CN104866593A - Database searching method based on knowledge graph - Google Patents

Database searching method based on knowledge graph Download PDF

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CN104866593A
CN104866593A CN201510289249.3A CN201510289249A CN104866593A CN 104866593 A CN104866593 A CN 104866593A CN 201510289249 A CN201510289249 A CN 201510289249A CN 104866593 A CN104866593 A CN 104866593A
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relation
entity
query
knowledge
node
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CN104866593B (en
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蒋锴
任志宏
傅军
杨怡
王辉
何加浪
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CETC 28 Research Institute
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/242—Query formulation
    • G06F16/243—Natural language query formulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models
    • G06F16/284—Relational databases
    • G06F16/288—Entity relationship models

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  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

The invention relates to a database searching method based on knowledge graph, and belongs to the field of structural data mining and searching. The method provided by the invention comprises: firstly analyzing factors such as a type of a database table and inter-table constraint, then generating a corresponding concept, an entity, and an inter-entity relation by using the table and the inter-table constraint, and establishing a knowledge graph service. After a natural language query input by a user is obtained, each factor queried by the user is detected to obtain a factor mode and a factor value of the query, then the factor mode is matched in a template base to obtain a corresponding query mode, then the factor value of the query is substituted into to the query model to obtain a knowledge graph query statement, and finally the query statement is executed in the knowledge graph service, to obtain corresponding knowledge queried by the user and return the knowledge to the user. According to the method provided by the invention, data and an internal relation in a database can be effectively organized and shown, and the natural language query by the user is supported, thereby improving user experience of database searching.

Description

A kind of database search method of knowledge based collection of illustrative plates
Technical field
The present invention relates to structural data to excavate and searching method, the database search method of particularly a kind of support user natural language querying of knowledge based collection of illustrative plates.
Background technology
Knowledge mapping technology (Knowledge Graph) is at present in the most noticeable technology of knowledge engineering field development.Knowledge mapping represents the relation between knowledge and knowledge with figure (Graph) model exactly in simple terms, the concept involved by node on behalf knowledge of figure or entity, the limit of figure represents the relation of concept or inter-entity, and the figure that numerous nodes and limit are formed just can carry out complete to knowledge and clearly describe.A large amount of knowledge mapping is integrated and according to knowledge hierarchy taxonomic organization, just define knowledge base (Knowledge Base).In recent years, built or algorithm Automatic Extraction by internet mass-rent mode, define the knowledge base that some comprise magnanimity entity, more well-known knowledge base has YAGO, DBpedia, Freebase etc.Current knowledge mapping technical support many revolutionary services and application.Such as Google, must answer, the main flow search engine such as Baidu adds the knowledge such as the entity relevant to query word at result of page searching; The smart mobile phone assistants such as the Siri of apple, the Cortana of Microsoft can answer the enquirement of user; Knowledge search engine Walfram Alpha directly can provide answer according to the problem of user's input, instead of provides relevant documentation as traditional search engine.
Although knowledge mapping technology is widely applied in the open data such as internet arena, but rarely has application in traditional field such as relational databases.Relational database stores data in a structured manner, supports that the query statements such as SQL are inquired about data, is a kind of reliable and efficient storage mode.But relational database is to the semanteme of data and the tissue of internal relation and show Shortcomings, and the SQL query mode that relational database provides requires that user has professional knowledge and experiences not good.
Summary of the invention
Goal of the invention: the present invention is directed to the deficiency that current database search exists, propose a kind of database search method of support user natural language querying of knowledge based collection of illustrative plates, uses difficulty to reduce database search, improves Consumer's Experience.
Technical scheme: in order to solve the problems of the technologies described above, the invention discloses a kind of database search method of knowledge based collection of illustrative plates, comprises knowledge mapping and builds and the large step of natural language querying process two;
Described knowledge mapping builds, and comprising:
A. carry out factor analysis to database, and table is divided into the relation table of relation between tables of data and storage object storing concrete object, described key element comprises table name, row name and restriction relation between showing;
B. concept node and entity node is set up according to the record in tables of data and tables of data;
C. according between tables of data between foreign key constraint relation and storage object the relation table of relation set up the relation between entity node;
D. utilize knowledge mapping instrument to store the internodal relation generated in the node generated in step b and step c, set up knowledge mapping service;
Described natural language querying process, comprising:
E. carry out participle to the query statement of user's input, and the vocabulary after participle is carried out query elements mapping, obtain feature schema and the key element value of inquiry, described query elements comprises variable, relation, entity and concept;
F. feature schema is carried out in template base mating and obtain corresponding query pattern;
G. key element value is filled in query pattern and obtains knowledge mapping query statement;
H. in knowledge mapping service, perform knowledge mapping query statement obtain user and inquire about corresponding knowledge.
