AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE)
Symposium Program and Papers
March 25–27, 2019 @ Stanford University, Palo Alto, California, USA
March 25–27, 2019 @ Stanford University, Palo Alto, California, USA
Symposium Opening • Andreas Martin (Slides) and Knut Hinkelmann (Slides), FHNW
9:00 - 9:30Keynote: It’s Statistical Learning and Knowledge Engineering All the Way Down • Doug Lenat, CEO of Cycorp, Inc. and AAAI Fellow
9:30 - 10:15Coffee Break
10:30 - 11:00Deconstructing the Final Frontier of Artificial Intelligence: Five Theses for a Constructivist Machine Learning • Thomas Schmid
Knowledge engineering and machine learning for design and use in cyber-physical environments • Michael Walch
From Demonstrations and Knowledge Engineering to a DNN Agent in a Modern Open-World Video Game • (Slides) Igor Borovikov and Ahmad Beirami
Leverage White-Collar Workers with AI • (Slides) Stephan Jüngling and Angelin Hofer
Lunch
12:30 - 14:00SEVA: A Systems Engineer’s Virtual Assistant • Jitin Krishnan, Patrick Coronado and Trevor Reed
Virtual Bartender: A Dialog System Combining Data-Driven and Knowledge-Based Recommendation • (Slides) Knut Hinkelmann, Monika Blaser, Oliver Faust, Alexander Horst and Carlo Mehli
Building Knowledge Base through Deep Learning Relation Extraction and Wikidata • Pero Subasic, Hongfeng Yin and Xiao Lin
Coffee Break
15:30 - 16:00Collaborative Learning of Concept Representations for Video Data • Francisco Torres, Hoda Eldardiry, Gaurang Gavai and Chad Ramos
Representation and Retrieval of Images by Means of Spatial Relations Between Objects • (Slides) Danilo Nunes, Leonardo Anjoletto Ferreira, Paulo E. Santos and Adam Pease
Modularity as a Means for Complexity Management in Neural Networks Learning • David Castillo-Bolado, Cayetano Guerra-Artal and Mario Hernandez-Tejera
Day 1 Concluding • Frank van Harmelen, VU University
17:10 - 17:30Reception
18:00 - 19:00Keynote • (Slides) Frank van Harmelen, VU University
9:00 - 09:40Applying the Concept of Knowledge Blockchains to Ontologies • Hans-Georg Fill
Combining Machine Learning with Knowledge Engineering to detect Fake News in Social Networks - A Survey • (Slides) Knut Hinkelmann, Sajjad Ahmed and Flavio Corradini
Coffee Break
10:30 - 11:00Improving Topic Modeling for Textual Content with Knowledge Graph Embeddings • Marco Brambilla and Birant Altinel
The Lokahi Prototype: Toward the automatic Extraction of Entity Relationship Models from Text • (Slides) Michael Kaufmann
DEXTER - Data EXTraction & Entity Recognition for Low Resource Datasets • Nihal V. Nayak, Pratheek Mahishi and Sagar Rao
CoKE : Word sense induction using contextualized knowledge embeddings • Sanjana Ramprasad and James Maddox
Lunch
12:30 - 14:00PLOW: Probabilistic Logic Over the Well-Founded Semantics • (Slides) Benjamin Grosof and Theresa Swift
Poster and Demonstration14:00 - 14:20Using Predicate Information from a Knowledge Graph to Identify Disease Trajectories • (Slides) Wytze Vlietstra, Rein Vos, Erik van Mulligen and Jan Kors
Poster14:20 - 14:35A proposal for determining the evidence types of biomedical documents using a drug-drug interaction ontology and machine learning • Linh Hoang, Richard D. Boyce, Mathias Brochhausen, Joseph Utecht and Jodi Schneider
Poster14:35 - 14:50On Datasets for Evaluating Architectures for Learning to Reason • Naveen Sundar Govindarajulu, Jean-Claude Paquin, Shreya Banerjee, Atriya Sen, Paul Mayol and Selmer Bringsjord
Poster14:50 - 15:05Learning Temporal Rules from Knowledge Graph Streams • Pouya Ghiasnezhad Omran, Kewen Wang and Zhe Wang
Poster and Full Paper15:05 - 15:20Coffee Break
15:30 - 16:00Duplicate Removal for Overlapping Clusters: A Study Using Social Media Data • (Slides) Amit Paul and Animesh Dutta
Performance measures fusion for experimental comparison of methods for multi-label classification • Tome Eftimov and Dragi Kocev
CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks • Asma Nouira, Nataliya Sokolovska and Jean-Claude Crivello
Tree-based Regularization for Interpretable Readmission Prediction • Jialiang Jiang, Sharon Hewner and Varun Chandola
Day 2 Concluding • Knut Hinkelmann, FHNW
17:20 - 17:40Plenary Session • (Slides) Aurona Gerber, University of Pretoria, reporting in plenary session
18:00 - 19:00Towards an Assistive and Pattern Learning-driven Process Modeling Approach • (Slides) Emanuele Laurenzi, Knut Hinkelmann, Stephan Jüngling, Devid Montecchiari, Charuta Pande and Andreas Martin
DeepLogic: Towards End-to-End Differentiable Logical Reasoning • Nuri Cingillioglu and Alessandra Russo
On the Capabilities of Logic Tensor Networks for Deductive Reasoning • Federico Bianchi and Pascal Hitzler
KINN: Incorporating Expert Knowledge in Neural Networks • (Slides)Muhammad Ali Chattha, Shoaib Ahmed Siddiqui, Muhammad Imran Malik, Ludger Van Elst, Andreas Dengel and Sheraz Ahmed
Coffee Break
10:30 - 11:00Learning and Engineering Similarity Functions for Business Recommenders and Clustering • (Slides) Hans Friedrich Witschel and Andreas Martin
Leveraging Wikipedia for Ontology Pattern Population from Text • (Slides) Michelle Cheatham, James Lambert and Charles Vardeman II
Metrics for Evaluating Quality of Embeddings for Ontological Concepts • Faisal Alshargi, Saeedeh Shekarpour, Tommaso Soru and Amit Sheth
Day 3 and Symposium Concluding • Knut Hinkelmann and Andreas Martin, FHNW
12:00 - 12:30