CN112487306A - Automatic event marking and classifying method based on knowledge graph - Google Patents

Automatic event marking and classifying method based on knowledge graph Download PDF

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CN112487306A
CN112487306A CN202011417045.0A CN202011417045A CN112487306A CN 112487306 A CN112487306 A CN 112487306A CN 202011417045 A CN202011417045 A CN 202011417045A CN 112487306 A CN112487306 A CN 112487306A
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CN112487306B (en
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王晓玲
赵鑫
袁佳豪
王韵弘
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East China Normal University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9536—Search customisation based on social or collaborative filtering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/35—Clustering; Classification
    • G06F16/355—Class or cluster creation or modification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
    • G06F16/367—Ontology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/951—Indexing; Web crawling techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities
    • G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking

Abstract

The invention discloses an automatic event marking and classifying method based on a knowledge graph, which comprises the steps of constructing the knowledge graph of a required field category, crawling each text data published in social media in a preset time window, extracting key phrases and screening to obtain burst phrases, clustering the burst phrases to obtain burst phrase clusters, calculating TF-IDF scores of the corresponding burst phrases of the burst events on each knowledge graph, summing to obtain the TF-IDF scores of the burst events on each knowledge graph, and marking the corresponding events as the field category if the TF-IDF scores are larger than a preset threshold value, thereby determining the marking and classification of the events. According to the method, the emergency is automatically determined by screening and clustering the emergency phrases of the text data in the social media, and then TF-IDF scores of the emergency on knowledge maps of various field categories are calculated, so that the automatic accurate marking and classification of the social media events are realized.

