NLP and Knowledge Graphs 2020

In this guide we will use neosemantics and APOC NLP to create a sports knowledge graph.

neosemantics (n10s)

neosemantics (n10s) is a plugin that enables the use of RDF and its associated vocabularies like (OWL,RDFS,SKOS and others) in Neo4j.

You can use n10s to easily build integrations with RDF-generating / RDF-consuming components. You can also use it to validate your graph against constraints expressed in SHACL or to run basic inferencing.

Learn more

Wikidata SPARQL API

Wikidata is a free and open knowledge base that can be read and edited by both humans and machines. It acts as central storage for the structured data of its Wikimedia sister projects including Wikipedia.

Query the API

Configuring n10s

CREATE CONSTRAINT n10s_unique_uri ON (r:Resource) ASSERT r.uri IS UNIQUE;

CALL n10s.graphconfig.init({handleVocabUris: "MAP"});

CALL n10s.nsprefixes.add('neo','neo4j://voc#');
CALL n10s.mapping.add("neo4j://voc#about","ABOUT");
CALL n10s.mapping.add("neo4j://voc#part_of","PART_OF");

Importing Wikidata’s Sports Taxonomy into Neo4j

Players

UNWIND ["Q324867","Q9448","Q15804"] as leagueId with leagueId
CALL n10s.rdf.import.fetch(
  'https://query.wikidata.org/sparql?query=prefix%20neo%3A%20%3Cneo4j%3A%2F%2Fvoc%23%3E%20%0ACONSTRUCT%20%7B%0A%20%20%3Fitem%20neo%3Aname%20%3Flabel%3B%0A%20%20%20%20%20%20%20%20neo%3Apart_of%20%3Fclub%3B%0A%20%20%20%20%20%20%20%20a%20neo%3AHuman.%0A%20%20%3Farticle%20a%20neo%3AWikipediaPage%3B%20%0A%20%20%20%20%20%20%20%20%20%20%20%20%20neo%3Aabout%20%3Fitem%20%3B%20%20%20.%0A%20%20%3Fclub%20a%20neo%3AClub%20%3B%20%0A%20%20%20%20%20%20%20%20%20%20neo%3Aname%20%3FclubName%20%3B%0A%20%20%20%20%20%20%20%20%20%20neo%3Apart_of%20%3Fleague%20.%0A%20%20%3Fleague%20a%20neo%3ALeague%20%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20neo%3Aname%20%3FleagueName%20.%0A%20%20%0A%20%20%3FclubArticle%20a%20neo%3AWikipediaPage%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20neo%3Aabout%20%3Fclub%20.%20%20%20%20%20%20%0A%7D%0AWHERE%20%0A%7B%0A%20%20%3Fclub%20wdt%3AP118%20wd%3A' + leagueId + '%2C%20%3Fleague%20.%0A%20%20%0A%20%20%3Fitem%20wdt%3AP106%20wd%3AQ937857%20%3B%20wdt%3AP569%20%3Fdob%20.%0A%20%20FILTER(xsd%3Adate(%3Fdob)%20%3E%3D%20%221970-01-01%22%5E%5Exsd%3Adate%20)%0A%20%20%3Fitem%20wdt%3AP54%20%3Fclub%20.%0A%20%20%0A%20%20%0A%20%20%3Fitem%20rdfs%3Alabel%20%3Flabel%20.%0A%20%20filter(lang(%3Flabel)%20%3D%20%22en%22)%0A%20%20%0A%20%20%3Fclub%20rdfs%3Alabel%20%3FclubName%20.%0A%20%20filter(lang(%3FclubName)%20%3D%20%22en%22)%0A%20%20%0A%20%20%3Fleague%20rdfs%3Alabel%20%3FleagueName%20.%0A%20%20filter(lang(%3FleagueName)%20%3D%20%22en%22)%0A%0A%20%20OPTIONAL%20%7B%0A%20%20%20%20%20%20%3Farticle%20schema%3Aabout%20%3Fitem%20%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20schema%3AinLanguage%20%22en%22%20%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20schema%3AisPartOf%20%3Chttps%3A%2F%2Fen.wikipedia.org%2F%3E%20.%0A%20%20%7D%0A%20%20%0A%20%20OPTIONAL%20%7B%0A%20%20%20%20%20%20%3FclubArticle%20schema%3Aabout%20%3Fclub%20%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20schema%3AinLanguage%20%22en%22%20%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20schema%3AisPartOf%20%3Chttps%3A%2F%2Fen.wikipedia.org%2F%3E%20.%20%20%20%0A%20%20%20%20%7D%0A%20%20%7D%20',
  'Turtle' ,
  { headerParams: { Accept: "application/x-turtle" } }
) yield terminationStatus, triplesLoaded, triplesParsed, namespaces, extraInfo, callParams
return leagueId, terminationStatus, triplesLoaded, triplesParsed, namespaces, extraInfo, callParams

