- (Chakaravarthy et al., 2006) ⇒ Venkatesan T. Chakaravarthy, Himanshu Gupta, Prasan Roy, Mukesh Mohania. (2006). “Efficiently Linking Text Documents with Relevant Structured Information.” In: Proceedings of the 32nd International Conference on Very Large Data Bases (VLDB 2006).
- It proposes an unsupervised algorithm, named EROCS, for an entity mention normalization task where Text Passages are linked to a single entity record.
- Its proposed algorithm uses of a TF-IDF-like ranking function that restricts itself to the terms that are available to describe entities.
- Its proposed algorithm requires that each passage be linked to at most one Entity Record. This restriction is acceptable for their scenario where each entity relates to a purchase transaction, because few Passages will discuss more than one Transaction.
- It proposes a greedy iterative cache refinement strategy in order to reduce the the data retrieved from the entity database.
- Its proposal is related to the Factoid QA Task, if the entity description is treated as the query and if the supporting passages is required as evidence. So, it would be interesting to test out the performance of the TF-IDF Vector Cosine Similarity approach used for the Factoid QA task.
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- QUOTE: We note with interest the EROCS system (Chakaravarthy, Gupta, Roy, & Mohania, 2006) where the authors tackle the problem of linking full text documents with relational databases. The technique involves filtering out all non-nouns from the text, and then finding the matches in the database. This is an intriguing approach; interesting future work would involve performing a similar filtering for larger documents and then applying the Phoebus algorithm to match the remaining nouns to reference sets.
- (Bhide et al., 2007) ⇒ Manish A. Bhide, Ajay Gupta, Rahul Gupta, Prasan Roy, Mukesh K. Mohania, and Zenita Ichhaporia. (2007). “LIPTUS: associating structured and unstructured information in a banking environment." Proceedings of the 2007 [[ACM SIGMOD] Conference.
Faced with growing knowledge management needs, enterprises are increasingly realizing the importance of interlinking critical business information distributed across structured and unstructured data sources. We present a novel system, called EROCS, for linking a given text document with relevant structured data. EROCS views the structured data as a predefined set of "entities" and identifies the entities that best match the given document. EROCS also embeds the identified entities in the document, effectively creating links between the structured data and segments within the document. Unlike prior approaches, EROCS identifies such links even when the relevant entity is not explicitly mentioned in the document. EROCS uses an efficient algorithm that performs this task keeping the amount of information retrieved from the database at a minimum. Our evaluation shows that EROCS achieves high accuracy with reasonable overheads.
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|2006 EffiLinkingTextDocs||Venkatesan T. Chakaravarthy|
|Efficiently Linking Text Documents with Relevant Structured Information||Proceedings of the 32nd International Conference on Very Large Data Bases||http://www.vldb.org/conf/2006/p667-chakaravarthy.pdf||2006|
|Author||Venkatesan T. Chakaravarthy +, Himanshu Gupta +, Prasan Roy + and Mukesh Mohania +|
|journal||Proceedings of the 32nd International Conference on Very Large Data Bases +|
|title||Efficiently Linking Text Documents with Relevant Structured Information +|