Datasets
Research datasets released by the group. Click any entry to expand. All datasets are openly hosted on OSF.
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Large Legal Text Corpus & Word Embeddings
Created for the paper Synergistic Union of Word2Vec and Lexicon for Domain Specific Semantic Similarity. Contains a large legal text corpus, several Word2Vec embedding models, and legal domain gazetteer lists.
- Legal Case Corpus: 39,155 cases including 22,776 US Supreme Court cases; 29,404 preprocessed; folder map.
- Word2Vec models: trained on raw text and lemmatized text.
- Legal gazetteer lists: domain-specific semantic groupings from a legal professional.
- Word2Vec results: [100×100] [100×200] [100×210] [100×500]
K. Sugathadasa, B. Ayesha, N. de Silva, A. Perera, V. Jayawardana, D. Lakmal, and M. Perera, "Synergistic Union of Word2Vec and Lexicon for Domain Specific Semantic Similarity," in 2017 IEEE International Conference on Industrial and Information Systems (ICIIS), IEEE, 2017, pp. 1--6,
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Legal Information Retrieval
Created for Legal Document Retrieval using Document Vector Embeddings and Deep Learning. Uses the Large Legal Text Corpus above; adds a mention map, edge list, and text ranking output.
- Text Corpus & Map: 2,500 cases plus mention map.
- Edge List: citation graph edge list.
- Outputs: text rank and serialized file.
K. Sugathadasa, B. Ayesha, N. de Silva, A. Perera, V. Jayawardana, D. Lakmal, and M. Perera, "Legal Document Retrieval using Document Vector Embeddings and Deep Learning," in Science and information conference, Springer, 2018, pp. 160--175,
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Legal Ontology Building
Created for Deriving a Representative Vector for Ontology Classes with Instance Word Vector Embeddings. Contains a limited legal ontology, extracted cases, gazetteer lists, and result vectors.
- Legal ontology built for this study.
- Case files extracted from the Large Legal Text Corpus.
- Gazetteer lists from a legal professional.
- Result vectors.
V. Jayawardana, D. Lakmal, N. de Silva, A. Perera, K. Sugathadasa, and B. Ayesha, "Deriving a Representative Vector for Ontology Classes with Instance Word Vector Embeddings," in 2017 Seventh International Conference on Innovative Computing Technology (INTECH), IEEE, 2017, pp. 79--84,
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Legal Ontology Population
Created for Word Vector Embeddings and Domain Specific Semantic based Semi-Supervised Ontology Instance Population. Uses the Large Legal Text Corpus and the Legal Ontology Building dataset (download separately). Contains class instances, a legal-words gazetteer, and result vectors.
- Class instances (5 models) for ontology population.
- Legal-domain gazetteer lists.
- Result vectors.
V. Jayawardana, D. Lakmal, N. de Silva, A. Perera, K. Sugathadasa, B. Ayesha, and M. Perera, "Word Vector Embeddings and Domain Specific Semantic based Semi-Supervised Ontology Instance Population," ICTer, vol. 11, no. 1, 2018,
V. Jayawardana, D. Lakmal, N. de Silva, A. Perera, K. Sugathadasa, B. Ayesha, and M. Perera, "Semi-Supervised Instance Population of an Ontology using Word Vector Embeddings," in Advances in ICT for Emerging Regions (ICTer), 2017 Seventeenth International Conference on, IEEE, Sep. 2017,
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Automatic Generation of Legal Arguments
Created for the thesis Ontology-Based Information Extraction for Automatic Generation of Legal Arguments. Raw data from the Large Legal Text Corpus; annotations by a domain expert.
- 77 sentences annotated as legal argument / not.
- 394 sentences tagged positive / neutral / negative.
- 87 sentence pairs with discourse tags (Elaboration, No Relation, Shift-in-View).
- 1,000 verb pairs annotated for legal-domain similarity.
