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Jens Lehmann | |
---|---|
Born | |
Nationality | German |
Scientific career | |
Fields | Knowledge 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 | jens-lehmann |
Jens Lehmann (born 29 March 1982) 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 the European Laboratory for Learning and Intelligent Systems. [2] He was formerly a full professor at the University of Bonn, Germany and lead scientist for Conversational AI and Knowledge Graphs at Fraunhofer IAIS.
In 2007, Lehmann co-founded the DBpedia knowledge graph project. [3] [4] [5]
Lehmann also works on Symbolic Artificial Intelligence, specifically as it pertains to 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]
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.
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.
Cyc is a long-term artificial intelligence project that aims to assemble a comprehensive ontology and knowledge base that spans the basic concepts and rules about how the world works. Hoping to capture common sense knowledge, Cyc focuses on implicit knowledge. The project began in July 1984 at MCC and was developed later by the Cycorp company.
Knowledge representation and reasoning is a 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 natural-language dialog. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge, in order to design formalisms that make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of reasoning.
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.
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.
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.
James Alexander Hendler is an artificial intelligence researcher at Rensselaer Polytechnic Institute, United States, and one of the originators of the Semantic Web. He is a Fellow of the National Academy of Public Administration.
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.
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.
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.
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 using OpenLink Virtuoso. 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.
Semantic Scholar is a research tool for scientific literature powered by artificial intelligence. It is developed at the Allen Institute for AI and was publicly released in November 2015. Semantic Scholar uses modern techniques in natural language processing to support the research process, for example by providing automatically generated summaries of 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.
In natural language processing, linguistics, and neighboring fields, Linguistic Linked Open Data (LLOD) describes a method and an interdisciplinary community concerned with creating, sharing, and (re-)using language resources in accordance with Linked Data principles. The Linguistic Linked Open Data Cloud was conceived and is being maintained by the Open Linguistics Working Group (OWLG) of the Open Knowledge Foundation, but has been a point of focal activity for several W3C community groups, research projects, and infrastructure efforts since then.
Semantic Web - Interoperability, Usability, Applicability is a bimonthly peer-reviewed scientific journal published by IOS Press. It was established in 2010 and covers the foundations and applications of semantic web technologies, knowledge graph, and linked data. The journal uses an open peer-review process. The journal publishes its metadata online in the form of linked data and provides scientometrics such as the geographic distribution of authors, citation networks, trends in research topics over time, and so forth. The founding editors-in-chief are Pascal Hitzler and Krzysztof Janowicz (2010-).
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, one of the Directors 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.
Sheila Ann McIlraith is a Canadian computer scientist specializing in artificial intelligence (AI). She is a Professor in the Department of Computer Science, University of Toronto. She is a Canada CIFAR AI Chair, a faculty member of the Vector Institute, and Associate Director and Research Lead of the Schwartz Reisman Institute for Technology and Society.
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 free-form semantics or relationships underlying these entities.