Proximal nodes

Navarro, Gonzalo; Baeza-Yates, Ricardo

doi:10.1145/263479.263482

Cited by 99 publications

(37 citation statements)

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“…This resulted in a number of models for querying and manipulating content and hierarchical structure such as the parsed strings model [10], PAT expressions [15], the containment model [5] and generalized concordance lists model [7]. Similar approaches were developed elsewhere, such as the proximal nodes model [13] and the nested region model [11]. The interest in structured text retrieval models has grown since the introduction of XML in 1998, and the emergence of standard data retrieval query languages (see XPATH/XQUERY) for XML data.…”

Section: Historical Backgroundmentioning

confidence: 99%

“…Explicit structure is assumed by amongst others the proximal nodes model [13] and the full match model [2]. In systems that use implicit structure, however, structure is not explicitly distinguished from content.…”

Section: Scientific Fundamentalsmentioning

confidence: 99%

“…Inside a single hierarchy, the structural elements are either disjoint or nested inside each other, but across hierarchies elements may partially overlap, i.e., a subsection might start half way a page, and end on the following page. Some approaches relate single views in one query [13]. An interesting approach is suggested by Alink [1], who introduces additional XPath steps (select-narrow and select-wide) that navigate from one hierarchy to another.…”

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confidence: 99%

“…An interesting approach is suggested by Alink [1], who introduces additional XPath steps (select-narrow and select-wide) that navigate from one hierarchy to another. For instance, in the following XQuery Full-Text-like query fragment navigates from the paragraph elements to another hierarchy with a Verb element that contains "killed", and to a hierarchy with a person element that contains "Abraham Lincoln": The need for multiple hierarchies is for instance addressed in the containment model [5], and the proximal nodes model [13]. In several publications, the hierarchies are called "stand-off annotation" or "offset annotation" to stress that the structural information (or annotations) are modeled separately from the textual data.…”

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Structured Text Retrieval Models

Hiemstra¹,

Baeza–Yates²

2017

Encyclopedia of Database Systems

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DEFINITIONStructured text retrieval models provide a formal definition or mathematical framework for querying semistructured textual databases. A textual database contains both content and structure. The content is the text itself, and the structure divides the database into separate textual parts and relates those textual parts by some criterion. Often, textual databases can be represented as marked up text, for instance as XML, where the XML elements define the structure on the text content. Retrieval models for textual databases should comprise three parts: 1) a model of the text, 2) a model of the structure, and 3) a query language [4]: The model of the text defines a tokenization into words or other semantic units, as well as stop words, stemming, synonyms, etc. The model of the structure defines parts of the text, typically a contiguous portion of the text called element, region, or segment, which is defined on top of the text model's word tokens. The query language typically defines a number of operators on content and structure such as set operators and operators like "containing" and "contained-by" to model relations between content and structure, as well as relations between the structural elements themselves. Using such a query language, the (expert) user can for instance formulate requests like "I want a paragraph discussing formal models near to a table discussing the differences between databases and information retrieval". Here, "formal models" and "differences between databases and information retrieval" should match the content that needs to be retrieved from the database, whereas "paragraph" and "table" refer to structural constraints on the units to retrieve. The features, structuring power, and the expressiveness of the query languages of several models for structured text retrieval are discussed below. HISTORICAL BACKGROUNDThe STAIRS system (Storage and Information Retrieval System), which was developed at IBM already in the late 1950's allowed querying both content and structure. Much like today's On-line Public Access Catalogues, it was used to store bibliographic data in records with fields such as keywords and title, providing structured search, but no overlapping or hierarchical structures nor full text search. At the end of the 1980's, researchers at the University of Waterloo in Canada researched database support for the creation of an electronic version of the Oxford English Dictionary. This resulted in a number of models for querying and manipulating content and hierarchical structure such as the parsed strings model [10], PAT expressions [15], the containment model [5] and generalized concordance lists model [7]. Similar approaches were developed elsewhere, such as the proximal nodes model [13] and the nested region model [11]. The interest in structured text retrieval models has grown since the introduction of XML in 1998, and the emergence of standard data retrieval query languages (see XPATH/XQUERY) for XML data. One might argue that the structured text retrieval approaches such...

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Section: Historical Backgroundmentioning

confidence: 99%

Section: Scientific Fundamentalsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Structured Text Retrieval Models

Hiemstra¹,

Baeza–Yates²

2017

Encyclopedia of Database Systems

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“…Regions models (Burkowski 1992;Clarke et al 1995;Navarro and Baeza-Yates 1997;Jaakkola and Kilpelainen 1999) Figure 3 shows a fragment from Shakespeare's Hamlet for which we numbered the word positions. The figure shows the region that starts at word 103 and ends at word 131.…”

Section: Region Modelsmentioning

confidence: 99%

Information Retrieval Models

Hiemstra

2009

Information Retrieval

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A survey in indexing and searching XML documents

Luk

Leong

Dillon

et al. 2002

J. Am. Soc. Inf. Sci.

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XML holds the promise to yield (1) a more precise search by providing additional information in the elements, (2) a better integrated search of documents from heterogeneous sources, (3) a powerful search paradigm using structural as well as content specifications, and (4) data and information exchange to share resources and to support cooperative search. We survey several indexing techniques for XML documents, grouping them into flatfile, semistructured, and structured indexing paradigms. Searching techniques and supporting techniques for searching are reviewed, including full text search and multistage search. Because searching XML documents can be very flexible, various search result presentations are discussed, as well as database and information retrieval system integration and XML query languages. We also survey various retrieval models, examining how they would be used or extended for retrieving XML documents. To conclude the article, we discuss various open issues that XML poses with respect to information retrieval and database research. IntroductionAn Internet search engine (e.g., Altavista or Infoseek) returns thousands of so-called matched documents from a single query, some of which are relevant and others irrelevant to the query. End users typically have problems with organizing and digesting such vast quantities of information, in which much (i.e., 75% as pointed out by Selberg and Etzioni, 1997) of the information retrieved is likely to be irrelevant. XML holds the promise that searching can be done more precisely because structural, self-describing information and meta-data (e.g., RDF) is available, to allow for context-based and/or category-based search. XML also holds the promise to model heterogeneous data, generated from databases (DBs) or from word processors, thereby enabling search engines to locate and process heterogeneous documents or records.An XML document consists of a set of elements, which are hierarchically structured, as defined by the user. Each element has a name (e.g., p for paragraph), which is defined by the user. Data of an element (say, p) can be stored inside the element delimited by its start tag (i.e., ͗p͘) and its end tag (i.e., ͗/p͘), or it can be stored as values in its attribute (e.g., ͗p idϭ "1"͘). Certain attribute value types are reserved for referencing (e.g., IDREF). An XML element is accessed typically using the XPath language. Child elements and their parent element are separated by a slash. For example, the XPath /header/author/first accesses the first element from the root element header, and then the author element.It is possible to use other mark-up languages (e.g., HTML) or proprietary formats but XML appears to be suitable for a wide variety of information retrieval (IR) tasks, specific enough to reduce modeling complexity and open enough for easy and rapid adoption. A major advantage of XML over HTML is that users can define their own tags. Tag names are typically chosen to incorporate some relationship to the semantics of the contents or the type of co...

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Proximal nodes

Cited by 99 publications

References 24 publications

Structured Text Retrieval Models

Structured Text Retrieval Models

Information Retrieval Models

A survey in indexing and searching XML documents

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