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2017-08-18 |
{:shortdesc: .shortdesc} {:new_window: target="_blank"} {:tip: .tip} {:pre: .pre} {:codeblock: .codeblock} {:screen: .screen} {:javascript: .ph data-hd-programlang='javascript'} {:java: .ph data-hd-programlang='java'} {:python: .ph data-hd-programlang='python'} {:swift: .ph data-hd-programlang='swift'}
This topic contains information about {{site.data.keyword.discoveryshort}} features that might still be available, but have been replaced with newer options. {: tip}
{: #AlchemyLanguage-enrichments}
Starting on 18 July, 2017 {{site.data.keyword.discoveryfull}} introduced a new enrichment technology, named {{site.data.keyword.nlushort}}. These enrichments are the same as your existing enrichments but require a slightly different configuration and schema. The original enrichments, named {{site.data.keyword.alchemylanguageshort}} enrichments, will be deprecated.
{{site.data.keyword.alchemylanguageshort}} enrichment support will end on 15 January, 2018.
Note: New collections should be enriched with {{site.data.keyword.nlushort}} and any existing collections with {{site.data.keyword.alchemylanguageshort}} configuration files migrated as soon as possible. {{site.data.keyword.alchemylanguageshort}} enrichment ingestion support ends 15 January, 2018. For information on migrating collections and configuration files that utilize the {{site.data.keyword.alchemylanguageshort}} enrichments, see Migrating enrichments to {{site.data.keyword.nlushort}}.
{: #entity-extraction-al}
Returns items such as persons, places, and organizations that are present in the input text. Entity extraction adds semantic knowledge to content to help understand the subject and context of the text that is being analyzed. The entity extraction techniques are based on sophisticated statistical algorithms and natural language processing technology, and are unique in the industry with their support for multilingual analysis, context-sensitive disambiguation, and quotations extraction.
Example portion of a document enriched with Entity Extraction:
{
"text": "The stockholders were pleased that Acme Corporation plans to build a new factory in Atlanta, Georgia.",
"enriched-text": {
"status": "OK",
"language": "english",
"entities": [
{
"type": "City",
"relevance": 0.532754,
"sentiment": {
"type": "positive",
"score": 0.527541,
"mixed": false
},
"count": 1,
"text": "Atlanta",
"disambiguated": {
"subType": [
"AdministrativeDivision",
"GovernmentalJurisdiction",
"OlympicHostCity",
"PlaceWithNeighborhoods"
],
"name": "Atlanta",
"website": "http://www.atlantaga.gov/",
"dbpedia": "http://dbpedia.org/resource/Atlanta",
"freebase": "http://rdf.freebase.com/ns/m.013yq"
}
}
]
}
}{: codeblock}
In the preceding example, you could query the entity type by accessing enriched_text.entities.type
sentiment is calculated for entity types even if the sentiment enrichment is not selected. To learn more about sentiment scoring, see Sentiment analysis.
The relevance score will range from 0.0 to 1.0. The higher the score, the more relevant the entity. The disambiguated field contains the disambiguation information for the entity, which includes the entity subType information and links to the resource(s), if applicable. The count is the number of times the entity is mentioned in the document.
{: #keyword-extraction-al}
Important topics in your content that are typically used when indexing data, generating tag clouds, or when searching. The {{site.data.keyword.discoveryshort}} service automatically identifies supported languages in your input content, and then identifies and ranks keywords in that content.
Example portion of a document enriched with Keyword Extraction:
{
"text": "The stockholders were pleased that Acme Corporation plans to build a new factory in Atlanta, Georgia.",
"enriched-text": {
"status": "OK",
"language": "english",
"keywords": [
{
"relevance": 0.66497,
"sentiment": {
"score": 0.527541,
"type": "positive",
"mixed": false
},
"text": "stockholders"
}
]
}
}{: codeblock}
In the preceding example, you could query the keyword text by accessing enriched_text.keywords.text
sentiment is calculated for keywords even if the sentiment enrichment is not selected. To learn more about sentiment scoring, see Sentiment analysis.
The relevance score will range from 0.0 to 1.0. The higher the score, the more relevant the keyword.
{: #taxonomy-classification-al}
Categorizes input text, HTML or web-based content into a hierarchical taxonomy up to five levels deep. Deeper levels allow you to classify content into more accurate and useful subsegments.