For annotated arguments:
G. Ratnayaka, T. Rupasinghe, N. de Silva, M. Warushavithana, V. Gamage, and A. Perera, "Identifying Relationships Among Sentences in Court Case Transcripts Using Discourse Relations," in 2018 18th International Conference on Advances in ICT for Emerging Regions (ICTer), IEEE, 2018, pp. 13--20,
For sentiment phrases or domain-similarity verb pairs:
V. Gamage, M. Warushavithana, N. de Silva, A. Perera, G. Ratnayaka, and T. Rupasinghe, "Fast Approach to Build an Automatic Sentiment Annotator for Legal Domain using Transfer Learning," in Proceedings of the 9th workshop on computational approaches to subjectivity, sentiment and social media analysis, 2018, pp. 260-265,
For discourse-annotated sentence pairs:
G. Ratnayaka, T. Rupasinghe, N. de Silva, V. Gamage, M. Warushavithana, and A. Perera, "Shift-of-Perspective Identification Within Legal Cases," in Proceedings of the 3rd Workshop on Automated Detection, Extraction and Analysis of Semantic Information in Legal Texts, 2019,
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Legal Party Extraction
Created for the thesis Party Identification of Legal Documents. Raw data from the Large Legal Text Corpus.
- 1,000 sentences annotated at word level for party membership.
- Data 750 — 750 sentences as numpy, model-ready.
- Labeled 1000 — 1,000 sentences tagged petitioner / defendant.
- GRU 512 model trained on party-annotated sentences.
- GRU 512 (P|D) model trained on Labeled 1000.
For party-annotated sentences or Data 750:
M. de Almeida, C. Samarawickrama, N. de Silva, G. Ratnayaka, and A. Perera, "Legal Party Extraction from Legal Opinion Text with Sequence to Sequence Learning," in 2020 20th International Conference on Advances in ICT for Emerging Regions (ICTer), IEEE, 2020, pp. 143--148,
For Labeled 1000 or GRU models:
C. Samarawickrama, M. de Almeida, N. de Silva, G. Ratnayaka, and A. Perera, "Party Identification of Legal Documents using Co-reference Resolution and Named Entity Recognition," in 2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS), IEEE, 2020, pp. 494--499,
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Winning Party Prediction
Created for the thesis SigmaLaw — Predicting Winning Party of a Legal Case Using Legal Opinion Texts. Raw data from the Large Legal Text Corpus. Includes extracted sentences, critical-sentence labels, embedded legal vocabulary, and expert-tagged legal causality pairs.
- Criminal Case Sentence Dataset (CSV, by token count): 1k 2k 3k 4k 5k 6k 7k 8k 9k 10k
- 1,844 critical sentences with victory, sentiment, and relative-sentiment annotations.
- Embedded Legal Vocabulary: min freq 3, BERT (10k), BERT (30k), TF (30k)
- Legal causality sentence pairs: raw tagged and merged.
For Criminal Case Sentence Dataset:
S. Jayasinghe, L. Rambukkanage, A. Silva, N. de Silva, and A. Perera, "Party-based Sentiment Analysis Pipeline for the Legal Domain," in 2021 21st International Conference on Advances in ICT for Emerging Regions (ICter), 2021, pp. 171-176,
For Critical Sentence Identification or Embedded Legal Vocabulary:
S. Jayasinghe, L. Rambukkanage, A. Silva, N. de Silva, and A. Perera, "Critical Sentence Identification in Legal Cases Using Multi-Class Classification," in 2021 IEEE 16th International Conference on Industrial and Information Systems (ICIIS), IEEE, 2021, pp. 146--151,
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PubMed Dataset
Collected for Discovering Inconsistencies in PubMed Abstracts Through Ontology-Based Information Extraction. Contains 36,877 PubMed abstracts, Stanford CoreNLP and OLLIE outputs, finalized triples compatible with the OMIT Ontology, a medical dictionary, and discovered inconsistencies.
- PubMed IDs (tar.gz, 111 kB)
- 36,877 PubMed abstracts (tar.gz, 18.5 MB)
- Stanford-parsed abstracts: Part 1 (97.7 MB) · Part 2 (103 MB) · Part 3 (104 MB) · Part 4 (71.3 MB)
- OLLIE triples (tar.gz, 27.3 MB)
- Final triples (tar.gz, 2.9 MB)
- Medical dictionary (4.8 MB)
- Raw inconsistencies (65 kB)
- Final inconsistencies (expanded) (63 kB)
N. de Silva, D. Dou, and J. Huang, "Discovering Inconsistencies in PubMed Abstracts Through Ontology-Based Information Extraction," in Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, 2017, pp. 362--371,
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SinMin — Sinhala Text Corpus
A multi-genre Sinhala corpus drawn from online newspapers, news sites, school textbooks, magazines, Wikipedia, fiction, the Mahawansa, blogs, subtitles, and the Sri Lanka Gazette. Covers both modern and classical Sinhala.
