CN116049148B - Construction method of domain meta knowledge engine in meta publishing environment - Google Patents
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Abstract
The invention provides a construction method of a domain meta knowledge engine in a meta publishing environment, which comprises the following steps: step 1, carrying out meta-knowledge demand analysis on the field; step 2, designing a personalized JSON template engine based on the meta-knowledge requirement; the JSON template engine is used for storing custom meta-knowledge attributes; step 3, constructing a four-layer ontology model; and 4, realizing a domain element knowledge engine based on the JSON template engine and the four-layer ontology model. The invention can solve the problem of different types and attributes of the meta-knowledge focused in each field, and the built field meta-knowledge engine ensures that the field meta-knowledge has the characteristics of simplicity, easiness in use, individuation and universality.
Description
Technical Field
The invention relates to the technical field of open academic publishing, in particular to a method for constructing a domain meta-knowledge engine in a meta-publishing environment.
Background
The development of open science is pushing the evolution of academic communication mechanism, because open science claims to improve the transparency and repeatability of research, strengthen the cooperation of scientific research innovation and accelerate the propagation and transformation of scientific research results through the opening of research methods, research tools, research processes, research results and other scientific research whole processes. Along with expansion of academic achievement types from characters to data, images, multimedia and multi-source fusion information, the achievement publishing mode is gradually expanded and changed from a traditional journal paper to a diversified mode comprising software, scientific data, a research method and the like, and the academic publishing system is also greatly changed to support the emerging publishing demands in a more open posture. The open academic Publishing process is more inclusive, meta knowledge flows faster, radiation is more distant, a program can be more transparent and is more convenient to monitor, and the concept of the open academic Publishing process is not easy to be framed in a traditional Publishing form, so that an open science-oriented Meta Publishing (Meta) concept is provided, the Meta (Meta) contains decomposition, foundation and surpassing meanings, the Meta Publishing is an academic Publishing mode integrating pre-pad Publishing, data Publishing and structured information Publishing into a whole and oriented to the open science concept, and the open academic Publishing method aims at providing a ubiquitous immersive open knowledge communication community which is completely integrated by scientists.
Meta Knowledge (Meta knowledges) refers to information of elements and structures, including contents such as research questions, research methods, research results, analysis conclusions, experimental tools, experimental materials, and the like, and realizes Meta publishing with Meta Knowledge as a core, which has a plurality of benefits:
firstly, the speed of the release of research results is improved. The contents submitted by the scientific researchers are not limited to a complete and mature paper, but the problems, the research methods and ideas, the experimental process, the experimental results and the research conclusion are submitted in a concise and brief manner, so that the writing and review processes are greatly simplified, and the readers are helped to understand the scientific research results deeply.
And secondly, the efficiency of reproduction of the meta-knowledge is improved. By means of big data technology and informatization, structured content representation is established, reproduction of meta-knowledge is stimulated, quick release and convergence of the meta-knowledge are promoted, and finally, the aim is to provide more effective reference for subsequent scientific research, reading and scientific integration on a normalized and standardized platform based on the meta-knowledge are more convenient, and scientific development is facilitated.
And thirdly, the integration degree of academic communication community development is enhanced. The method provides research and development full-flow embedded information service for generalized science and technology workers, builds academic communities for ubiquitous meta-knowledge transmission and communication, is beneficial to follow-up science and technology development dynamics, communicate technological front problems and find collaboration opportunities, and improves the meta-knowledge communication and collaboration efficiency of the science and technology workers.
However, the meta-knowledge has a strong association relation with the field, the field is different, and the type of the meta-knowledge concerned is also different. The domain meta-knowledge engine is an intelligent meta-knowledge management meta-knowledge base, and relates to link modules of domain meta-knowledge type and attribute design, meta-knowledge ontology construction, meta-knowledge extraction, storage and the like, so that the domain meta-knowledge engine is a technical foundation for supporting a meta-publishing concept, the specificity of the domain meta-knowledge cannot be presented through a traditional three-layer ontology model consisting of domains, concepts and examples, and an effective construction method of the domain meta-knowledge engine is lacking currently.