Description

Automatic event marking and classifying method based on knowledge graph
Technical Field
The invention belongs to the technical field of event marking and classification, and particularly relates to an automatic event marking and classifying method based on a knowledge graph.
Background
In recent years, with the rapid development of social media, social media such as twitter and microblog gradually become important ways for people to acquire news information. Therefore, more and more work is focused on and analyzes social media information, wherein one important type of work is event extraction work of social media data, namely, events described by the social media information are extracted according to the social media data. However, for extracted events (event key phrases, abstracts, etc.), some events which are not concerned are inevitably generated, so that the extracted events need to be marked and classified (several categories such as military, politics, geography, etc.) to obtain the category to which each event belongs, so as to filter out the events which are not concerned about the category and screen out the events which are interested. However, how to accurately obtain the categories of events according to a small amount of information describing the events and how to solve the problem that a certain event may belong to multiple categories, and a better solution is not available, all of which need to be further researched and solved.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an automatic event marking and classifying method based on a knowledge graph, so that automatic and accurate marking and classification of social media events are realized.
In order to achieve the above object, the automatic event marking and classifying method based on knowledge graph of the present invention comprises the following steps:
s1: setting N field categories according to actual needs, respectively collecting text data of each field category and constructing a knowledge graph Gn，n＝1,2…,N；
S2: presetting a time window T, crawling social media in the time windowAnd extracting key phrases from each piece of document data of the cloth, and forming a key phrase set A by using the extracted key phrases. Respectively calculating the burst degree W of each key phrase s in the key phrase set AsThe calculation formula is as follows:
Ws＝ps×log(us)×log(rs)×log(log(fs))
wherein p issRepresenting the probability of a burst, u, of a key phrase s within a time window TsRepresenting the number of users, r, who used the key phrase s within the time window TsRepresenting the number of times a text containing a key phrase s is forwarded within a time window T, fsRepresents the sum of the number of interests of the user using the key phrase s within the time window T;
sorting all key phrases according to the burst degree from high to low, and selecting the first K key phrases as burst phrases to be added into a burst phrase set B;
s3: averagely dividing the time window T into M continuous disjoint sub-time windows, and recording the mth sub-time window as TmFor each burst phrase e, e ∈ B, each sub-time window T is countedmText set text (e, m) and number of texts f containing the burst phrase e1(e, m), and the amount of text f containing the burst phrase e over the time window T2(e) Calculating the time window T of each burst phrase e in the sub-time windowmThe ratio d (e, m) is f1(e,m)/f2(e)；
Let two burst phrases be e respectivelya、ebFirst, text sets text (e) are calculated separatelyaM) and text set text (e)bM) similarity sim (text (e)a,m),text(ebM)), and then the similarity S (e) of the two burst phrases is calculated using the following formulaa,eb)：
Figure BDA0002820446500000021
Clustering burst phrases according to similarity between burst phrasesGet K burst phrase clusters CkK is 1,2, …, K, each burst phrase cluster CkI.e. an emergency event within the time window T;
s4: for burst phrase cluster CkCorresponding emergencies are constructed according to the knowledge graph G of each field category constructed in the step S1nCovered text data, calculating a burst phrase cluster CkKnowledge graph G of each burst phrase v in each domain classnThe score of the TF-IDF is score (v, n), and then the sum is carried out to obtain the burst phrase cluster CkKnowledge graph G of corresponding emergency in each field categorynUpper TF-IDF score (k, n):
Figure BDA0002820446500000022
s5: presetting TF-IDF score threshold
Figure BDA0002820446500000023
For burst phrase cluster CkCorresponding emergency, if it is in domain class knowledge map GnA TF-IDF score of score (k, n) above a threshold value
Figure BDA0002820446500000024
The incident is marked as the domain category to determine the marking and classification of the incident.
The invention relates to an automatic event marking and classifying method based on a knowledge graph, which comprises the steps of constructing the knowledge graph of a required field category, crawling each text data published in social media in a preset time window, extracting key phrases and screening to obtain burst phrases, clustering the burst phrases to obtain burst phrase clusters, calculating TF-IDF scores of the corresponding burst phrases of the burst events on each knowledge graph, summing to obtain the TF-IDF scores of the burst events on each knowledge graph, and marking the corresponding events as the field category if the TF-IDF scores are larger than a preset threshold value, thereby determining the marking and classification of the events.
According to the method, the emergency is automatically determined by screening and clustering the emergency phrases of the text data in the social media, and then TF-IDF scores of the emergency on knowledge maps of various field categories are calculated, so that the automatic accurate marking and classification of the social media events are realized.
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FIG. 1 is a flow diagram of an embodiment of a method for knowledge-graph based automated event tagging and classification in accordance with the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided in order to better understand the present invention for those skilled in the art with reference to the accompanying drawings. It is to be expressly noted that in the following description, a detailed description of known functions and designs will be omitted when it may obscure the subject matter of the present invention.
Examples
FIG. 1 is a flow diagram of an embodiment of a method for knowledge-graph based automated event tagging and classification in accordance with the present invention. As shown in FIG. 1, the automatic event labeling and classifying method based on knowledge graph of the present invention comprises the following specific steps:
s101: constructing knowledge graphs of various field categories:
setting N field categories according to actual needs, respectively collecting text data of each field category and constructing a knowledge graph Gn，n＝1,2…,N。
Knowledge Graph (Knowledge Graph) is a series of different graphs displaying the relation between the Knowledge development process and the structure, and uses visualization technology to describe Knowledge resources and carriers thereof, and excavates, analyzes, constructs, draws and displays Knowledge and the mutual relation between the Knowledge resources and the carriers. For internet social media, the domain categories set in the present embodiment include Military (milery), People (peoples), Industry (Industry), security (Safety), weather (meteorology), and Geography (geographics), relevant data of these domain categories on wikipedia is crawled, a knowledge graph is constructed based on these data, and the event is labeled and classified by means of these knowledge graph information.
