An ontological investigation over human relations in linked data

Is there anything better than foaf:knows? The relationship FOAF module developed in 2002 by E. Vitiello8

Inclusion of some of these properties seems debatable. For example, if a person describes someone as his/her enemy, then the person surely knows the enemy. However, the opposite may not be true. There may be an unknown enemy of anybody (the same holds, e.g., for children knowing their parents).

Also inclusion of enemies in a friend of a friend vocabulary seems counterintuitive, because the general intuition about knows in the semantic context of FOAF is a positive (or at least neutral) relation between people.

Since 2004 the relationship module has been modified to a more general FOAF Relationship Vocabulary and is continually maintained and enhanced.9

The Relationship Vocabulary is represented in OWL and is presented as a vocabulary for expressing human relations in general. The Relationship Vocabulary is not used as widely as FOAF core, but there are substantial linked datasets that use it, e.g., BBC Music10

The reason why we go into such depth with the analysis of the FOAF project and the Relationship Vocabulary is to analyze the difficulties when we link these data to similar datasets using different vocabularies. Subsumption and other logical relations are important because they are used even in the simplest reasoners and information aggregators like Tabulator by Berners-Lee et al. (2006).

Limitations of the foaf:knows relation led to some other proposals. E.g., Matsuo et al. (2004) proposed a different classification of human relations: 1) relationship by event – relationships of people based on their participation in the same event (a conference, a baseball game, a walk); 2) relationship by common property – relationships based on property like working place or hobby; and 3) relationship by communication – these are relationships based on some type of communication: face-to-face conversation, telephone call, e-mail, chat, posting to web blogs etc.

However, even this approach has problems. Firstly, all criteria used in that classification are events: communications, face-to-face meetings, telephone calls, sending e-mail messages. Therefore, it is not clear why relationships based on communication should form a separate top level class from that based on events. Secondly, having a common property in most cases does not imply a knows relation. People can work at the same work place, study in the same school, have the same hobbies etc., without knowing each other or having any other significant relationship. It seems that authors actually think about criteria of plausibility to be able to infer that someone knows someone else. Indeed, communicating is probably a safer plausibility criterion than working in a same company, which is safer than participating in a same generic event. Coherently, Matsuo’s approach is only explicitly tailored to automatic relationship discovery, e.g., from a frequent email exchange between two persons we can conclude that they know each other.

Two other, more general, ontologies are those by Gangemi and Mika (2003) and Masolo et al. (2004), which lay down the foundations for the Neo-Davidsonian representation of human relations proposed by Mika and Gangemi (2004).

Gangemi and Mika (2003) developed the Descriptions and Situations ontology design pattern that provides a very generic way to capture relations between a description and a described situation. The authors rely on the distinction between individual (extensional) relationships (i.e. situations), and their generic descriptions (intensional relations, i.e. situation types aka situation descriptions). These two levels need to be represented separately, in order to support a class of use cases, e.g., the relation between a norm, and a case to which a norm should be applied. The separation is not enough though, because intensional or extensional social relations can have multiple arities (they are multivaried predicates), which cannot be known in advance. A famous example is due to Davidson (1967): when representing, e.g., the intensional relation preparing a coffee, how many arguments are needed? The agent preparing the coffee, the amount of coffee, its quality, the coffee-pot, the heating, the kind of water, the time and place of preparing it, etc.? In order to support both separation of intension and extension, and multivaried predicates, the generic pattern of Description and Situations applies a double reification strategy, which allows to represent separately (extensional) relationships such as a certain coffee making situation, (intensional) relations with any arity required, such as a coffee making recipe, and to relate them systematically.

Related to work by Gangemi and Mika (2003), paper by Masolo et al. (2004), on a rich axiomatic setting, provides a “general formal framework for developing a foundational ontology of socially constructed entities”, based on formal analysis of roles and their descriptions (understood as intensional relations).