Disclosure of Invention
The invention aims to provide a construction method of a domain meta-knowledge engine in a meta-publishing environment, so as to solve the problem that the types and the attributes of the meta-knowledge concerned in each domain are different.
The invention provides a construction method of a domain meta knowledge engine in a meta publishing environment, which comprises the following steps:
step 1, carrying out meta-knowledge demand analysis on the field;
step 2, designing a personalized JSON template engine based on the meta-knowledge requirement; the JSON template engine is used for storing custom meta-knowledge attributes;
step 3, constructing a four-layer ontology model;
and 4, realizing a domain element knowledge engine based on the JSON template engine and the four-layer ontology model.
Further, in step 2, the meta-knowledge attribute is composed of a public attribute and an owned attribute; wherein:
the public attribute comprises a meta-knowledge storage type, a meta-knowledge name, a meta-knowledge ordering, a meta-knowledge description and meta-knowledge content, wherein the meta-knowledge storage type comprises five types, including a plurality of lines of plain text, a single line of plain text, numbers, tables and uploading files;
the self attribute comprises a custom attribute name, a custom attribute description, a custom attribute filling rule, a custom attribute display state, a custom attribute type and custom attribute content; the custom attribute filling rule consists of a verification mechanism after field filling, verification failure reminding and whether filling is needed or not.
Further, in step 3, the four-layer ontology model includes a domain category layer, a meta knowledge concept layer, a meta knowledge attribute layer and a meta knowledge instance layer; specifically:
the domain category layer consists of specific domains, and comprises a primary domain and a secondary domain;
the meta-knowledge concept layer consists of meta-knowledge types contained in the field; the meta-knowledge type comprises research questions, research methods, research results, research data, analysis conclusions, experimental tools and experimental materials;
the meta-knowledge attribute layer consists of meta-knowledge attributes; the meta-knowledge attribute specifies the specific technical characteristics of each meta-knowledge;
the meta-knowledge instance layer consists of meta-knowledge instances; the meta-knowledge example comprises meta-knowledge filled by scientific researchers and triples automatically extracted from the meta-knowledge.
Further, in step 3, the four-layer ontology model further includes a meta-knowledge type view, a meta-knowledge attribute view, and a meta-knowledge structure view; specifically:
the meta-knowledge type view is a specific description of a domain category layer and a meta-knowledge concept layer in the four-layer ontology model;
the meta-knowledge attribute view is a specific description of a meta-knowledge concept layer and a meta-knowledge attribute layer in the four-layer ontology model;
the meta-knowledge structure view is a specific description of a meta-knowledge attribute layer and a meta-knowledge instance layer in the four-layer ontology model.
Further, the four-layer ontology model consists of classes and relations; the classes of the four-layer ontology model comprise a field set, a meta knowledge concept set, a meta knowledge attribute set and a meta knowledge instance set; the relation of the four-layer ontology model is represented by a triplet, and the triplet comprises a field set, a meta-knowledge concept set, a meta-knowledge attribute set and a meta-knowledge instance set, wherein the triplet is formed by various relations.
Further, step 4 comprises the following sub-steps:
step 4.1, generating a meta-knowledge template based on a JSON template engine, filling meta-knowledge in the meta-knowledge template, and storing the filled meta-knowledge into a first database in a JSON format;
step 4.2, based on the four-layer ontology model, performing ontology modeling by using ontology editing and meta-knowledge acquisition software, and extracting all triples from JSON format data stored in a database;
and 4.3, storing the triplet data set obtained in the steps 4.1-4.2 into a second database, and constructing a distributed multidimensional index based on an open source distributed full text search engine to realize a domain element knowledge engine.