Leagues

CALL n10s.rdf.import.fetch("https://query.wikidata.org/sparql?query=prefix%20neo%3A%20%3Cneo4j%3A%2F%2Fvoc%23%3E%20%0ACONSTRUCT%20%7B%0A%20%20%3Fleague%20a%20neo%3ALeague%20%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20neo%3Aname%20%3FleagueName%20.%0A%20%20%0A%20%20%3FleagueArticle%20a%20neo%3AWikipediaPage%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20neo%3Aabout%20%3Fleague%20.%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%0A%7D%0AWHERE%20%0A%7B%0A%20%20%3Fclub%20wdt%3AP118%20%3Fleague%20.%0A%20%0A%20%20FILTER%20(%3Fleague%20IN%20(wd%3AQ324867%2C%20wd%3AQ9448%2C%20wd%3AQ15804%20)%20)%20%20%0A%20%20%0A%20%20%3Fleague%20rdfs%3Alabel%20%3FleagueName%20.%0A%20%20filter(lang(%3FleagueName)%20%3D%20%22en%22)%0A%0A%20%20OPTIONAL%20%7B%20%20%20%20%0A%20%20%20%20%20%20%3FleagueArticle%20schema%3Aabout%20%3Fleague%20%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20schema%3AinLanguage%20%22en%22%20%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20schema%3AisPartOf%20%3Chttps%3A%2F%2Fen.wikipedia.org%2F%3E%20.%20%20%20%20%0A%20%20%20%20%7D%0A%20%20%7D", 'Turtle' , { headerParams: { Accept: "application/x-turtle" } })

Managers

CALL n10s.rdf.import.fetch("https://query.wikidata.org/sparql?query=prefix%20neo%3A%20%3Cneo4j%3A%2F%2Fvoc%23%3E%20%0ACONSTRUCT%20%7B%0A%20%20%3Fitem%20neo%3Aname%20%3Flabel%3B%0A%20%20%20%20%20%20%20%20neo%3Apart_of%20%3Fclub%3B%0A%20%20%20%20%20%20%20%20a%20neo%3AHuman.%0A%20%20%3Farticle%20a%20neo%3AWikipediaPage%3B%20%0A%20%20%20%20%20%20%20%20%20%20%20%20%20neo%3Aabout%20%3Fitem%20%3B%20%20%20.%0A%20%20%3Fclub%20a%20neo%3AClub%20%3B%20%0A%20%20%20%20%20%20%20%20%20%20neo%3Aname%20%3FclubName%20%3B%0A%20%20%20%20%20%20%20%20%20%20neo%3Apart_of%20%3Fleague%20.%0A%20%20%3Fleague%20a%20neo%3ALeague%20%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20neo%3Aname%20%3FleagueName%20.%0A%20%20%0A%20%20%3FclubArticle%20a%20neo%3AWikipediaPage%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20neo%3Aabout%20%3Fclub%20.%20%20%20%20%20%20%20%20%20%0A%7D%0AWHERE%20%0A%7B%0A%20%20%3Fitem%20wdt%3AP106%20wd%3AQ628099%20.%0A%20%20%3Fitem%20wdt%3AP6087%20%3Fclub%20.%0A%20%20%3Fclub%20wdt%3AP118%20%3Fleague%20.%0A%20%20%0A%20%20FILTER%20(%3Fleague%20IN%20(wd%3AQ324867%2C%20wd%3AQ9448%2C%20wd%3AQ15804%20)%20)%20%20%0A%0A%20%20%0A%20%20%3Fitem%20rdfs%3Alabel%20%3Flabel%20.%0A%20%20filter(lang(%3Flabel)%20%3D%20%22en%22)%0A%20%20%0A%20%20%3Fclub%20rdfs%3Alabel%20%3FclubName%20.%0A%20%20filter(lang(%3FclubName)%20%3D%20%22en%22)%0A%20%20%0A%20%20%3Fleague%20rdfs%3Alabel%20%3FleagueName%20.%0A%20%20filter(lang(%3FleagueName)%20%3D%20%22en%22)%0A%0A%20%20OPTIONAL%20%7B%0A%20%20%20%20%20%20%3Farticle%20schema%3Aabout%20%3Fitem%20%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20schema%3AinLanguage%20%22en%22%20%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20schema%3AisPartOf%20%3Chttps%3A%2F%2Fen.wikipedia.org%2F%3E%20.%0A%20%20%20%20%0A%20%20%20%20%20%20%3FclubArticle%20schema%3Aabout%20%3Fclub%20%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20schema%3AinLanguage%20%22en%22%20%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20schema%3AisPartOf%20%3Chttps%3A%2F%2Fen.wikipedia.org%2F%3E%20.%0A%20%20%20%20%7D%0A%20%20%7D", 'Turtle' , { headerParams: { Accept: "application/x-turtle" } });

