Ontology-based Information Extraction (OBIE) Task
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An Ontology-based Information Extraction (OBIE) Task is an information extraction from text task that requires an ontology.
- Context:
- input: Information Resource, Ontology.
- It can range from being an Open Ontology-based Information Extraction (OBIE) Task to being a Closed Ontology-based Information Extraction (OBIE) Task.
- It can be solved by an Ontology-based Information Extraction System (that implements a Ontology-based IE algorithm).
- It can range from being Supervised Ontology-based IE from Text, to being Semi-Supervised Ontology-based IE from Text, to being Unsupervised Ontology-based IE from Text.
- ...
- Example(s):
- Counter-Example(s):
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- See: GM-RKB Ontology, Ontology Population Task, Ontology, Ontology Learning Task.
References
2024
- Perplexity
- Ontology-based Information Extraction (OBIE) is a process of automatically extracting information from unstructured or semi-structured text sources, guided by an ontology that formally represents the domain knowledge.[1][2][3]
- **Ontology-Driven**: The information extraction process is guided by an ontology, which defines the concepts, relationships, and constraints within a specific domain. The ontology serves as a knowledge base, providing a structured representation of the domain.[1][2]
- **Semantic Annotation**: OBIE systems aim to semantically annotate text by identifying instances of concepts, relations, and properties defined in the ontology. This involves mapping textual mentions to their corresponding ontological entities.[1][3]
- **Knowledge Base Population**: The extracted information is typically used to populate a knowledge base or ontology with instances and relationships derived from the text.[1][2]
- **Domain-Specific**: OBIE systems are often tailored to specific domains, as the ontologies used are designed to capture the relevant concepts and relationships within that domain.[2][4]
- **Hybrid Approaches**: OBIE systems may employ a combination of techniques, such as rule-based methods, machine learning, and linguistic analysis, to leverage the ontological knowledge and extract relevant information from text.[1][3]
- Here are three examples of Ontology-based Information Extraction (OBIE) tasks:
- Biomedical Domain
- **Task**: Extract information about genes, proteins, diseases, and their relationships from scientific literature and research papers.
- **Ontology**: Gene Ontology (GO), Unified Medical Language System (UMLS), or domain-specific ontologies developed for biomedical research.
- **Example**: Given a research paper on cancer, an OBIE system could identify mentions of genes (e.g., BRCA1, TP53), proteins (e.g., p53), diseases (e.g., breast cancer, lung cancer), and extract relationships such as "BRCA1 gene is associated with breast cancer" or "p53 protein regulates cell cycle."
- 2. Business Intelligence
- **Task**: Extract information about companies, products, financial data, and market trends from news articles, reports, and websites.
- **Ontology**: Domain ontologies for business entities, products, financial concepts, and market sectors.
- **Example**: Given a news article about a company's quarterly earnings report, an OBIE system could identify the company name, financial figures (revenue, profit, earnings per share), and extract relationships such as "Company X reported a revenue of $Y billion" or "Product Z contributed to X% of total sales."
- Legal Domain
- **Task**: Extract information about legal entities, regulations, court cases, and their relationships from legal documents and case law.
- **Ontology**: Legal ontologies covering concepts like legal entities, jurisdictions, laws, court cases, and legal relationships.
- **Example**: Given a court case document, an OBIE system could identify the parties involved (e.g., plaintiff, defendant), legal concepts (e.g., contract breach, intellectual property), and extract relationships such as "Company A sued Company B for patent infringement" or "The court ruled in favor of the defendant."
- Biomedical Domain
- Citations:
- Ontology-based Information Extraction (OBIE) is a process of automatically extracting information from unstructured or semi-structured text sources, guided by an ontology that formally represents the domain knowledge.[1][2][3]
[1] https://citeseerx.ist.psu.edu/document?doi=f53373c8eba5a75f7f5eb5ba61f986accef6bee9&repid=rep1&type=pdf [2] https://ceur-ws.org/Vol-2721/paper563.pdf [3] https://www.researchgate.net/publication/220195792_Ontology-based_information_extraction_An_Introduction_and_a_survey_of_current_approaches [4] https://www.gabormelli.com/RKB/Ontology-based_Information_Extraction_Task [5] https://www.semanticscholar.org/paper/Ontology-Based-Information-Extraction-Wimalasuriya/3c5ce0b7e541c8dd53bb5c8ce10fdbab6d85211d
2013
- (Anantharangachar et al., 2013) ⇒ Raghu Anantharangachar, Srinivasan Ramani, and S. Rajagopalan. (2013). “Ontology Guided Information Extraction from Unstructured Text.” In: International Journal of Web & Semantic Technology (IJWesT), 4(1). doi:10.5121/ijwest.2013.4102
2010a
- (Melli, 2010b) ⇒ Gabor Melli. (2010). “Supervised Ontology to Document Interlinking..” Ph.D. Thesis, Simon Fraser University.
2010b
- (Wimalasuriya & Dou, 2010) ⇒ Daya C. Wimalasuriya, and Dejing Dou. (2010). “Ontology-based Information Extraction: An introduction and a survey of current approaches.” In: Journal of Information Science, 36(3). doi:10.1177/0165551509360123
2009
- (Wimalasuriya & Dou, 2009) ⇒ Daya C. Wimalasuriya, and Dejing Dou. (2009). “Using Multiple Ontologies in Information Extraction.” In: Proceedings of the Eighteenth Conference on Information and Knowledge Management (CIKM 2009) doi:10.1145/1645953.1645985
2008
- (Buitelaar et al., 2008) ⇒ Paul Buitelaar, Philipp Cimiano, Anette Frank, Matthias Hartung, Stefania Racioppa. (2008). “Ontology-based Information Extraction and Integration from Heterogeneous Data Sources.” In: International Journal of Human-Computer Studies, 66(11). doi:10.1016/j.ijhcs.2008.07.007
- (Yankova et al., 2008) ⇒ Milena Yankova, Horacio Saggion, and Hamish Cunningham. (2008). “A Framework for Identity Resolution and Merging for Multi-source Information Extraction.” In: Proceedings of LREC Conference (LREC 2008).
- QUOTE: Ontology-based extraction (OBIE) is the process of identifying in text or other sources relevant concepts, properties, and relations expressed in an ontology. In the context of ontology-based information extraction, one fundamental problems to be addressed is that of identification and merging ontological instances extracted from multiple sources (the problem is known as ontology population in the Semantic Web community).
2006
- (Buitelaar et al., 2006) ⇒ Paul Buitelaar, Philipp Cimiano, Stefania Racioppa, and Melanie Siegel. (2006). “Ontology-based Information Extraction with SOBA.” In: Proceedings of the International Conference on Language Resources and Evaluation.