S102: screening burst phrases:
and presetting a time window T, crawling each text data published in social media in the time window, extracting key phrases of each file data, and forming a key phrase set A by the extracted key phrases. Respectively calculating the burst degree W of each key phrase s in the key phrase set AsThe calculation formula is as follows:
Ws＝ps×log(us)×log(rs)×log(log(fs))
wherein p issRepresenting the probability of a burst of a key phrase s within a time window T, i.e.
Figure BDA0002820446500000041
ts、ts′Respectively representing the times of occurrence of key phrase s and key phrase s 'in the time window T, s, s' being equal to A and usRepresenting the number of users, r, who used the key phrase s within the time window TsRepresenting the number of times a text containing a key phrase s is forwarded within a time window T, fsRepresenting the sum of the number of interests of the user using the key phrase s within the time window T.
And sorting all key phrases according to the burst degree from high to low, selecting the first K key phrases as burst phrases to be added into a burst phrase set B, and setting the value of K according to the requirement.
Table 1 is a list of the degree of burstiness of some key phrases in this example.
Key phrases Degree of burst
Iran 20.1310
Zarif 6.91061
foreign minister 3.68816
Human rights 3.21209
president 2.54122
resignation 2.53455
fellow diplomats 1.22547
foreign policy 0.32457
hinting at 0.30289
condemns 0.02536
In front of 0.01785
except for 0.00566
TABLE 1
In this embodiment, the first 3 key phrases are selected as burst phrases to be added to the burst phrase set B, i.e., B ═ Iran, Zarif, forign minimum }.
S103: constructing an emergency based on the burst phrase clustering:
averagely dividing the time window T into M continuous disjoint sub-time windows, and recording the mth sub-time window as TmM1, 2, …, M, for each burst phrase e, e ∈ B, each sub-time window T is countedmText set text (e, m) and number of texts f containing the burst phrase e1(e, m), and the amount of text f containing the burst phrase e over the time window T2(e) Calculating the time window T of each burst phrase e in the sub-time windowmThe ratio d (e, m) is f1(e,m)f2(e)。
And calculating the similarity of the burst phrases in the burst phrase set B pairwise, wherein the calculation method comprises the following steps:
let two burst phrases be e respectivelya、ebFirst, text sets text (e) are calculated separatelyaM) and text set text (e)bM) similarity sim (text (e)a,m),text(ebM)), and then the similarity S (e) of the two burst phrases is calculated using the following formulaa,eb)：
Figure BDA0002820446500000051
Clustering the burst phrases according to the similarity between the burst phrases to obtain K burst phrase clusters CkK is 1,2, …, K, each burst phrase cluster CkI.e. an emergency event of the time window T.
In this embodiment, the similarity between text sets is TF-IDF (term frequency-inverse document frequency) similarity. TF-IDF is a commonly used weighting technique for information retrieval (information retrieval) and text mining (text mining). TF-IDF is a statistical method to assess how important a word is for one of a set of documents or a corpus. The importance of a word increases in proportion to the number of times it appears in a document, but at the same time decreases in inverse proportion to the frequency with which it appears in the corpus. The method for calculating the TF-IDF similarity of the text set in the embodiment comprises the following steps:
1) and respectively segmenting the two text sets, and combining the two obtained word sets to obtain a word set phi.
2) For each word in the set of words phi, the TF-IDF value p of the word in the two text sets is calculated separatelyi,1、pi,2I ═ 1,2, …, | φ |, | φ | represents the number of words in the set of words φ. The TF-IDF value is calculated by the following method: for the word i, the word frequency TF (i) and the inverse text frequency IDF (i) log (D/D) of the word i in the text set are countedi) D denotes the number of texts in the text set, DiRepresenting the number of texts in the text set containing word i, the TF-IDF value is TF (i) x IDF (i).
3) A TF-IDF vector P corresponding to each text set is constructed and obtained according to the TF-IDF value of each word1＝(p1,1,p2,1,…p|φ|,1)、P2＝(p1,2,p2,2,…p|φ|,2)。
4) And calculating cosine similarity between TF-IDF vectors corresponding to the two text sets, namely taking the cosine similarity as the similarity between the text sets. In this embodiment, the clustering of the burst phrases adopts a Jarvis-Patrick clustering algorithm, and the algorithm can perform clustering based on the similarity between burst knowledge, and the method can be briefly described as follows: drawing an SNN (shared neighbor similarity) similarity graph according to the burst phrase similarity, thinning the SNN similarity graph by using a similarity threshold, and finding out a communication branch of the thinned SNN similarity graph to obtain a clustering result.
Table 2 shows the burst phrase clusters obtained by clustering the burst phrases in this embodiment.
Figure BDA0002820446500000061
TABLE 2
S104: calculating the TF-IDF score of the emergency:
for burst phrase cluster CkCorresponding events are constructed according to the knowledge graph G of each field category constructed in the step S101nCovered text data, calculating a burst phrase cluster CkKnowledge graph G of each burst phrase v in each domain classnThe score of the TF-IDF is score (v, n), and then the sum is carried out to obtain the burst phrase cluster CkKnowledge graph G of corresponding emergency in each field categorynUpper TF-IDF score (k, n):
Figure BDA0002820446500000062
table 3 shows TF-IDF scores of events corresponding to the burst phrase clusters on the domain category knowledge graph in the embodiment.
Figure BDA0002820446500000063
Figure BDA0002820446500000071
TABLE 3
S105: event tagging and classification:
presetting TF-IDF score threshold
Figure BDA0002820446500000072
For burst phrase cluster CkCorresponding emergency, if it is in domain class knowledge map GnA TF-IDF score of score (k, n) above a threshold value
Figure BDA0002820446500000073
The incident is marked as the domain category to determine the marking and classification of the incident.
Setting the score threshold in the present embodiment
Figure BDA0002820446500000074
The event can be derivedThe labels and classifications are "Military (Military)" and "People (People)", consistent with human observation.
Although illustrative embodiments of the present invention have been described above to facilitate the understanding of the present invention by those skilled in the art, it should be understood that the present invention is not limited to the scope of the embodiments, and various changes may be made apparent to those skilled in the art as long as they are within the spirit and scope of the present invention as defined and defined by the appended claims, and all matters of the invention which utilize the inventive concepts are protected.

