| # Text2GQL Syntax Manual |
| This manual enumerates common syntax elements of GQL along with reference prompts, enabling users to formulate GQL statements by referring to the provided example queries. |
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| | Syntax | Query Example | Result | |
| |--------------------------------------------------------------| --- |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | Find Vertex | Locate vertices of type person | match(a:person) return a | |
| | Find Edge | Return all edges labeled as knows | match(a)-[e:knows]->(b) return e | |
| | Find Relationships | Query 10 universities located in Beijing<br>Identify 5 students related to Teacher Xiao Zhang | match(a:city where a.name = '北京')<-[:belong]-(b:university) return b limit 10<br />match(a:teacher where a.name='Xiao Zhang')-[e]-(b:student) return b limit 5 | |
| | Find Multi-Degree Relationships | Find people known by friends of Student Xiao Wang<br>Retrieve departments connected to universities, then students linked to those departments, and courses chosen by those students<br>Identify software co-created by Tencent and Google, return 5 results | match(a:student where a.name = 'Xiao Wang')-[e:friend]->(b)-[e2:knows]->(c:person) return c<br /><br />match(a:university)-[e:has]->(b:department)-[e2:has]->(c:student)-[e3:selects]->(d:course) return d<br /><br />match(a:company where a.name='Tencent')-[e:creates]->(b:software)<-[e2:creates]-(c:company where c.name='Google') return b.name limit 5 | |
| | Loop | From person Zhang Siqi, traverse through pay edges, reach vertices within 2 to 4 degrees | match(a:person where a.name='Zhang Siqi')-[e:pay]->{2,4}(b:person) return b | |
| | Loop | Identify 3-hop cycles involving persons who know Li Hong | match(a:person where name = 'Li Hong')-[e:knows]->{1,2}(b)->(a) return a.id, b.id as b_id<br /> | |
| | Filter Criteria | Find people known by Xiaohong, aged over 20, earning more than 5000<br>Fetch 10 nodes not female, shorter than 160cm, or with an id greater than 5 | match(a:person where a.name='Xiaohong')-[e:knows]->(b:person where b.age > 20 and b.salary > 5000) return b<br /><br />match(a where (a.gender <> 'female' and a.height < 160) or a.id > 5) return a limit 10 | |
| | Let Single Value | Query software created by Ant Group, set minPrice of software equal to its minimum price, return company id and software's minPrice | match(a:company where a.name = 'Ant Group')-[e:creates]->(b:software) let b.minPrice = MIN(b.price) return a.id, b.minPrice | |
| | Let Subquery | Find employees of Ant Group, set their countSalary equal to the sum of salaries of those who know them, then find the software they purchase<br>Identify the country that city id 10 belongs to, assign the average count of companies related to the country as avgCnt | match(a:company where a.name = 'Ant Group')-[e:employee]->(b:person) let b.countSalary = SUM((b:person)<-[e2:knows]-(c:person) => c.salary) match(b:person)-[e3:buy]->(d:software) return b.countSalary, d<br /><br />match(a:city where id = '10')-[e:belong]->(b:country)<-[e2:belong]-(c:company) let b.avgCnt = AVG(c.peopleNumber) return b | |
| | Function Call | Invoke SSSP function with 'arg1', 10 as inputs, return id and distance | match(a:person) call sssp(a, 10) yield (id, distance) return id, distance | |
| | order | Return software created by companies, sorted by company scale descending and software price ascending | match(a:company)-[e:creates]->(b:software) return a.scale,b.price order by a.scale desc, b.price asc | |
| | group by | Find people known by Xiaohong, grouped by gender, return max salary<br>For Peking University affiliates, return the average count of people per company, grouped by company scale | match(a:person where person.name = 'Xiaohong')-[e:knows]->(b:person) return MAX(b.salary) group by b.gender<br /><br />match(a:university where a.name='北京大学')-[e]-(b:company) return AVG(b.peopleNumber) group by b.scale | |