Example portion of a document enriched with Taxonomy Classification:
{
"text": "The stockholders were pleased that Acme Corporation plans to build a new factory in Atlanta, Georgia.",
"enriched-text": {
"status": "OK",
"language": "english",
"taxonomy": [
{
"label": "/business and industrial/company/merger and acquisition",
"score": 0.517533,
"confident": false
}
]
}
}{: codeblock}
In the preceding example, you could query the taxonomy label by accessing enriched_text.taxonomy.label
The label is the detected taxonomy category. The hierarchy levels are separated by forward slashes. The score for that category will range from 0.0 to 1.0. The higher the score, the greater the confidence in that category.
{: #concept-tagging-al}
Identifies concepts with which the input text is associated, based on other concepts and entities that are present in that text. Concept tagging understands how concepts relate, and can identify concepts that are not directly referenced in the text. For example, if an article mentions CERN and the Higgs boson, the Concepts API functions will identify Large Hadron Collider as a concept even if that term is not mentioned explicitly in the page. Concept tagging enables higher level analysis of input content than just basic keyword identification.
Example portion of a document enriched with Concept Tagging:
{
"text": "The stockholders were pleased that Acme Corporation plans to build a new factory in Atlanta, Georgia.",
"enriched_text": {
"status": "OK",
"language": "english",
"concepts": [
{
"text": "Acme Corporation",
"relevance": 0.91136,
"dbpedia": "http://dbpedia.org/resource/Acme_Corporation",
"freebase": "http://rdf.freebase.com/ns/m.0dndy",
"yago": "http://yago-knowledge.org/resource/Acme_Corporation"
}
]
}
}{: codeblock}
In the preceding example, you can query the concept text type by accessing enriched_text.concepts.text
The relevance score will range from 0.0 to 1.0. The higher the score, the more relevant the concept. Links to the resource(s) are provided, if applicable.
{: #relation-extraction-al}
Identifies subject, action, and object relations within sentences in the input content. Relation information can be used to automatically identify buying signals, key events, and other important actions.
Example portion of a document enriched with Relation Extraction:
{
"text": "The stockholders were pleased that Acme Corporation plans to build a new factory in Atlanta, Georgia.",
"enriched-text": {
"status": "OK",
"language": "english",
"relations": [
{
"sentence": " The stockholders were pleased that Acme Corporation plans to build a new factory in Atlanta, GA.",
"subject": {
"text": "The stockholders",
"keywords": [
{
"text": "stockholders"
}
]
},
"action": {
"text": "were",
"lemmatized": "be",
"verb": {
"text": "be",
"tense": "past"
}
},
"object": {
"text": "pleased that Acme Corporation plans to build a new factory in Atlanta, GA",
"sentiment": {
"type": "positive",
"score": 0.834516,
"mixed": false
},
"entities": [
{
"type": "Company",
"text": "Acme Corporation"
},
{
"type": "City",
"text": "Atlanta",
"disambiguated": {
"subType": [
"AdministrativeDivision",
"GovernmentalJurisdiction",
"OlympicHostCity",
"PlaceWithNeighborhoods"
],
"name": "Atlanta",
"website": "http://www.atlantaga.gov/",
"dbpedia": "http://dbpedia.org/resource/Atlanta",
"freebase": "http://rdf.freebase.com/ns/m.013yq"
}
},
{
"type": "StateOrCounty",
"text": "GA"
}
],
"keywords": [
{
"text": "Acme Corporation"
},
{
"text": "new factory"
},
{
"text": "GA"
},
{
"text": "Atlanta"
}
]
}
}
]
}
}{: codeblock}
In the preceding example, you could query the relation subject text by accessing enriched_text.relations.subject.text
sentiment is calculated for relations even if the sentiment enrichment is not selected. To learn more about sentiment scoring, see Sentiment analysis. It will not extract entities or keywords (as shown in the example) unless you also select the entity and keyword enrichments. See Entity extraction and Keyword extraction for more information on those enrichments.
The subject, action, and object are extracted for every sentence that contains a relation.
{: #sentiment-analysis-al}
Identifies attitude, opinions, or feelings in the content that is being analyzed. The {{site.data.keyword.discoveryshort}} service can calculate overall sentiment within a document, sentiment for user-specified targets, entity-level sentiment, quotation-level sentiment, directional-sentiment, and keyword-level sentiment. The combination of these capabilities supports a variety of use cases ranging from social media monitoring to trend analysis.
