institute for Technology and Society.

<span class="mw-page-title-main">Knowledge graph</span> Type of knowledge base

In knowledge representation and reasoning, a knowledge graph is a knowledge base that uses a graph-structured data model or topology to represent and operate on data. Knowledge graphs are often used to store interlinked descriptions of entities – objects, events, situations or abstract concepts – while also encoding the semantics or relationships underlying these entities.

Table extraction is the process of recognizing and separating a table from a large document, possibly also recognizing individual rows, columns or elements. It may be regarded as a special form of information extraction.

References

  1. ↑ "TU Dresden: The Faculty Of Computer Science Welcomes Honorary Professor Jens Lehmann". TU Dresden. 21 November 2022. Retrieved 7 November 2022.
  2. ↑ ELLIS. "European Laboratory for Learning and Intelligent Systems". European Laboratory for Learning and Intelligent Systems. Retrieved 19 December 2020.
  3. ↑ DBpedia: A nucleus for a web of open data, S. Auer, C. Bizer, G. Kobilarov, J. Lehmann, R. Cyganiak, Z. Ives, International Semantic Web Conference 2008, 722-735, the ISWC 10 Year Best Paper Award
  4. ↑ DBpedia – A crystallization point for the web of data, J. Lehmann et al. (shared first author), Journal of Web Semantics, 7(3):154–165, 2009, Journal of Web Semantics 2006–2010 Award.
  5. ↑ DBpedia SPARQL benchmark – performance assessment with real queries on real data, M. Morsey, J. Lehmann, S. Auer, and A. Ngonga Ngomo, International Semantic Web Conference 2011, ISWC Best Paper Award.
  6. ↑ Concept learning in description logics using refinement operators, J. Lehmann and P. Hitzler, Machine Learning
  7. ↑ DL-Learner: Learning concepts in description logics, J. Lehmann, Journal of Machine Learning Research, 10:2639–2642, 2009.
  8. ↑ book. "Learning OWL class expressions". German National Library. Retrieved 8 August 2020.
  9. ↑ Template-based question answering over RDF data, C. Unger, L. Buhmann, J. Lehmann, A. Ngonga-Ngomo, D. Gerber, P. Cimiano, World Wide Web Conference 2012, 639-648.
  10. ↑ Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework, Ali, M., Berrendorf, M., Hoyt, C. T., Vermue, L., Galkin, M., Sharifzadeh, S., Fischer, A., Tresp, V. and Lehmann, J., arXiv preprint arXiv:2006.13365 (2020).
  11. ↑ PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings, Ali, M., Berrendorf, M., Hoyt, C. T., Vermue, L., Sharifzadeh, S., Tresp, V. and Lehmann, J., arXiv preprint arXiv:2007.14175 (2020).
  12. ↑ "Fraunhofer und TU Dresden: Neues Zentrum für Kognitive Produktionssysteme und KI-Zentrum". Silicon Saxony. 11 February 2019. Retrieved 15 October 2020.
  13. ↑ Schön, Stephan (12 February 2019). "Dieses Forschungszentrum wird Dresden verändern". Wirtschaft in Sachsen. Retrieved 15 October 2020.
  14. ↑ Stelzer, Gerhard (6 December 2019). "Wie intelligent ist KI?: Kommunikation mit Missverständnissen". Elektronik.de (in German). Retrieved 7 August 2020.
  15. ↑ Lauer, Bernhard (4 March 2019). "Smarter Sprachassistent mit Domänenwissen". dotnetpro. Retrieved 7 August 2020.
  16. ↑ Interview, phys (1 March 2019). "Smart voice assistant answers your questions". phys. Retrieved 19 August 2020.
  17. ↑ "Projekt Speaker startet Aufbau einer KI-Plattform für B2B Sprachassistenten". EAD-portal. 15 April 2020. Retrieved 15 October 2020.
  18. ↑ Lehmann, Jens (2010-01-05). Learning OWL Class Expressions (PDF). Leipzig: Dissertation Universität Leipzig. p. 223. Retrieved 7 August 2020.
  19. ↑ Horrocks, Ian. "SWSA 10-year award 2017". Semantic Web Science Association. Retrieved 7 August 2020.
  20. ↑ Hitzler, Pascal. "Semantic Web journal Awards 2014". Semantic Web – Interoperability, Usability, Applicability an IOS Press Journal. Retrieved 7 August 2020.
  21. ↑ ILP (4 November 2022). "Semantic Web Journal". SWJ. Retrieved 21 November 2022.
  22. ↑ ILP. "Best Student Paper Award". IIP. Retrieved 21 November 2022.
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