#
Extraction Patterns to Derive Social Networks from Linked Open Data Using SPARQL^{ †}

^{*}

^{†}

## Abstract

**:**

## 1. Introduction

- We propose several techniques to extract social networks from linked open data.
- We express those techniques in a formal way using SPARQL algebra.
- We present formal translations into social networks.
- We present several case studies that apply some of the presented techniques.

## 2. Motivation

`yago:actedIn`predicate, which relates an actor (subject) to a movie (object) he/she acted in. Table 1 shows a subset of RDF triples from the YAGO dataset about this relation. Thus, we can visually represent this relation as a two-mode affiliation network (two types of nodes). Figure 1 demonstrates a smaple of such a network obtained from triples in Table 1.

## 3. Related Work

## 4. Background

#### 4.1. Social Networks

#### 4.2. SPARQL Algebra

- all variables in $\mathbf{X}$ and all terms in $\mathbf{I}\cup \mathbf{L}$ are expressions;
- if ?$x\in \mathbf{X}$, then
`bound`(?x) is an expression; - if $t\in \mathbf{T}\cup \mathbf{X}$, then
`isIRI`(t),`isLiteral`(t), and`isBlank`(t) are expressions; - if ${E}_{1}$ and ${E}_{2}$ are expressions, then so are: $({E}_{1}+{E}_{2})$, $({E}_{1}-{E}_{2})$, $({E}_{1}*{E}_{2})$, $({E}_{1}/{E}_{2})$, $({E}_{1}\doteq {E}_{2})$, $({E}_{1}<{E}_{2})$, $({E}_{1}>{E}_{2})$, $(\neg E)$, $({E}_{1}\wedge {E}_{2})$, and $({E}_{1}\vee {E}_{2})$;
`exists`(P) is an expression, if P is a pattern.

- a basic graph pattern (BGP) is a set of triple patterns, that is, elements of the set$$(\mathbf{I}\cup \mathbf{L}\cup \mathbf{X})\times (\mathbf{I}\cup \mathbf{X})\times (\mathbf{I}\cup \mathbf{L}\cup \mathbf{X})$$
- $Join({P}_{1},{P}_{2})$, $Union({P}_{1},{P}_{2})$ and $SetMinus({P}_{1},{P}_{2})$ are patterns if ${P}_{1}$ and ${P}_{2}$ are patterns;
- $Filter(E,P)$ is a pattern if P is a pattern and E is an expression;
- $LeftJoin(E,{P}_{1},{P}_{2})$ is a pattern if ${P}_{1}$ and ${P}_{2}$ are patterns and E is an expression;
- $GroupAgg(Z,$?$x,f,E,P)$, where Z is a set of variables, called grouping variables, ?x is a variable called aggregation variable, f is an aggregate function, E is an expression, and P is a pattern;
- $Extend($?$x,E,P)$ is a pattern (which captures
`BIND`and`VALUES`constructs), where ?x is a variable, E is an expression, and P is a pattern;

`Count`,

`Sum`,

`Avg`,

`Min`, or

`Max`), and E is the expression (often a variable) we are aggregating over.

## 5. Network Extraction Techniques

#### 5.1. Extraction Using One Predicate

#### 5.2. Pattern with One Triple (Direct Extraction)

SELECT ?u ?v WHERE { ?u p ?v. } |

`yago:isMarriedTo`, then the relation becomes bidirectional). This means the extracted social network will be a directed network. In this type of extraction, one tie in the network is extracted from one triple in the RDF graph.

**Example**

**1.**

`yago:influences`that relates persons to others who have influence on them (on their opinions or work). To extract a social network based on this influence relationship, we can use this query:

#### 5.3. Patterns with Two Triples

#### 5.3.1. In-2-Star

SELECT ?u ?v WHERE { ?u p ?o. ?v p ?o.} |

**Example**

**2.**

`dbo:almaMater`that relates people to academic institutions they studied in. This predicate can be used to derive a so-called alumni relationship among people who attended the same university. The following query can be used for this purpose:

**Example**

**3.**

`yago:actedIn`can be used to derive co-acting network among actors who acted in same movies. The query can be expressed as:

`Count`, the aggregation expression is simply the variable $?o$, and the pattern P is $\left\{\right(?u,p,?o),(?v,p,?o\left)\right\}$. Finally, we select the three variables $u,v$ and w. The query can be syntactically expressed as:

SELECT ?u ?v (COUNT(?o) AS ?w) WHERE { ?u p ?o. ?v p ?o. } GROUP BY ?u ?v |

#### 5.3.2. Out-2-Star

SELECT ?u ?v WHERE { ?s p ?u. ?s p ?v.} |

`{ ?s p ?u, ?v.}`since the two triple patterns share the same subject and predicate.

