Blog:Session1 Automation of Ontology Development

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Session 1, Track A: Automation of Knowledge Extraction and Ontology Learning

SessionChair: Gary Berg-Cross


Context: Building & maintaining knowledge bases & ontologies is hard work and could use some automated help.

Perception: Various parts of AI, such as NLP and machine learning are developing rapidly and could offer help.

Motivation: bring together various researchers to discuss the issues and state of the art.

Sample Questions:

  • What are the ranges of methods used to extract knowledge and build ontologies and other knowledge structures?
  • How have been techniques enhanced and expanded over time?
  • What issues of knowledge building and reuse have been noted?
  • Are there hybrid efforts?

Agenda and Speakers

  • Introduction: Gary Berg-Cross Slides


  • Estevam Hruschka (Associate Professor at Federal University of Sao Carlos DC-UFSCar & adjunct Professor at Carnegie Mellon University) will speak on work growing out of Never-Ending Language Learning (NELL) Slides

Abstract: Never-Ending Learning approach for Populating and Extending an Ontology

Abstract: NELL (Never-Ending Language Learner) is a computer system that runs 24/7, forever, learning to read the web and, as a result, populating and extending its own ontology. NELL has two main tasks to be performed each day: i) extract (read) more facts from the web, and integrate these into its growing ontology; and ii) learn to read better than yesterday, enabling it to go back to the text it read yesterday, and today extract more facts, more accurately. This system has been running 24 hours/day for over seven years now. The result so far is an ontology having +100 million interconnected instances (e.g., servedWith(coffee, applePie), isA(applePie, bakedGood)), that NELL is considering at different levels of confidence, along with hundreds of thousands of learned phrasings, morphological features, and web page structures that NELL uses to extract beliefs from the web. The main motivation for building NELL is based on the belief that we will never really understand machine learning until we can build machines that learn many different things, over years, and become better learners over time.

Short bio Estevam R. Hruschka Jr. is co-leader of the Carnegie Mellon Read the Web project –, and the head of the Machine Learning Lab (MaLL) at Federal University of Sao Carlos (UFSCar), in Brazil. He is also adjunct professor in the Machine Learning Department at Carnegie Mellon University, USA, associate professor at UFSCar, Brazil and member of the AI4Good Foundation ( Steering Committee. Estevam has been ”young research fellow” at FAPESP (Sao Paulo state research agency, Brazil) and, currently, he is ”research fellow” at CNPq (Brazilian research agency). His main research interests are never-ending learning, machine learning, probabilistic graphical models and natural language understanding. He has been working on machine learning with many international research teams, collaborating with research groups from companies and universities.

Abstract: A machine reader is a tool able to transform natural language text to formal structured knowledge so as the latter can be interpreted by machines, according to a shared semantics. FRED is a machine reader for the semantic web: its output is a RDF/OWL graph, whose design is based on frame semantics. Nevertheless, FRED’s graph are domain and task independent making the tool suitable to be used as a semantic middleware for domain- or task- specific applications. To serve this purpose, it is available both as REST service and as Python library. In this talk I will give an overview of the method and principles behind FRED’s implementation.


Short bio Valentina Presutti is a researcher at the Semantic Technology Laboratory of the National Research Council (CNR) in Rome, and she is associated at LIPN (University Paris 13 and CNRS). She received her Ph.D in Computer Science in 2006 at the University of Bologna (Italy). She contributes as researchers to, and is the scientific responsible at CNR for, EU as well as national funded project e.g. MARIO (H2020), IKS (FP7). She is a board member of the Association for Ontology Design and Patterns. She contributes to the Semantic Web community serving in scientific event roles e.g. Workshop Chair at ISWC 2017, Program Chair at ESWC 2013 and I-Semantics 2012. She has more than 90 publications in international journals/conferences/workshops on topics such as Semantic Web, knowledge extraction, and ontology design. She developed her expertise in ontology design, knowledge extraction also serving as a consultant for private as well as public organizations. Her research interests include Semantic Web and Linked Data, ontology quality, ontology design and patterns, assistive robotics, natural language understanding.

  • Alessandro Oltramari (Research Scientist at Bosch) "From machines that learn to machines that know: the role of ontologies in machine intelligence" Slides

Abstract Deep learning networks are getting better and better at recognizing visual and acoustic information, from pictures of cats to human faces, from gunshots to voices. But to make sense of a movie scene, or to interpret speech, machines need world models, namely semantic structures capable of connecting the dots from perception to knowledge. In my presentation I will talk about the state of the art of the art in the integration between machine learning algorithms and ontologies. I will also briefly illustrate how this integration is nowadays a key requirement for enabling intelligent services in the Internet of Things.

Short bio Alessandro Oltramari is a Research Scientist and Project Leader at Bosch. Prior to this position, he was a Research Associate at Carnegie Mellon University. He received his Ph.D. from University of Trento (Italy) in Cognitive Science, in co-tutorship with the Institute for Cognitive Science and Technology of the Italian National Research Council (ISTC- CNR). He has held a research position at the Laboratory for Applied Ontology (ISTC-CNR) in Trento from 2000 to 2010. He has been a Visiting Research Associate at Princeton University (Cognitive Science Laboratory) in 2005 and 2006. His primary interests are centered on theoretical and applied research on knowledge representation and agent technologies. In particular, his research activity mainly deals with integrating ontologies and cognitive architectures for high-level reasoning in knowledge-intensive tasks.

Virtual Meeting Details

Date: Wed., 8-March-2017 Start Time: 9:30am PST / 12:30pm EST / 6:30pm CEST / 5:30pm BST / 1630 UTC

Expected Call Duration: ~90 minutes

Video Teleconference: (3) Meeting ID: 768423137 (4) Chat room:



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