WO2022037103A1 - Procédé d'alignement d'indices valeur-qualité-capacité de services multipartites orientés vers les limites spatiotemporelles - Google Patents

Procédé d'alignement d'indices valeur-qualité-capacité de services multipartites orientés vers les limites spatiotemporelles Download PDF

Info

Publication number
WO2022037103A1
WO2022037103A1 PCT/CN2021/089373 CN2021089373W WO2022037103A1 WO 2022037103 A1 WO2022037103 A1 WO 2022037103A1 CN 2021089373 W CN2021089373 W CN 2021089373W WO 2022037103 A1 WO2022037103 A1 WO 2022037103A1
Authority
WO
WIPO (PCT)
Prior art keywords
index
relationship
service
semantic
space
Prior art date
Application number
PCT/CN2021/089373
Other languages
English (en)
Chinese (zh)
Inventor
涂志莹
李敏
王忠杰
徐晓飞
徐汉川
Original Assignee
哈尔滨工业大学
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 哈尔滨工业大学 filed Critical 哈尔滨工业大学
Publication of WO2022037103A1 publication Critical patent/WO2022037103A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06395—Quality analysis or management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/10—Text processing
    • G06F40/166—Editing, e.g. inserting or deleting
    • G06F40/169—Annotation, e.g. comment data or footnotes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/205—Parsing
    • G06F40/216—Parsing using statistical methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/237—Lexical tools
    • G06F40/242—Dictionaries
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
    • G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
    • G06V10/758—Involving statistics of pixels or of feature values, e.g. histogram matching

