CN106250910B - Semi-structured data classification method based on label sequence and nGrams - Google Patents

Semi-structured data classification method based on label sequence and nGrams Download PDF

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CN106250910B
CN106250910B CN201610555498.7A CN201610555498A CN106250910B CN 106250910 B CN106250910 B CN 106250910B CN 201610555498 A CN201610555498 A CN 201610555498A CN 106250910 B CN106250910 B CN 106250910B
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张利军
李宁
高锦涛
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Abstract

The invention discloses a semi-structured data classification method based on a label sequence and nGrams, which is used for solving the technical problem of poor accuracy of the existing semi-structured data classification method. The technical scheme includes that TSGrams features are used as a basic unit for representing semi-structured data, structural information of the semi-structured data is captured through a label sequence, content information of the semi-structured data is captured through nGrams, the TSGrams features and the content information are fused to serve as inclusion relation between feature capture structures and content, mutual relation among different keywords in the content information is considered, information gain is used for screening the TSGrams features, a TSGrams feature construction feature space with strong classification capacity is obtained, a category classification model is built according to the mutual information between the TSGrams features and categories, similarity among different structures is considered during classification, and accuracy of classification of the semi-structured data is improved.

Description

Semi-structured data classification method based on label sequence and nGrams
Technical Field
The invention relates to a semi-structured data classification method, in particular to a semi-structured data classification method based on a label sequence and nGrams.
Background
Semi-structured data classification is generally divided into three steps: firstly, extracting features from a semi-structured data set of known classes, then constructing a classification model by using the extracted features, and finally classifying the data of the unknown classes by using the constructed model. The semi-structured data contains structure and content information, and for the classification based on the structure and the content, the following factors need to be considered when extracting the features:
1. the containment relationship between structure and content, i.e., the content is organized to be contained in different hierarchies;
2. the mutual relations among the elements in the structure, namely brother relations, father-son relations, ancestor-descendant relations and the like among the elements;
3. the interrelationship between keywords in the content.
Most of the existing semi-structured data classification methods based on structures and contents extend the traditional unstructured text document vector space model on the basis of the vector space model, so that the structured text document vector space model contains structural information and is then used for classification. For example, documents 1 "train T, Nayak R, Bruza P d. combining Structure and Content properties for XML Document clustering. proceedings of the 7th architecture Data Mining Conference (AusDM '08)," 2008.219-226 "and" ghos, Mitra P. combining Content and Structure Similarity for XML Document Classification Composite SVM kernels. proceedings of19th International Conference on Pattern Recognition (ICPR'08), Tampa,2008.1-4 "all employ such methods in that the Structure information and the Content information respectively indicate that the mutual relationship between the Structure and the Content is broken, i.e., the first factor mentioned above is ignored.
There are also methods that consider the relationship between structure and Content, such as document 2 "Yuanjia, Goodde, Bohong. XML Web Classification research based on the correlation between structure and text keywords. computer research and development, 2006,43(8): 1361-. Although these methods consider the inclusion relationship between the structure and the content, the structure is modeled as a path, and the path represents the hierarchical (parent-child, ancestor-descendant) relationship between elements, but neglects the mutual relationship between different paths and the similarity of paths, and the like, i.e. the second factor cannot be processed well.
Document 4 "Yang J, Zhang F. XML Document Classification Using Extended VSM. proceedings of Focused Access to XML Documents, the 6th International works for the Evaluation of XML recommendation (INEX'07), Dagstuhl cast, Germany: springer Berlin/Heidelberg,2008.234-244, "extends the vector space model, represents semi-structured data as a matrix, thereby capturing the association between structure and content, and the relationship among the internal (elements) of the structure is embodied by a core matrix, and the structure element structure subtrees are replaced by structure subtrees in the Document 5 'Yang J, Wang S.extended VSM for XML Document Classification Using frequency subtrees of Focused Retrieval and Evaluation, the 8th International Workshop of the Initiative for the Evaluation of XML Retrieval (INEX'09), spring Berlin/Heidelberg,2010.441-448 ], so as to further embody the relevance. This approach, while taking into account the first two factors to some extent, has not yet explicitly addressed the third factor.
