WO2021143014A1 - Method and device for generating knowledge graph, and computer readable storage medium - Google Patents
Method and device for generating knowledge graph, and computer readable storage medium Download PDFInfo
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- WO2021143014A1 WO2021143014A1 PCT/CN2020/093056 CN2020093056W WO2021143014A1 WO 2021143014 A1 WO2021143014 A1 WO 2021143014A1 CN 2020093056 W CN2020093056 W CN 2020093056W WO 2021143014 A1 WO2021143014 A1 WO 2021143014A1
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/367—Ontology
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- the embodiments of the present invention relate to, but are not limited to, the field of data processing, in particular to a method and device for generating a knowledge graph, and a computer-readable storage medium.
- the embodiment of the present invention provides a method and device for generating a knowledge graph and a computer-readable storage medium, which can directly convert speech into a knowledge graph and improve the accuracy of the knowledge graph.
- the embodiment of the present invention provides a method for generating a knowledge graph, including:
- the method further includes:
- Generating a knowledge graph based on the corrected text information of the spoken language includes:
- the knowledge graph is generated according to the adjusted text information of sentence segmentation and punctuation.
- adding punctuation marks between two adjacent sentences in the text information after sentence segmentation includes:
- the oral correction of the text information with punctuation marks includes:
- generating a knowledge graph based on the adjusted text information of sentence segmentation and punctuation includes:
- the list of triples is drawn as a knowledge graph in a visual form.
- the embodiment of the present invention provides a device for generating a knowledge graph, which includes a processor and a computer-readable storage medium.
- the computer-readable storage medium stores instructions. When the instructions are executed by the processor, it realizes any of the above-mentioned knowledge graphs. Generation method.
- the processor is further configured to execute the following steps:
- the colloquial and punctuation adjustments are performed on the colloquially revised text information
- the processor is used for generating a knowledge graph based on the corrected text information of the spoken language through the following steps: generating a knowledge graph based on the adjusted text information of sentence segmentation and punctuation.
- the processor is further configured to add punctuation marks between two adjacent sentences in the text information after sentence segmentation through the following steps:
- the processor is further configured to implement the oral correction of the text information added with punctuation through the following steps:
- the embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of any of the above-mentioned methods for generating a knowledge graph are realized.
- the embodiment of the present invention includes: converting the speech signal into text information; segmenting the text information, adding punctuation marks between two adjacent sentences in the text information after the segmentation; performing oral correction on the text information with punctuation marks; The corrected text information of the spoken language generates a knowledge graph.
- the text information is segmented, punctuation marks and spoken language correction are added, and then a knowledge graph is generated.
- FIG. 1 is a flowchart of a method for generating a knowledge graph proposed by an embodiment of the present invention
- FIG. 2 is a schematic diagram of the structural composition of a device for generating a knowledge graph according to another embodiment of the present invention.
- an embodiment of the present invention proposes a method for generating a knowledge graph, including:
- Step 100 Convert the voice signal into text information.
- Step 101 Perform sentence segmentation on the text information, and add punctuation marks between two adjacent sentences in the text information after the sentence segmentation.
- adding punctuation marks between two adjacent sentences in the sentenced text information includes: sequentially inputting the sentenced text information into the trained punctuation prediction model to predict the difference between two adjacent sentences. Between the punctuation marks (such as comma, period, ellipsis, etc.), the predicted punctuation marks are added between two adjacent sentences.
- the punctuation prediction model can be trained in any of the following ways.
- Treat the punctuation prediction problem as a language model problem treat the punctuation marks as ordinary words, train the n-gram language model according to the corpus, and calculate the first n words, the next one is the punctuation mark Probability. In other words, given the first n words, calculate the probability of the n+1th word, and select the word with the highest occurrence probability as the n+1th word among all the candidate words.
- the classification model can use Long Short-Term Memory (LSTM) to continuously input characters into the LSTM, and each time it is judged whether the next character is a punctuation mark, and if so, which punctuation mark should be.
- LSTM Long Short-Term Memory
- the classification model can also choose Bi-LSTM as a training model. Compared with LSTM, this model has the advantage of not only being able to use the above characters, but also the following characters as features. Predict whether the current character should be followed by punctuation marks.
- Step 102 Perform oral correction on the text information added with punctuation marks.