These two ontologies share the basic assumptions, and provide the groundwork for paper by Mika and Gangemi (2004), which proposes an extension of social ontologies such as FOAF with reification (first-order treatment) of social relationships. Mika and Gangemi (2004) introduce a Social Relationship class as a subclass of Context, and particular social relationships such as Friendship as subclasses of Social Relationship. As contexts, social relationships can have, e.g., parameters, roles and tasks as components. A typical class of roles is Relationship Role, a subclass of the Social Role concept introduced by Masolo et al. (2004). An example of a relationship role is Friend as in a friendship relation, Student and Professor as in a student/advisor relationship, and Uncle and Nephew as kinship roles. This modeling approach also restricts relationship roles to be played by members of the class Natural Person, and introduces some characteristics of social relationships that can be associated with it: sign (valence: positive, negative, etc.), strength, provenance, history, etc.

From the point of view of formal ontology, the design pattern proposed by Mika and Gangemi (2004) is more expressive and powerful, and should be preferred whenever a use case requires substantial expressiveness and reasoning capabilities. However the Neo-Davidsonian pattern has not got a wide usage specifically for human relations in the Linked Data domain, since ‘direct’ binary relation patterns are usually preferred, probably because of their simplicity and closeness to the basic RDF data model. The analysis presented in this paper is therefore directed at binary human relations, although the results of the analysis can also be used for ontologies following more expressive patterns.

3.1. Formal concepts used in analysis of Relationship Vocabulary: Ontology pattern and reification

The meaning of term “ontology pattern” is summarized by Gangemi (2005): historically it is based on latin word “patronus” (patron) – someone proposed for imitation. In knowledge engineering Clark and Porter (1997) define “pattern” as “a theory ‘template’ or ‘schema’, which denotes a structure of objects and relationships, but whose axioms are not directly part of the global KB”. In ontology engineering “ontology pattern” is often defined more flexibly as various schemas and macros for UML, OWL, core ontologies, etc. See works by Reich (2000), Gangemi et al. (2003), Guizzardi et al. (2004) and Svátek (2004).

The term “reification” is historically based on latin words “res” (thing) and “facere” (to make) so it can be translated as “thingmaking”, i.e. turning of something abstract into a thing. Stevens and Lord (2010) claim that in the case of ontology engineering, the term “reification” describes technique that represents a property as an object enabling a richer description of a property.

3.2. Structure of Relationship Vocabulary

At the beginning we will analyze what ontological patterns are used in Relationship Vocabulary to describe various types of human relations.

The first observation is that almost all properties of Relationship Vocabulary have the class Person as both domain and range. An example is the property livesWith – a relation between two persons, in this case symmetric.

On the other hand, there are also three terms that do not fit within this description: the properties participant and participantIn, and the class Relationship. The domain of participantIn is Person, while its range is Relationship. The opposite holds for participant. Intuitively, we would expect these two properties to be inverse of each other, but this is not formally declared in the Relationship Vocabulary.

We therefore observe that Relationship Vocabulary includes two different ontology patterns for human relations.

OPEN IN VIEWER

Fig. 2.

Ontology pattern of Relationship Vocabulary 1.

OPEN IN VIEWER

Fig. 3.

Ontology pattern of Relationship Vocabulary 2.

Let us look at the first pattern first. Historically the limitation of the original FOAF relationship module was that it consisted of just rdfs:subPropertyOf triples (binary relation subsumption axioms). The Relationship Vocabulary extended it and turned it into a generic vocabulary. The description of extended properties now includes some semantics with a richer subsumption structure. Still, probably for backward compatibility, properties like childOf are all subsumed by knows. A new knowsOf property, which is not symmetric, has been introduced, and recently (February 2010) its semantics has been changed so that the knowsOf (which is not symmetric) is no longer subproperty of the (symmetric) knows. It has been stated that knows is subproperty of knowsOf.

A recent (February 2010) revision of the Relationship Vocabulary eventually acknowledged that for distant descendants/ancestors it may not be possible to know reciprocally each other, hence the property descendantOf is no longer subsumed by knows. However, the editors failed to notice that we run into exactly the same problem when we consider the property grandparentOf and even parentOf. For a grandparent (and even parent) of a person could die before the person was born, so there are real-world cases where people could not know their children or grandchildren.

In many cases the assumption that being parentOf implies knows may be true. But from strictly ontological point of view these properties are independent.

The second pattern seems a bit puzzling. What is that modeling approach meant for? For a person to be participant in a relationship, we need that members of that class are reifications of extensional, rather than intensional, relationships.