Further, the method setting up concept node and entity node according to the record in tables of data and tables of data in described step b is: to each the tables of data T in database a, set up a concept node C with table name A a; Then for table T ain each record T ai, set up an entity E ai, and using this record each row name and corresponding train value as this entity attributes and property value, then by concept node C apoint to entity node E ai, i is the sequence number of record.
Further, the method setting up relation between entity node according to foreign key constraint relation between tables of data in described step c is: if table T ain row B be table T cthe external key of middle row D, then according to the value corresponding relation of row B and row D, set up table T ain every line item T aiconstructed entity E ai, to table T cmiddle record T cjconstructed entity E cjbetween relation, the title of relation is determined according to the conceptual relation between table name A and C.
Further, the method setting up relation between entity node according to relation table in described step c is: if relation table T cstore tables of data T ato T bmany-one relationship, then according to table T cthe Table A major key of record, to the mapping relations of table B major key, sets up table T athe entity E built aito table T bthe entity E built bjbetween relation, the title of relation is determined according to table name C.
Further, carry out participle, and the vocabulary after participle is carried out query elements mapping in step e to the query statement of user's input, the method obtaining feature schema and the key element value of inquiring about comprises:
Use the inquiry O={o that natural language processing segmentation methods inputs user 1o 2... o n, o 1o 2... o nfor individual character, carry out participle and obtain word segmentation result W={w 1=o 1o 2... o k, w 2=o k+1... ..., w n=o m... o n, the dictionary that participle uses comprises general dictionary, and the concept generated in step c, entity, vocabulary that attribute is relevant with relation;
Defining variable (Variable, V), relation (Relation, R), entity (Entity, E), concept (Concept, C) four class query elements, each vocabulary in word segmentation result is mapped to a key element in described four class query elements, is designated as: < P, W >={ < p 1, w 1>, < p 2, w 2> ... < p n, w n> }, the element identification that vocabulary each in word segmentation result maps is extracted the feature schema obtaining inquiring about O, be designated as: P={p 1, p 2... p n, p i∈ { V, R, E, C}.
Further, the query pattern in described step f is designated as X=f (V, R, E, C), and f (V, R, E, C) is the generalized function of four class key elements, is the combination of knowledge mapping inquiry primitive and four class key elements.
Beneficial effect of the present invention is two aspects: the first, utilizes knowledge mapping to organize the data in relational database, can the semanteme of mining data and inner link better; The second, by carrying out automatic analysis and conversion to natural language querying, can directly return relevant knowledge according to the natural language querying of user's input, thus reduce search use difficulty and improve Consumer's Experience.
Accompanying drawing explanation
To do the present invention below in conjunction with the drawings and specific embodiments and further illustrate, above-mentioned and/or otherwise advantage of the present invention will become apparent.
Fig. 1 is the overview flow chart of the embodiment of the present invention;
Fig. 2 is the process flow diagram that the embodiment of the present invention is applied to movie database;
Fig. 3 is based on the knowledge mapping structural representation that movie database builds in the embodiment of the present invention.
Embodiment
As shown in Figure 1, a kind of database search method of knowledge based collection of illustrative plates disclosed in the embodiment of the present invention comprises knowledge mapping and builds and the large step of natural language querying process two, wherein knowledge mapping builds and mainly comprises database elements analysis, concept and solid generation, inter-entity relation generates and knowledge mapping service is built step, and natural language querying process mainly comprises the step of key element detection, template matches, query generation and query execution.Get information about content of the present invention in order to clearer, will take movie database as example below, and elaborate the concrete steps of the inventive method.Fig. 2 illustrates the process flow diagram that embodiment of the present invention method is applied in movie database.Table 1 lists table name, the element information such as row name and constraint of the movie database that example uses.
Table 1 movie database key element
Before each step of detailed description, first technical solution of the present invention part and the letter character that occurs below are explained as follows: tee is the initial of table Table, and the table in representation database, the subscript of T represents table name.Letter C is the initial of concept Concept, represents concept node.Letter e is the initial of entity Entity, represents entity node.T airepresent i-th record in tables of data, be general reference, represent and same operation is all done to each line item of tables of data.Points relationship between the arrow representation node in formula, the title of the textual representation relation above arrow.
