Modern Information Retrieval
An Empirical Approach to Spanish Anaphora Resolution
Machine Translation
MDA Explained: The Model Driven Architecture: Practice and Promise
MDA Explained: The Model Driven Architecture: Practice and Promise
The Pragmatics of Model-Driven Development
IEEE Software
Ontology-based Integration of OLAP and Information Retrieval
DEXA '03 Proceedings of the 14th International Workshop on Database and Expert Systems Applications
The integration of business intelligence and knowledge management
IBM Systems Journal
Question Answering in Restricted Domains: An Overview
Computational Linguistics
Contextualizing data warehouses with documents
Decision Support Systems
Improving the performance of question answering with semantically equivalent answer patterns
Data & Knowledge Engineering
The benefits of the interaction between data warehouses and question answering
Proceedings of the 2010 EDBT/ICDT Workshops
Using AliQAn in monolingual QA@CLEF 2008
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
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Business Intelligence (BI) applications no longer limit their analysis to structured databases, but they also need to obtain actionable information from unstructured sources (e.g. data from the Web, etc.). Interestingly, Question Answering (QA) systems are good candidates for these purposes, since they allow users to obtain concise answers to questions stated in natural language from a collection of text documents. Traditionally, QA systems include patterns for dealing with a large spectrum of general questions, namely open-domain question answering (ODQA). However, BI users should be aware of asking questions related to a specific activity of the business (e.g. healthcare, agricultural, transportation, etc.). Therefore, adapting ODQA systems to new restricted domains is an increasingly necessity for these systems to be precisely used in BI. Unfortunately, research addressing this topic has two main drawbacks: (i) patterns are manually tuned, which requires a huge effort in time and cost, and (ii) tuning of patterns is based on analyzing potential questions to be answered, which is not a realistic situation since, in restricted domains, questions are highly complex and difficult to be acquired. To overcome these drawbacks, this paper presents a novel approach based on model-driven development in order to use knowledge resources to automatically and effortlessly adapt patterns of ODQA systems to be useful for restricted-domain BI scenarios.