Definitions

  • the invention belongs to the technical field of enterprise interoperability in software engineering, in particular to the field of multi-participant service non-functional attribute alignment, and relates to a time-space-oriented multi-party service value-quality-capability index alignment method.
  • Enterprise interoperability is a prerequisite for the exchange of data and information among service participants, to reach consensus on service requirements and service goals, and to establish a stable cooperative relationship and a reliable cooperative model.
  • the "European Interoperability Framework for Pan-European E-Government Services" (ElF) identifies three types of organizational interoperability, technical interoperability and semantic interoperability.
  • organizational interoperability is related to enterprise organizational structure and business implementation process, which can be solved with the help of modeling specifications and model transformation methods; technical interoperability includes interactive interfaces, data integration, representation and exchange, usually with the help of standardized metadata formats and meanings As a reference to achieve data consistency; semantic interoperability is to eliminate inconsistencies in the exchange of information between different enterprises.
  • Service evaluation index is a statistical index to measure and evaluate service value-quality-capability. It is an effective reference information for service decision-making and optimization, and it is also an important negotiation content for various service providers to establish cooperative relations.
  • the evaluation indicators contain not only rich semantic information, but also detailed qualitative and quantitative description information. Different participants have their own norms and habits in the definition, interpretation, quantification, and empowerment of indicators.
  • the premise of the cooperation between the two parties is to realize the alignment of the semantics and quantitative methods of the multi-party service evaluation indicators, so as to ensure that the content expressed by each other's indicators and the meaning of the values can be accurately understood in the process of multi-party cooperation and cooperation.
  • the traditional research on semantic interoperability of heterogeneous enterprise models mainly focuses on using ontology as the semantic model, establishing domain ontology through ontology construction or reconstruction techniques (ontology hybridization, synthesis, mutation, etc.), and providing semantic reference for model interoperability.
  • Ontology-based model semantic mapping rules and strategies realize semantic alignment between heterogeneous enterprise models, including term alignment, conceptual granularity alignment, angle alignment, coverage alignment, etc., but these alignment schemes cannot solve the alignment of indicator measurement methods; Semantic conflicts between various heterogeneous models, including the same name but different names, different names for the same name, inconsistent scope of the concept, etc.; finally realize the information sharing and business cooperation between the alliances.
  • the present invention provides a method for aligning multi-party service evaluation indicators oriented to the space-time boundary.
  • a multi-party service value-quality-capability index alignment method oriented to the space-time boundary comprising the following steps:
  • Step 1 Extract keyword groups including service content, business activities, index evaluation aspects and index evaluation rules from the index definition, wherein:
  • the indicator definition includes indicator name, abbreviation/idiom, English abbreviation, indicator explanation, superior direction, dimension (unit + order of magnitude), value range, and calculation formula;
  • Service content including service providers (personnel roles, system tools, software applications, etc.), service carriers (commodities, orders, knowledge, data, etc.) and service execution environment and context, generally Noun phrases;
  • 2Business activities including specific implementation behaviors of service providers and detailed disposal methods of service carriers, generally verb phrases;
  • 3Evaluation aspects including service content and business activities modifiers, generally represented by XX rate
  • Step 2 According to the public dictionary, the domain dictionary and the self-built dictionary, calculate the morpheme relationship between the four key groups of the two indicators, and obtain the semantic similarity matrix between the indicators, where:
  • Described public dictionary includes synonym word forest (extended version), HowNet dictionary, Baidu Chinese dictionary;
  • the domain dictionary includes Sogou industry thesaurus and Baidu industry thesaurus, including six entries: concept identifier, concept name, synonym, English name, semantic description, and application field. It is established by field experts based on their understanding and experience of the field. A list of domain-specific concepts;
  • the definition content of the phrase in the self-built dictionary includes ID, phrase, part of speech, the category (one of service content, business activity, index evaluation side, index evaluation rule), synonyms, antonyms, similar words, hypernyms, Hyponymy, causal-related phrases, belonging/source-related phrases, usage/tool-related phrases, composition/total score-related phrases, and execution-dependent-related phrases;
  • the morpheme relationship includes four types: similar (highly similar), similar (weaker than similar), related, and similar;
  • the semantic similarity matrix is a two-dimensional matrix, which are four types of keyword group sets of two indicators;
  • Step 3 Determine the semantic relationship between the indicators with the help of the semantic similarity matrix, and calculate the relationship confidence, where:
  • the semantic relationship includes similarity relationship (1same index; 2 conjugate index; 3 subordinate index; ), related relationship (4 service content related; 8 Similar business; 9 Similar service content);
  • Step 4 Determine the semantic relationship of all indicators according to Step 3 to obtain a semantic relationship network, delete redundant edges according to the direction and quantity of the semantic relationship between the indicators, and simplify the semantic network, wherein:
  • the semantic relationship network refers to a network with indicators as nodes and semantic relationships between indicators as edges.
  • the edge attributes are the semantic relationship type and confidence, and the direction of the edge includes two kinds of directed and undirected. 5 Business-related is directional;
