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# System prepended metadata

title: Joint NLIWOD and PROFILES workshop @ ISWC 2020

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# Joint NLIWOD and PROFILES workshop @ ISWC 2020 

Websites:
* https://www.nliwod.org/program
* http://profiles2020.l3s.uni-hannover.de/index.php/programme-2

## Program and Notes

* *NLIWOD* - **Keynote**: [Bhaskar Mitra](https://twitter.com/UnderdogGeek) -  Benchmarking for Neural Information Retrieval: MS MARCO, TREC, and Beyond
  * If you use proprietary datasets, no one can reproduce them
  * Neural Models are now in 79% of SIGIR papers
  * We still lack public IR benchmarks with large scale training data
  * Even industrial Teams now use BERT on a day-to-day basis
  * Challenges:
      * Single-Deadline/Single-Submission Challenges (such as TREC) 
      * Leaderborad benchmarking lead to overfitting
      * Approaches have to work on more than one dataset
      * Bender-Rule: English is not the only Language
      * Cross-flow between communities needed!
  * Questions:
      * Are we hitting a glass ceiling with current ML models? A: More general purpose models
      * What advice would you do to researchers working on other languages, where the challenge for benchmark is even harder? A: Start building Benchmarks and gather a community around it


* *NLIWOD* -Chatbot For Interacting with SDMX Databases - Guillaume Thiry, Ioana Manolescu and Leo Liberti
    * Ranking of queries/datasets can be supported by metadata
    * Usage of DataCubes still relevant
    * Real world use on the horizon - OECD
    * Questions: 
        * What is important to your approach to generalize? 

* *NLIWOD* -Verbalizing the Evolution of Knowledge Graphs with Formal Concept Analysis - Martin Arispe, Mayesha Tasnim, Damien Graux, Fabrizio Orlandi and Diego Collarana
    * Formal Concept Analysis to find hierachies in real-world KGs
    * Questions:
        * Which verbalisation functions did you use? A: We are currently in the phase of trying out different ones.
        * What is the performance? A: FCA can deal with big data already now.
        
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* *PROFILES* - **Keynote**: Prof. Dr. Felix Naumann - Data Profiling in the Relational World
    * Commercial tools are still not there yet
    * How to efficiently find good dependencies? Algorithms!
    * Questions:
        * Have you considered how users can be involved to quickly reduce the search space? A: Show the results as they created and show them to users as early as possible. 
        * Databases usually follow the closed-world assumption. What to consider for your proposed algorithms if that is not given?
        * What happens in the presence of NULLs? A: Algorithms can deal with the answer but there are challenges!

* *PROFILES* - An Architecture for Cell-Centric Indexing of Datasets - Lixuan Qiu, Haiyan Jia, Brian Davison and Jeff Heflin
    * Table indexes are typically created on the table-level or column-level
    * Usage of cell-centric index that involves metadata, cell values and other values (context) in the respective row
    * Question:
        * How flexible is your cell indexing approach towards enriching the set of indexed fields (title, context,...), in particular w.r.t. dataset profiles? A: ElasticSearch easily allows addition of further search fields.

* *PROFILES* - A Template-Based Approach for Annotating Long-Tail Datasets - Daniel Garijo, Ke-Thia Yao, Amandeep Singh and Pedro Szekely
    * Table annotation typically requires expertise in semantic technologies
    * Users add meta data to the table to support the transformation of the table into a KG
    * Question:
        * Which Wikifier do you use? How do you understand columns? A: External Service based on Wikidata, but that is not the bottleneck. For example, property linking.

* *NLIWOD* - Generating Knowledge Graphs from Unstructured Texts: Experiences in the eCommerce Field for Question Answering - Diogo Sant'Anna, Rodrigo Caus, Lucas Ramos, Victor Hochgreb and Julio Cesar Dos Reis
    * Question Answering in GoBots can increase sales by 120%
    * Entity and Intent based QA systems
    * Question: 
        * What do you use for training? Propriatary data and the Rasa framework.
        * Your precision is really high, how about the recall? We did measure it, please see paper.

* *NLIWOD* - Generating Grammars from lemon lexica for Questions Answering over Linked Data: a Preliminary Analysis - Philipp Cimiano, Basil Ell, Viktoria Benz and Mohammad Fazleh Elahi
    * Question Grammar generation from a lemon lexicon based on LTAG grammars and LexInfo ideas
    * Advantage: portability between domains without training data and auto-completion
    * Question:
        * Have you thought of combining word embedding model with lemon lexicon? We will look into it since it can add synonyms also in high-dimensional space?

## Community Discussion and Best Presentation Awards

For choosing the best presentation please go to www.menti.com and use the code 71 88 38 7

Feedback:
* More people can participate due to lower entrance barrier
* Split it in two events to ensure people from all time zones can particpate more easily
* How can we open the mic better to have more people ask more question?
* Live conference is preferred over pre-recorded videos in the workshops