Example portion of a document enriched with Sentiment Analysis:
{
"text": "The stockholders were pleased that Acme Corporation plans to build a new factory in Atlanta, Georgia.",
"enriched_text": {
"status": "OK",
"language": "english",
"docSentiment": {
"type": "positive",
"score": 0.0966252,
"mixed": true
}
}
}{: codeblock}
In the preceding example, you could query the docSentiment type by accessing enriched_text.docSentiment.type
The type is the overall sentiment of the document (positive, negative, or neutral). The sentiment type is based on the score; a score of 0.0 would indicate that the document is neutral, a positive number would indicate the document is positive, a negative number would indicate the document is negative. If mixed is true, it indicates that the document contains both positive and negative sentiments (this field is not determined by the score).
{: #emotion-analysis-al}
Detects anger, disgust, fear, joy, and sadness implied in English text. Emotion Analysis can detect emotions that are associated with targeted phrases, entities, or keywords, or it can analyze the overall emotional tone of your content.
Example portion of a document enriched with Emotion Analysis:
{
"text": "The stockholders were pleased that Acme Corporation plans to build a new factory in Atlanta, Georgia.",
"enriched_text": {
"status": "OK",
"language": "english",
"docEmotions": {
"anger": "0.077394",
"disgust": "0.044024",
"fear": "0.092664",
"joy": "0.553327",
"sadness": "0.3969"
}
}
}{: codeblock}
In the preceding example, you could query the joy docEmotion by accessing enriched_text.docEmotions.joy
Emotion Analysis analyzes your text and calculates a score for each emotion (anger, disgust, fear, joy, sadness) on a scale of 0.0 to 1.0. If the score of any emotion is 0.5 or higher, then that emotion has been detected (the higher the score above 0.5, the higher the relevance). In the snippet shown, joy has a score above 0.5, so {{site.data.keyword.watson}} detected joy.
A new version of {{site.data.keyword.discoverynewsfull}} debuted on 31, July 2017. {{site.data.keyword.discoverynewsfull}} Original was retired with a removal from service date of 15, January 2018. See Watson Discovery News for information on this new version.
{{site.data.keyword.discoverynewsfull}} Original is a dataset of primarily English-language news sources that is updated continuously, with approximately 300,000 new articles and blogs added daily. This indexed dataset is pre-enriched with the following {{site.data.keyword.alchemylanguageshort}} enrichments: Keyword Extraction, Entity Extraction, Concept Tagging, Relation Extraction, Sentiment Analysis, and Taxonomy Classification. The following additional metadata is also added: crawl date, publication date, URL ranking, host rank, and anchor text. Historical search is available for the past 60 days of news data.
{{site.data.keyword.discoverynewsfull}} Original is enriched with {{site.data.keyword.alchemylanguageshort}} enrichments. For more information about these enrichments, see {{site.data.keyword.alchemylanguageshort}} enrichments.
A new version of {{site.data.keyword.discoverynewsfull}} debuted on 31, July 2017. {{site.data.keyword.discoverynewsfull}} Original was retired with a removal from service date of 15, January 2018. See Watson Discovery News for information on this new version.
{{site.data.keyword.discoverynewsfull}} Original uses a similar, but slightly different JSON schema from the one used for private collections. You do not need to include enriched_text in your queries, for example:
How to structure a {{site.data.keyword.discoverynewsfull}} Original query
The following example query returns the top 10 articles in {{site.data.keyword.discoverynewsfull}} Original about the Pittsburgh Steelers that have a positive sentiment.
- On the Your data screen, choose the {{site.data.keyword.discoverynewsfull}} collection.
- Click Query this collection, then Build your own query.
- Under Search for documents, click Use the {{site.data.keyword.discoveryshort}} Query Language, then enter
text:Pittsburgh Steelers, docSentiment.type:positiveinto the Enter query here field. - Click Customize display options, then enter
10(this is the default) in theNumber of documents to returnfield. - Click Run query. The top 10 articles about the Pittsburgh Steelers with a positive sentiment are displayed.
Additional example {{site.data.keyword.discoverynewsfull}} Original queries
concepts.text:"Health care"- Under Search for documents, click Use the {{site.data.keyword.discoveryshort}} Query Language, then enter this query. It returns all articles that include the concept ofhealth care. If you specify a count, such as 50, in the Number of documents to return field you receive only the top 50 most relevant articles.