Querying the Software Taxonomy - Finding all children

MATCH path = (c:Resource {name: "Premier League"})<-[:PART_OF*]-(child)
RETURN path
LIMIT 25;

Querying the Software Taxonomy - Finding siblings

MATCH path = (:Resource {name: "Manchester United F.C."})-[:PART_OF*]->(root)<-[:PART_OF]-(sibling)
WHERE not((root)-[:PART_OF]->())
RETURN path
LIMIT 50;

Guardian Football

Guardian Football

Importing Guardian Football articles

We’re going to some articles from the Guardian football section into Neo4j. We can access an RSS feed of the articles are https://www.theguardian.com/football/rss

CALL apoc.load.xml('https://www.theguardian.com/football/rss','rss/channel/item')
YIELD value
WITH value WHERE value._type = 'item'
RETURN value AS c;

Importing Guardian articles

We’re going to use the apoc.load.xml procedure to extract the body and title from each of these articles. We’ll wrap the call to apoc.load.xml with a call to apoc.periodic.iterate so that we can process the articles concurrently:

CREATE CONSTRAINT on (c:Category)
ASSERT c.name is UNIQUE;

CREATE CONSTRAINT on (a:Article)
ASSERT a.uri is UNIQUE;

Import this static feed for predictable results: https://raw.githubusercontent.com/neo4j-examples/nlp-knowledge-graph/master/import/guardian.football.rss.xml

CALL apoc.periodic.iterate(
  "CALL apoc.load.xml('https://www.theguardian.com/football/rss','rss/channel/item')
   YIELD value
   WITH value WHERE value._type = 'item'
   RETURN value AS c",
  "WITH c, [i in c._children where i._type = 'title' | i][0]._text AS title,
        [i in c._children where i._type = 'link' | i][0]._text AS link,
        [i in c._children where i._type = 'description' | i][0]._text AS description,
        [i in c._children where i._type = 'date' | i][0]._text AS date,
        [cat in [i in c._children where i._type = 'category' | i] | cat._text] AS categories
   MERGE (a:Article {uri: link})
   SET a.body = apoc.text.regreplace(description, '<[^>]*>', ' ') , a.title = title, a.datetime = datetime(date)
   FOREACH(c in categories | MERGE (category:Category {name: c}) MERGE (a)-[:IN_CATEGORY]->(category) )",
  {batchSize: 5, parallel: false}
)
YIELD batches, total, timeTaken, committedOperations
RETURN batches, total, timeTaken, committedOperations;

Querying articles

MATCH (a:Article)
RETURN a.uri, a.title, a.body, a.datetime
ORDER BY a.datetime DESC;

MATCH (n:Article)
RETURN n.uri, n.title,
       [(n)-[:IN_CATEGORY]->(c) | c.name] AS categories;

Entity extraction with APOC NLP

APOC is Neo4j’s standard utility library. It includes over 450 standard procedures, providing functionality for utilities, conversions, graph updates, and more.

It has procedures that wrap the Natural Language Processing APIs for the major cloud providers, AWS, GCP, and Azure.