  • Step 5 Fit the distribution characteristics of the indicator in the single domain and the rich domain according to the sample data of the indicator in different space-time boundaries, where:
  • Time refers to different time domains
  • space refers to different geographic domains
  • boundary refers to different service implementation environments (online or offline), different service implementation platforms or different service participants;
  • the single domain distribution feature refers to the probability distribution feature of the indicator in one service domain
  • the rich domain distribution feature refers to the probability distribution feature of the indicator in two or more service domains
  • Step 6 Establish an alignment relationship in the way of index quantification with the probability quantile as a reference, in which:
  • the alignment relationship in the index quantification method refers to finding the corresponding index value range of a certain type of service level under different space-time boundary characteristics, or determining the corresponding service level of the index value under a specific space-time boundary.
  • the present invention has the following advantages:
  • the present invention does not depend on the construction of ontology, but uses common methods of natural language processing to extract key words contained in the sentences defined and explained by indicators, and uses public dictionaries and domain
  • the lexical information and morpheme relationships contained in the dictionary are used to mine the correlation between different indicators.
  • the present invention summarizes the factors that lead to inconsistent quantification methods in the process of collaboration among multiple participants, and considers the relationship between the specific value of the indicator in the multi-dimensional service implementation environment and the actual service level to be expressed from the perspective of space and time. Mapping relationship, to achieve the alignment of index quantification.
  • Fig. 1 is the multi-party service value-quality-capability index alignment method framework oriented to the space-time boundary of the present invention
  • Fig. 2 is the method framework of the multi-participant service value-quality-capability index semantic alignment oriented to domain features of the present invention
  • Fig. 3 is the method framework of the multi-participant service value-quality-capability index quantification method alignment oriented to spatiotemporal features of the present invention
  • Fig. 4 is the principle of index relation judgment in the semantic alignment stage of the present invention.
  • FIG. 5 is an example diagram of the keyword analysis of the domain feature-oriented service evaluation index of the present invention.
  • FIG. 6 is a schematic diagram of semantic alignment of domain feature-oriented multi-participant service evaluation indicators of the present invention.
  • FIG. 7 is an example diagram of a single-domain distribution feature of indicators oriented to spatiotemporal features of the present invention.
  • FIG. 8 is an exemplary diagram of a spatiotemporal feature-oriented index rich domain distribution feature of the present invention.
  • FIG. 9 is a theoretical diagram of alignment of the spatiotemporal feature-oriented multi-participant service evaluation index quantification method according to the present invention.
  • the invention provides a multi-party service value-quality-capability index alignment method oriented to the space-time boundary.
  • the method is divided into two parts: the semantic alignment of the multi-participant service evaluation index oriented to the domain characteristics and the multi-participant service oriented to the characteristics of the time-space boundary.
  • the quantification methods of evaluation indicators are aligned, and the framework is shown in Figure 1-3.
  • the purpose of semantic alignment of the present invention is to extract key elements of indicators through natural language processing related technologies on the premise of knowing the multi-domain and multi-participant service value-quality-capability evaluation index system, and then calculate with the help of public dictionaries, domain dictionaries and self-built dictionaries.
  • the semantic relationship between the four types of phrases is finally determined on the basis of the lexical relationship matrix and the relationship confidence is calculated.
  • the multi-domain and multi-participant index semantic relationship network is obtained.
  • Each participant can learn the relationship between its own service index and other party's index from the semantic relationship network. This relationship is not limited to the situation of the same name but different names or different names, and can also mine richer semantic relationships.
  • the original index definition includes index name, abbreviation/idiom, English abbreviation, index explanation, superior direction, dimension (unit + order of magnitude), value range, calculation formula, etc.
  • the abbreviation/idiom and English abbreviation include: Strong domain expertise, it is necessary to use the relevant explanations contained in the domain dictionary to assist understanding; the index names and explanations lack normative, and the naming methods and explanation details of different participants are inconsistent; the calculation content also implies index related relation.
  • the present invention completes the index preprocessing in the first step, extracts the key elements of the index through natural language processing technologies such as word segmentation, part-of-speech tagging, dependency syntax analysis, word frequency statistics, etc., and eliminates those that are difficult to understand or irrelevant to service evaluation.
  • Words get [service content, business activities, index evaluation side, index evaluation rules] four types of phrases.
  • Service content It includes the roles of personnel involved in service implementation, the resources that service execution depends on, tangible products or valuable knowledge information accompanying the service delivery process, etc., generally represented by proper nouns.
  • Indicator evaluation side describe the nouns that modify service content or business activities, generally with specific suffixes, such as XX rate, XX degree, XX effect, XX nature.
  • the evaluation indicators have specific evaluation frequency and objects, such as daily average, monthly average, annual average; or per person, per order, per case.
  • the main judging basis of the index relationship of the present invention is three types of dictionaries: public dictionaries, domain dictionaries and self-built dictionaries.