**Example**

**4.**

`dbo:starring`that relates a movie (or TV show) to people who have a starring role in it. This predicate can be used to derive a relationship among actors who have starring roles in same movies. The following query can be used for this purpose:

SELECT ?u ?v (COUNT(?s) AS ?w) WHERE { ?s p ?u, ?v. } GROUP BY ?u ?v |

#### 5.3.3. Chain

SELECT ?u ?v WHERE { ?u p ?x. ?x p ?v.} |

SELECT ?u ?v WHERE { ?u p/p ?v.} |

**Example**

**5.**

`dbo:parent`that relates a person to his parent(s). This predicate can be used to derive a grandparent relationship among people. A person u has a grandparent v, when the parent of u is x and the parent of x is v. The corresponding query is:

SELECT ?u ?v (COUNT(?x) AS ?w) WHERE { ?u p ?x. ?x p ?v } GROUP BY ?u ?v |

## 6. Extraction Using Two Predicates

#### 6.1. Patterns with Two Triples

#### 6.1.1. In-2-Star

SELECT ?u ?v WHERE { ?u p ?o. ?v q ?o.} |

**Example**

**6.**

`yago:exports`and

`yago:imports`that relate countries to products they export and import, respectively. These predicates can be used to derive a so-called commercial-dependency relationship among countries. If we want the relation to be such that a country is linked to another when the former exports a product imported by the later, then we consider

`exports`predicate as primary, and

`imports`as secondary, hence, the corresponding query is:

`imports`as primary and

`exports`as secondary, here the derived tie from u to v would mean that u depends on v.

SELECT ?u ?v (COUNT(?o) AS ?w) WHERE { ?u p ?o. ?v q ?o } GROUP BY ?u ?v |

#### 6.1.2. Out-2-Star

SELECT ?u ?v WHERE { ?s p ?u. ?s q ?v } |

`{?s p ?u; q v?.}`since the triples share the same subject.

SELECT ?u ?v (COUNT(?s) AS ?w) WHERE { ?s p ?u; q ?v. } GROUP BY ?u ?v |

**Example**

**7.**

`dc:creator`, and to a contributor using

`dc:contributor`, both predicates come from Dublin Core vocabulary (http://dublincore.org/documents/dcmi-terms/). Thus, a collaboration social network can be derived such that it relates creators of bibliographic resources to other contributors using this query:

**Example**

**8.**

`dbo:director`predicate that relates a movie to the person who directed it. Thus, along with

`dbo:starring`predicate, we can derive a network between actors and directors who have worked together on a movie. However, we should specify the direction we desire for the ties; if we want the ties to be from directors to stars, then the pattern should be:

#### 6.1.3. Chain

SELECT ?u ?v WHERE { ?u p/q ?v. } GROUP BY ?u ?v |

SELECT ?u ?v (COUNT(?x) AS ?w) WHERE { ?u p ?x. ?x q ?v. } GROUP BY ?u ?v |

**Example**

**9.**

`dbo:spouse`and

`dbo:parent`, to derive a social network where a person is related to his parent-in-low using the following query:

**Example**

**10.**

`dbo:employer`that relates a person (an employee) to an organization where he is employed, and the predicate

`dbo:president`that relates an organization to its president. Using these two predicates with chain pattern, we can derive a social network that relates employees to their employers:

#### 6.2. Patterns with Three Triples

#### 6.2.1. Parallel-In

SELECT ?u ?v WHERE { ?x p ?u. ?y p ?v. ?x q ?y. } |

**Example**

**11.**

`dc:contributor`relates a document to its author, while

`bibo:cites`relates a document to another document that cites the first document. Thus, using these predicates, we can extract a citation social network among authors, such that it relates an author to another one when the first writes a document that cites a document written by the second author. This network is extracted using the following query:

SELECT ?u ?v (COUNT(*) AS ?w) |

WHERE { ?x p ?u. ?y p ?v. ?x q ?y. } |

GROUP BY ?u ?v |

#### 6.2.2. Parallel-Out

SELECT ?u ?v WHERE { ?u p ?x. ?v p ?y. ?x q ?y. } |

SELECT ?u ?v (COUNT(*) AS ?w) |

WHERE { ?u p ?x. ?v p ?y. ?x q ?y. } |

GROUP BY ?u ?v |

#### 6.3. Patterns with Four Triples

#### 6.3.1. Straight In-2-Star

SELECT ?u ?v WHERE { ?u p/q ?o. ?v p/q ?o. } |

**Example**

**12.**

`dailymed:producesDrug`relates a pharmaceutical company to a drug it produces, and the predicate