- Blog data (5 MB)
- News data (223 MB)
- Wikipedia data (38 MB)
D. Upeksha, C. Wijayarathna, M. Siriwardena, L. Lasandun, C. Wimalasuriya, N. H. N. D. De Silva, and G. Dias, "Implementing a Corpus for Sinhala Language," in Symposium on Language Technology for South Asia 2015, 2015,
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SiClaEn — Sinhala & English News Classification
A paired Sinhala/English news corpus across five topics (Business, Entertainment, Politics, Science & Technology, Sports). English from Reuters (7,103 sentences, 383 posts); Sinhala from AdaDerana and NewsFirst (5,221 sentences, 471 posts).
N. de Silva, "Sinhala Text Classification: Observations from the Perspective of a Resource Poor Language," , 2015,
Datasets and models released alongside papers from the group, discovered automatically from publication metadata.
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Sri Lankan Tourism Review Sentiment-Rating Incongruence Dataset
Data Science Sentiment Analysis
This dataset contains 16,156 tourism reviews of destinations in Sri Lanka, sourced from Mendeley Data, and used to study incongruence between review text sentiment and the numerical star rating given by reviewers. It supports the analysis presented in *"Fault of Our Stars: Behavioral Drivers of Rating–Sentiment Incongruence."*
R. Abaiyan, R. Sutharsan, K. Amantha, A. Krishnathas, A. Rauff, K. Sriyathurshan, P. Narasinghe, N. Munasinghe, N. de Silva, and S. Wickramanayake, "Fault of Our Stars: Behavioral Drivers of Rating-Sentiment Incongruence," in 2026 Moratuwa Engineering Research Conference (MERCon), 2026, pp. 443-448,
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Sri Lanka Environmental & Respiratory Health Panel (2014–2024)
Data Science
This dataset provides a district-level environmental and respiratory health panel for Sri Lanka from 2014 to 2024. It covers all 25 administrative districts and integrates environmental, atmospheric, vegetation, fire activity, demographic, and respiratory health indicators.
R. Iqbal, A. Ahamed, I. Nisfer, S. Shaheed, M. Ilham, N. Athukorala, M. Mendis, N. de Silva, and S. Wickramanayake, "Environmental Drivers of Respiratory Disease: A District Level Analysis," in 2026 Moratuwa Engineering Research Conference (MERCon), 2026, pp. 413-418,
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JPetStore CHGNN Call Graph
Microservices Software Architecture Monolith Decomposition Graph Neural Networks
This dataset contains the call graph generated from the JPetStore monolithic application for use with the CHGNN-based microservice decomposition pipeline.
M. Weerasinghe, H. Kularathne, M. Madhushika, D. Lakshan, N. de Silva, A. Wijayasiri, and S. Perera, "TripleBound: Triplet-Guided Heterogeneous Graph Learning for Microservice Decomposition," arXiv preprint arXiv:2609.11212, 2026,
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Sri Lankan Import-Isolated Vegetable Price Volatility Dataset
Data Science
This dataset supports forecasting of agricultural price volatility in Sri Lanka's import-isolated vegetable market. It integrates retail and farmer-gate prices with origin-aligned weather variables, diesel costs, and exchange rates, covering **12 vegetable varieties across 14 market centres from 2013 to 2019**. An additional, fully unseen **2024 hyperinflationary period** is included for out-of-ti
R. Weerasekara, H. Nethmina, M. Ranathunga, V. Wettasinghe, D. Navodya, S. Arumugam, N. Munasinghe, N. de Silva, and S. Wickramanayake, "When Prices Double in a Week: Forecasting of Agricultural Volatility in Import-Isolated Markets," in 2026 Moratuwa Engineering Research Conference (MERCon), 2026, pp. 419-424,
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Sri Lanka Daily Tourist Arrivals & Search Intent Dataset (2023–2025)
Data Science
This dataset provides a unified, daily time-series panel designed for forecasting international tourist arrivals to Sri Lanka over the post-crisis recovery period of **January 2023 to December 2025**. It integrates five disparate data streams to capture the macroeconomic, environmental, and behavioural signals that drive tourism demand.