APOC NLP - GCP

Specifying credentials

:params key => ("<insert-key-here>")

Connecting sports articles and the Sports Taxonomy

CALL apoc.periodic.iterate(
  "MATCH (a:Article)
   WHERE not(exists(a.processed))
   RETURN a",
  "CALL apoc.nlp.gcp.entities.stream([item in $_batch | item.a], {
     nodeProperty: 'body',
     key: $key
   })
   YIELD node, value
   SET node.processed = true
   WITH node, value
   UNWIND value.entities AS entity
   WITH entity, node
   WHERE not(entity.metadata.wikipedia_url is null)
   WITH  node, entity.metadata.wikipedia_url AS uri
   MERGE (page:Resource {uri: 'https://en.wikipedia.org/wiki/' + apoc.text.urlencode(n10s.rdf.getIRILocalName(uri))})
   SET page:WikipediaPage
   MERGE (node)-[:HAS_ENTITY]->(page)",
  {batchMode: "BATCH_SINGLE", batchSize: 10, params: {key: $key}});

Removing redundant structure

MATCH (a:Article)-[:HAS_ENTITY]->()-[:ABOUT]->(category)-[:PART_OF*]->(parent),
      (a)-[parentEntity:HAS_ENTITY]->()-[:ABOUT]->(parent)
DELETE parentEntity;

Querying the Knowlege Graph - Basic Search

MATCH (n:Article)
RETURN n.uri, n.title,
       [(n)-[:IN_CATEGORY]->(c) | c.name] AS categories,
       [(n)-[:HAS_ENTITY]->(e) | apoc.text.urldecode(n10s.rdf.getIRILocalName(e.uri))] AS entities;

Querying the Knowledge Graph - Semantic Search

We can write a query that starts from a top level category and finds all the articles attached to the underlying taxonomy. The n10s.inference.nodesInCategory procedure automates this for us, as shown below:

MATCH (c:Resource { name : "Real Madrid CF" })
CALL n10s.inference.nodesInCategory(c, {
  inCatRel: "ABOUT",
  subCatRel: "PART_OF"
})
YIELD node
MATCH (node)<-[:HAS_ENTITY]-(article)
RETURN article.uri AS uri, article.title AS title, article.datetime AS date,
       collect([ p in (node)-[:ABOUT]->() | nodes(p)[-1].name][0])  as connectingTopics,
       [ p in (article)-[:HAS_ENTITY]->()-[:ABOUT]->() | nodes(p)[-1].name ] as allTopics
ORDER BY date DESC

Querying the Knowledge Graph - Similar articles

MATCH (a:Article {uri: "https://www.theguardian.com/football/2020/oct/14/weston-mckinnie-juventus-covid-19-cristiano-ronaldo-coronavirus"}),
      path = (a)-[:HAS_ENTITY]->(wiki)-[:ABOUT]->(cat),
      otherPath = (wiki)<-[:HAS_ENTITY]-(other)
return path, otherPath;

Querying the Knowledge Graph - Similar articles

We are exploring the :PART_OF hierarchy up to two levels deep -[:PART_OF*..2]- but we are excluding top level nodes none(x in nodes(path) where x:League) as they are too "noisy"

MATCH (a:Article {uri: "https://www.theguardian.com/football/2020/oct/14/weston-mckinnie-juventus-covid-19-cristiano-ronaldo-coronavirus"}),
      entityPath = (a)-[:HAS_ENTITY]->(wiki)-[:ABOUT]->(cat),
      path = (cat)-[:PART_OF*..2]->(parent)<-[:PART_OF*..2]-(otherCat),
      otherEntityPath = (otherCat)<-[:ABOUT]-(otherWiki)<-[:HAS_ENTITY]-(other)
WHERE other <> a and none(x in nodes(path) where x:League)
RETURN [(a)-[:HAS_ENTITY]->()-[:ABOUT]->(entity) | entity.name] AS articlesEntities, other.title as similarArticle, other.uri,
       [(other)-[:HAS_ENTITY]->()-[:ABOUT]->(entity) | entity.name] AS similarArticleEntities,
       count(path) as weight,
       collect(substring(reduce(res = "", node in nodes(path) | res + "--(" + node.name + ")"),2)) AS explanation
ORDER by weight DESC;

Querying the Knowledge Graph - Similar articles

For other articles we get no similarities at all. This one in particular (https://www.theguardian.com/football/ng-interactive/2020/oct/06/david-squires-on-anarchy-in-the-premier-league-and-extinct-dinosaurs) is about Manchester United and Liverpool FC

