Full-Text Manipulation in Databases

Full-Text Manipulation in Databases

László Kovács (University of Miskolc, Hungary) and Domonkos Tikk (Budapest University of Technology and Economics, Hungary)
DOI: 10.4018/978-1-60566-242-8.ch092
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The textual data format is one of the most important data types in database management. Databases support a wide range of special textual types that can be used to store string data. In the case of textual data, information retrieval mostly concerns the selection and the ranking of documents. In the traditional database management systems (DBMS), text manipulation is related to the usual string manipulation facilities, i.e. the exact matching of substrings. The main disadvantages of the traditional string-level operations are the high cost as they work without task-oriented index structures and the restrictionto the syntax level.
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The subfield of document management that aims at processing, searching, and analyzing text documents is text mining. Text mining is an application oriented interdisciplinary field of machine learning which exploits tools and resources from computational linguistics, natural language processing, information retrieval, and data mining. The general application schema of text mining is depicted in Figure 1 (Fan, Wallace, Rich & Zhang, 2006). For giving a brief summary of text mining, four main areas are presented here: information extraction, text categorization/classification, document clustering, and summarization.

Figure 1.

The text mining module

The goal of information extraction (IE) is to collect the text fragments (facts, places, people, etc.) from documents relevant to the given application. The IE module includes among others the following subtasks: named entity recognition (Sibanda & Uzuner, 2006); co-reference resolution (Ponzetto & Strube, 2006); identification of roles and their relations (Ruppenhofer et al, 2006).

Text categorization (TC) techniques aim at sorting documents into a given category system (see Sebastiani, 2002 for a good survey). Typical application examples of TC include among many others: document filtering (Lewis, 1995); patent document routing (Larkey, 1999); assisted categorization (Tikk et al, 2007), automatic metadata generation (Liddy et al, 2002),

Key Terms in this Chapter

Ontology: Explicit specification of conceptualization. The ontology is used to define the concepts, relationships and other distinctions that are relevant for modeling a domain.

Full-Text: Text data without any predefined structure. It contains usually sentences given in some natural language.

Computational Linguistics: Application of computer for modeling and investigation of natural languages.

Indexer: It builds one or more indices for the speed up information retrieval from free text. These indices usually contain the following information: terms (words), occurrence of the terms, format attributes.

Full-Text Search (FTS) Engine: A module within a database management system that supports efficient search in free texts. The main operations supported by the FTS engine are the exact matching, position-based matching, similarity-based matching, grammar-based matching and semantic-based matching.

Semantic Operators: operators that are based on the semantic content of the text. Specialization and synonyms are base examples of semantic operators.

Stemmer: It is a language-dependent module that determines the stem form of a given word. The stem form is usually identical to the morphological root. It requires a language dictionary.

Fuzzy Matching: A special type of word matching where the similarity of two terms are calculated as the cost of the transformation from one into the other. The most widely used cost calculation method is the edit distance method.

Thesaurus: A special repository of terms, which contains not only the words themselves but the similarity, the generalization and specialization relationships. It describes the context of a word but it does not give an explicit definition for the word.

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