- performing oral correction on the punctuation-added text information includes: removing any one or more of the mood word and the mantra in the punctuation-added text information.
- the modal particle may be: “um”, “ah”, etc.
- the mantra may be: “this”, “that”, “right”, “right”, etc.
- a dictionary of modal particles and mantras is used to store pre-obtained modal particles and mantras, such as: “this, that” with modal particles before and after; consecutive "um, ah, ah,” shortened For one.
- Step 103 Generate a knowledge graph based on the corrected text information of the spoken language.
- generating a knowledge graph based on the corrected text information of the spoken language includes:
- the knowledge elements are extracted from the corrected text information of the spoken language, and the knowledge elements are expressed in the form of a list of triples, and the list of triples is drawn on a graph in a visual form, and the drawn graph is a knowledge graph.
- the above-mentioned knowledge elements may include, but are not limited to: concepts, entities, relationships, and attributes.
- the foregoing triplet may include but is not limited to: parameter 1, relationship, parameter 2.
- parameter 1 is a noun representing an entity, concept or attribute
- parameter 2 is a noun representing an entity or concept, or a specific value, geographic location, date, etc.
- a relationship is a verb or noun representing a relationship.
- the method further includes:
- the generating a knowledge graph based on the corrected text information of the spoken language includes:
- the knowledge graph is generated according to the adjusted text information of sentence segmentation and punctuation.
- the complete voice signal after receiving the complete voice signal, the complete voice signal can be converted into text information, and the subsequent process can be executed; the voice signal can also be converted into text information in real time during the process of receiving the voice signal , And execute subsequent processes in real time.
- generating a knowledge graph based on the adjusted text information of sentence segmentation and punctuation includes:
- the knowledge elements are extracted from the text information after the adjustment of sentence segmentation and punctuation, and the knowledge elements are expressed in the form of a list of triples, and the list of triples is drawn on a graph in a visual form.
- the drawn graph is Knowledge graph.
- the text information is segmented, punctuation marks and spoken language correction are added, and then a knowledge graph is generated. Because the text information is segmented, adding punctuation marks and spoken language correction is helpful for word segmentation , Part-of-speech analysis and syntactic analysis, and word segmentation, part-of-speech analysis and syntactic analysis are often required in the process of generating knowledge graphs, thereby improving the accuracy of knowledge graphs.
- Another embodiment of the present invention provides an apparatus for generating a knowledge graph, which includes a processor and a computer-readable storage medium.
- the computer-readable storage medium stores instructions. When the instructions are executed by the processor, Realize any of the above-mentioned methods for generating knowledge graphs.
- Another embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of any of the above-mentioned methods for generating a knowledge graph are realized.
- FIG. 2 another embodiment of the present invention provides a knowledge graph generating device, including:
- the speech recognition module 201 is configured to convert the speech signal into text information
- the text processing module 202 is configured to segment the text information, add punctuation marks between two adjacent sentences in the text information after the sentence segmentation; and make oral corrections to the text information with the added punctuation marks;
- the knowledge graph generating module 203 is configured to generate a knowledge graph based on the corrected text information of the spoken language.
- the text processing module 202 is specifically configured to add punctuation marks between two adjacent sentences in the segmented text information in the following manner: input the segmented text information into the trained punctuation marks in sequence
- the prediction model predicts the punctuation between two adjacent sentences (such as comma, period, ellipsis, etc.), and adds the predicted punctuation between two adjacent sentences.
- the text processing module 202 may use any of the following methods to train the punctuation prediction model.
- Treat the punctuation prediction problem as a language model problem treat the punctuation marks as ordinary words, train the n-gram language model according to the corpus, and calculate the first n words, the next one is the punctuation mark Probability. In other words, given the first n words, calculate the probability of the n+1th word, and select the word with the highest occurrence probability as the n+1th word among all the candidate words.
- the classification model can use Long Short-Term Memory (LSTM) to continuously input characters into the LSTM, and each time it is judged whether the next character is a punctuation mark, and if so, which punctuation mark should be.
- LSTM Long Short-Term Memory
- the classification model can also choose Bi-LSTM as a training model. Compared with LSTM, this model has the advantage of not only being able to use the above characters, but also the following characters as features. Predict whether the current character should be followed by punctuation marks.