In summary, when the existing semi-structured data classification method extracts features from semi-structured data, the three factors are not fully considered, so that the semi-structured data classification model constructed by the methods lacks part of intrinsic information between the semi-structured data features and the semi-structured data categories, thereby affecting the accuracy of semi-structured data classification.
Disclosure of Invention
In order to overcome the defect of poor accuracy of the existing semi-structured data classification method, the invention provides a semi-structured data classification method based on a label sequence and nGrams. The method comprises the steps of taking TSGrams characteristics as a basic unit for representing semi-structured data, capturing structure information of the semi-structured data by using a label sequence, capturing content information of the semi-structured data by using nGrams, fusing the TSGrams characteristics and the content information as an inclusion relation between a characteristic capture structure and the content, considering the mutual relation between different keywords in the content information, screening the TSGrams characteristics by using information gain, obtaining a TSGrams characteristic structure characteristic space with strong classification capability, constructing a category classification model according to the mutual information between the TSGrams characteristics and the categories, and considering the similarity between different structures during classification, so that the accuracy of classification of the semi-structured data is improved.
The technical scheme adopted by the invention for solving the technical problems is as follows: a semi-structured data classification method based on a tag sequence and nGrams is characterized by comprising the following steps:
step one, constructing a TSGrams characteristic space.
(1) And (5) extracting TSGrams characteristics. For each data document D in the data set D, traversing all text nodes of D by using a tree model, forming a label sequence by using paths from a root node to a parent node of the root node, and extracting all nGrams with the length less than or equal to 3 from text contents. And then combining the tag sequences and nGrams to form TSGrams characteristics, and recording a set formed by all the TSGrams characteristics as TSGramsSet.
(2) An information gain is calculated. An information gain IG (f) is calculated for each TSGrams feature f: < s, g > in TSGramsSet. The calculation method is as follows:
Figure BDA0001049512060000031
wherein the content of the first and second substances,
Figure BDA0001049512060000032
Figure BDA0001049512060000033
Figure BDA0001049512060000034
(3) sorting all TSGrams characteristics with the length of1 in the TSGramsSet from large to small according to information gain, and setting the information gain of the Nth characteristic as IGN. Wherein N is a parameter.
(4) Selecting all information gain values in TSGramsSet to be larger than IGNConstitutes a feature space omega.
And step two, constructing a classification model.
(1) Calculating each TSGrams feature f in the feature space omega:<s,g>and class CiMutual information MI (f, C)i). The calculation method is as follows:
Figure BDA0001049512060000035
(2) the feature space Ω is divided into k disjoint subsets, each subset representing a class CiThe subset is called the class CiIs recorded as a classification model of
Figure BDA0001049512060000036
Any feature f in the feature space omega is divided into a classification model phi with the highest mutual informationC*In (1). Namely:
Figure BDA0001049512060000037
(3) according to the above division, any one of the classes CiClassification model of
Figure BDA0001049512060000038
Can be expressed as a vector in the TSGrams eigenspace Ω:
Figure BDA0001049512060000041
wherein, wi,jFor TSGrams feature fj:<sj,gj>In class CiWeight in (1), if fjNot being CiIf the weight is 0, otherwise, the two information are mutually obtained and normalized, that is to say
Figure BDA0001049512060000042
And step three, classifying the unknown class data.
(1) Preprocessing the unknown class semi-structured data d to be classified, obtaining the TSGrams features in the unknown class semi-structured data d by the method, and discarding the features which appear in the unknown class semi-structured data d but are not contained in the feature space omega, so that the unknown class semi-structured data d is represented as a vector in the feature space omega:
Figure BDA0001049512060000043
wherein, wjIs the jth feature f in the TSGrams feature space omegaj:<sj,gj>The values obtained after normalization of the frequencies that appear in document d.
(2) Computing the unknown class semi-structured data d and any class C using its vector representationiClassification model of
Figure BDA0001049512060000044
The similarity between the two is calculated as follows:
Figure BDA0001049512060000045
wherein, wd(＜sj,gj>) For TSGrams characteristic < sj,gj>The weights in the unknown class semi-structured data d,
Figure BDA0001049512060000046
for TSGrams characteristic < sk,gj>In-category model
Figure BDA0001049512060000047
The weights in (1), d, and
Figure BDA0001049512060000048
are respectively d and
Figure BDA0001049512060000049
euclidean norm of, and sim(s)j,sk) Is a tag sequence sjAnd skThe similarity between them is defined as:
Figure BDA00010495120600000410
wherein m and n are respectively a tag sequence sjAnd skLength of (d), and ed(s)j,sk) Is s isjAnd skThe edit distance of (1).
