README.md

---
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](https://qpp.dei.unipd.it/) paper on [A Large-Scale Dataset for Known-Item Question Performance Prediction](https://downloads.webis.de/publications/papers/froebe_2023c.pdf).

# 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](https://huggingface.co/docs/datasets/index).

# 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

```jsonl
{
  "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.