Further, step 4.2 comprises the following sub-steps:
step 4.2.1, extracting the relation in the JSON format data based on the meta-knowledge type view, the meta-knowledge attribute view and the meta-knowledge structure view respectively to form a triplet data set;
step 4.2.2, performing triplet extraction on JSON format data with element knowledge attributes of a plurality of lines of plain texts, wherein manual labeling is performed on the field data, and a labeling model is trained by using a triplet automatic extraction tool, so that triples are extracted from the field data, and the relation of the triples to a triplet data set is supplemented;
and 4.2.3, performing similarity calculation on all the triplet objects based on the open-source word vector tool, finding out the triplet objects with similarity threshold values larger than the set threshold value, and supplementing the relation of the triplet objects to the triplet data set.
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
aiming at the problem of different types and attributes of the meta-knowledge focused in each field, the invention designs the personalized JSON template by analyzing the meta-knowledge demands of the field, constructs and realizes a four-layer ontology model from a field category layer, a meta-knowledge concept layer, a meta-knowledge attribute layer, a meta-knowledge instance layer and the like, and finally constructs a field meta-knowledge engine, so that the field meta-knowledge has the characteristics of simplicity, easiness, individuation and universality.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following description will briefly describe the drawings in the embodiments, it being understood that the following drawings only illustrate some embodiments of the present invention and should not be considered as limiting the scope, and that other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a general flow chart of a method for building a domain meta-knowledge engine in a meta-publishing environment in accordance with an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of an example of a four-layer ontology model according to an embodiment of the present invention.
FIG. 3 is a meta-knowledge type view of an example of a four-layer ontology model in an embodiment of the present invention.
FIG. 4 is a meta-knowledge attribute view of an example of a four-layer ontology model in an embodiment of the present invention.
FIG. 5 is a diagram of an exemplary meta-knowledge structure of a four-layer ontology model in an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples
As shown in fig. 1, this embodiment proposes a method for constructing a domain meta-knowledge engine in a meta-publishing environment, including the following steps:
step 1, carrying out meta-knowledge demand analysis on the field;
and decomposing the research content of the field into the meta-knowledge such as research problems, research methods, research results, research data, analysis conclusions, experimental tools, experimental materials and the like by carrying out meta-knowledge demand analysis on the field, and further determining the specific attributes contained in the meta-knowledge.
Taking the field of natural products belonging to one of the interdisciplinary subjects of chemistry and biology as an example, the meta-knowledge demand analysis is performed: the research results of scientific researchers are decomposed into the meta-knowledge of research objects, experimental materials, research methods, research results and the like; wherein:
the study object further describes the number and source of the study objects (samples or products, animals and plants, etc.), etc.;
experimental materials further illustrate technical specifications, main chemical physical properties, manufacturer names, addresses and the like of experimental materials (including scientific research instruments, chemical reagents, medicines and the like);
the research methods further illustrate the detailed information corresponding to each research method, including (1) experimental environments (such as voltage, temperature, etc.); (2) an experimental material; (3) an experimental procedure; (4) a specific method; (5) statistical analysis methods, etc.;
the results of the study further illustrate the results of the study by each of the study methods.
Step 2, designing a personalized JSON template engine based on the meta-knowledge requirement; the JSON template engine is used for storing custom meta-knowledge attributes;
the JSON is a flexible data organization form and is one of important data storage and transmission standards, so that the personalized JSON template engine is designed based on the meta-knowledge requirement and is applied to user-defined meta-knowledge attributes. The meta-knowledge attribute consists of a public attribute and an owned attribute; wherein:
common attributes include type (meta-knowledge storage type), fieldName (meta-knowledge name), index (meta-knowledge order), note (meta-knowledge description), and value (meta-knowledge content), wherein type mainly has five types of textarea (multi-line plain text), string (single-line plain text), int (number), table (table), and file (upload file).
The custom attributes include name (custom attribute name), note (custom attribute description), rules (custom attribute filling rule), status (custom attribute display state), type (custom attribute type) and value (custom attribute content); the rule is composed of trigger (verification mechanism after field filling), message (verification failure reminder) and required (whether filling is necessary or not).
