Advanced Topics in Knowledge Bases

Block seminar, 7 ECTS credits, winter semester 2018–19

Basic Information

  • Type: Block seminar
  • Lecturer: Simon Razniewski
  • Credits: 7 ECTS credits
  • Registration: Sign up under this link until September 28
  • Dates: 3 introductory meetings on October 23, 25 and 30, two days block seminar in winter 2019 (probably February, to be agreed among the participants).

The seminar is a block seminar and will take place on two consecutive days in winter 2019. There will also be three meetings at the beginning of the semester, for which participation is mandatory.


  • July 4 -- first version of the course website online
  • July 24 -- registration deadline fixed to September 28


A detailed list is below

Representation, collection and extraction of general knowledge in knowledge bases (KBs) is at the core of many AI applications. In this seminar, we cover a range of topics around KBs, in particular factual KBs (e.g., Wikidata, YAGO, DBPedia), common-sense KBs (e.g., science knowledge, howto-knowledge, script knowledge) and non-textual KBs (e.g., ImageNet). We explore how these KBs are constructed and how they are used in various applications such as question answering (QA), story/script prediction and biography generation. We also explore learning new facts over KBs.


  • The number of participants is limited
  • To apply for registration, please fill the form under this link. You will receive an email confirmation
  • Places will be allocated based on background match (courses taken) and motivation


  • September 28 -- Registration deadline
  • October 3 -- Notification of placement
  • October 23 -- Kick-off meeting and introduction to knowledge bases (participation is mandatory) [pdf]
    • Time: 10:00-11:30
    • Place: MPII building (E1 4), room 24 (ground floor)
      • Explanation of the structure and organization of the seminar
      • Introduction to knowledge bases
  • October 25 -- Introduction to knowledge bases (ctnd.) and topic presentation
    • Time: 10:00-11:30
    • Place: MPII building (E1 4), room 24 (ground floor)
      • Introduction to knowledge bases (ctnd.)
      • Presentation of the topics
  • October 30 -- "How to prepare and present a seminar talk" (participation is mandatory)
    • Time: 10:00-11:30
    • Place: MPII building (E1 4), room 24 (ground floor)
      • As this is a block seminar, it is particularly crucial that the students' presentations are of high quality. This lecture aims at preparing the participants in such a way that their slides and presentations will be of high quality.
      • Topic assignment: students prepare a preferred ranked list of proposed topics, we will do the live assignment based on a randomly decided order.
  • November 30 -- students send a suggestion of the outline of their seminar paper, including an itemization of the planned content for each section.
  • December 5/6 -- students have in-person feedback meetings
  • January 31, 2019 -- students submit their final seminar paper.
  • TBD -- students send preliminary slides
  • TBD -- students send their final slides which they will use in the block seminar
  • TBD -- Block seminar, Day 1
  • TBD -- Block seminar, Day 2

Rules and Grading

  • Participation in the introductory meetings, the "How to prepare and present a seminar talk" lecture, and both days of the block seminar is mandatory.
  • Students will be assigned a particular topic and have to submit a seminar paper (template will be provided) and give a presentation (25 minutes + 10 minutes for discussion) over the topic.
  • Grading will be based on:
    • the report
    • the presentation
    • knowledge on the subject (as evidenced in the discussion after the presentation)
    • activity in the discussions
    • ability to stick to deadlines
  • Attention: According to the study regulations, you are only allowed to withdraw from the seminar within three weeks after the kick-off meeting, i.e., until TBD. Later withdrawal counts as "failed".


  1. A WordNet-based image KB: ImageNet
    • ImageNet: A large-scale hierarchical image database (Deng et al., CVPR 2009) [pdf]
    • WordNet: A lexical database for English (Miller, CACM 1995) [pdf]
  2. Spatial common sense from text and images
    • Acquiring Common Sense Spatial Knowledge through Implicit Spatial Templates, Collell et al., AAAI 2018 [pdf]
    • Automatic Extraction of Commonsense LocatedNear Knowledge, Xu et al., ACL 2018 [pdf]
  3. Schema learning
    • Are All People Married?: Determining Obligatory Attributes in Knowledge Bases, Lajus and Suchanek, WWW 2018 [pdf]
    • Recoin: Relative Completeness in Wikidata, Balaraman et al., Wiki workshop at WWW 2018 [pdf]
  4. Change prediction and updating
    • "A Spousal Relation Begins with a Deletion of engage and Ends with an Addition of divorce": Learning State Changing Verbs from Wikipedia Revision History, Wijaya et al., EMNLP 2015 [pdf]
    • How to Keep a Knowledge Base Synchronized with Its Encyclopedia Source, Liang et al, IJCAI 2017 [pdf]
  5. Named entity recognition and coreference resolution
    • Joint Coreference Resolution and Named-Entity Linking with Multi-pass Sieves, Hajishirzi et al., EMNLP 2013 [pdf]
    • Improving Coreference Resolution by Learning Entity-Level Distributed Representations, Clark and Manning, ACL 2016 [pdf]
  6. Fact ranking and explanation
    • The unusual suspects: Deep learning based mining of interesting entity trivia from knowledge graphs (Fatma et al., AAAI 2017) [pdf]
    •  Tell Me Why Is It So? Explaining Knowledge Graph Relationships by Finding Descriptive Support Passages, Bathia et al., ISWC 2018 [pdf]
  7. Recall assessment
    • Demand-Weighted Completeness Prediction for a Knowledge Base, Hopkinson et al., NAACL 2018 [pdf]
    • What Knowledge is Needed to Solve the RTE5 Textual Entailment Challenge?, Clark, ArXiv 2018 [pdf]
    • Domain-targeted, high precision knowledge extraction, Dalvi et al., TACL 2017 (Section 5.1) [pdf]
  8. Open information extraction
    • Minie: minimizing facts in open information extraction, Gashteovski et al., EMNLP 2017. [pdf]
    • Open Language Learning for Information Extraction, Mausam et al., EMNLP 2012 [pdf]
  9. State Changes

    • What Happened? Leveraging VerbNet to Predict the Effects of Actions in Procedural Text, Clark et al., ArXiv 2018 [pdf]
    • Tracking State Changes in Procedural Text: a Challenge Dataset and Models for Process Paragraph Comprehension, Dalvi et al., NAACL 2018 [pdf]

More to come...