Claims (3)

1. An automatic event marking and classifying method based on knowledge graph is characterized by comprising the following steps:
s1: setting N field categories according to actual needs, respectively collecting text data of each field category and constructing a knowledge graph Gn，n＝1,2…,N；
S2: and presetting a time window T, crawling each text data published in social media in the time window, extracting key phrases of each file data, and forming a key phrase set A by the extracted key phrases. Respectively calculating the burst degree W of each key phrase s in the key phrase set AsThe calculation formula is as follows:
Ws＝ps×log(us)×log(rs)×log(log(fs))
wherein p issRepresenting the probability of a burst, u, of a key phrase s within a time window TsRepresenting the number of users, r, who used the key phrase s within the time window TsRepresenting the number of times a text containing a key phrase s is forwarded within a time window T, fsRepresents the sum of the number of interests of the user using the key phrase s within the time window T;
sorting all key phrases according to the burst degree from high to low, and selecting the first K key phrases as burst phrases to be added into a burst phrase set B;
s3: dividing the time window T into M consecutive disjoint sub-time windows on averagem sub-time windows are TmFor each burst phrase e, e ∈ B, each sub-time window T is countedmText set text (e, m) and number of texts f containing the burst phrase e1(e, m), and the amount of text f containing the burst phrase e over the time window T2(e) Calculating the time window T of each burst phrase e in the sub-time windowmThe ratio d (e, m) is f1(e,m)/f2(e)；
Let two burst phrases be e respectivelya、ebFirst, text sets text (e) are calculated separatelyaM) and text set text (e)bM) similarity sim (text (e)a,m),text(ebM)), and then the similarity S (e) of the two burst phrases is calculated using the following formulaa,eb)：
Figure FDA0002820446490000011
Clustering the burst phrases according to the similarity between the burst phrases to obtain K burst phrase clusters CkK is 1,2, …, K, each burst phrase cluster CkI.e. an emergency event within the time window T;
s4: for burst phrase cluster CkCorresponding emergencies are constructed according to the knowledge graph G of each field category constructed in the step S101nCovered text data, calculating a burst phrase cluster CkKnowledge graph G of each burst phrase v in each domain classnThe score of the TF-IDF is score (v, n), and then the sum is carried out to obtain the burst phrase cluster CkKnowledge graph G of corresponding emergency in each field categorynUpper TF-IDF score (k, n):
Figure FDA0002820446490000021
s5: presetting TF-IDF score threshold
Figure FDA0002820446490000022
For burst phrase cluster CkCorresponding emergency, if it is in domain class knowledge map GnA TF-IDF score of score (k, n) above a threshold value
Figure FDA0002820446490000023
The incident is marked as the domain category to determine the marking and classification of the incident.
2. The automated event labeling and classification method according to claim 1, wherein the text set similarity in step S3 is TF-IDF similarity, and the calculation method comprises the following steps:
1) respectively segmenting the two text sets, and combining the two obtained word sets to obtain a word set phi;
2) for each word in the set of words phi, the TF-IDF value p of the word in the two text sets is calculated separatelyi,1、pi,2I ═ 1,2, …, | φ |, | φ | represents the number of words in the set of words φ;
3) a TF-IDF vector P corresponding to each text set is constructed and obtained according to the TF-IDF value of each word1＝(p1,1,p2,1,…p|φ|,1)、P2＝(p1,2,p2,2,…p|φ|,2)；
4) And calculating cosine similarity between TF-IDF vectors corresponding to the two text sets, namely taking the cosine similarity as the similarity between the text sets.
3. The automated event labeling and classification method according to claim 1, wherein the clustering of the burst phrases in step S3 adopts a Jarvis-Patrick clustering algorithm.
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