  • the lexical richness, lexical relationship detail, lexical explanation detail, and lexical organization structure in the dictionaries will affect the calculation result. reliability.
  • the present invention selects the synonym Cilin (extended version), HowNet dictionary, and Baidu Chinese dictionary as public dictionaries that can be referred to; Sogou industry thesaurus and Baidu industry thesaurus are domain dictionaries that can be referred to; the self-built dictionary contains ID, phrase , part of speech, described category (one of the four of service content, business activity, evaluation side, evaluation rules), synonyms, antonyms, similar words, hypernyms, hyponyms, causally related phrases, belonging/source related phrases, usage/tools Related phrases, composition/total score related phrases, execution-dependent related phrases, etc. Then comprehensively use the above dictionary information to calculate the relationship between the four types of phrases.
  • the present invention defines three major categories and nine sub-categories for the correlation between indexes on the semantic level, wherein: the nine categories of relations are explained as follows:
  • the same indicator It means that the service content, business activities, indicator evaluation aspects and modifiers can all correspond, and all have highly similar semantics. eg. Food packaging rate, food packaging efficiency.
  • Conjugate index It means that the service content and business activities are highly similar, but the evaluation aspects of the index are antonyms to each other. eg. The cleanliness of the restaurant and the degree of clutter in the dining environment.
  • word A is a component of word B, or word A is a subcategory of word B).
  • word A is a subcategory of word B.
  • Commodity defective rate fresh defective rate.
  • Relevance of service content refers to similar business activities (if both exist), similar aspects of index evaluation (weaker than similar approximation), and there is a certain correlation between service content, such as the health status of the chef and the hygiene of the dishes, and the dishes are made by the chef. , health and hygiene are similar.
  • Business-related Refers to similar service content, similar indicators and evaluation aspects, and there is a certain correlation between business activities, such as the firmness of food packaging and the degree of non-destructiveness of food transportation, because packaging is a pre-order activity of transportation, and the degree of firmness and non-destructiveness are similar .
  • Indicator correlation It means that there is no obvious correlation between service content and business activities, but when the indicator description contains accompanying words such as "with XXX” and "more XX, more XX", it indicates that there is a correlation between the two indicators. If the change trend is consistent, it is positive correlation; otherwise, it is negative correlation. For example, the delivery time of dishes is negatively correlated with the degree of quality assurance of the dishes. Obviously, the longer the delivery time, the worse the quality assurance of the dishes.
  • Similar indicators/service evaluation aspects Refers to the similar service evaluation aspects, but the service content and business activities are neither similar nor related, or the service content and business activities are not extracted. In this case, a similar relationship can be roughly defined. eg. Dishes packaging accuracy, order accounting accuracy.
  • Similar business refers to similar business activities, but the service content and evaluation aspects are neither similar nor related. eg. The accuracy of food packaging and the firmness of food packaging.
  • Similar service content Refers to the similar service content, but the business activities and evaluation aspects are neither similar nor related. eg. Commodity storage time, the proportion of finishing commodities.
  • the main work of the preprocessing stage is to extract indicators
  • the keyword groups of these four types of information contained in The reason why it is not four words but phrases is that some indicators may contain words such as "such as XX", “including XX", "XX, etc.” in the content of the indicator explanation.
  • the input in the preprocessing stage is a sentence S i defined and explained by an indicator.
  • the purpose of word segmentation is to extract all the words belonging to the above four categories of keywords from the sentence and remove unnecessary stop words to obtain WG (WG represents the number of key words).
  • important words containing actual semantics such as nouns, verbs, quantifiers, adverbs, adjectives, conjunctions, etc., can be identified from WG, and corresponding to the service content phrase WG services , business activity phrase WG business , and indicator evaluation side phrases WG indicators , modifier phrase WG adjunctword .
  • the dependency/modification relationship between words of different parts of speech can be obtained at the stage of dependency syntax analysis.
  • association relationships By synthesizing the analysis results of all evaluation indicators, the following four types of association relationships can be summarized: 1What are the related business actions of a certain service content; 2 Who are the implementers of a business activity and who are the recipients; 3 What are the specific evaluation aspects of a service content or business activity; 4 Which evaluation aspects are public (most service content or business activities will be considered) .
  • dependency syntactic analysis can also clarify the co-ordinated words related to conjunctions, and can further delete unimportant words.
  • the above preprocessing work can be completed by relying on natural language processing toolkits such as StanfordNLPCore and language models trained on public large corpora.
  • table turnover rate the original definition of the indicator is as follows: [Table turnover rate; the average number of times each table is used in a hotel in a day, the table turnover rate is an important indicator to measure the profitability of a restaurant and is closely related to the average daily passenger flow of the restaurant; (table turnover rate) The number of times of use - the total number of units) ⁇ the total number of units].
  • the four types of phrases obtained after preprocessing are as follows:
  • WG services ⁇ restaurant, table, dining room, table ⁇ ;
  • WG indicators ⁇ number of times, total number of stations, passenger flow ⁇ ;
  • WG adjunctword ⁇ one day, per sheet, daily average ⁇ .