T. Perera, J. Mahanama, M. Kumarasinghe, B. Udurawana, P. Nawarathna, P. Narasinghe, N. Athukorala, S. Wickramanayake, and N. de Silva, "The Effect of Search Intent Data on Predicting Post-Crisis Daily Tourist Arrivals in Sri Lanka," in 2026 Moratuwa Engineering Research Conference (MERCon), 2026, pp. 395-400,
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Forecasting Weather-Driven Price Dynamics Across Sri Lankan Tea Market Catalogues
Forecasting
This dataset contains structured historical price data from the Colombo Tea Auction (CTA) in Sri Lanka, synchronized with localized meteorological lag features. It is designed for segment-specific and pooled time-series regression models to investigate how market structures and localized weather patterns drive tea pricing in Sri Lanka.
H. Mallawarachchi, S. Madurapperumage, N. Kulathunge, T. Angeesa, N. Gunaweera, S. Weerasekara, P. Narasinghe, N. de Silva, and S. Wickramanayake, "Forecasting Weather-Driven Price Dynamics Across Sri Lankan Tea Market Catalogues," in 2026 Moratuwa Engineering Research Conference (MERCon), 2026, pp. 431-436,
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Sri Lankan Medical Institutional Data (2024)
Data Science
This dataset accompanies the research paper:
S. Kirindage, V. Nimsara, S. Rajapaksa, K. Hathurusinghe, L. Dilshan, S. Arumugam, N. Athukorala, S. Wickramanayake, and N. de Silva, "Golden Hour Divide: Trauma Care Accessibility and Resource Vulnerability in Sri Lanka," in 2026 Moratuwa Engineering Research Conference (MERCon), 2026, pp. 425-430,
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SiDiaC v2.0: Sinhala Diachronic Corpus
Sinhala Diachronic Corpus Corpus Language Change Corpus Linguistics Diachrony
> **Note:** The latest corpus statistics can be found at [https://arxiv.org/abs/2603.10861](https://arxiv.org/abs/2603.10861), superseding the figures reported in the main paper referenced below.
N. Jayatilleke, N. de Silva, U. Nimanthi, G. Kulathilaka, A. Safrullah, and J. Sofalas, "SiDiaC-v. 2.0: Sinhala Diachronic Corpus Version 2.0," , ,
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Monsoon Mayhem to Market Waves: Forecasting Fisheries Resilience in Sri Lanka
Data Science
The **Sri Lanka Fisheries Resilience Dataset** provides comprehensive, high-frequency (weekly and monthly) data on fish production, market prices (retail and wholesale), and climate anomalies in Sri Lanka from 2019 to 2025.
R. Ahmedh, Y. Jayasinghe, T. Gamage, I. Ikram, H. Lawanya, N. Munasinghe, P. Narasinghe, N. de Silva, and S. Wickramanayake, "Monsoon Mayhem to Market Waves: Forecasting Fisheries Resilience in Sri Lanka," in 2026 Moratuwa Engineering Research Conference (MERCon), 2026, pp. 401-406,
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Sinhala Diachronic POS & Lemma Corpus
Sinhala Diachronic Corpus Corpus Language Change Corpus Linguistics Diachrony
Token-level part-of-speech tags and lemmas for a corpus of 59 OCR'd historical Sinhala documents (5th–20th century), built as part of a study on diachronic semantic change in Sinhala (see **Paper** below). Provided in two formats:
N. Jayatilleke, and N. de Silva, "Dynamics of meaning: Towards the Evaluation of Diachronic Semantic Change in Sinhala," arXiv preprint arXiv:2609.08609, 2026,
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Sri Lanka Labour Emigration and Remittances Dataset (1994–2025)
Data Science
The study based on the dataset can be accessed via https://arxiv.org/abs/2606.28190
D. Fernando, D. Ginige, K. Lakshan, C. Gurusinghe, L. Pahanga, S. Arumugam, S. Weerasekara, S. Wickramanayake, and N. de Silva, "The Remittance Blueprint: Data-driven Intelligence for Sri Lanka," in 2026 Moratuwa Engineering Research Conference (MERCon), 2026, pp. 61-66,
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HelaBERT: Enhancing Sinhala Language Understanding with Dual Pooling Classification Head
Embedding
HelaBERT is a BERT-based masked language model pre-trained from scratch on a large Sinhala text corpus. With approximately **23.3 million parameters**, it is designed to produce contextual representations of Sinhala language text and can be used for downstream NLP tasks such as text classification, named entity recognition, semantic similarity, and information retrieval.