Taking a meta-knowledge attribute for defining a research object in the field of natural products as an example, the meta-knowledge attribute is stored in a form of a table (table), and the custom attribute also comprises an object name, number, ages, sources, a preparation method, an attachment uploading and the like, wherein the object name, the ages and the sources are string types, the number is an int type, the preparation method is a textarea type, and the attachment uploading is a file type. Then the JSON template engine is defined as follows:
[ { "filedName": "study object", "name": "," note ":" TableRule "is used in the event of" event "is used in the event of" name "is used in the event of" event "is used in the event of" is used in the event "is" does "the" "the".
Step 3, constructing a four-layer ontology model;
as shown in fig. 2, the four-layer ontology model includes a domain category layer, a meta-knowledge concept layer, a meta-knowledge attribute layer and a meta-knowledge instance layer; specifically:
the domain category layer consists of specific domains, and comprises a primary domain and a secondary domain;
the meta-knowledge concept layer consists of meta-knowledge types contained in the fields, wherein the meta-knowledge types change along with the fields, the fields are different, and the meta-knowledge types are different; the meta-knowledge type comprises research questions, research methods, research results, research data, analysis conclusions, experimental tools, experimental materials and the like;
the meta-knowledge attribute layer consists of meta-knowledge attributes which detail the specific technical characteristics of each meta-knowledge;
the meta-knowledge instance layer consists of meta-knowledge instances, wherein the meta-knowledge instances comprise meta-knowledge filled by scientific researchers and triples automatically extracted from the meta-knowledge.
The four-layer ontology model mainly comprises classes (class) and relationships (relation); wherein, the class (class) of the four-layer ontology model comprises a domain set (Domains), a meta-knowledge concept set (Concepts), a meta-knowledge attribute set (Attributes) and a meta-knowledge instance set (Instances); the relationship of the four-layer ontology model is represented by a triplet (triplet), which is assembled into eight types, as follows:
Triple 1 ={(D i ,r,D j )|D i ,D j ∈Domains,r=has_domain}；
Triple 2 ={(D,r,C)|D∈Domains,C∈Concepts,r=has_concept}；
Triple 3 ={(C,r,A)|C∈Concepts,A∈Attributes,r=has_attribute}；
Triple 4 ={(A,r,I)|A∈Attributes,I∈Instances,r=has_instance}；
Triple 5 ={(S,r,O)|S∈Subjects,O∈Objects,r∈predicates}；
Triple 6 ={(C ix ,r,C jx )|C ix ,C jx ∈Concepts,r=same_as}；
Triple 7 ={(I,r,S)|I∈Instances,S∈Subjects,r=relate_to,same_as}；
Triple 8 ={(O p ,r,O q )|O p, O q ∈Objects,r=relate_to,same_as}；
wherein D represents a domain, D i Represents the i-th field, D j Represents a j-th domain; c represents a meta-knowledge concept, C ix An x-th meta-knowledge concept representing an i-th domain, C jx An xth meta-knowledge concept representing a jth domain; a represents a meta-knowledge attribute, A xy A y-th meta-knowledge attribute representing an x-th meta-knowledge concept; i represents a meta-knowledge instance, I m Represents the m-th meta knowledge instance, S represents the subject, S n Represents the nth subject, O represents the object, O p Representing the p-th object, O q Representing the q-th object; r represents the relationship, r 1 For the has_domain relationship, the has_domain represents the inclusion relationship among the fields; r is (r) 2 For the has_concept relationship, has_concept represents the inclusion relationship between the domain and the meta-knowledge concept; r is (r) 3 For the has_attribute relationship, the has_attribute represents the inclusion relationship between the meta-knowledge concept and the meta-knowledge attribute; r is (r) 4 For the has_instance relationship, has_instance represents the inclusion relationship between the meta-knowledge attribute and the meta-knowledge instance; r is (r) 5 As the predictes relationship, predictes represents an association relationship between a subject and an object; r is (r) 6 For the same_as relationship, same_as represents the same relationship; r is (r) 7 For the relationship, the relationship_to represents a similar relationship; triple (Triple) 1 Representing triples formed by the hos_domain relationship among the fields; triple (Triple) 2 A triplet formed by the has_concept relation between the representation field and the meta-knowledge concept; triple (Triple) 3 Representing a triplet formed by a has_attribute relation between the meta-knowledge concept and the meta-knowledge attribute; triple (Triple) 4 Representing a triplet formed by the has_instance relation between the meta-knowledge attribute and the meta-knowledge instance; triple (Triple) 5 Representing triples extracted from text, consisting of subjects (subjects), predicates (predictes), and binomials (objects); triple (Triple) 6 Representing triples formed by the same_as relation among the meta-knowledge concepts; triple (Triple) 7 Representing a triplet formed by a relation of a meta knowledge instance and a triplet subject through a relation of a related_to or a same_as; triple (Triple) 8 Representing triples between triples objects that are formed by a relationship of either relay_to or same_as.