  • the present invention uses the ID-IDF method to quantify The importance of each word is analyzed, and the unimportant words are deleted. At the same time, this importance will also be involved in the subsequent indicator relationship determination.
  • the calculation formula is as follows:
  • n i,j is the total number of occurrences of a specific word i in an indicator j
  • n k,j is the total number of occurrences of other words k in the indicator j
  • represents the number of all indicators
  • idf i denotes the degree of exclusiveness of the word in the explanation of the index.
  • index correlation is directly affected by lexical semantic association.
  • the existing open dictionaries partially meet the needs in this regard, but most of them only include hyponymous relations, synonymous relations, antonymous relations, homogeneous relations, etc.
  • the related relationship has not been included.
  • the present invention summarizes the common lexical semantic relationships of service evaluation indicators, but there is no excellent method to accurately extract these semantic relationships from the public domain, so it is temporarily replaced by a rough lexical semantic relationship dictionary and a user-built dictionary.
  • A is a kind of B
  • A is a hyponym of B
  • B is a hypernym of A.
  • a and B have a common abstract parent class in the tree-like upper-lower relationship. Such as “dishes” and “meat products”.
  • A is the raw material of B, and B is processed by A. Such as “dishes” and “ingredients”.
  • A is a tool of B related business, such as "dish” and "refrigerator”.
  • a activity is the pre-order activity of B activity, and B activity is the successor activity of A activity, such as "packaging” and "delivery”;
  • a and B belong to the same class of quantifiers, so they can be converted with the help of conversion formulas, such as "daily average” and “monthly average”.
  • the index system builder can configure the "similar judgment threshold TH hs ", "similar judgment threshold TH s ", “similar judgment threshold TH ls ", and "related judgment threshold TH r " (thresholds take The value range is between 0 and 1. There is no value limit for the relevant judgment threshold. The other three thresholds need to satisfy TH hs > TH s > TH ls ). On the other hand, you can configure the "lower limit of relationship number” and “upper limit of relationship number”, and automatically adjust the size of the above four thresholds on the premise of ensuring the number of relationships as much as possible.
  • the present invention expresses it as the following six categories:
  • High similarity the calculated value of similarity between words is greater than the similarity judgment threshold TH hs ;
  • Antonym of each other refers to the words of the adjective part of speech that are antonyms to each other in the dictionary, or the sum of the sentiment values expressed is approximately 1;
  • LS Hyponymy relationship
  • NULL means that there is neither a highly similar relationship nor a related relationship; or the category of words does not exist in the definition of one indicator.
  • the determination of the above semantic relationship can be obtained by calculating the position, number, identifier and dictionary structure of the word in the dictionary.
  • Step 3 Determining the relationship between indicators
  • the relationship between the four types of words is determined with the help of an open public dictionary.
  • the synonym forest, HowNet and Baidu Chinese dictionary are adopted in the experiment of the present invention, which contains information such as word frequency, part of speech, synonyms, hypernyms, word codes, and related words.
  • users can also build their own dictionaries to supplement.
  • To determine whether there is a certain semantic relationship between the two indicators In, Im first calculate the same type of phrases The semantic association that exists between k ⁇ ⁇ services,bu sin ess,indicators,adjunctword ⁇ .
  • the relationship between homogeneous phrases can be calculated using a matrix Express:
  • the index In contains p words
  • the index Im contains q words
  • each word has a corresponding IF-IDF value
  • the matrix size is p ⁇ q.
  • Each element a i,j in the matrix is a two-tuple ⁇ RelarionType, Confidence> including the relation type and confidence between words, where RelationType ⁇ HS,AN,SY,LS,RE,NULL ⁇ and Confidence ⁇ [0 ,1].
  • the r Max corresponding to the maximum value of SD r is the semantic association type of this type of phrase, and the confidence level of this semantic association is the mean of the confidences of all elements of the same type in the matrix (other statistics can also be adopted).
  • n and m represent the index I n and the index I m respectively
  • k refers to the four types of keyword groups
  • num refers to the number of words.
  • Step 4 Optimize the relationship between indicators
  • the present invention defines the following evaluation indicators:
  • the in-degree of a node indicates the degree of dependence of the node in the comprehensive index evaluation system, which means that many related variables or indicators will determine or affect the value of the index. If the maximum in-degree of the node is larger, it means that the index system The structure level is shallower, the fault tolerance rate is lower, and the error propagation probability is also lower.
  • the out-degree of a node indicates the importance of the node in the comprehensive index evaluation index system, which means that the index can determine or affect the value of multiple indicators. If the maximum node out-degree is larger, it means that the index system structure The more complex and unstable it is, the more likely it is to cause problems that affect the whole body.
  • the hit rate means that the index semantic relationship mined by the above method includes the deterministic centralized index Among them, e j represents the jth edge in the indicator semantic relation network, and ⁇ e ("Condition") represents the number of indicators that an element meets a certain condition.
  • This step is only to analyze the alignment effect of the above methods in detail. If the relationship between similar indicators is high, it means that the index evaluation system has high redundancy; The high proportion of index relationship means that the index system is more detailed.
  • This method is highly dependent on the lexicon and word semantic association judgment threshold, so the result of the index semantic alignment obtained by the artificial initial input may have insufficient relationship mining or relationship mining error.
  • the hit rate, error rate, and innovation degree mentioned in the above alignment result evaluation are all proportional to coverage.