Elaborate below in conjunction with the knowledge mapping structure in accompanying drawing 2 pairs of the present embodiment methods and natural language querying processing procedure.Knowledge mapping builds and comprises the following steps:
The analysis of step 1. database elements.Extract and relevant factor in analytical database, comprise each table and table name, the row name of each row in table, the restriction relation between table, and further table is divided into the tables of data storing concrete object, and the relation table of relation between storage object.As: analyzed by the database shown in his-and-hers watches 1, obtain tables of data: film, performer, director, film types, relation table: film performer's table, movie director's table, and the restriction relation between tables of data.
Step 2. utilizes tables of data structure concept and entity node.To each the tables of data T in database a, set up a concept node C with table name A a, then for table T ain each record T ai, set up an entity E ai, and by concept node C apoint to entity node E ai.Namely
for each T ai∈ T a
Entity E aithe attribute comprised is table T aeach row name of definition, property value is T aithe value of each row.
As: with cast T performerfor example, first set up a concept node C performer, for the record T of each in cast performer i, set up an entity node E performer i, and by concept node C performerpoint to entity node E performer i, relationships between nodes is " comprising ".Other tables of data are also all adopted and uses the same method.Fig. 3 illustrates the concept node and part entity node that build.In order to reach display effect clearly, concept node adopts dotted arrow to represent to the relation of inclusion of entity node, and relation name " comprises " omission and do not draw.Each entity node only depicts title, and numbering has carried out omitting and not drawing.
Step 3. utilizes constraint or relation table between table to build the relation between entity node.Following foreign key constraint relation is defined: table T in tentation data storehouse ain row B be table T cthe external key of middle row D, is designated as
T A(B)＝Foreign(T C(D))
Then according to the value corresponding relation of row B and row D, set up table T ain every line item T aiconstructed entity E ai, to table T cmiddle record T cjconstructed entity E cjbetween relation.The title of relation is determined according to the conceptual relation between table name A and C, is designated as R (A, C).Namely
work as T ai(B)=T cj(D)
In description above, T ai(B) table T is represented ain the value of row B of the i-th line item, T cj(D) table T is represented cthe value of the row D of middle jth line item.
When a record of certain table in database exists corresponding relation with another many records shown, the independent relation of table to this one-to-many usually can be used to store.Because one-to-many and many-to-one relation are symmetrical, thus below only illustrate utilize many-one relationship table build entity relationship.If table T cstore table T ato T bmany-one relationship, then can according to table T cthe Table A major key of record, to the mapping relations of table B major key, sets up table T athe entity E built aito table T bthe entity E built bjbetween relation.The title of relation is determined according to table name C, is designated as R (C).Namely
as R (T ai(pk), T bj(pk)) ∈ T c
As in example, there is foreign key constraint relation in film table and film types table, then for movie property node, if entity node E film j" film types numbering " attribute equal film types node E film types k" film types numbering " attribute, then set up E film jpoint to E film types krelation, relation name is " type ".Can set up the relation of actor node to film node for relation table " film performer's table " and " movie director's table ", and director's node is to the relation of film node.For film performer's table, if certain a line T in table film performer lfilm numbering attribute equal film node E film mfilm numbering attribute, performer's numbering attribute equals actor node E performer nperformer's numbering attribute, then set up node E performer npoint to E film mrelation, relation name is " performing ".In like manner set up the relation of director's node to film node, relation name is " directing ".So far, the knowledge mapping based on movie database just builds and completes, and Fig. 3 illustrates a sample of knowledge mapping.
The service of step 4. knowledge mapping is built.Knowledge mapping instrument of the increasing income Cayley of Google is utilized to store the knowledge mapping that step 3 has built.Cayley provides Gremlin grammatical query to serve, and Gremlin is a kind of language of mapping traversal, can carry out the inquiry of node according to the title on the points relationship on limit and limit.Table 2 illustrates some query statements based on movie database application and Query Result example, the inquiry of ' V ' representation node (Vertex) in query statement, and limit is pointed out in ' Out ' representative, and ' In ' representative refers to into limit.
Table 2 Gremlin inquires about example
Natural language querying process comprises the following steps:
Step 5. dictionary builds.Vocabulary involved by entity and entity relationship is added the dictionary of participle software.Vocabulary in this example involved by entity comprises title, actor name, director names, the film types title of film.Vocabulary involved by relation has " comprising ", " directing ", relation name such as " performances ", and the vocabulary equivalent in meaning and close with these vocabulary.Be understandable that, the structure of dictionary can be independently build based on a large amount of prioris and safeguard, relative words is added dictionary herein, can expand dictionary, improves word segmentation processing effect.