`dailymed:activeIngredient`relates a drug to its active ingredient. Using these two predicates, we can extract a social network of competing pharmaceutical companies where competition is defined by selling drugs with the same active ingredient [28]. The required query can be written as:

#### 6.3.2. Revert In-2-Star

SELECT ?u ?v |

WHERE { ?x p ?u; q ?o. |

?y p ?v; q ?o. } |

**Example**

**13.**

`bibo:presentedAt`that relates a document to an event—for example, a paper to a conference. Thus, using this predicate and

`dc:contributor`that relates a document to his author, we can extract a social network among authors whose papers are presented at the same event. The required query can be written as:

#### 6.3.3. Straight Out-2-Star

SELECT ?u ?v WHERE { ?s q/p ?u, ?v. } |

#### 6.3.4. Revert Out-2-Star

SELECT ?u ?v WHERE { ?u p ?x. ?v p ?y. ?s q ?x; q ?y. } |

## 7. Translation into Networks

#### 7.1. Binary Networks

#### 7.2. Weighted Networks

## 8. Discussion

`owl:sameAs`,

`owl:equivalentProperty`and

`owl:equivalentClass`, since many publishers use such equivalence relationships, for declaring that their URIs are equivalent with URIs of other datasets” [35]. As most of LOD datasets are interlinked, there are considerable amounts of overlap of RDF resources within datasets in the whole LOD cloud. Thus, such overlap is also reflected onto the social networks extracted from different datasets.

`yago:actedIn`(which relates an actor to a movie) with the in-2-star extraction pattern (Section 5.3.1) as demonstrated in Example 3. In this case, the size of the network is 225,790 edges, connecting 26,544 nodes (actors).

`dbo:starring`(which relates a movie to an actor) with the out-2-star extraction pattern (Section 5.3.2) as mentioned in Example 4. The SPARQL query is shown in Figure 8. In this case, the size of the network is 829,887 edges. This network is different from the one extracted from YAGO, not only in terms of the number of entities and edges, but also in terms of the entities themselves (RDF resources), as the entities in YAGO belong to the namespace

`http://yago-knowledge.org/resource/`, whereas the entities in DBpedia belong to the namespace

`http://dbpedia.org/resource/`.

`yago:Brad_Pitt`from yago is the same as the entity

`dbr:Brad_Pitt`from DBpedia (Here, the prefix

`dbr`refers to DBpedia resources namespace: http://dbpedia.org/resource/). The good news is that, thanks to the interlinking of DBpedia and YAGO, such equivalences of entities are available via the OWL property

`owl:sameAs`. Thus, the overlap between the two co-acting social networks (from YAGO and DBpedia) can be easily detected. Figure 9 shows another version of the previous SPARQL query (to extract the network from DBpedia) where each entity from DBpedia is associated with its equivalent entity from YAGO. The results of this query consist of 94,311 ties/edges that correspond to the intersection of the the two social networks.

`SERVICE`operator) which allow for combining graph patterns that can be evaluated over several endpoints within a single query [15].

## Author Contributions

## Funding

## Conflicts of Interest

## Abbreviations

LOD | Linked Open Data |

SNA | Social Network Analysis |

RDF | Resource Description Framework |

SPARQL | Simple Protocol and RDF Query Language |

W3C | World Wide Web Consortium |

YAGO | Yet Another Great Ontology |

BGP | Basic Graph Pattern |

OWL | Web Ontology Language |

URI | Uniform Resource Identifier |

IRI | Internationalized Resource Identifier |

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**Figure 3.**Extraction patterns with one predicate. (

**a**) Direct Extraction: one direct connection from u to v. (

**b**) In-2-Star: two connections are coming-into o from u and v. (

**c**) Out-2-Star: two connections are going-out s to u and v. (

**d**) Chain: two successive connections between u and v via x.

**Figure 4.**Extraction patterns with two predicates p, q and two triples. (

**a**) In-2-Star: two connections are coming-into o from u (using p) and v (using q). (

**b**) Out-2-Star: two connections are going-out s to u (using p) and to v (using q). (

**c**) Chain: two successive connections between u and v via x, using p and q respectively.

**Figure 5.**Extraction patterns with two predicates and three triples. (

**a**) Parallel-In: u and v have in-coming connections (using p) from x and y respectively. (

**b**) Parallel-Out: u and v have out-going connections (using p) to x and y respectively. In both patterns, x is connected to y using q.