MATCH (a:Article {uri: "https://www.theguardian.com/football/ng-interactive/2020/oct/06/david-squires-on-anarchy-in-the-premier-league-and-extinct-dinosaurs"}),
      entityPath = (a)-[:HAS_ENTITY]->(wiki)-[:ABOUT]->(cat),
      path = (cat)-[:PART_OF*..2]->(parent)<-[:PART_OF*..2]-(otherCat),
      otherEntityPath = (otherCat)<-[:ABOUT]-(otherWiki)<-[:HAS_ENTITY]-(other)
WHERE other <> a and none(x in nodes(path) where x:League)
RETURN [(a)-[:HAS_ENTITY]->()-[:ABOUT]->(entity) | entity.name] AS articlesEntities,
       other.title as similarArticle,
       other.uri,
       [(other)-[:HAS_ENTITY]->()-[:ABOUT]->(entity) | entity.name] AS similarArticleEntities,
       count(path) as weight,
       collect(substring(reduce(res = "", node in nodes(path) | res + "--(" + node.name + ")"),2)) AS explanation
ORDER by weight DESC;

Adding a custom ontology

We’re now going to add a custom ontology of football rivalries.

 CALL n10s.nsprefixes.add('skos','http://www.w3.org/2004/02/skos/core#');
 CALL n10s.nsprefixes.add('rdfs','http://www.w3.org/2000/01/rdf-schema#');
 CALL n10s.mapping.add("http://www.w3.org/2004/02/skos/core#broader","PART_OF");
 CALL n10s.mapping.add("http://www.w3.org/2000/01/rdf-schema#label","name");
 CALL n10s.mapping.add("http://www.w3.org/2004/02/skos/core#Concept","Concept");

call n10s.rdf.preview.fetch("http://www.nsmntx.org/2020/10/fo-riv",'Turtle')

call n10s.rdf.import.fetch("http://www.nsmntx.org/2020/10/fo-riv",'Turtle')
YIELD terminationStatus, triplesLoaded, triplesParsed, namespaces, callParams
RETURN terminationStatus, triplesLoaded, triplesParsed, namespaces, callParams;

Querying the Knowledge Graph - Similar articles

And now if we try our similarity query again with the article that returned no similar ones, we’ll see articles popping up based on the relationships defined in the football rivalry ontology.

MATCH (a:Article {uri: "https://www.theguardian.com/football/ng-interactive/2020/oct/06/david-squires-on-anarchy-in-the-premier-league-and-extinct-dinosaurs"}),
      entityPath = (a)-[:HAS_ENTITY]->(wiki)-[:ABOUT]->(cat),
      path = (cat)-[:PART_OF*0..2]->(parent)<-[:PART_OF*0..2]-(otherCat),
      otherEntityPath = (otherCat)<-[:ABOUT]-(otherWiki)<-[:HAS_ENTITY]-(other)
WHERE other <> a and none(x in nodes(path) where x:League)
RETURN [(a)-[:HAS_ENTITY]->()-[:ABOUT]->(entity) | entity.name] AS articlesEntities,
       other.title as similarArticle,
       other.uri,
       [(other)-[:HAS_ENTITY]->()-[:ABOUT]->(entity) | entity.name] AS similarArticleEntities,
       count(path) as weight,
       collect(substring(reduce(res = "", node in nodes(path) | res + "--(" + node.name + ")"),2)) AS explanation
ORDER by weight DESC;

Some of the articles ranking at the top are connected to the initial article via the Team Rivalry, which isn’t a good measure of similarity. Let’s fix that.

Querying the Knowledge Graph - Similar articles

Let’s remove any paths that contain the Concept label, which will sort out that problem.