How to structure a {{site.data.keyword.discoverynewsfull}} Original aggregation
The following example aggregation returns the number of articles found in {{site.data.keyword.discoverynewsfull}} Original about the Pittsburgh Steelers by sentiment.
- On the Your data screen, choose the {{site.data.keyword.discoverynewsfull}} Original collection.
- Click Query this collection, then Build your own query.
- Under Include analysis of your results, enter
filter(text:"Pittsburgh Steelers").term(docSentiment.type,count:3)in the Write an aggregation query using the {{site.data.keyword.discoveryshort}} Query Language field. - Click Customize display options, then enter
0in the Number of documents to return field. - Click Run query. The results show the number of documents about the Pittsburgh Steelers and how many of those results have a
positive,negative, orneutraldocSentiment.
Additional example {{site.data.keyword.discoverynewsfull}} Original aggregation
filter(entities.text:twitter).term(docSentiment.type,count:3)- If you enter this aggregation query in the Write an aggregation query using the {{site.data.keyword.discoveryshort}} Query Language field, it will first narrow down (filter) the set of articles to only the ones that include the entities text of twitter, then divides those articles by the document sentiment types. Only the top three document sentiment types (positive,negative,neutral) are returned.
Adding nested before an aggregation query restricts the aggregation to the area of the results specified. For example: nested(text.entities) means that only the text.entities components of any result are used to aggregate against. This affect can be seen easily by looking at the differences between the following two queries: filter(text.entities.type::City) - the aggregation counts the number of Results that contain one or more entity with the type City and nested(text.entities).filter(text.entities.type::City) - the aggregation counts the number of instances of an entity with the type City in the results. Additionally, any subsequent operation will further restrict the result set that can be aggregated against. For example nested(text.entities).filter(text.entities.type::City) means that any only entities of type::City will be aggregated. For example: nested(text.entities).filter(text.entities.type::City).term(text.entities.text,count:3) will aggregate the top three entities of type City, Where as: filter(text.entities.type::City).term(text.entities.text,count:3) will return the top three entities whereas the result contains at least one entity of type City.
Note: You cannot adjust the {{site.data.keyword.discoverynewsfull}} Original configuration, train, or add documents to this collection.
You can integrate a custom model from {{site.data.keyword.knowledgestudiofull}} with the {{site.data.keyword.discoveryshort}} service to provide custom enrichments. {: shortdesc}
Before you can integrate a custom model from {{site.data.keyword.knowledgestudioshort}} with the {{site.data.keyword.discoveryshort}} service, you must create and deploy the model by using {{site.data.keyword.knowledgestudioshort}}. See the {{site.data.keyword.knowledgestudioshort}} documentation for information on creating and deploying models. You need the unique ID of the deployed model to integrate it with the {{site.data.keyword.discoveryshort}} service.
You can use a custom model developed in {{site.data.keyword.knowledgestudioshort}} to enrich documents in the {{site.data.keyword.discoveryshort}} service. This gives you the flexibility to apply the {{site.data.keyword.discoveryshort}} service's document-enhancing capabilities with information specific to areas of particular focus, such as industry or scientific discipline. You can use both public data and your own proprietary data in your enrichment model.
You must use the service API to integrate a {{site.data.keyword.knowledgestudioshort}} model with the {{site.data.keyword.discoveryshort}} service. You cannot use the {{site.data.keyword.discoveryshort}} tooling to integrate a custom model.
-
Get the ID of your {{site.data.keyword.discoveryshort}} environment as described at List environments
{: new_window}. Note the environment ID. -
List the IDs of your current {{site.data.keyword.discoveryshort}} configuration or configurations as described at List configurations
{: new_window} Note the ID of the configuration that you want to integrate with your {{site.data.keyword.knowledgestudiofull}} custom model. -
Download a copy of your current {{site.data.keyword.discoveryshort}} configuration by running the following commands in a bash shell or equivalent, such as Cygwin for Windows. Substitute
{environment_id}and{configuration_id}with the IDs you noted down in the previous two steps.curl -u "{username}":"{password}" "https://gateway.watsonplatform.net/discovery/api/v1/environments/{environment_id}/configurations/{configuration_id}?version=2017-08-01" > my_config.json
{: pre}
This command lists the contents of your collection file and puts them in a JSON file named
my_config.json. -
Open the
my_config.jsonfile in a text editor and make the following changes:-
Change the value of the
"name"field to something that indicates the purpose of the new configuration. You can optionally change the value of the"description"field as well.... "name": "wks-config", "description": "This is a configuration to use with a WKS model", ...