- the text processing module 202 is specifically configured to implement the oral correction of the punctuation-added text information in the following manner: removing any one or more of the mood word and the mantra in the punctuation-added text information.
- the modal particle may be "um”, “ah”, etc., and the mantra may be: “this”, “that”, “right”, “right”, etc.
- a dictionary of modal particles and mantras is used to store pre-obtained modal particles and mantras, such as: “this, that” with modal particles before and after; consecutive "um, ah, ah,” shortened For one.
- the knowledge graph generation module 203 is specifically configured as:
- the knowledge elements are extracted from the corrected text information of the spoken language, and the knowledge elements are expressed in the form of a list of triples, and the list of triples is drawn on a graph in a visual form, and the drawn graph is a knowledge graph.
- the above-mentioned knowledge elements may include, but are not limited to: concepts, entities, relationships, and attributes.
- the foregoing triplet may include but is not limited to: parameter 1, relationship, parameter 2.
- parameter 1 is a noun representing an entity, concept or attribute
- parameter 2 is a noun representing an entity or concept, or a specific value, geographic location, date, etc.
- a relationship is a verb or noun representing a relationship.
- the text processing module 202 is further configured to:
- the knowledge graph generation module 203 is specifically set as follows:
- the knowledge graph is generated according to the adjusted text information of sentence segmentation and punctuation.
- the complete voice signal after receiving the complete voice signal, the complete voice signal can be converted into text information, and the subsequent process can be executed; the voice signal can also be converted into text information in real time during the process of receiving the voice signal , And execute the follow-up process in real time.
- the knowledge graph generation module 203 is specifically configured as:
- the knowledge elements are extracted from the text information after the adjustment of sentence segmentation and punctuation, and the knowledge elements are expressed in the form of a list of triples, and the list of triples is drawn on a graph in a visual form.
- the drawn graph is Knowledge graph.
- the text information is segmented, punctuation marks and spoken language correction are added, and then a knowledge graph is generated. Because the text information is segmented, adding punctuation marks and spoken language correction is helpful for word segmentation , Part-of-speech analysis and syntactic analysis, and word segmentation, part-of-speech analysis and syntactic analysis are often required in the process of generating knowledge graphs, thereby improving the accuracy of knowledge graphs.
- the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may consist of several physical components.
- the components are executed cooperatively.
- Some or all of the components may be implemented as software executed by a processor, such as a digital signal processor or a microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit.
- Such software may be distributed on a computer-readable medium, and the computer-readable medium may include a computer storage medium (or a non-transitory medium) and a communication medium (or a transitory medium).
- computer storage medium includes volatile and non-volatile data implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data).
- Information such as computer-readable instructions, data structures, program modules, or other data.
- Computer storage media include but are not limited to RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or Any other medium used to store desired information and that can be accessed by a computer.
- communication media usually contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as carrier waves or other transmission mechanisms, and may include any information delivery media. .
- the foregoing computer-readable storage medium may be configured to store a computer program for executing the following steps:
- the text information is segmented, and punctuation marks are added between two adjacent sentences in the text information after the segmentation;
- the integrated unit in the foregoing embodiment is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in the foregoing computer-readable storage medium.
- the technical solution of the present invention essentially or the part that contributes to the related technology or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make one or more computer devices (which may be personal computers, servers, or network devices, etc.) execute all or part of the steps of the methods described in the various embodiments of the present invention.
- the disclosed client can be implemented in other ways.
- the device embodiments described above are only illustrative.
- the division of the units is only a logical function division.
- there may be other division methods for example, multiple units or components may be combined or may be Integrate into another system, or some features can be ignored or not implemented.
- the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, units or modules, and may be in electrical or other forms.
- the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solutions of the embodiments.
- the functional units in the various embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
- the above-mentioned integrated unit can be implemented in the form of hardware or software functional unit.
- the embodiment of the present invention includes: converting the speech signal into text information; segmenting the text information, adding punctuation marks between two adjacent sentences in the text information after the segmentation; performing oral correction on the text information with punctuation marks; The corrected text information of the spoken language generates a knowledge graph.
- the text information is segmented, punctuation marks and spoken language correction are added, and then a knowledge graph is generated.