**Figure 6.**Extraction patterns with two predicates and four triples. (

**a**) Straight In-2-Star: u and v have out-going connections (using p) to x and y, respectively, while x and y have out-going connections (using q) to o. (

**b**) Revert In-2-Star: u and v have in-coming connections (using p) from x and y, respectively, while x and y have out-going connections (using q) to o. (

**c**) Straight Out-2-Star: u and v have in-coming connections (using p) from x and y, respectively, while x and y have in-coming connections (using q) from s. (

**d**) Revert Out-2-Star: u and v have out-going connections (using p) to x and y, respectively, while x and y have in-coming connections (using q) from s.

Jodie_Foster | actedIn | Contact_(1997_US_film) |

Matthew_McConaughey | actedIn | Contact_(1997_US_film) |

Matthew_McConaughey | actedIn | Sahara_(2005_film) |

Penélope_Cruz | actedIn | Sahara_(2005_film) |

Steve_Zahn | actedIn | Sahara_(2005_film) |

Penélope_Cruz | actedIn | Bandidas |

Salma_Hayek | actedIn | Bandidas |

Steve_Zahn | actedIn | Bandidas |

⋯ | ⋯ | ⋯ |

Variant | Basic Graph Pattern | Extracted Network | ||
---|---|---|---|---|

Directed? | Weighted? | |||

1-triple | direct | $\left\{\right(?u,\mathtt{p},?v\left)\right\}$ | yes | no |

1-pred., 2-triples | in-2-star | $\left\{\right(?u,\mathtt{p},?o),(?v,\mathtt{p},?o\left)\right\}$ | no | yes |

out-2-star | $\left\{\right(?s,\mathtt{p},?u),(?s,\mathtt{p},?v\left)\right\}$ | no | yes | |

chain | $\left\{\right(?u,\mathtt{p},?x),(?x,\mathtt{p},?v\left)\right\}$ | yes | yes | |

2-pred., 2-triples | in-2-star | $\left\{\right(?u,\mathtt{p},?o),(?v,\mathtt{q},?o\left)\right\}$ | yes | yes |

out-2-star | $\left\{\right(?s,\mathtt{p},?u),(?s,\mathtt{q},?v\left)\right\}$ | yes | yes | |

chain | $\left\{\right(?u,\mathtt{p},?x),(?x,\mathtt{q},?v\left)\right\}$ | yes | yes | |

2-pred., 3-triples | parallel-in | $\left\{\right(?x,\mathtt{p},?u),(?y,\mathtt{p},?v),(?x,\mathtt{q},?y\left)\right\}$ | yes | yes |

parallel-out | $\left\{\right(?u,\mathtt{p},?x),(?v,\mathtt{p},?y),(?x,\mathtt{q},?y\left)\right\}$ | yes | yes | |

2-pred., 4-triples | straight in-2-star | $\left\{\right(?u,\mathtt{p},?x),(?v,\mathtt{p},?y),(?x,\mathtt{q},?o),(?y,\mathtt{q},?o\left)\right\}$ | no | yes |

revert in-2-star | $\left\{\right(?x,\mathtt{p},?u),(?y,\mathtt{p},?v),(?x,\mathtt{q},?o),(?y,\mathtt{q},?o\left)\right\}$ | no | yes | |

straight out-2-star | $\left\{\right(?x,\mathtt{p},?u),(?y,\mathtt{p},?v),(?s,\mathtt{q},?x),(?s,\mathtt{q},?y\left)\right\}$ | no | yes | |

revert out-2-star | $\left\{\right(?u,\mathtt{p},?x),(?v,\mathtt{p},?y),(?s,\mathtt{q},?x),(?s,\mathtt{q},?y\left)\right\}$ | no | yes |

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Ghawi, R.; Pfeffer, J.
Extraction Patterns to Derive Social Networks from Linked Open Data Using SPARQL. *Information* **2020**, *11*, 361.
https://doi.org/10.3390/info11070361

**AMA Style**

Ghawi R, Pfeffer J.
Extraction Patterns to Derive Social Networks from Linked Open Data Using SPARQL. *Information*. 2020; 11(7):361.
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**Chicago/Turabian Style**

Ghawi, Raji, and Jürgen Pfeffer.
2020. "Extraction Patterns to Derive Social Networks from Linked Open Data Using SPARQL" *Information* 11, no. 7: 361.
https://doi.org/10.3390/info11070361