Rule-based Algorithm

A Rule-based Algorithm is an Algorithm that applies a Rule-based Model to an Input.

• AKA: Rule-based Method, Rule-based Approach.
• Context:
  • It can be applied by a Rule-based Computing System.
• Example(s):
  • a Classification Algorithm that uses a Rule-based Model.
• See: Rule-based Model Induction Algorithm, Knowledge Engineering System.

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References

2007

• (Schmid, 2007) ⇒ Helmut Schmid. (2007). “Tokenizing.” In: Corpus Linguistics: An International Handbook. Walter de Gruyter, Berlin.
  • There are four major tokenization approaches for these languages: rule-based methods**, statistical methods based on word n-grams, tagging approaches, and systems which integrate tokenization with POS tagging or parsing.

2005

• (Hanisch et al., 2005) ⇒ Daniel Hanisch, Katrin Fundel, Heinz-Theodor Mevissen, Ralf Zimmer, and Juliane Fluck. (2005). “Prominer: Rule-based protein and gene entity recognition.” In: BMC Bioinformatics, 6 (Suppl 1), S14.
  • The ProMiner system uses a pre-processed synonym dictionary to identify potential name occurrences in the biomedical text and associate protein and gene database identifiers with the detected matches. It follows a rule-based approach and its search algorithm is geared towards recognition of multi-word names.

2004

• (Krauthammer & Nenadic, 2004) ⇒ Michael Krauthammer, and Goran Nenadic. (2004). “Term Identification in the Biomedical Literature.” In: Journal of Biomedical Informatics, 37(6). doi:10.1016/j.jbi.2004.08.004
  • Rule-based approaches generally attempt to recover terms by re-establishing associated term formation patterns that have been used to coin the terms in question.6 The main approach is to (typically manually) develop rules that describe common naming structures for certain term classes using either orthographic or lexical clues, or more complex morpho-syntactic features. Also, in many cases, dictionaries of typical term constituents (e.g. terminological heads, affixes, specific acronyms) are used to assist in term recognition. However, knowledge engineering approaches are known to be extremely time-consuming for development, and – since rules are typically very specific – their adjustment to other entities is usually difficult.

2003

• (Friedman-Hill, 2003) ⇒ Ernest Friedman-Hill. (2003). “Jess in Action: Java Rule-based Systems." Manning Publications. ISBN:1930110898
  • A practical handbook for anyone interested in programming rule-based systems and written by the creator of the popular Java rule engine, Jess, this book is structured around a series of large, fully developed practical examples of rule-based programming in Java. After the topic of rule-based systems is introduced, software developers and architects are shown the Jess rule programming language in an accessible, tutorial style. Demonstrated is how to quickly progress from building freestanding interactive applications to rule-based Web and Enterprise software. Specific issues covered in this process include designing the application, embedding Jess in Java applications, and using a rule engine in the J2EE environment.

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