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Abstract
Description
Claims (10)
- 一种知识图谱的生成方法,包括:A method for generating knowledge graphs, including:将语音信号转换为文本信息;Convert the voice signal into text information;对文本信息进行断句,在断句后的文本信息中相邻两个句子之间添加标点符号;To segment the text information, add punctuation marks between two adjacent sentences in the text information after the segmentation;对添加标点符号的文本信息进行口语修正;Oral correction of the text information with punctuation marks;根据口语修正后的文本信息生成知识图谱。Generate a knowledge graph based on the corrected text information of the spoken language.
- 根据权利要求1所述的生成方法,其中,所述对添加标点符号的文本信息进行口语修正后,在根据口语修正后的文本信息生成知识图谱之前,该方法还包括:The generating method according to claim 1, wherein after the oral correction is performed on the text information with punctuation marks, the method further comprises: before generating a knowledge graph based on the corrected text information of the spoken language, the method further comprises:对口语修正后的文本信息进行断句和标点符号的调整;Adjust the sentence segmentation and punctuation of the corrected text information of the spoken language;所述根据口语修正后的文本信息生成知识图谱包括:The generating a knowledge graph based on the corrected text information of the spoken language includes:根据进行断句和标点符号的调整后的文本信息生成所述知识图谱。The knowledge graph is generated according to the adjusted text information of sentence segmentation and punctuation.
- 根据权利要求1或2所述的生成方法,其中,所述在断句后的文本信息中相邻两个句子之间添加标点符号包括:The generating method according to claim 1 or 2, wherein the adding punctuation marks between two adjacent sentences in the text information after the sentence segmentation comprises:将断句后的文本信息依次输入到训练好的标点符号预测模型中预测相邻两个句子之间的标点符号;Input the text information after segmentation into the trained punctuation prediction model to predict the punctuation between two adjacent sentences;将预测的标点符号添加到相邻两个句子之间。Add the predicted punctuation marks between two adjacent sentences.
- 根据权利要求1或2所述的生成方法,其中,所述对添加标点符号的文本信息进行口语修正包括:The generating method according to claim 1 or 2, wherein said correcting the text information added with punctuation marks in spoken language comprises:去除所述添加标点符号的文本信息中的语气词和口头禅中的任意一个或多个。Remove any one or more of modal particles and mantras in the punctuation-added text information.
- 根据权利要求2所述的生成方法,其中,所述根据进行断句和标点符号的调整后的文本信息生成所述知识图谱包括:The generating method according to claim 2, wherein the generating the knowledge graph according to the adjusted text information of sentence segmentation and punctuation includes:从进行断句和标点符号的调整后的文本信息中抽取出知识要素,并以 三元组列表的形式表达所述知识要素;Extract knowledge elements from the text information after the adjustment of sentence segmentation and punctuation marks, and express the knowledge elements in the form of a list of triples;将所述三元组列表以可视化的形式绘制为所述知识图谱。The list of triples is drawn as the knowledge graph in a visual form.
- 一种知识图谱的生成装置,包括处理器和计算机可读存储介质,所述计算机可读存储介质中存储有指令,当所述指令被所述处理器执行时,实现如权利要求1~5任一项所述的知识图谱的生成方法。A device for generating a knowledge graph, comprising a processor and a computer-readable storage medium. The computer-readable storage medium stores instructions. When the instructions are executed by the processor, any of claims 1 to 5 can be implemented. The method for generating the knowledge graph described in one item.
- 根据权利要求6所述的生成装置,其中,所述处理器还用于执行以下步骤:The generating device according to claim 6, wherein the processor is further configured to perform the following steps:所述对添加标点符号的文本信息进行口语修正后,在根据口语修正后的文本信息生成知识图谱之前,对口语修正后的文本信息进行断句和标点符号的调整;After the colloquial correction of the text information with punctuation marks is performed, sentence segmentation and punctuation adjustment are performed on the colloquial correction text information before generating a knowledge graph based on the colloquial text information;所述处理器用于通过以下步骤实现所述根据口语修正后的文本信息生成知识图谱:根据进行断句和标点符号的调整后的文本信息生成所述知识图谱。The processor is configured to realize the generating of the knowledge graph based on the corrected text information of the spoken language by the following steps: generating the knowledge graph based on the adjusted text information of sentence segmentation and punctuation.
