Research hypothesis generation over scientific knowledge graphs
Generating research hypotheses is a crucial step in scientific investigation that involves the creation of precise, verifiable, and logically valid statements that can be empirically examined. Therefore, many efforts have been made to automate or assist this process through the use of various Artifi...
| Authors: | ; ; ; ; ; ; ; ; |
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| Format: | Article |
| Status: | Published version |
| Publication date: | 2025 |
| Country: | España |
| Institution: | Universidad de Sevilla (US) |
| Repository: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/181544 |
| Online Access: | https://hdl.handle.net/11441/181544 https://doi.org/10.1016/j.knosys.2025.113280 |
| Access Level: | Open access |
| Keyword: | Hypothesis generation Knowledge graphs Link prediction Scholarly domain Scientific facts Artificial Intelligence |
| _version_ | 1878729247458066432 |
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| author | Borrego Díaz, Agustín |
| author2 | Dessì, Danilo Ayala Hernández, Daniel Hernández Salmerón, Inmaculada Concepción Osborne, Francesco Recupero, Diego Reforgiato Buscaldi, Davide Ruiz Cortés, David Motta, Enrico |
| author2_role | author author author author author author author author |
| author_browse | Ayala Hernández, Daniel Borrego Díaz, Agustín Buscaldi, Davide Dessì, Danilo Hernández Salmerón, Inmaculada Concepción Motta, Enrico Osborne, Francesco Recupero, Diego Reforgiato Ruiz Cortés, David |
| author_facet | Borrego Díaz, Agustín Dessì, Danilo Ayala Hernández, Daniel Hernández Salmerón, Inmaculada Concepción Osborne, Francesco Recupero, Diego Reforgiato Buscaldi, Davide Ruiz Cortés, David Motta, Enrico |
| author_role | author |
| collection | idUS. Depósito de Investigación de la Universidad de Sevilla |
| description | Generating research hypotheses is a crucial step in scientific investigation that involves the creation of precise, verifiable, and logically valid statements that can be empirically examined. Therefore, many efforts have been made to automate or assist this process through the use of various Artificial Intelligence solutions. However, most existing methods are tailored to very specific domains, particularly within the biomedical field. There have been recent attempts to formalize hypothesis generation as a link prediction task over knowledge graphs. This solution is potentially domain-independent and applicable across diverse disciplines. Nevertheless, current approaches for link prediction, which typically rely on embedding models or path-based methods, have shown limited success in accurately predicting new hypotheses. To address these limitations, this paper introduces ResearchLink, an innovative and domain-independent methodology for hypothesis generation over knowledge graphs. ResearchLink combines path-based features and knowledge graph embeddings with text embeddings, capturing the semantic context of entities within a given corpus, and integrates additional information from bibliometric databases to improve research collaboration predictions. To conduct a rigorous evaluation of ResearchLink, we constructed CSKG-600, a new dataset for hypothesis generation, consisting of 600 statements that were manually labeled by domain experts. ResearchLink achieved outstanding performance (78.7% P@20), significantly outperforming alternative approaches such as TransH (71.8%), TransD (71.7%), and RotatE (70.7%). |
| eu_rights_str_mv | openAccess |
| format | article |
| id | ES_042fc2eefb4ca2bbe6ef72104ca026a8 |
| institution | Universidad de Sevilla (US) |
| instname_str | Universidad de Sevilla (US) |
| network_acronym_str | ES |
| network_name_str | España |
| oai_identifier_str | oai:idus.us.es:11441/181544 |
| publishDate | 2025 |
| publishDateSort | 2025 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| reponame_str | idUS. Depósito de Investigación de la Universidad de Sevilla |
| spelling | Research hypothesis generation over scientific knowledge graphs Borrego Díaz, Agustín Dessì, Danilo Ayala Hernández, Daniel Hernández Salmerón, Inmaculada Concepción Osborne, Francesco Recupero, Diego Reforgiato Buscaldi, Davide Ruiz Cortés, David Motta, Enrico Hypothesis generation Knowledge graphs Link prediction Scholarly domain Scientific facts Artificial Intelligence Generating research hypotheses is a crucial step in scientific investigation that involves the creation of precise, verifiable, and logically valid statements that can be empirically examined. Therefore, many efforts have been made to automate or assist this process through the use of various Artificial Intelligence solutions. However, most existing methods are tailored to very specific domains, particularly within the biomedical field. There have been recent attempts to formalize hypothesis generation as a link prediction task over knowledge graphs. This solution is potentially domain-independent and applicable across diverse disciplines. Nevertheless, current approaches for link prediction, which typically rely on embedding models or path-based methods, have shown limited success in accurately predicting new hypotheses. To address these limitations, this paper introduces ResearchLink, an innovative and domain-independent methodology for hypothesis generation over knowledge graphs. ResearchLink combines path-based features and knowledge graph embeddings with text embeddings, capturing the semantic context of entities within a given corpus, and integrates additional information from bibliometric databases to improve research collaboration predictions. To conduct a rigorous evaluation of ResearchLink, we constructed CSKG-600, a new dataset for hypothesis generation, consisting of 600 statements that were manually labeled by domain experts. ResearchLink achieved outstanding performance (78.7% P@20), significantly outperforming alternative approaches such as TransH (71.8%), TransD (71.7%), and RotatE (70.7%). Elsevier https://hdl.handle.net/11441/181544 https://doi.org/10.1016/j.knosys.2025.113280 |
| spellingShingle | Research hypothesis generation over scientific knowledge graphs Borrego Díaz, Agustín Hypothesis generation Knowledge graphs Link prediction Scholarly domain Scientific facts Artificial Intelligence |
| status_str | publishedVersion |
| title | Research hypothesis generation over scientific knowledge graphs |
| title_full | Research hypothesis generation over scientific knowledge graphs |
| title_fullStr | Research hypothesis generation over scientific knowledge graphs |
| title_full_unstemmed | Research hypothesis generation over scientific knowledge graphs |
| title_short | Research hypothesis generation over scientific knowledge graphs |
| title_sort | Research hypothesis generation over scientific knowledge graphs |
| topic | Hypothesis generation Knowledge graphs Link prediction Scholarly domain Scientific facts Artificial Intelligence |
| topic_facet | Hypothesis generation Knowledge graphs Link prediction Scholarly domain Scientific facts Artificial Intelligence |
| url | https://hdl.handle.net/11441/181544 https://doi.org/10.1016/j.knosys.2025.113280 |