The four-layer ontology model further comprises a meta-knowledge type view, a meta-knowledge attribute view and a meta-knowledge structure view; specifically:
the meta-knowledge type view is a specific description of a domain category layer and a meta-knowledge concept layer in a four-layer ontology model. The meta-knowledge type of each domain is designed based on meta-knowledge requirements, as shown in FIG. 3, D in the meta-knowledge type view of one example of a four-layer ontology model 4 Is D 1 Is the sub-domain of D 7 Is D 3 Is C 41 、C 42 、C 43 And C 44 Is field D 4 Meta-knowledge type, C 71 、C 72 And C 73 Is field D 7 Meta-knowledge type of (D), thus D 1 And D 4 ，D 4 And C 44 And C 42 And C 72 And have has domain (r) 1 ）、has_concept（r 2 ) And same_as (r 6 ) These several relationships, all of which together constitute a meta-knowledge type view.
The meta-knowledge attribute view is a specific description of a meta-knowledge concept layer and a meta-knowledge attribute layer in a four-layer ontology model. The meta-knowledge concepts correspond to different amounts of meta-knowledge attributes, such as C in the meta-knowledge attribute view of one example of the four-layer ontology model shown in FIG. 4 11 And C 21 Is a meta knowledge concept, A 11 、A 12 、A 13 、A 21 、A 22 And A 23 Is a meta-knowledge attribute, C 11 And C 21 ，C 11 And A is a 11 And C 21 And A is a 21 And has a has_attribute (r 3 ) And same_as (r 6 ) These two relationships, all of which together constitute a meta-knowledge attribute view.
The meta-knowledge structure view is a specific description of a meta-knowledge attribute layer and a meta-knowledge instance layer in the four-layer ontology model. Each meta-knowledge attribute needs to be filled with a specific meta-knowledge instance, as in an example meta-knowledge structure view of a four-layer ontology model shown in fig. 5, a 11 、A 12 And A 13 Is a meta-knowledge attribute, I 1 、I 2 And I 3 Is an example of meta knowledge, S 1 、S 2 And S is 3 Is the subject of the triplet O 1 、O 2 、O 3 、O 4 、O 5 And O 6 Object of triplet, A 11 And I 1 ，I 1 And S is equal to 1 ，S 1 With O 1 And O 1 With O 6 And has a has_instance (r) 4 ）、predicates（r 5 ）、same_as（r 6 ) And related_to (r) 7 ) These several relationships, all of which together constitute a meta-knowledge structure view.
And 4, realizing a domain element knowledge engine based on the JSON template engine and the four-layer ontology model. The method specifically comprises the following substeps:
and 4.1, generating a meta-knowledge template based on a JSON template engine, filling meta-knowledge in the meta-knowledge template, and storing the filled meta-knowledge in a first database (such as a MongoDB database) in JSON format data.