  • the richness of the index content will also affect the determination of the index relationship. If the index content is too concise (the description of service content, business activities, and evaluation aspects is incomplete), it is often easy to be classified into the same index relationship. Therefore, if the relationship between similar indicators is high and the error rate is high, the content optimization can be explained by supplementary indicators.
  • the purpose of the quantitative alignment method of the present invention is to define the space-time boundary and divide the service domain based on the sample data of the known index under different space-time boundary conditions, and then use the kernel density estimation to fit the spatio-temporal boundary characteristic distribution of the index on the single domain and the rich domain. , solve the probability distribution function according to the fitted probability density function, and then use the quantile as the benchmark to solve the corresponding value of the index under different space-time boundary characteristics.
  • the mapping relationship between the specific value of the index and the actual service level is not unique and constant.
  • the same index value may also correspond to different service levels under different space-time boundary conditions, and different service levels are under different space-time boundary conditions. It is possible for the indicator to take the same value.
  • the price level and average price of commodities vary significantly in different regions.
  • the same commodity average price is high in Harbin but low in Shanghai; or distribution efficiency and delivery time also exist in time, space and field.
  • the efficient delivery time during the off-peak dining period only takes 20 minutes
  • the high-efficiency delivery time during the dining peak period is generally about 30-40 minutes
  • the efficient delivery time at midnight is 50-60 minutes.
  • the difference in characteristic distribution of indicators in different time and space boundaries is not considered, it will lead to the failure or imbalance of service decision-making and optimization.
  • an enterprise formulates a unified commodity price adjustment strategy across the country it will be obvious to low-income areas. Rising and high-income regions did not feel a significant difference.
  • the decision maker can perceive the distribution difference of the index value in different time and space boundaries, and formulate a reasonable enterprise decision plan according to the alignment mapping function.
  • the time domain has natural continuity and can be described by interval numbers.
  • the specific definition is as follows:
  • T start ,T end take a certain moment in the past or the current moment as T start , and define a specific deadline as T end ;
  • [T start ,T end ] period define fixed T start and T end , define a clock period period;
  • [N i , N j ] slice defines a fixed time slice slice, starting with the N i th slice and ending with the N j th slice.
  • T E-start , T E-end Event , taking the event occurrence as T E-start , taking the event’s influence end as T E-end , and Event being the trigger event in the time domain.
  • the spatial domain is the geographic domain, which can be described in the form of set algebra.
  • the specific definition is as follows:
  • Location 1 a geographic location with latitude and longitude attributes; 2 streets, business districts, communities, etc. with proper names; 3 names of provinces and municipalities determined according to the division of national administrative regions.
  • Regional attributes can be ranked by regional advantages (such as regional economic development, population density, education level, consumption index, etc.), and each region will correspond to a Rank value, thereby determining the partial order relationship.
  • the generalized domain is to divide the service domain into several sub-domains according to a certain boundary rule, highlighting the characteristics of different sub-domains and the fusion and transition between sub-domains with business optimization and service collaboration.
  • Boundary rules can be formulated according to the industry field, service content and nature, and the technology platform on which service execution depends.
  • the traditional definition of service boundaries is limited to the existence of management boundaries between autonomous organizations, and other boundaries are equivalent to the separation of technology platforms and service content caused by organizational boundaries.
  • organizational boundaries It is not enough to fully describe the existence of service boundaries. It is necessary to define richer service boundaries to provide a basis for judgment in service collaboration and integration.
  • Step 3 Calculate the alignment relationship of the indicators in terms of quantitative methods
  • step 2 we obtained the characteristic distribution of the indicators in different time-space boundary service domains.
  • the quantile ⁇ is used as the alignment reference, and it is assumed that the indicator I presents two distributions cdf(I a ) and cdf(I b ) on the two service domains a and b.
  • ⁇ [0,1] is the function of the independent variable, each quantile ⁇ ' corresponds to two index values i' a , i' b , so that the correspondence between the index values on the two service domains can be established relationship, as shown in Figure 9.
  • the alignment of multiple space-time boundary indicators is also established on the basis of quantiles.
  • the service level can be converted into a number between [0, 1], and it can be known that a certain service level is under different space-time boundary conditions. The corresponding specific index value.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Artificial Intelligence (AREA)
  • General Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Business, Economics & Management (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Computational Linguistics (AREA)
  • Human Resources & Organizations (AREA)
  • Evolutionary Computation (AREA)
  • Evolutionary Biology (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Economics (AREA)
  • Development Economics (AREA)
  • Strategic Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Probability & Statistics with Applications (AREA)
  • Educational Administration (AREA)
  • Computing Systems (AREA)
  • Medical Informatics (AREA)
  • Databases & Information Systems (AREA)
  • Multimedia (AREA)
  • Game Theory and Decision Science (AREA)
  • Software Systems (AREA)
  • Marketing (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Machine Translation (AREA)

