Create README.md

README.md

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+---
+# For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
+{}
+---
+
+# Dataset Card for Dataset Name
+
+<!-- Provide a quick summary of the dataset. -->
+
+This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
+
+## Dataset Details
+
+### Dataset Description
+
+This dataset includes structural properties of knowledge graphs of 33,803 patents sampled from USPTO. For more, please read,
+
+Siddharth, L., 2025. Modularizing artefact knowledge promotes technological impact. https://doi.org/10.31219/osf.io/fd36m_v1
+
+The knowledge graphs were populated as part of earlier works as listed below.
+
+Siddharth, L., Luo, J., 2024. Knowledge of Technological Artefacts: Investigating the Linguistic and Structural Foundations. https://doi.org/10.31219/osf.io/ncqz3
+Siddharth, L, Luo, J., 2024. Retrieval augmented generation using engineering design knowledge. KNOWLEDGE-BASED SYSTEMS 303. https://doi.org/10.1016/j.knosys.2024.112410
+
+- **Curated by:** L. Siddharth, siddharthl.iitrpr.sutd@gmail.com
+- **Language(s) (NLP):** English
+- **License:** Creative Commons Attribution 4.0 International (CC BY 4.0)
+
+### Dataset Sources
+
+- **Repository:** Open Science Framework (OSF)
+- **Paper :** Siddharth, L., 2025. Modularizing artefact knowledge promotes technological impact. https://doi.org/10.31219/osf.io/fd36m_v1
+
+## Uses
+
+The dataset can be used for studies that involve patent analytics, network science, and technology forecasting.
+The dataset includes structural properties of the knowledge graphs of 33,803 patents and their technological impact as measured via citation received.
+These structural properties can related to other patent quality/impact measures in future studies.
+
+## Dataset Creation
+
+The dataset include properties of patent knowledge graphs that were populated using earlier methods as stated below.
+
+Siddharth, L., Luo, J., 2024. Knowledge of Technological Artefacts: Investigating the Linguistic and Structural Foundations. https://doi.org/10.31219/osf.io/ncqz3
+Siddharth, L, Luo, J., 2024. Retrieval augmented generation using engineering design knowledge. KNOWLEDGE-BASED SYSTEMS 303. https://doi.org/10.1016/j.knosys.2024.112410
+
+
+### Curation Rationale
+
+This dataset is intended to advance technology and innovation literature by delving knowledge structures of patents and investigating their role is how patents create impact in the technology space and in the society as a whole.
+
+### Source Data
+
+USPTO
+
+#### Who are the source data producers?
+
+L. Siddharth (siddharthl.iitrpr.sutd@gmail.com)
+
+## Citation [optional]
+
+**BibTeX:**
+
+
+@misc{siddharth_knowledge_2024,
+	title = {Knowledge of {Technological} {Artefacts}: {Investigating} the {Linguistic} and {Structural} {Foundations}},
+	url = {https://osf.io/preprints/osf/ncqz3_v2},
+	publisher = {OSF Preprints},
+	author = {Siddharth, L. and Luo, Jianxi},
+	month = dec,
+	year = {2024},
+	doi = {10.31219/osf.io/ncqz3},
+}
+
+@article{siddharth_retrieval_2024,
+	title = {Retrieval augmented generation using engineering design knowledge},
+	volume = {303},
+	issn = {0950-7051},
+	doi = {10.1016/j.knosys.2024.112410},
+	abstract = {Aiming to support Retrieval Augmented Generation (RAG) in the design process, we present a method to identify explicit, engineering design facts - \{head entity:: relationship:: tail entity\} from patented artefact descriptions. Given a sentence with a pair of entities (selected from noun phrases) marked in a unique manner, our method extracts their relationship that is explicitly communicated in the sentence. For this task, we create a dataset of 375,084 examples and fine-tune language models for relation identification (token classification task) and relation elicitation (sequence-to-sequence task). The token classification approach achieves up to 99.7\% accuracy. Upon applying the method to a domain of 4,870 fan system patents, we populate a knowledge base of over 2.93 million facts. Using this knowledge base, we demonstrate how Large Language Models (LLMs) are guided by explicit facts to synthesise knowledge and generate technical and cohesive responses when sought out for knowledge retrieval tasks in the design process.},
+	language = {English},
+	journal = {KNOWLEDGE-BASED SYSTEMS},
+	author = {Siddharth, L and Luo, JX},
+	month = nov,
+	year = {2024},
+	keywords = {Knowledge graphs, Knowledge Graphs, Patent documents, Graph neural networks, Engineering Design Knowledge, Graph Neural Networks, Large-Language Models, Patent Documents, Retrieval-Augmented Generation, Engineering design knowledge, Large-language models, Retrieval-augmented generation},
+	annote = {Times Cited in Web of Science Core Collection:  0Total Times Cited:  0Cited Reference Count:  32},
+	annote = {Times Cited in Web of Science Core Collection:  2Total Times Cited:  2Cited Reference Count:  32},
+}
+
+@misc{siddharth_modularizing_2025,
+	title = {Modularizing artefact knowledge promotes technological impact},
+	url = {https://osf.io/preprints/osf/fd36m_v1},
+	publisher = {OSF Preprints},
+	author = {Siddharth, L.},
+	month = mar,
+	year = {2025},
+	doi = {10.31219/osf.io/fd36m_v1},
+}
+
+
+**APA:**
+
+Siddharth, L. (2025, March). Modularizing artefact knowledge promotes technological impact. OSF Preprints. https://doi.org/10.31219/osf.io/fd36m_v1
+
+Siddharth, L., & Luo, J. (2024a). Retrieval augmented generation using engineering design knowledge. KNOWLEDGE-BASED SYSTEMS, 303. https://doi.org/10.1016/j.knosys.2024.112410
+
+Siddharth, L., & Luo, J. (2024b, December). Knowledge of Technological Artefacts: Investigating the Linguistic and Structural Foundations. OSF Preprints. https://doi.org/10.31219/osf.io/ncqz3
+
+## Dataset Card Authors [optional]
+
+L. Siddharth
+
+## Dataset Card Contact
+
+siddharthl.iitrpr.sutd@gmail.com