In practice, each individual relationship becomes an instance of the class Relationship. For example the relation friendOf(Peter, John) would be reified to, e.g., an instance friendOf1 and the relation friendOf(Mary, Jane) would be reified to, e.g., an instance friendOf2. Once reified, one can use them to represent participation, as depicted in Fig. 4.

Notice that even with this interpretation, reified relationships are instances of the general class Relationship, but there is no subclass structure for specific relations such as Friendship or Marriage).

OPEN IN VIEWER

Fig. 4.

Instances friendOf1 and friendOf2 of class Relationship and other instances.

Finally, it is not clear how to express the semantics of the relations, e.g., not being symmetric: how can we use fanOf to say that Peter is fan of Beethoven but Beethoven is not fan of Peter?

The Relationship Vocabulary has some other gaps. For example, it does not include intuitive axioms such as childOf and grandchildOf being subsumed by descendantOf, or define siblings as having common parents, etc. Therefore, it does not take advantage of the expressivity of OWL that could be used for representing some common-sense requirements.

Another problem is related to employment relations like employedBy, which has the consequence (because of being subsumed by knows) of having both its domain and range set to the class Person. In a real-world scenario, an entity that employs other persons is usually a legal entity – company, institute or some other type of organization. This may be called a legal person; however, in the FOAF vocabulary such kind of entity is represented by the class Organization, which is formally asserted to be disjoint with class Person. If we use this formalization then we cannot express that a (physical) person is employed by an organisation without introducing logical inconsistencies into our system. Indeed, employment relationships are usually expressed in FOAF by the property workplaceHomepage, which ranges on the class Document. Then documents can be related to organizations using the property homepage. It would be intuitive to say that if the workplaceHomepage of a person is a document that is the homepage of some organisation, then the person is employedBy this organisation. This is however impossible in the scope of the Relationship Vocabulary semantics, which defines employedBy as a relationship between two (physical) persons.

A clarification of the intended semantics of the Relationship Vocabulary, as far as we can reconstruct it from the known documentation, is provided in Section 4.

5.1. Philosophical problems relating to human relations

We may start philosophical analysis with some remarks concerning theory of intentionality; it is overview was made by Jacob (2010). Theory of intentionality, as founded by Brentano (1874), deals among other topics with the analysis of intentional inexistence: the characteristic specific of every mental phenomenon – that it includes something as an object and a direction or reference to it. Mental phenomena that interest us here are mental acts and these can have different types. Any such act has an object and is directed toward this object; e.g., in imagining something is imagined, in judgment something is affirmed or denied, in love something is loved etc.

We must therefore differentiate between three paradigmatic cases represented by these examples of forms of assertions:

Notice that while cases 1 and 2 deal with mental acts, assertion 3 concerns no mental phenomena but describes state of affairs. Notice also that the meaning of verb “to know” is in cases 1 and 2 completely different. While the intentional object of assertion 1 is a person (person y), the intentional object of assertion 2 is a proposition – assertion 2 can be rephrased: “x knows/believes that proposition ‘y is child of x’ is true”. Assertions similar to case 2 are usually called belief-ascriptions and some authors like, e.g., Schiffer (1992) interpret them not only using x as person, p as proposition, but introduce even third component m as mode of presentation of under which x believes p (main idea is that x can believe p under m 1 but not under m 2 ).

Notice also that in assertion 2 we have written “knows/believes”. The reason is that while in common language is the word “to know” used relatively freely with respect to various propositions whose certainty is not assured, in philosophical discourse the verb “to believe” is preferred in such contexts. Usage of the word “to know” is defined usually with reference to “justified true belief” (credited to Plato, see Chisholm (1982)) and there are additional complex requirements to assure certainty of “known” proposition. In philosophical context we should therefore use the verb “to believe” unless these requirements are shown to be fulfilled.

Assertions like 1 are quite different – they still describe intentional acts and their intentional object is a person, however mode of existence of this object is intentional inexistence. As already Brentano (1874) noted: while in other relations (like the relation child of) both fundament and terminus are real,19

Concerning assertions like 1 we have also to ask what is meant by “knowing”? Russell (1911) distinguishes knowing by acquaintance and by description. Knowing by acquaintance means the first hand knowledge based in case of persons primarily on personal meeting accompanied with sufficient amount of empiric and other experience. The knowledge by description is based only on indirect propositional (verbal, textual, theoretical) information about its object. Notice that only knowledge by acquaintance can guarantee existence of know person.