(3) Assigning the category of document d to C with the highest similarity*I.e. by
Figure BDA00010495120600000411
The invention has the beneficial effects that: the method takes TSGrams characteristics as a basic unit for representing semi-structured data, captures structural information of the semi-structured data by using a label sequence, captures content information of the semi-structured data by using nGrams, fuses the two to be used as an inclusion relation between a characteristic capture structure and the content, considers the mutual relation between different keywords in the content information, screens the TSGrams characteristics by using information gain, obtains a TSGrams characteristic structure characteristic space with strong classification capability, constructs a category classification model according to the mutual information between the TSGrams characteristics and the categories, considers the similarity between different structures during classification, and improves the accuracy of classification of the semi-structured data.
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
Drawings
FIG. 1 is a flow chart of the present invention label sequence and nGrams based semi-structured data classification method.
Detailed Description
Refer to fig. 1. The method for classifying semi-structured data based on the label sequence and nGrams comprises the following specific steps:
1. a TSGrams feature space is constructed.
1> TSGrams feature extraction: for each data Document D in The data set D, traversing all text nodes of D in a tree model, constructing a tag sequence from The path of The root node to its parent node, and extracting all nGrams with a length less than or equal to 3 from The text content by a method similar to The documents "Tesar R, Strnad V, Jezek K, et al. And then combining the tag sequences and nGrams to form TSGrams characteristics, and recording a set formed by all the TSGrams characteristics as TSGramsSet.
And 2, calculating information gain. An information gain IG (f) is calculated for each TSGrams feature f: < s, g > in TSGramsSet.
The calculation method is as follows:
Figure BDA0001049512060000051
wherein:
Figure BDA0001049512060000052
Figure BDA0001049512060000053
Figure BDA0001049512060000061
3>general in TSGramsSetThe TSGrams features with the length of1 are sorted from large to small according to the information gain, and the information gain of the Nth feature is set as IGN. Wherein N is a parameter, different values are taken according to different training data sets, and adjustment is needed according to experimental results.
4>Selecting all information gain values in TSGramsSet to be larger than IGNConstitutes a feature space omega.
2. And constructing a classification model.
1>Calculating each TSGrams feature f in the feature space omega:<s,g>and class CiMutual information MI (f, C)i). The calculation method is as follows:
Figure BDA0001049512060000062
2>the feature space Ω is divided into k disjoint subsets, each subset representing a class CiThe subset is called the class CiIs recorded as a classification model of
Figure BDA0001049512060000063
Any feature f in the feature space omega is divided into a classification model phi with the highest mutual informationC*In (1). Namely:
Figure BDA0001049512060000064
3>according to the above division, any one of the classes CiClassification model of
Figure BDA0001049512060000065
Can be expressed as a vector in the TSGrams eigenspace Ω:
Figure BDA0001049512060000066
wherein, wi,jFor TSGrams feature fj:<sj,gj>In class CiWeight in (1), if fjNot being CiIf the weight is 0, otherwise, the two information are mutually obtained and normalized, that is to say
Figure BDA0001049512060000067
3. And classifying the unknown class data.
1, preprocessing unknown class semi-structured data d to be classified, acquiring TSGrams features in the unknown class semi-structured data d by the method, and discarding features which appear in the unknown class semi-structured data d but are not included in a feature space Ω, so that the unknown class semi-structured data d can be represented as a vector in the feature space Ω:
Figure BDA0001049512060000068
wherein, wjIs the jth feature f in the TSGrams feature space omegaj:<sj,gj>The values obtained after normalization of the frequencies that appear in document d.
2>Computing the unknown class semi-structured data d and any class C using its vector representationiClassification model of
Figure BDA0001049512060000071
The similarity between the two is calculated as follows:
Figure BDA0001049512060000072
wherein, wd(＜sj,gj>) For TSGrams characteristic < sj,gj>The weight in d is such that,
Figure BDA0001049512060000073
for TSGrams characteristic < sk,gj>In-category model
Figure BDA0001049512060000074
The weights in (1), d, and
Figure BDA0001049512060000078
are respectively d and
Figure BDA0001049512060000075
euclidean norm of, and sim(s)j,sk) Is a tag sequence sjAnd skThe similarity between them is defined as:
Figure BDA0001049512060000076
wherein m and n are respectively a tag sequence sjAnd skLength of (d), and ed(s)j,sk) Is s isjAnd skFor the calculation of the edit distance, please refer to the document "Levenshtein VI. binary codes capable of correcting spectral errors and deletions of ones. schemes of Information transmission.1965", which is different from the basic unit edited in the method is the label (tag).
3>Assigning the category of document d to C with the highest similarity*I.e. by
Figure BDA0001049512060000077