Taking study object meta-knowledge as an example, two pieces of JSON format data examples containing object names, ages, sources, numbers, preparation methods and attachment uploads are stored:
{ "TableValue" ({ "id": 1"," object name_String ":" SPF grade healthy male Balb/c mouse "," week_String ": 6-8 weeks", "number_Int": 36"," Source_String ":" Hunan Stokes Lesion laboratory animal Limited "," preparation method_texttarea ": all mice are free to feed standard feed and water under standard conditions", "accessory upload_file": "mouse 1.Jpg" }, { "id": 2"," name_String ": SPF grade healthy male Balb/c mouse", "week_String": 1-5 weeks "," number_Int ": 36", "Source_String": "Hunan Stokes Lesion laboratory animal Limited", "preparation method_texttarea": all mice are free to feed standard feed and water under standard conditions "," accessory upload_file "}" mouse 1.Jpg "}
And 4.2, based on the four-layer ontology model, performing ontology modeling by using ontology editing and meta-knowledge acquisition software (such as Prot g e) and extracting all triples from JSON format data stored in a database. Specifically, the method comprises three links:
the first link is: based on the meta-knowledge type view, the meta-knowledge attribute view and the meta-knowledge structure view, relationships (such as has_domain, has_concept, has_ attribute, has _ instance, same _as relationships) in the JSON format data are extracted to form a triplet data set.
The second link is: and performing triplet extraction on JSON format data with a meta-knowledge attribute of textarea (multi-line plain text), wherein the field data is manually marked, and a labeling model is trained by using a triplet automatic extraction tool (such as deep), so that triples are extracted from the field data, and the precursors are added to the triplet data set.
And the third link: and (3) performing similarity calculation on all the triplet objects based on an open source Word vector tool (such as Word2 vec), finding the triplet objects with similarity threshold value larger than a set threshold value (such as 0.9), and supplementing the relation_to the triplet data set.
Taking the field of natural products as an example, in the meta-knowledge type view, the chemical, biological and natural products form a has_domain relationship, and the natural products, a research object, experimental materials, a research method and a research result form a has_concept relationship; in the meta-knowledge attribute view, research objects and object names, numbers, ages, sources, preparation methods, accessory uploading and the like form a has_attribute relationship, experimental materials and material names, manufacturers, manufacturer addresses, technical specifications and the like form a has_attribute relationship, a research method and method names, experimental programs, experimental environment conditions, research object selection, experimental material selection and the like form a has_attribute relationship, and a research result and result names, detailed description, accessory uploading and the like form a has_attribute relationship; in the meta-knowledge structure view, the object name and mouse, source and Hunan Szechwan laboratory animal Co., ltd.and the like constitute the has_instance relationship, and the mouse and feed and the like constitute the predictes relationship.
And 4.3, storing the triplet data set obtained in the steps 4.1-4.2 into a second database (such as a Neo4j graph database), and constructing a distributed multidimensional index based on an open source distributed full-text search engine (such as an elastic search) to realize a domain meta-knowledge engine.