We can observe also some general problems regarding semantics of names used in these assertions. Frege (1892) noted that one may have some beliefs about “Hesperus” but not about “Phosphorus” because of not knowing that these two names denote the same object (planet Venus). He developed distinction between the sense and reference of the word and used it to clarify such questions. Similarly one may know “Cicero” but not “Tully” because of not knowing that these two names denote the same person. One may have even different set of beliefs concerning both of these names. Russell (1905) later enhanced this theory with addition of distinction between the name (e.g., “Scott”) and definite descriptions (e.g., “author of Waverley”) that exhibit similar problems.

These questions are also connected with problem of referential opacity. According to Quine (1960) we say that two terms are referentially opaque if they cannot be substituted salva veritate (i.e. without changing the truth value of the statement). This theory claims that possibility of such substitution depends on context: in context like assertion 1 or 2 are called referentially opaque contexts because we cannot substitute terms salva veritate: “John knows Cicero” may be true, but “John knows Tully” may be false. Similarly with assertion 2. However Quine (1960) claims that cases such as our assertion 3 are referentially transparent contexts so truth value of “Marcus is child of Cicero” and truth value of “Marcus is child of Tully” are always the same.

Conclusion of our analysis is that assertion 3 is completely different from assertions 1 and 2. While assertions 1 and 2 concern mental states of individual human beings and also their truth value depends on these mental states, the assertion 3 concerns actual state of affairs, independent of knowledge and beliefs of any human being. While assertion 3 represents a real world relation, assertions 1 and 2 represent quasi-relations. For relation 3 to obtain, both fundament and terminus (x and y) must exist, assertions 1 and 2 may be true even if terminus (y) does not exist. The conclusion of this analysis is that assertions 1, 2 and 3 are in some very fundamental aspects different and irreducible. Using our terminology introduced above – assertions 1 and 2 concern epistemic state of affairs, while assertion 3 concerns ontological state of affairs.

Principles of reasoning about knowledge are formalized by epistemic logic described by Rescher (2005) and autoepistemic logic presented by (Moore, 1985).

Autoepistemic logic is one of formalisms trying to describe nonmonotonic reasoning. It focuses on reasoning of perfectly rational agent about his own knowledge and utilizes classic propositional logic enhanced with unary operator K – denoting knowledge or belief. If p signifies proposition, then K p signifies that p is believed by the rational agent. Every rational agent has a belief set of propositions believed by that agent. Because the agent is perfectly rational the believe set is closed under positive introspection ( p ∈ E ) → ( K p ∈ E ) – if agent believes p, he also believes that he believes p – and also closed under negative introspection. Kripke’s modal semantics of possible worlds is used for autoepistemic logic, which corresponds to K45 modal logics (because axioms like K p → p hold). Marek and Truszczynski (1991) demonstrated that autoepistemic logic has many applications related to non-monotonic programming and Lakemeyer (1993) showed that introduction of multi-agent systems led to development of multi-agent autoepistemic logic similar to more general epistemic logic.

Epistemic logic introduces the notational binary operator K. Formula K ( x , p ) is read as “x knows p”, where x is a “knower” (i.e. a conscious rarional agent) and p is a proposition. Rescher (2005) writes K x p – we rewrite it for better readability as K ( x , p ) .

On its turn, the interpersonal relation “knows”, which we baptize here p-knows, is problematic because it is related to epistemic reasoning, but at the same time it refers to the social aspects of knowing certain facts about persons, which lead to the ability of performing certain actions, having rights and duties, etc. In other words, p-knows is a consequence of the ‘application’ of epistemic reasoning to interpersonal and social worlds: if I know some facts about a person, then I am entitled to do certain actions; if I have a certain connection (e.g., a kinship relation) to someone then I am expected to know other facts that make me eligible to other actions, etc. So from the point of view of epistemic logic we have to distinguish two relations:

Grounding this reflection in logic, what is meant by saying that “person x knows person y”? What exactly does person x know? Is it possible to define how the relations knows and p-knows are interrelated?