MATCH (a:Article {uri: "https://www.theguardian.com/football/ng-interactive/2020/oct/06/david-squires-on-anarchy-in-the-premier-league-and-extinct-dinosaurs"}),
      entityPath = (a)-[:HAS_ENTITY]->(wiki)-[:ABOUT]->(cat),
      path = (cat)-[:PART_OF*0..2]->(parent)<-[:PART_OF*0..2]-(otherCat),
      otherEntityPath = (otherCat)<-[:ABOUT]-(otherWiki)<-[:HAS_ENTITY]-(other)
WHERE other <> a and none(x in nodes(path) where x:League OR x:Concept)
RETURN [(a)-[:HAS_ENTITY]->()-[:ABOUT]->(entity) | entity.name] AS articlesEntities,
       other.title as similarArticle,
       other.uri,
       [(other)-[:HAS_ENTITY]->()-[:ABOUT]->(entity) | entity.name] AS similarArticleEntities,
       count(path) as weight,
       collect(substring(reduce(res = "", node in nodes(path) | res + "--(" + node.name + ")"),2)) AS explanation
ORDER by weight DESC;

That looks better. At the moment we’re ordering the results by the number of paths that exist between the articles. Let’s update that so that we favour articles with shorter paths.

Querying the Knowledge Graph - Similar articles

We’ll build a score that sums the inverse path lengths. This means that:

A path of length 5 adds (1/5) to the score
A path of length 2 adds (1/2) to the score

MATCH (a:Article {uri: "https://www.theguardian.com/football/ng-interactive/2020/oct/06/david-squires-on-anarchy-in-the-premier-league-and-extinct-dinosaurs"}),
      entityPath = (a)-[:HAS_ENTITY]->(wiki)-[:ABOUT]->(cat),
      path = (cat)-[:PART_OF*0..2]->(parent)<-[:PART_OF*0..2]-(otherCat),
      otherEntityPath = (otherCat)<-[:ABOUT]-(otherWiki)<-[:HAS_ENTITY]-(other)
WHERE other <> a and none(x in nodes(path) where x:League OR x:Concept)
WITH [(a)-[:HAS_ENTITY]->()-[:ABOUT]->(entity) | entity.name] AS articlesEntities,
       other.title as similarArticle,
       other.uri AS uri,
       [(other)-[:HAS_ENTITY]->()-[:ABOUT]->(entity) | entity.name] AS similarArticleEntities,
       count(path) as weight,
       collect(DISTINCT substring(reduce(res = "", node in nodes(path) | res + "--(" + node.name + ")"),2)) AS explanation,
       collect(path) AS paths
RETURN articlesEntities,
       similarArticle, uri,
       similarArticleEntities, weight,
       explanation,
       reduce(score=0.0, x in paths | score + 1.0/length(x)) AS weightedScore
ORDER by weightedScore DESC;

Hmmm, our top ranking article is still a quiz that contains the names of lots of ex Liverpool and Manchester United players. That probably isn’t the most relevant article, so let’s dampen the score by the number of entities that an article has.

Querying the Knowledge Graph - Similar articles

We can do this by dividing the weightedScore by the size of similarArticleEntities:

MATCH (a:Article {uri: "https://www.theguardian.com/football/ng-interactive/2020/oct/06/david-squires-on-anarchy-in-the-premier-league-and-extinct-dinosaurs"}),
      entityPath = (a)-[:HAS_ENTITY]->(wiki)-[:ABOUT]->(cat),
      path = (cat)-[:PART_OF*0..2]->(parent)<-[:PART_OF*0..2]-(otherCat),
      otherEntityPath = (otherCat)<-[:ABOUT]-(otherWiki)<-[:HAS_ENTITY]-(other)
WHERE other <> a and none(x in nodes(path) where x:League OR x:Concept)
WITH [(a)-[:HAS_ENTITY]->()-[:ABOUT]->(entity) | entity.name] AS articlesEntities,
       other.title as similarArticle,
       other.uri AS uri,
       [(other)-[:HAS_ENTITY]->()-[:ABOUT]->(entity) | entity.name] AS similarArticleEntities,
       count(path) as weight,
       collect(DISTINCT substring(reduce(res = "", node in nodes(path) | res + "--(" + node.name + ")"),2)) AS explanation,
       collect(reduce(res = [], l in [node in nodes(path) | [l in labels(node) where l <> "Resource"]] | res + l)) AS nodeTypes
RETURN articlesEntities,
       similarArticle, uri,
       similarArticleEntities, weight,
       explanation,
       reduce(score=0.0, x in nodeTypes | score + 1.0/size(x)) / size(similarArticleEntities) AS weightedScore
ORDER by weightedScore DESC;

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knowledge-graphs-nodes2020.adoc

knowledge-graphs-nodes2020.adoc

NLP and Knowledge Graphs 2020