HelaBERT-Large is a BERT-based masked language model pre-trained from scratch on a large Sinhala text corpus. With approximately **110 million parameters**, it is designed to produce contextual representations of Sinhala language text and can be used for downstream NLP tasks such as text classification, named entity recognition, semantic similarity, and information retrieval.
T. Ekanayake, and N. de Silva, "HelaBERT: Enhancing Sinhala Language Understanding with Dual Pooling Classification Head," arXiv preprint arXiv:2608.22922, 2026,
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Sri Lanka Parliamentary Hansard (2017-2026)
Topic Modeling Tamil
Sri Lanka Parliamentary Hansard is a trilingual parliamentary speech corpus built from publicly available Hansard records of the Parliament of Sri Lanka. It contains Sinhala (සිංහල), Tamil (தமிழ்), English, and code-mixed speeches from 2017 to 2026, with speaker names, dates, and topic-modeling labels.
H. Dhanapala, H. Daishika, H. Kuruppu, S. Seneviratne, A. Kavindya, P. Narasinghe, S. Weerasekara, N. de Silva, and S. Wickramanayake, "Trilingual Topic Modeling of Sri Lankan Parliamentary Debates," in 2026 Moratuwa Engineering Research Conference (MERCon), 2026, pp. 389-394,
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Global PIQA Non-Parallel
Common Sense Reasoning Physical Reasoning Low-resource Languages Language Resources LLM Cultural Alignment Multilingual
Global PIQA is a participatory commonsense reasoning benchmark for over 100 languages, constructed by hand by over 350 researchers from over 65 countries around the world. The non-parallel split covers 136 language varieties, covering five continents, 18 language families, and 24 writing systems. In this non-parallel split, over 50% of examples reference local foods, customs, traditions, or other \xe2\x80\xa6
N. de Silva, and S. Ranathunga, "Sinhala Physical Common Sense Reasoning Dataset for Global PIQA," arXiv preprint arXiv:2602.02207, 2026,
T. Chang, C. Arnett, A. Eldesokey, A. Sadallah, A. Kashar, A. Daud, A. Olanihun, A. Mohammed, A. Praise, A. Sharma, et al., "Global PIQA: Evaluating Physical Commonsense Reasoning Across 100+ Languages and Cultures," arXiv preprint arXiv:2510.24081, 2025,
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SalAngaBhava: A Sinhala Market Dataset for Aspect-based Sentiment Analysis
Aspect-based Sentiment Analysis Aspect Extraction Sentiment Classification
The SalAngaBhava dataset is designed for research in Natural Language Processing (NLP), with a focus on Aspect-Based Sentiment Analysis (ABSA) for Sinhala language data. It supports reproducible research and enables fine-grained sentiment understanding at the aspect level.
L. Galwatta, N. de Silva, S. Aththanayake, and A. Galwatta, "SalAngaBhava: A Sinhala Market Dataset for Aspect-based Sentiment Analysis," in 2026 Moratuwa Engineering Research Conference (MERCon), 2026, pp. 664-669,
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Sri Lanka Bird Diversity Dataset
Data Science
The **Sri Lanka Bird Diversity Dataset with Environmental and Climate Features** is a large-scale biodiversity dataset containing **1,552,048 bird occurrence records** covering **429 bird species** across all **25 provinces of Sri Lanka**.
