metadata

license: apache-2.0
language:
  - en
tags:
  - ir
  - trec
  - tip-of-my-tongue
  - known-item-search
  - natural-language-processing
  - information-retrieval
pretty_name: tip-of-my-tongue-known-item-search
size_categories:
  - 1M<n<10M

The TOMT-KIS (tip-of-my-tongue-known-item-search) Dataset

Searchers who cannot resolve a known-item information need using a search engine might post a respective question on a question answering platform, hoping that the discussion with other people can help to identify the item. TOMT-KIS is a large-scale dataset of 1.28 million known-item questions from the r/tipofmytongue subreddit in the QPP++@ECIR'23 paper on A Large-Scale Dataset for Known-Item Question Performance Prediction.

Citation

If you use the TOMT-KIS dataset, please cite the corresponding paper:

@InProceedings{froebe:2023c,
  author =                   {Maik Fr{\"o}be and Eric Oliver Schmidt and Matthias Hagen},
  booktitle =                {QPP++ 2023: Query Performance Prediction and Its Evaluation in New Tasks},
  month =                    apr,
  publisher =                {CEUR-WS.org},
  series =                   {CEUR Workshop Proceedings},
  site =                     {Dublin, Irland},
  title =                    {{A Large-Scale Dataset for Known-Item Question Performance Prediction}},
  year =                     2023
}

Use of TOMT-KIS

You can download our full dataset using the datasets library.

from datasets import load_dataset
tomt_kis = load_dataset("webis/tip-of-my-tongue-known-item-search")

To see the size of the dataset execute

len(tomt_kis['train'])

You can access the first respectively last row in TOMT-KIS using

# Get the first row in the dataset
tomt_kis['train'][0]


# Get the last row in the dataset
tomt_kis['train'][-1]

... and a specific column:

# Returns a list of all the values in the the title column
tomt_kis['train']['title']

# Returns the title of the first column
tomt_kis['train'][0]['title']

Iterate over the TOMT-KIS dataset:

for element in tomt_kis['train']:
    print(element)

For further information see the Datasets documentation.

Dataset Structure

Our TOMT-KIS dataset is available in JSONL format. For each question, we include all the attributes available in the crawled data and add the chosen answer when our heuristic could extract it.

Data Instances

{
  "id": "2gbnla",
  "author": "alany611",
  "url": "http://www.reddit.com/r/tipofmytongue/comments/2gbnla/tomt_1990s_educational_cartoon_for_kids_to_learn/",
  "permalink": "/r/tipofmytongue/comments/2gbnla/tomt_1990s_educational_cartoon_for_kids_to_learn/",
  "title": "1990's Educational Cartoon for kids to learn French",
  "content": "Hi all,\n\nWhen I was really young, 3-5, I remember watching a cartoon that I think was supposed to teach kids French. I would guess it was made from 1990-1995, but possibly earlier.\n\nIt was in color and the episodes I remember featured a guy with a long, narrow, and crooked nose and greenish skin teaching kids how to count? There was also a scene that had some character running up a clock tower to change the time.\n\nOverall, it was a pretty gloomy feel, iirc, and I'd love to see it again if possible.",
  "created_utc": "1410647042",
  "link_flair_text": "Solved",
  "comments": [
    {
      "author": "scarpoochi",
      "body": "Muzzy?\n\nhttps://www.youtube.com/watch?v=mD9i39GENWU",
      "created_utc": "1410649099",
      "score": 11,
      "comments": [
        {
          "author": "alany611",
          "body": "thank you!!!",
          "created_utc": "1410666273",
          "score": 1
        }
      ]
    },
    {
      "author": "pepitica",
      "body": "Muzzy! It's been driving me crazy for a while now!",
      "created_utc": "1410649896",
      "score": 6
    }
  ],
  "answer_detected": True,
  "solved_utc": "1410649099",
  "chosen_answer": "Muzzy?\n\nhttps://www.youtube.com/watch?v=mD9i39GENWU",
  "links_on_answer_path": [
    "https://www.youtube.com/watch?v=mD9i39GENWU"
  ],
  "categories": [ "[TOMT]" ]
}

Data Fields

Overall, TOMT-KIS includes 129 attributes for each question, such as

id (string): unique Reddit-identifier of a question
title (string): the title of a question without category tags
content (string): the main text content of a question
created_utc (string): the posted question's timestamp
link_flair_text (string): indicates whether the question is solved; set by moderators
comments (string, json): the complete tree of the discussion on each question
categories (list of strings): list of all category tags that were part of the original title

We run our precision-oriented answer identification heuristic on questions tagged as solved by a moderator and add four "new" attributes when the heuristic could identify an answer:

answer_detected (bool): indicates whether our heuristic could extract an answer
solved_utc(string): the timestamp when the identified answer was posted
chosen_answer(string): the extracted answer
links_on_answer_path(list of strings): contains all Reddit-external pages that were found in posts between the question and the post with the answer.