| | join | Find all people liked by Zheng Wei and all who know him, return together<br>Find schools related to person Alice, denote as X, further find companies and persons associated with X | match(a:person where a.name = 'Zheng Wei')-[e:likes]->(b:person),(a:person where a.name = 'Zheng Wei')<-[e2:knows]-(c:person) return a, b, c<br /><br />match(a:person where a.name = 'alice')-[e]-(b:school), (b:school)-[e2]-(c:company),(b:school)-[e3]-(d:person) return a, b, c, d | |
| | Schema Query with Graph (Automatically appended in Console) | Using this graph schema:CREATE GRAPH g ( Vertex film ( id int ID, name varchar, category varchar, value int ), Vertex cinema ( id int ID, name varchar, address varchar, size int ), Vertex person ( id int ID, name varchar, age int, gender varchar, height int, salary int ), Vertex comment ( id int ID, name varchar, createTime bigint, wordCount int ), Vertex tag ( id int ID, name varchar, value int ), Edge person_likes_comment ( srcId int FROM person SOURCE ID, targetId int FROM comment DESTINATION ID, weight double, f0 int, f1 boolean ), Edge cinema_releases_film ( srcId int FROM cinema SOURCE ID, targetId int FROM film DESTINATION ID, weight double, f0 int, f1 boolean ), Edge person_watch_film ( srcId int FROM person SOURCE ID, targetId int FROM film DESTINATION ID, weight double, f0 int, f1 boolean, timeStamp bigint ), Edge film_has_tag ( srcId int FROM film SOURCE ID, targetId int FROM tag DESTINATION ID, weight double, f0 int, f1 boolean ), Edge person_creates_comment ( srcId int FROM person SOURCE ID, targetId int FROM comment DESTINATION ID, weight double, f0 int, f1 boolean, timeStamp bigint ), Edge comment_belong_film ( srcId int FROM comment SOURCE ID, targetId int FROM film DESTINATION ID, weight double, f0 int, f1 boolean ));Find comments created by Sun Mei and liked by Sun Jiancong, return all | match(a:person where a.name = 'Sun Mei')-[e:person_creates_comment]->(b:comment),(c:person where c.name = 'Sun Jiancong')-[e2:person_likes_comment]->(d:comment)return a, b, c, d | |
| | Multi-Query with Graph Schema | Using this graph schema:CREATE GRAPH g ( Vertex book ( id int ID, name varchar, id int ID, name varchar, category varchar, price int, wordCount int, createTime bigint ), Vertex publisher ( id int ID, name varchar, age int, gender varchar, height int, salary int ), Vertex reader ( id int ID, name varchar, age int, gender varchar, height int, salary int ), Vertex author ( id int ID, name varchar, age int, gender varchar, height int, salary int ), Edge author_write_book ( srcId int FROM author SOURCE ID, targetId int FROM book DESTINATION ID, weight double, f0 int, f1 boolean, timeStamp bigint ), Edge publisher_publish_book ( srcId int FROM publisher SOURCE ID, targetId int FROM book DESTINATION ID, weight double, f0 int, f1 boolean, timeStamp bigint ), Edge book_refers_book ( srcId int FROM book SOURCE ID, targetId int FROM book DESTINATION ID, weight double, f0 int, f1 boolean ), Edge reader_likes_book ( srcId int FROM reader SOURCE ID, targetId int FROM book DESTINATION ID, weight double, f0 int, f1 boolean ), Edge author_knows_author ( srcId int FROM author SOURCE ID, targetId int FROM author DESTINATION ID, weight double, f0 int, f1 boolean ));Execute 4 queries: 1. Writers known by Huang Jiacong; 2. Edges labeled author_knows_author; 3. IDs of books related to "Computer Networks"; 4. 152 books related to both He Xue and Zhang Jiancong | Queries:1: match(a:author)<-[e:author_knows_author]-(b:author where b.name='Huang Jiacong') return a, b;2: match(a:author)-[e:author_knows_author]->(b:author) return e;3: match(a:book where a.name='Computer Networks')-[e]-(b:book) return b.id;4: match(a where a.name='He Xue')-[e]->(b:book)<-[e2]-(c where c.name='Zhang Jiancong') return b limit 152; | |
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