Specifically, an open source distributed full text search engine (such as an elastic search) is utilized to support navigation browsing of domain element knowledge, and the presentation mode of a search result set is enriched by carrying out layered display on microscopic element knowledge units containing multiple association relations such as co-occurrence, semantics and grammar, so that search experience is improved; and (3) complex network analysis and big data mining are carried out by utilizing the Neo4j graphic database, massive data storage and inquiry are supported, and big data mining applications such as similarity calculation, optimal path, collaborative recommendation and the like are realized by utilizing a Neo4j built-in graph algorithm.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (3)
1. The construction method of the domain meta-knowledge engine in the meta-publishing environment is characterized by comprising the following steps:
step 1, carrying out meta-knowledge demand analysis on the field;
step 2, designing a personalized JSON template engine based on the meta-knowledge requirement; the JSON template engine is used for storing custom meta-knowledge attributes;
step 3, constructing a four-layer ontology model;
step 4, realizing a domain element knowledge engine based on a JSON template engine and a four-layer ontology model;
the meta-knowledge attribute consists of a public attribute and an owned attribute; wherein:
the public attribute comprises a meta-knowledge storage type, a meta-knowledge name, a meta-knowledge ordering, a meta-knowledge description and meta-knowledge content, wherein the meta-knowledge storage type comprises five types, including a plurality of lines of plain text, a single line of plain text, numbers, tables and uploading files;
the self attribute comprises a custom attribute name, a custom attribute description, a custom attribute filling rule, a custom attribute display state, a custom attribute type and custom attribute content; the custom attribute filling rule consists of a verification mechanism after field filling, verification failure reminding and whether filling is necessary or not;
in the step 3, the four-layer ontology model comprises a domain category layer, a meta-knowledge concept layer, a meta-knowledge attribute layer and a meta-knowledge instance layer; specifically:
the domain category layer consists of specific domains, and comprises a primary domain and a secondary domain;
the meta-knowledge concept layer consists of meta-knowledge types contained in the field; the meta-knowledge type comprises research questions, research methods, research results, research data, analysis conclusions, experimental tools and experimental materials;
the meta-knowledge attribute layer consists of meta-knowledge attributes; the meta-knowledge attribute specifies the specific technical characteristics of each meta-knowledge;
the meta-knowledge instance layer consists of meta-knowledge instances; the meta-knowledge example comprises meta-knowledge filled by scientific researchers and triples automatically extracted from the meta-knowledge;
step 4 comprises the following sub-steps:
step 4.1, generating a meta-knowledge template based on a JSON template engine, filling meta-knowledge in the meta-knowledge template, and storing the filled meta-knowledge into a first database in a JSON format;
step 4.2, based on the four-layer ontology model, performing ontology modeling by using ontology editing and meta-knowledge acquisition software, and extracting all triples from JSON format data stored in a database;
step 4.3, storing the triplet data set obtained in the steps 4.1-4.2 into a second database, and constructing a distributed multidimensional index based on an open source distributed full text retrieval engine to realize a domain element knowledge engine;
step 4.2 comprises the following sub-steps:
step 4.2.1, extracting the relation in the JSON format data based on the meta-knowledge type view, the meta-knowledge attribute view and the meta-knowledge structure view respectively to form a triplet data set;
step 4.2.2, performing triplet extraction on JSON format data with element knowledge attributes of a plurality of lines of plain texts, wherein manual labeling is performed on the field data, and a labeling model is trained by using a triplet automatic extraction tool, so that triples are extracted from the field data, and the relation of the triples to a triplet data set is supplemented;
and 4.2.3, performing similarity calculation on all the triplet objects based on the open-source word vector tool, finding out the triplet objects with similarity threshold values larger than the set threshold value, and supplementing the relation of the triplet objects to the triplet data set.
2. The method for building a domain meta-knowledge engine in a meta-publishing environment according to claim 1, wherein in step 3, the four-layer ontology model further includes a meta-knowledge type view, a meta-knowledge attribute view, and a meta-knowledge structure view; specifically:
the meta-knowledge type view is a specific description of a domain category layer and a meta-knowledge concept layer in the four-layer ontology model;
the meta-knowledge attribute view is a specific description of a meta-knowledge concept layer and a meta-knowledge attribute layer in the four-layer ontology model;
the meta-knowledge structure view is a specific description of a meta-knowledge attribute layer and a meta-knowledge instance layer in the four-layer ontology model.
3. The method for building a domain meta knowledge engine in a meta publishing environment according to claim 1 or 2, wherein the four-layer ontology model is composed of classes and relationships; the classes of the four-layer ontology model comprise a field set, a meta knowledge concept set, a meta knowledge attribute set and a meta knowledge instance set; the relation of the four-layer ontology model is represented by a triplet, and the triplet comprises a field set, a meta-knowledge concept set, a meta-knowledge attribute set and a meta-knowledge instance set, wherein the triplet is formed by various relations.
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