D. Chandrasiri, M. Herath, Y. Hewarathna, M. Herath, G. Bandara, M. Mendis, N. Athukorala, N. de Silva, and S. Wickramanayake, "How Environment and Urbanization Shape Bird Diversity in Sri Lanka," in 2026 Moratuwa Engineering Research Conference (MERCon), 2026, pp. 437-442,
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SiDiaC: Sinhala Diachronic Corpus
Sinhala Diachronic Corpus Corpus Language Change Corpus Linguistics Diachrony
N. Jayatilleke, and N. de Silva, "SiDiaC: Sinhala Diachronic Corpus," in Proceedings of the 39th Pacific Asia Conference on Language, Information and Computation, 2025, pp. 511--527,
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Zero-shot OCR Accuracy of Low-Resourced Languages: A Comparative Analysis on Sinhala and Tamil
OCR Low-resource Languages Tamil
This dataset contains images of Tamil text generated from 6 different fonts:
N. Jayatilleke, and N. de Silva, "Zero-shot OCR Accuracy of Low-Resourced Languages: A Comparative Analysis on Sinhala and Tamil," in Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing-Natural Language Processing in the Generative AI Era, 2025, pp. 471--480,
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SinLlama-A Large Language Model for Sinhala
Low-resource Languages LLM Pre-trained Models Text Classification
base_model: meta-llama/Meta-Llama-3-8B library_name: peft ---
H. Aravinda, R. Sirajudeen, S. Karunathilake, N. de Silva, S. Ranathunga, and R. Kaur, "SinLlama-A Large Language Model for Sinhala," in Moratuwa Engineering Research Conference (MERCon), 2025,
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Dungeons \& Dragons Fantasy Adventure Generation
Text Generation LLM
https://huggingface.co/datasets/Akila/ForgottenRealmsWikiDataset
A. Peiris, "Dungeons \& Dragons Fantasy Adventure Generation," , 2024,
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Shoulders of Giants: A Look at the Degree and Utility of Openness in NLP Research
Low-resource Languages
S. Ranathunga, N. de Silva, D. Jayakody, and A. Fernando, "Shoulders of Giants: A Look at the Degree and Utility of Openness in NLP Research," in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics, 2024,
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Fine Tuning Named Entity Extraction Models for the Fantasy Domain
Named Entity Recognition
A. Sivaganeshan, and N. De Silva, "Fine Tuning Named Entity Extraction Models for the Fantasy Domain," in 2023 Moratuwa Engineering Research Conference (MERCon), IEEE, 2023, pp. 346--351,
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Sinhala-English Parallel Word Dictionary Dataset
Word Embeddings Word Embedding Alignment Multilingual Bilingual Lexicon Induction Alignment Dictionaries Language-agnostic Processing Multilingual Embedding Inflected Languages Measure Alignment Parallel Corpora
English-Sinhala parallel word dictionary and resources
K. Wickramasinghe, and N. de Silva, "Sinhala-English Parallel Word Dictionary Dataset," in 2023 IEEE 17th International Conference on Industrial and Information Systems (ICIIS), IEEE, 2023, pp. 61--66,
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Context Sensitive Verb Similarity Dataset for Legal Information Extraction
Semantic Analysis Word Embeddings
Please check the below sections for more information
G. Ratnayaka, N. de Silva, A. Perera, G. Kavirathne, T. Ariyarathna, and A. Wijesinghe, "Context Sensitive Verb Similarity Dataset for Legal Information Extraction," Data, vol. 7, no. 7, 2022,
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Some Languages are More Equal than Others: Probing Deeper into the Linguistic Disparity in the NLP World
Low-resource Languages Linguistic Disparity
AACL 2022 - Some Languages are More Equal than Others : Probing Deeper into the Linguistic Disparity in the NLP World. Code and Data
S. Ranathunga, and N. de Silva, "Some Languages are More Equal than Others: Probing Deeper into the Linguistic Disparity in the NLP World," in Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing, 2022, pp. 823--848,
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Synthesis and Evaluation of a Domain-specific Large Data Set for Dungeons \& Dragons
Text Generation
Akila Peiris, and Nisansa de Silva, "Synthesis and Evaluation of a Domain-specific Large Data Set for Dungeons \& Dragons," in Proceedings of the 36th Pacific Asia Conference on Language, Information and Computation, 2022, pp. 415--424,
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OmniSearch: a semantic search system based on the Ontology for MIcroRNA Target (OMIT) for microRNA-target gene interaction data
Ontology Development Data Annotation Data Integration Information Retrieval Knowledge Graph
J. Huang, F. Gutierrez, H. Strachan, D. Dou, W. Huang, B. Smith, J. Blake, K. Eilbeck, D. Natale, Y. Lin, B. Wu, N. de Silva, et al., "OmniSearch: a semantic search system based on the Ontology for MIcroRNA Target (OMIT) for microRNA-target gene interaction data," Journal of biomedical semantics, vol. 7, no. 1, pp. 25, 2016,