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Documents authored by Jain, Nitisha

Document
DOI: 10.4230/TGDK.3.1.2
Talking Wikidata: Communication Patterns and Their Impact on Community Engagement in Collaborative Knowledge Graphs

Authors: Elisavet Koutsiana, Ioannis Reklos, Kholoud Saad Alghamdi, Nitisha Jain, Albert Meroño-Peñuela, and Elena Simperl

Published in: TGDK, Volume 3, Issue 1 (2025). Transactions on Graph Data and Knowledge, Volume 3, Issue 1

Abstract
We study collaboration patterns of Wikidata, one of the world's largest open source collaborative knowledge graph (KG) communities. Collaborative KG communities, play a key role in structuring machine-readable knowledge to support AI systems like conversational agents. However, these communities face challenges related to long-term member engagement, as a small subset of contributors often is responsible for the majority of contributions and decision-making. While prior research has explored contributors' roles and lifespans, discussions within collaborative KG communities remain understudied. To fill this gap, we investigated the behavioural patterns of contributors and factors affecting their communication and participation. We analysed all the discussions on Wikidata using a mixed methods approach, including statistical tests, network analysis, and text and graph embedding representations. Our findings reveal that the interactions between Wikidata editors form a small world network, resilient to dropouts and inclusive, where both the network topology and discussion content influence the continuity of conversations. Furthermore, the account age of Wikidata members and their conversations are significant factors in their long-term engagement with the project. Our observations and recommendations can benefit the Wikidata and semantic web communities, providing guidance on how to improve collaborative environments for sustainability, growth, and quality.
Cite as

Elisavet Koutsiana, Ioannis Reklos, Kholoud Saad Alghamdi, Nitisha Jain, Albert Meroño-Peñuela, and Elena Simperl. Talking Wikidata: Communication Patterns and Their Impact on Community Engagement in Collaborative Knowledge Graphs. In Transactions on Graph Data and Knowledge (TGDK), Volume 3, Issue 1, pp. 2:1-2:27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@Article{koutsiana_et_al:TGDK.3.1.2,
  author =	{Koutsiana, Elisavet and Reklos, Ioannis and Alghamdi, Kholoud Saad and Jain, Nitisha and Mero\~{n}o-Pe\~{n}uela, Albert and Simperl, Elena},
  title =	{{Talking Wikidata: Communication Patterns and Their Impact on Community Engagement in Collaborative Knowledge Graphs}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{2:1--2:27},
  ISSN =	{2942-7517},
  year =	{2025},
  volume =	{3},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.3.1.2},
  URN =		{urn:nbn:de:0030-drops-230114},
  doi =		{10.4230/TGDK.3.1.2},
  annote =	{Keywords: collaborative knowledge graph, network analysis, graph embeddings, text embeddings}
}
Document
Invited Paper
DOI: 10.4230/OASIcs.AIB.2022.4
Combining Embeddings and Rules for Fact Prediction (Invited Paper)

Authors: Armand Boschin, Nitisha Jain, Gurami Keretchashvili, and Fabian Suchanek

Published in: OASIcs, Volume 99, International Research School in Artificial Intelligence in Bergen (AIB 2022)

Abstract
Knowledge bases are typically incomplete, meaning that they are missing information that we would expect to be there. Recent years have seen two main approaches to guess missing facts: Rule Mining and Knowledge Graph Embeddings. The first approach is symbolic, and finds rules such as "If two people are married, they most likely live in the same city". These rules can then be used to predict missing statements. Knowledge Graph Embeddings, on the other hand, are trained to predict missing facts for a knowledge base by mapping entities to a vector space. Each of these approaches has their strengths and weaknesses, and this article provides a survey of neuro-symbolic works that combine embeddings and rule mining approaches for fact prediction.
Cite as

Armand Boschin, Nitisha Jain, Gurami Keretchashvili, and Fabian Suchanek. Combining Embeddings and Rules for Fact Prediction (Invited Paper). In International Research School in Artificial Intelligence in Bergen (AIB 2022). Open Access Series in Informatics (OASIcs), Volume 99, pp. 4:1-4:30, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{boschin_et_al:OASIcs.AIB.2022.4,
  author =	{Boschin, Armand and Jain, Nitisha and Keretchashvili, Gurami and Suchanek, Fabian},
  title =	{{Combining Embeddings and Rules for Fact Prediction}},
  booktitle =	{International Research School in Artificial Intelligence in Bergen (AIB 2022)},
  pages =	{4:1--4:30},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-228-0},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{99},
  editor =	{Bourgaux, Camille and Ozaki, Ana and Pe\~{n}aloza, Rafael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.AIB.2022.4},
  URN =		{urn:nbn:de:0030-drops-160021},
  doi =		{10.4230/OASIcs.AIB.2022.4},
  annote =	{Keywords: Rule Mining, Embeddings, Knowledge Bases, Deep Learning}
}
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