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Round 1: 4 days left #benchmark #semtab Weight: 1.0

Cell Entity Annotation by DBpedia (CEA-DBP)

This is a task of ISWC 2021 “Semantic Web Challenge on Tabular Data to Knowledge Graph Matching”. It is to annotate column cells (entity mentions) in a table with DBpedia (2016-10) entities. Click here for the official challenge website.

Task Description

The task is to annotate each target cell with an entity of DBpedia.

Each submission should contain the annotation of the target cell. One cell can be annotated by one entity. Any of the wiki page redirected entities of the ground truth entity (defined by dbo:wikiPageRedirects) are regarded as correct. Case is NOT sensitive.

The submission file should be in CSV format. Each line should contain the annotation of one cell which is identified by a table id, a column id and a row id. Namely one line should have four fields: “Table ID”, “Column ID”, “Row ID” and “DBpedia entity IRI” (these field headers should be excluded from the submission file). Each cell should be annotated by at most one entity. Here is an example: “9206866_1_8114610355671172497”,”0”,”121”,”http://dbpedia.org/resource/Norway”

Notes:

1) Table ID does not include filename extension; make sure you remove the .csv extension from the filename.

2) Column ID is the position of the column in the table file, starting from 0, i.e., first column’s ID is 0.

3) Row ID is the position of the row in the table file, starting from 0, i.e., first row’s ID is 0.

4) One submission file should have NO duplicate lines for one cell.

5) Annotations for cells out of the target cells are ignored.

Datasets

Table set for Round #1: Tables, Target Cells

Data Description: One table is stored in one CSV file. Each line corresponds to a table row. The first row may either be the table header or content. The target columns for annotation are saved in a CSV file.

Evaluation Criteria

Precision, Recall and F1 Score are calculated:

Precision = (Correct Annotations #) / (Submitted Annotations #)

Recall = (Correct Annotations #) / (Ground Truth Annotations #)

F1 Score = (2 * Precision * Recall) / (Precision + Recall)

Notes:

1) # denotes the number.

2) F1 Score is used as the primary score; Precision is used as the secondary score.

3) One target cell, one ground truth annotation, i.e., # ground truth annotations = # target cells. The ground truth annotation has already covered all equivalent entities (e.g., wiki page redirected entities); the groud truth is hit if one of its equivalent entities is hit. 

Prizes

To appear :=)

Submission

1. One participant is allowed to make at most 5 submissions per day in Round #1.

Rules

  1. Selected systems with the best results will be invited to present their results during the ISWC conference and the Ontology Matching workshop.

  2. The prize winners will be announced during the ISWC conference (October 24 - 28, 2021). We will take into account all evaluation rounds specially the ones running till the conference dates.

  3. Participants are encouraged to submit a system paper describing their tool and the obtained results. Papers will be published online as a volume of CEUR-WS as well as indexed on DBLP. By submitting a paper, the authors accept the CEUR-WS and DBLP publishing rules.

  4. Please see additional information at our official website