{: codeblock}
-
Update the enrichment fields with information for the {{site.data.keyword.knowledgestudioshort}} model. Assuming that the enrichment fields originally read:
"enrichments": [ { "destination_field": "enriched_text", "source_field": "text", "enrichment": "alchemy_language", "options": { "extract": "keyword, entity, doc-sentiment, taxonomy, concept, relation", "sentiment": true, "quotations": true } } ]
{: codeblock}
-
Update the file as follows, substituting the unique ID of the {{site.data.keyword.knowledgestudioshort}} model described in "Before you begin" for
{watson_knowledge_studio_model_ID}."enrichments": [ { "destination_field": "enriched_text", "source_field": "text", "enrichment": "alchemy_language", "options": { "extract": "keyword, entity, doc-sentiment, taxonomy, concept, relation, typed-rels", "sentiment": true, "quotations": true, "model": "{watson_knowledge_studio_model_ID}" } } ]
{: codeblock}
-
-
Optionally enable entity normalization as described in Creating a custom configuration to normalize entities.
-
Save the
my_config.jsonfile. -
Use a JSON validator, such as JSLint
{: new_window} to validate and, if necessary, correct your edited JSON before you perform the next steps. -
Update the configuration as follows. You again need the
{environment_id}and{configuration_id}IDs you collected at the start of this procedure.curl -X PUT -u "{username}":"{password}" -H "Content-Type: application/json" -d @my_config.json "https://gateway.watsonplatform.net/discovery/api/v1/environments/{environment_id}/configurations/{configuration_id}?version=2017-08-01"
{: pre}
The command returns the contents of the updated configuration file.
-
Use the {{site.data.keyword.discoveryshort}} service normally. Documents you ingest with the updated configuration are automatically enriched with the data from your custom model.
{: #normalizing-entities}
You can configure {{site.data.keyword.discoveryshort}} service to include normalized entities, also known as canonical names, in the output of your queries. {: shortdesc}
Note: Editing the configuration to enable normalized entities is a manual task that must be performed by using a text editor and API calls. It is not currently supported by the Tooling.
Note: Entity normalization is available only when you use the Discovery service with a custom model generated by Watson Knowledge Studio, as described in Integrating with {{site.data.keyword.knowledgestudiofull}}.
Entity normalization inserts normalized (canonical) names for different references to the same person or object in the source document. For instance, if you enable entity normalization and then ingest a document or documents that discuss "J.R. Cash" and "John R. Cash," the processed output will include the canonical_name "Johnny Cash" along with each matched term. It will also include relevant canonical names for other text entities encountered in the document. See the end of this section for example output.
After you enrich a document with canonical names, you can search more easily for specific items with the same canonical name.
Canonical names are derived from a public dictionary. If a suitable canonical name cannot be located in the dictionary, the service uses the most suitable entity reference in the document as the canonical name. Before querying an entity-normalized document for a canonical name or names, examine the enriched JSON document to verify that the canonical name or names generated by the service match the names you expect.
-
Get the ID of your {{site.data.keyword.discoveryshort}} environment as described at List environments
{: new_window}. Note the environment ID. -
List the IDs of your current {{site.data.keyword.discoveryshort}} configuration or configurations as described at List configurations
{: new_window}. Note the ID of the configuration that you want to update. -
Download a copy of your current {{site.data.keyword.discoveryshort}} configuration by running the following commands in a bash shell or equivalent, such as Cygwin for Windows. Substitute
{environment_id}and{configuration_id}with the IDs you noted down in the previous two steps.curl -u "{username}":"{password}" "https://gateway.watsonplatform.net/discovery/api/v1/environments/{environment_id}/configurations/{configuration_id}?version=2017-08-01" > new_config.json
{: pre}
This command lists the contents of your collection file and puts them in a JSON file named
new_config.json. -
Open the
new_config.jsonfile in a text editor and make the following changes:-
Change the value of the
"name"field to something that indicates the purpose of the new configuration. You can optionally change the value of the"description"field as well.... "name": "normalize-entities-config", "description": "This configuration enables entity normalization", ...