Abstract

L'invention concerne un procédé d'alignement d'indices valeur-qualité-capacité de services multipartites orientés vers les limites spatiotemporelles. Le procédé est divisé en deux parties : alignement sémantique d'indices d'évaluation valeur-qualité-capacité de services à plusieurs participants orientés vers les attributs de domaine et alignement de procédés de quantification d'indices d'évaluation valeur-qualité-capacité de services à plusieurs participants orientés vers les attributs de domaine de limites spatiotemporelles. Le procédé ne repose pas sur la construction d'une ontologie, mais utilise des moyens communs de traitement de la langue naturelle pour extraite du vocabulaire clé contenu dans une phrase définie et expliqué par un indicateur, et, en vertu informations de vocabulaire contenu dans des dictionnaires publics et des dictionnaires de domaine et d'une relation de morphèmes, extrait la corrélation entre différents indicateurs. En ce qui concerne l'alignement de procédés de quantification, le procédé résume les facteurs qui conduisent des procédés de quantification à être incohérents dans le processus collaboratif de plusieurs participants, et prend en compte, du point de vue du temps et de l'espace, le mappage entre une valeur spécifique d'un indice dans un environnement multidimensionnel de mise en œuvre de services et le niveau de service réel nécessitant l'expression, assurant ainsi l'alignement de procédés de quantification d'indices.
PCT/CN2021/089373 2020-08-18 2021-04-23 Procédé d'alignement d'indices valeur-qualité-capacité de services multipartites orientés vers les limites spatiotemporelles WO2022037103A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202010833133.2A CN111898928B (zh) 2020-08-18 2020-08-18 面向时空界的多方服务价值-质量-能力指标对齐方法
CN202010833133.2 2020-08-18

Publications (1)

Publication Number Publication Date
WO2022037103A1 true WO2022037103A1 (fr) 2022-02-24

Family

ID=73229209

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2021/089373 WO2022037103A1 (fr) 2020-08-18 2021-04-23 Procédé d'alignement d'indices valeur-qualité-capacité de services multipartites orientés vers les limites spatiotemporelles

Country Status (2)

Country Link
CN (1) CN111898928B (fr)
WO (1) WO2022037103A1 (fr)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111898928B (zh) * 2020-08-18 2021-08-31 哈尔滨工业大学 面向时空界的多方服务价值-质量-能力指标对齐方法
CN112732251A (zh) * 2020-12-25 2021-04-30 哈尔滨工业大学 一种面向服务互联网的服务价值网的半自动化生成方法

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102622675A (zh) * 2012-03-14 2012-08-01 浙江大学 一种实现集群式供应链环境下企业互操作的方法及系统
CN106156082A (zh) * 2015-03-31 2016-11-23 华为技术有限公司 一种本体对齐方法及装置
CN107315768A (zh) * 2017-05-17 2017-11-03 上海交通大学 基于异构信息模型映射的配网信息交互方法及系统
US20170364503A1 (en) * 2016-06-17 2017-12-21 Abbyy Infopoisk Llc Multi-stage recognition of named entities in natural language text based on morphological and semantic features
CN111898928A (zh) * 2020-08-18 2020-11-06 哈尔滨工业大学 面向时空界的多方服务价值-质量-能力指标对齐方法

