Towards a natural language user interface: an approach of fuzzy query

Wang, Fangju

doi:10.1080/02693799408901991

Cited by 50 publications

(27 citation statements)

References 9 publications

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“…However, their research focuses on the fuzzy representation of spatial entities and formalized topological relations rather than establishing interpretation models between NLSR and geometric representation. In Wang et al [17] and Wang [18], mapping models between non-spatial attributes in conventional GISs and natural language are developed, which enhances the representation ability of database and facilitates natural language query of non-spatial attributes. However, these methods are limited in handling natural language query involving NLSR terms.…”

Section: Discussionmentioning

confidence: 99%

“…Indeed, it has been proven to be useful at handling non-spatial information, spatial objects, formalized relations and remote sensing images [15,16]. Wang et al [17] and Wang [18] developed fuzzy models to improve the representation, analysis, and query of non-spatial information in natural languages, such as low humidity and high elevation. Modeling fuzzy spatiotemporal objects [19][20][21] was studied to facilitate fuzzy spatial queries.…”

Section: Related Workmentioning

confidence: 99%

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Interpreting the Fuzzy Semantics of Natural-Language Spatial Relation Terms with the Fuzzy Random Forest Algorithm

Wang

Feng

et al. 2018

IJGI

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Naïve Geography, intelligent geographical information systems (GIS), and spatial data mining especially from social media all rely on natural-language spatial relations (NLSR) terms to incorporate commonsense spatial knowledge into conventional GIS and to enhance the semantic interoperability of spatial information in social media data. Yet, the inherent fuzziness of NLSR terms makes them challenging to interpret. This study proposes to interpret the fuzzy semantics of NLSR terms using the fuzzy random forest (FRF) algorithm. Based on a large number of fuzzy samples acquired by transforming a set of crisp samples with the random forest algorithm, two FRF models with different membership assembling strategies are trained to obtain the fuzzy interpretation of three line-region geometric representations using 69 NLSR terms. Experimental results demonstrate that the two FRF models achieve good accuracy in interpreting line-region geometric representations using fuzzy NLSR terms. In addition, fuzzy classification of FRF can interpret the fuzzy semantics of NLSR terms more fully than their crisp counterparts.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Interpreting the Fuzzy Semantics of Natural-Language Spatial Relation Terms with the Fuzzy Random Forest Algorithm

Wang

Feng

et al. 2018

IJGI

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“…The usefulness of fuzzy concepts in geoscience applications from a modeling standpoint has, for example, been demonstrated in Burrough, van Gaans & Macmillan (2000) for modeling geomorphological units, De Gruijter, Walvoort & Vangaans (1997), Lagacherie, Andrieux & Bouzigues (1996) for representing soil types and boundaries, Cheng, Molenaar & Lin (2001) for designing landscape objects, Brown (1998) for describing forest types, Hendricks Franssen, van Eijnsbergen & Stein (1997) for determining soil pollution classes in environmental applications, and Bogàrdi, Bárdossy & Duckstein (1990) for performing hydrological studies. Approaches nearer to computer science have been given by Burrough (1996) introducing fuzzy geographical objects for modeling natural objects with indeterminate boundaries, Dutta (1989Dutta ( , 1991 and Kollias & Voliotis (1991) dealing with qualitative spatial and temporal reasoning using fuzzy logic, Edwards (1994) and Wang & Hall (1996) dealing with fuzzy representations of geographical boundaries in GIS, Usery (1996) defining concepts like core and boundary of a fuzzy region, and Wang, Hall & Subaryono (1990) and Wang (1994) presenting a fuzzy query approach in order to introduce more natural language expressions into GIS user interfaces. Approaches that deal with the issue of representing, storing, retrieving, and querying fuzzy spatial objects in fuzzy spatial databases are rare.…”

Section: A Classification Of Models For Vague Spatial Objectsmentioning

confidence: 99%

Soft Computing Techniques in Spatial Databases

Schneider

2010

Soft Computing Applications for Database Technologies

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Spatial database systems and geographical information systems are currently only able to support geographical applications that deal with crisp spatial objects, that is, objects whose extent, shape, and boundary are precisely determined. Examples are land parcels, school districts, and state territories. However, many new, emerging applications are interested in modeling and processing geographic data that are inherently characterized by spatial vagueness or spatial indeterminacy. Examples are air polluted areas, temperature zones, and lakes. These applications require novel concepts due to the lack of adequate approaches and systems. In this chapter, we show how soft computing techniques can provide a solution to this problem. We give an overview of two type systems or algebras that can be integrated into database systems and utilized for the modeling and handling of spatial vagueness. The first type system, called Vague Spatial Algebra (VASA), is based on well known, general, and exact models of crisp spatial data types and introduces vague points, vague lines, and vague regions. This enables an exact definition of the vague spatial data model since we can build it upon an already existing theory of spatial data types. The second type system, called Fuzzy Spatial Algebra (FUSA), leverages fuzzy set theory and fuzzy topology and introduces novel fuzzy spatial data types for fuzzy points, fuzzy lines, and fuzzy regions. This enables an even more fine-grained modeling of spatial objects that do not have sharp boundaries and interiors or whose boundaries and interiors cannot be precisely determined. This chapter provides a formal definition of the structure and semantics of both type systems. Further, we introduce spatial set operations for both algebras and obtain vague and fuzzy versions of geometric intersection, union, and difference. Finally, we describe how these data types can be embedded into extensible databases and show some example queries.

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“…120 -130] identiÿes a number of issues related to fuzzy set theory. Most relevant is that although fuzzy logic claims to address the Law of Excluded Middle, and to overturn some aspects [83] of classic logic, it is in fact based on classic logic [87,92].…”

Section: Fuzzy Set Theorymentioning

confidence: 99%