Step 6. key element detects.The inquiry of user's input side is denoted as O={o 1o 2... o n, o 1o 2... o nfor individual character, utilize Chinese word segmentation instrument (such as ICTCLAS etc.) that participle is carried out in the natural language querying that user inputs, word segmentation result is W={w 1=o 1o 2... o k, w 2=o k+1... ..., w n=o m... o n.By defining four class key elements: variable (Variable, V), relation (Relation, R), entity (Entity, E), concept (Concept, C), vocabulary in word segmentation result can be mapped to above-mentioned each key element, be designated as: < P, W >={ < p 1, w 1>, < p 2, w 2> ... < p n, w n> }, wherein each two tuple illustrates key element and corresponding key element value.Such as two tuple < p 1, w 1w in > 1=o 1... o kbe a vocabulary, be also key element value simultaneously, p 1it is then the element identification of this vocabulary.The element identification that vocabulary each in word segmentation result maps is extracted the feature schema that just can obtain inquiring about O, be designated as: P={p 1, p 2... p n, p i∈ { V, R, E, C}, the span of element identification in formula: { V, R, E, C} are the initials of aforementioned four class key element English.
The inquiry of such as user has been which actor features of O={ Around the World in 80 Days }, word segmentation result is W={w 1=which, w 2=performer, w 3=perform, w 4=global the earth 80 days }.Word segmentation result is mapped to variable (Variable, V), relation (Relation, R), entity (Entity, E), concept (Concept, C) four class key elements, key element and key element value corresponding relation < P, W >={ < V, w can be obtained 1>, < C, w 2>, < R, w 3>, < E, w 4> }, then the feature schema inquiring about O is P=VCRE.
Step 7. template base builds.Set up the mapping template storehouse of feature schema to Gremlin query pattern according to syntax rule, each template in template base is the mapping ruler of a feature schema to query pattern.Query pattern is the combination that Gremlin inquires about primitive and four class query elements, can regard the generalized function of four class key elements as, be designated as f (V, R, E, C).Table 3 gives a feature schema to query pattern mapping template storehouse example.
Table 3 feature schema is to query pattern mapping template storehouse example
Feature schema Query pattern
C V g.V(C).Out()
V C g.V(C).Out()
E R V g.V(E).Out(R)
E R V C g.V(E).Out(R).And(g.V(C).Out())
V C R E g.V(E).In(R).And(g.V(C).Out())
… …
Step 8. template matches.Mate in the template base that the feature schema obtained in step 6 is built in step 7, obtain corresponding query pattern.Such as mated by the feature schema P=V C R E that query by example in step 6 produces, can obtain query pattern is g.V (E) .In (R) .And (g.V (C) .Out ()).
Step 9. query generation.According to the corresponding relation of key element in step 6 and key element value, key element value is inserted query pattern X=f (V, R, E that step 8 obtains, C), just can generate corresponding knowledge mapping query statement, Y=f (V, R, E, C, < P, W >)=f (w 1, w 2..., w n).Such as, key element in step 6 example and key element value corresponding relation are < P, W >={ < V, w 1>, < C, w 2>, < R, w 3>, < E, w 4> }, by vocabulary w 1, w 2, w 3, w 4insert the query pattern obtained in step 8, obtain Q=f (V, R, E, C, < P, W >)=g.V (w 4) .In (w 3) .And (g.V (w 2) .Out ())=g.V (' the global earth 80 days ') .In (' performing ') .And (g.V (' performer ') .Out ()).
Step 10. query execution.The query statement generated in step 9 is performed in the Cayley knowledge mapping that step 4 builds is served, obtains Query Result.The query execution of such as, example in step 9 is result: " Cheng Long, big vast treasure ... "
It is pointed out that step 5 dictionary in above-mentioned steps build and step 7 template base to build the degree of coupling of these two steps and whole natural language querying treatment scheme more loose: can at Reusability in repeatedly query processing after dictionary and template base have once built; If specific area has existed suitable dictionary and template base in implementation process, directly or slightly can deal with rear introducing and use, and need not start anew to build.Therefore, although dictionary and template base are the necessary component of this method, the process building dictionary and template base is comparatively flexible in implementation process, in the process flow diagram shown in Fig. 1, therefore do not mark these two processes.No matter but what kind of building process dictionary and template base adopt, as long as meet the method shown in Fig. 1 flow process all should be considered as protection scope of the present invention.