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101645934B (zh) * 2009-08-31 2012-02-29 东软集团股份有限公司 基于加权的Web服务评价方法、Web服务查找方法及其装置
CN105740237B (zh) * 2016-02-03 2018-04-13 湘潭大学 一种基于词汇语义相似度的学生能力达成度评价度量方法
CN110175325B (zh) * 2019-04-26 2023-07-11 南京邮电大学 基于词向量和句法特征的评论分析方法及可视化交互界面

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102622675A (zh) * 2012-03-14 2012-08-01 浙江大学 一种实现集群式供应链环境下企业互操作的方法及系统
CN106156082A (zh) * 2015-03-31 2016-11-23 华为技术有限公司 一种本体对齐方法及装置
US20170364503A1 (en) * 2016-06-17 2017-12-21 Abbyy Infopoisk Llc Multi-stage recognition of named entities in natural language text based on morphological and semantic features
CN107315768A (zh) * 2017-05-17 2017-11-03 上海交通大学 基于异构信息模型映射的配网信息交互方法及系统
CN111898928A (zh) * 2020-08-18 2020-11-06 哈尔滨工业大学 面向时空界的多方服务价值-质量-能力指标对齐方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
LI JIA, ZHU, MING; LIU, CHENG; YANG, ZHENG-QIU: "Research and Implementation on Chinese Ontology Mapping", JOURNAL OF CHINESE INFORMATION PROCESSING, vol. 21, no. 4, 31 July 2007 (2007-07-31), XP055902041, ISSN: 1003-0077 *

Also Published As

Publication number Publication date
CN111898928A (zh) 2020-11-06
CN111898928B (zh) 2021-08-31

Similar Documents

Publication Publication Date Title
WO2021196520A1 (fr) Procédé et système de construction de carte de connaissances orientée domaine fiscal
Maedche et al. Discovering conceptual relations from text
Maedche et al. Semi-automatic engineering of ontologies from text
Maedche et al. Mining ontologies from text
Navigli et al. Ontology learning and its application to automated terminology translation
CN113704451B (zh) 一种电力用户诉求筛选方法、系统、电子设备和存储介质
WO2022037103A1 (fr) Procédé d'alignement d'indices valeur-qualité-capacité de services multipartites orientés vers les limites spatiotemporelles
CN107103100B (zh) 一种容错的基于图谱架构的智能语义搜索方法
Elbendak et al. Parsed use case descriptions as a basis for object-oriented class model generation
Bella et al. Language and domain aware lightweight ontology matching
Allen et al. Broad coverage, domain-generic deep semantic parsing
Yan et al. Response selection from unstructured documents for human-computer conversation systems
Zhu et al. A method for the dynamic collaboration of the public and experts in large-scale group emergency decision-making: Using social media data to evaluate the decision-making quality
Ruskanda et al. Comparative study on language rule based methods for aspect extraction in sentiment analysis
Wueest et al. Electoral campaigns and relation mining: Extracting semantic network data from newspaper articles
Sonbol et al. A Machine Translation Like Approach to Generate Business Process Model from Textual Description
Costa et al. Knowledge representations with ontology support for collaborative engineering in architecture engineering and construction
Mariani et al. Reuse and plagiarism in Speech and Natural Language Processing publications
Cao et al. A text-based mining approach for real estate policy impact monitoring and analysis
Savin et al. Using computational linguistics to analyse main research directions in Economy of Regions
Qin et al. Semantic document exchange through mediation of machine natural language
Xie et al. Chinese Event Discourse Deixis Resolution: Design of the Dataset and Model
Yu Hazard information extraction and classification based on domain ontology
Zhang et al. Aspect Level Sentiment Classification Based on Viewpoint Information Unit
Wu et al. An enterprise public opinion emergency response system

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21857204

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 21857204

Country of ref document: EP

Kind code of ref document: A1

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 09/08/2023)

122 Ep: pct application non-entry in european phase

Ref document number: 21857204

Country of ref document: EP

Kind code of ref document: A1