{: codeblock}
-
Update the enrichment fields with information for the {{site.data.keyword.knowledgestudioshort}} model. Assuming that the enrichment fields originally read:
"enrichments": [ { "destination_field": "enriched_text", "source_field": "text", "enrichment": "alchemy_language", "options": { "extract": "keyword, entity, doc-sentiment, taxonomy, concept, relation, typed-rels", "sentiment": true, "quotations": true, "model": "{watson_knowledge_studio_model_ID}" } } ]
{: codeblock}
-
Update the file as follows.
"enrichments": [ { "destination_field": "enriched_text", "source_field": "text", "enrichment": "alchemy_language", "options": { "extract": "keyword, entity, doc-sentiment, taxonomy, concept, relation, typed-rels", "sentiment": true, "quotations": true, "model": "{watson_knowledge_studio_model_ID}" "normalizeEntities": 1 } } ]
{: codeblock}
-
Save the
new_config.jsonfile.
-
-
Use a JSON validator, such as JSLint
{: new_window}, to validate your edited JSON before you perform the next steps. -
Update the configuration as follows. You again need the
{environment_id}and{configuration_id}IDs you collected at the start of this procedure.curl -X PUT -u "{username}":"{password}" -H "Content-Type: application/json" -F configuration-@new_config.json "https://gateway.watsonplatform.net/discovery/api/v1/environments/{environment_id}/configurations/{configuration_id}?version=2017-08-01"
{: pre}
The command returns the contents of the updated configuration file.
-
Use the {{site.data.keyword.discoveryshort}} service normally. Documents you ingest with the updated configuration are automatically enriched with normalized entities, as shown in the following output excerpts.
Output snippet without "normalizeEntities": 1:
{
"enriched_text": {
...
...
...
"entity_relations": {
"entities": {
"entity": [
{
"class": "SPC",
"eid": "-E0",
"generic": false,
"level": "NAM",
"mentref": [
{
"mid": "-M0",
"text": "J.R. Cash"
},
{
"mid": "-M6",
"text": "musician"
},
{
"mid": "-M7",
"text": "who"
},
{
"mid": "-M13",
"text": "He"
},
{
"mid": "-M20",
"text": "He"
}
],
"score": 0.7874817061794613,
"subtype": "OTHER",
"type": "PERSON"
},
...
...
...
]
},
"relations": {
"relation": [
{
"rel_entity_arg": [
{
"argnum": 1,
"eid": "-E0"
},
{
"argnum": 2,
"eid": "-E1"
}
],
"relmentions": {
"relmention": [
{
"class": "SPECIFIC",
"modality": "ASSERTED",
"rel_mention_arg": [
{
"argnum": 1,
"mid": "-M0",
"text": "John R. Cash",
},
{
"argnum": 2,
"mid": "-M1",
"text": "country"
}
],
"rmid": "-R1-1",
"score": 0.49918343781296,
"tense": "UNSPECIFIED"
}
]
},
"rid": "-R1",
"subtype": "OTHER",
"type": "knownAs"
},
...
...
...
]
}
}
}
}{: codeblock}
Output snippet with "normalizeEntities": 1:
{
"enriched_text": {
...
...
...
"entity_relations": {
"entities": {
"entity": [
{
"class": "SPC",
"eid": "-E0",
"generic": false,
"level": "NAM",
"mentref": [
{
"mid": "-M0",
"text": "J.R. Cash"
},
{
"mid": "-M6",
"text": "musician"
},
{
"mid": "-M7",
"text": "who"
},
{
"mid": "-M13",
"text": "He"
},
{
"mid": "-M20",
"text": "He"
}
],
"score": 0.7874817061794613,
"subtype": "OTHER",
"type": "PERSON",
"canonical_name": "Johnny Cash"
},
...
...
...
]
},
"relations": {
"relation": [
{
"rel_entity_arg": [
{
"argnum": 1,
"eid": "-E0"
},
{
"argnum": 2,
"eid": "-E1"
}
],
"relmentions": {
"relmention": [
{
"class": "SPECIFIC",
"modality": "ASSERTED",
"rel_mention_arg": [
{
"argnum": 1,
"mid": "-M0",
"text": "John R. Cash",
"canonical_name": "Johnny Cash"
},
{
"argnum": 2,
"mid": "-M1",
"text": "country",
"canonical_name": "country music"
}
],
"rmid": "-R1-1",
"score": 0.49918343781296,
"tense": "UNSPECIFIED"
}
]
},
"rid": "-R1",
"subtype": "OTHER",
"type": "knownAs"
},
...
...
...
]
}
}
}
}{: codeblock}