Through above-mentioned steps, a specific area, as film relational database, search service just build and complete.This search service make use of knowledge mapping technology, can support the natural language querying of user.Such as, inquiry as user in this embodiment is " which actor features the global earth 80 days ", and this search service just can directly provide corresponding answer: " Cheng Long, big vast treasure ... "
The invention provides a kind of database search method of knowledge based collection of illustrative plates, the method and access of this technical scheme of specific implementation is a lot, and the above embodiment is only the preferred embodiment of the present invention.It should be pointed out that for those skilled in the art, under the premise without departing from the principles of the invention, can also make some improvements and modifications, these improvements and modifications also should be considered as protection scope of the present invention.The all available prior art of each ingredient not clear and definite in the present embodiment is realized.

Claims (6)

1. a database search method for knowledge based collection of illustrative plates, is characterized in that, comprises knowledge mapping and builds and the large step of natural language querying process two;
Described knowledge mapping builds, and comprising:
A. carry out factor analysis to database, and table is divided into the relation table of relation between tables of data and storage object storing concrete object, described key element comprises table name, row name and restriction relation between showing;
B. concept node and entity node is set up according to the record in tables of data and tables of data;
C. according between tables of data between foreign key constraint relation and storage object the relation table of relation set up the relation between entity node;
D. utilize knowledge mapping instrument to store the internodal relation generated in the node generated in step b and step c, set up knowledge mapping service;
Described natural language querying process, comprising:
E. carry out participle to the query statement of user's input, and the vocabulary after participle is carried out query elements mapping, obtain feature schema and the key element value of inquiry, described query elements comprises variable, relation, entity and concept;
F. feature schema is carried out in template base mating and obtain corresponding query pattern;
G. key element value is filled in query pattern and obtains knowledge mapping query statement;
H. in knowledge mapping service, perform knowledge mapping query statement obtain user and inquire about corresponding knowledge.
2. the database search method of knowledge based collection of illustrative plates according to claim 1, is characterized in that,
The method setting up concept node and entity node according to the record in tables of data and tables of data in described step b is:
To each the tables of data T in database a, set up a concept node C with table name A a; Then for table T ain each record T ai, set up an entity E ai, and using this record each row name and corresponding train value as this entity attributes and property value, then by concept node C apoint to entity node E ai, i is the sequence number of record.
3. the database search method of knowledge based collection of illustrative plates according to claim 2, is characterized in that,
The method setting up relation between entity node according to foreign key constraint relation between tables of data in described step c is:
If table T ain row B be table T cthe external key of middle row D, then according to the value corresponding relation of row B and row D, set up table T ain every line item T aiconstructed entity E ai, to table T cmiddle record T cjconstructed entity E cjbetween relation, the title of relation is determined according to the conceptual relation between table name A and C.
4. the database search method of knowledge based collection of illustrative plates according to claim 3, is characterized in that,
The method setting up relation between entity node according to relation table in described step c is:
If relation table T cstore tables of data T ato T bmany-one relationship, then according to table T cthe Table A major key of record, to the mapping relations of table B major key, sets up table T athe entity E built aito table T bthe entity E built bjbetween relation, the title of relation is determined according to table name C.
5. the database search method of knowledge based collection of illustrative plates according to claim 4, is characterized in that,
Carry out participle to the query statement of user's input in step e, and the vocabulary after participle is carried out query elements mapping, the method obtaining feature schema and the key element value of inquiring about comprises:
Use the inquiry O={o that natural language processing segmentation methods inputs user 1o 2... o n, o 1o 2... o nfor individual character, carry out participle and obtain word segmentation result W={w 1=o 1o 2... o k, w 2=o k+1... ..., w n=o m... o n, the dictionary that participle uses comprises general dictionary, and the concept generated in step c, entity, vocabulary that attribute is relevant with relation;
Defining variable (Variable, V), relation (Relation, R), entity (Entity, E), concept (Concept, C) four class query elements, each vocabulary in word segmentation result is mapped to a key element in described four class query elements, is designated as: < P, W >={ < p 1, w 1>, < p 2, w 2> ... < p n, w n> }, the element identification that vocabulary each in word segmentation result maps is extracted the feature schema obtaining inquiring about O, be designated as: P={p 1, p 2... p n, p i∈ { V, R, E, C}.
6. the database search method of knowledge based collection of illustrative plates according to claim 5, is characterized in that,
Query pattern in described step f is designated as X=f (V, R, E, C), and f (V, R, E, C) is the generalized function of four class key elements, is the combination of knowledge mapping inquiry primitive and four class key elements.
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