Claims (1)

1. A semi-structured data classification method based on label sequences and nGrams is characterized by comprising the following steps:
step one, constructing a TSGrams characteristic space:
(1) TSGrams feature extraction: for each data document D in the data set D, traversing all text nodes of D by using a tree model, forming a label sequence from a path from a root node to a parent node of the data document D, extracting nGrams with the length less than or equal to 3 from text content, combining the label sequence and the nGrams to form TSGrams characteristics, and recording a set formed by all the TSGrams characteristics as TSGramsSet;
(2) calculating information gain: and calculating the information gain IG (f) of each TSGrams characteristic f: < s, g > in the TSGramsSet, wherein the calculation method comprises the following steps:
Figure FDF0000010457700000011
wherein the content of the first and second substances,
Figure FDF0000010457700000012
Figure FDF0000010457700000013
Figure FDF0000010457700000014
(3) sorting all TSGrams characteristics with the length of1 in the TSGramsSet from large to small according to information gain, and setting the information gain of the Nth characteristic as IGNWherein N is a parameter;
(4) selecting all information gain values in TSGramsSet to be larger than IGNThe characteristic of (a) constitutes a characteristic space Ω;
step two, constructing a classification model:
(1) calculating each TSGrams feature f in the feature space omega:<s,g>and class CiMutual information MI (f, C)i) The calculation method comprises the following steps:
Figure FDF0000010457700000015
(2) the feature space Ω is divided into k disjoint subsets, each subset representing a class CiThe subset is called the class CiIs recorded as a classification model of
Figure FDF0000010457700000016
Any feature f in feature space omega is divided into classification models with highest mutual information
Figure FDF0000010457700000017
In (1), namely:
Figure FDF0000010457700000021
(3) according to the above division, any one of the classes CiClassification model of
Figure FDF0000010457700000022
Can be expressed as a vector in the TSGrams eigenspace Ω:
Figure FDF0000010457700000023
wherein, wi,jFor TSGrams feature fj:<sj,gj>In class CiWeight in (1), if fjNot being CiIf the weight is 0, otherwise, the two information are mutually obtained and normalized, that is to say
Figure FDF0000010457700000024
Step three, classifying the unknown class data:
(1) preprocessing the unknown class semi-structured data d to be classified, obtaining the TSGrams features in the unknown class semi-structured data d by the method, and discarding the features which appear in the unknown class semi-structured data d but are not contained in the feature space omega, so that the unknown class semi-structured data d is represented as a vector in the feature space omega:
Figure FDF0000010457700000025
wherein, wjIs the jth feature f in the TSGrams feature space omegaj:<sj,gj>The value obtained after normalization of the frequency of occurrence in the document d;
(2) computing the unknown class semi-structured data d and any class C using its vector representationiClassification model of
Figure FDF0000010457700000026
The similarity between the two groups is calculated by the following method:
Figure FDF0000010457700000027
wherein, wd(＜sj,gj>) is TSGrams characteristic < sj,gjWeight in the unknown class semi-structured data d,
Figure FDF0000010457700000028
for TSGrams characteristic < sk,gjIn class model
Figure FDF0000010457700000029
The weights in (1), d, and
Figure FDF00000104577000000210
are respectively d and
Figure FDF00000104577000000211
euclidean norm of, and sim(s)j,sk) Is a tag sequence sjAnd skThe similarity between them is defined as:
Figure FDF00000104577000000212
wherein m and n are respectively a tag sequence sjAnd skLength of (e) andd(sj,sk) Is s isjAnd skThe edit distance of (d);
(3) assigning the category of document d to C with the highest similarity*I.e. by
Figure FDF00000104577000000213
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