- 根据权利要求6或7所述的生成装置,其中,所述处理器还用于通过以下步骤实现所述在断句后的文本信息中相邻两个句子之间添加标点符号:The generating device according to claim 6 or 7, wherein the processor is further configured to implement the adding punctuation marks between two adjacent sentences in the text information after sentence segmentation through the following steps:将断句后的文本信息依次输入到训练好的标点符号预测模型中预测相邻两个句子之间的标点符号;Input the text information after segmentation into the trained punctuation prediction model to predict the punctuation between two adjacent sentences;将预测的标点符号添加到相邻两个句子之间。Add the predicted punctuation marks between two adjacent sentences.
- 根据权利要求6或7所述的生成装置,其中,所述处理器还用于通过以下步骤实现所述对添加标点符号的文本信息进行口语修正:The generating device according to claim 6 or 7, wherein the processor is further configured to implement the oral correction of the text information with punctuation marks through the following steps:去除所述添加标点符号的文本信息中的语气词和口头禅中的任意一个或多个。Remove any one or more of modal particles and mantras in the punctuation-added text information.
- 一种计算机可读存储介质,其上存储有计算机程序,所述计算机程序被处理器执行时实现如权利要求1~5任一项所述的知识图谱的生成方法的步骤。A computer-readable storage medium with a computer program stored thereon, and when the computer program is executed by a processor, the steps of the method for generating a knowledge graph according to any one of claims 1 to 5 are realized.
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Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6598043B1 (en) * | 1999-10-04 | 2003-07-22 | Jarg Corporation | Classification of information sources using graph structures |
CN106156365A (en) * | 2016-08-03 | 2016-11-23 | 北京智能管家科技有限公司 | A kind of generation method and device of knowledge mapping |
CN107633005A (en) * | 2017-08-09 | 2018-01-26 | 广州思涵信息科技有限公司 | A kind of knowledge mapping structure, comparison system and method based on class teaching content |
CN110297907A (en) * | 2019-06-28 | 2019-10-01 | 谭浩 | Generate method, computer readable storage medium and the terminal device of interview report |
CN110543574A (en) * | 2019-08-30 | 2019-12-06 | 北京百度网讯科技有限公司 | knowledge graph construction method, device, equipment and medium |
Family Cites Families (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8407165B2 (en) * | 2011-06-15 | 2013-03-26 | Ceresis, Llc | Method for parsing, searching and formatting of text input for visual mapping of knowledge information |
CN104142915B (en) * | 2013-05-24 | 2016-02-24 | 腾讯科技(深圳)有限公司 | A kind of method and system adding punctuate |
CN107247706B (en) * | 2017-06-16 | 2021-06-25 | 中国电子技术标准化研究院 | Text sentence-breaking model establishing method, sentence-breaking method, device and computer equipment |
CN108549628B (en) * | 2018-03-16 | 2021-08-27 | 云知声智能科技股份有限公司 | Sentence-breaking device and method for stream type natural language information |
CN109255115B (en) * | 2018-10-19 | 2023-04-07 | 科大讯飞股份有限公司 | Text punctuation adjustment method and device |
CN109708256B (en) * | 2018-12-06 | 2020-07-03 | 珠海格力电器股份有限公司 | Voice determination method and device, storage medium and air conditioner |
CN109684638B (en) * | 2018-12-24 | 2023-08-11 | 北京金山安全软件有限公司 | Clause method and device, electronic equipment and computer readable storage medium |
CN110457466A (en) * | 2019-06-28 | 2019-11-15 | 谭浩 | Generate method, computer readable storage medium and the terminal device of interview report |
-
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Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6598043B1 (en) * | 1999-10-04 | 2003-07-22 | Jarg Corporation | Classification of information sources using graph structures |
CN106156365A (en) * | 2016-08-03 | 2016-11-23 | 北京智能管家科技有限公司 | A kind of generation method and device of knowledge mapping |
CN107633005A (en) * | 2017-08-09 | 2018-01-26 | 广州思涵信息科技有限公司 | A kind of knowledge mapping structure, comparison system and method based on class teaching content |
CN110297907A (en) * | 2019-06-28 | 2019-10-01 | 谭浩 | Generate method, computer readable storage medium and the terminal device of interview report |
CN110543574A (en) * | 2019-08-30 | 2019-12-06 | 北京百度网讯科技有限公司 | knowledge graph construction method, device, equipment and medium |
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