A comprehensive ontology (“NIC”) of how knowledge, norms, and social behavior are entrenched in the lifecycle of social collectives is presented by Gangemi (2008) by using the Description and Situations pattern (cf. Section 2.3). However, the scope and design of NIC are probably too complex: as we have noticed before, here we want to respect the simplicity of the use case faced by the Relationship Vocabulary, therefore we focus on a rigorous, but logically simple, design.

On the other hand, in line with (Rescher, 2005, p. 6), p-knows can be reduced to knows by a formula following an axiom schema such as: (59) p-knows ( x , y ) ≡ ∃ p ( identifies ( p , y ) ∧ K ( x , p ) ) Notice that a) p denotes a proposition, i.e. another formula, therefore such formulas as (59) actually represent assertions about assertions, b) operator K is intensional (value of K ( x , p ) is not function of x and p, see paper by Gabbay and Guenthner (2003)), c) the identifies relation is a shortcut for a complex of social relations (as e.g. formalised in NIC). These three characteristics make the epistemic approach too complex for e.g. linked data practices.

Eventually, we probably need to live without epistemic extensions, and treat p-knows as a plain empirical relation. Still, the considerations about epistemic and ontological states of affairs may provide us some help for a better organization of the Relationship Vocabulary. For each relation R in the Relationship Vocabulary, we may ask whether axiom (60) holds: (60) R ( x , y ) ↔ K ( x , R ( x , y ) )

That means that the relation R between persons x and y holds if and only if person x knows that relation R holds between persons x and y. This is a non-trivial assumption, because while ( K ( x , p ) → p ) is the most general principle of epistemic logic, our formula also says the inverse: that for a human relation R, axiom (61) holds: (61) R ( x , y ) → K ( x , R ( x , y ) )

It is thus never the case that the assertion R ( x , y ) evaluates to true without person x also knowing that it evaluates to true. Axiom (60) seems then quite strong: it may be true for some relations but not for others. It means that those relations are equivalent on epistemic and ontological aspects. For this reason, we introduce the metaproperty of “being an ontoepistemic relation” when axiom (60) holds for a relation.

5.3. Ontoepistemic relations

Ontoepistemic relations are in many cases those describing our mental states. Sometimes, as Robb and Heil (2009) note, they are called mental properties. Assuming that ontoepistemic relations are only mental predicates can be debatable for relations such us apprenticeOf, which are not purely mental (they may have social or institutional content), but still it seems unlikely that a person could be other person’s apprentice without knowing it.

The domain of an ontoepistemic relation is that of “knowers”, and when a state of affairs occurs, in which R ( x , y ) is true, then the knower x also knows it. Formally, it is the representation of the ontoepistemicity property based on axiom (60): (62) OE ( R ) ≡ Def ( R ( x , y ) ↔ K ( x , R ( x , y ) ) )

E.g., the predicate hates is ontoepistemic because if x hates y then x always knows that s/he hates y (this is a simple example because the hates property is mental). On the other hand, the predicate isFatherOf is not ontoepistemic because there can be situations when x isFatherOf y, but x does not know it.

It is also quite common that a relation is ontoepistemic while its inverse relation is not. E.g., if a person likes-to-eat ( R lte ) some food then she knows it, however the food does not know that it is the favorite food of some person. This is consistent with the definition of ontoepistemic relation because such predicate is not symmetric and the domain of the relation is different from the domain of its inverse. If this asymmetric relation R lte has domain X and range Y that are disjoint X ∩ Y = ∅ then the following holds: (63) ∀ x ∀ y R lte ( x , y ) ↔ ( K ( x , ( R lte ( x , y ) ) ) ∧ ¬ K ( y , ( R lte ( x , y ) ) ) )

In the case of symmetric relations then obviously if a relation is ontoepistemic then also inverse relation is ontoepistemic.

This analysis is by no means restricted to the FOAF Relationship Vocabulary: using vocabularies such as SIOC (Semantically-Interlinked Online Communities)20

Other examples include the Sport Ontology,22

All these examples are taken from vocabularies considered to be part of the Linked Data initiative and listed, e.g., on the LOV (Linked Open Vocabulary) portal.24

We can now determine what relations from the Relationship Vocabulary are ontoepistemic. We have also performed an analysis of these predicates from the point of view of general formal ontology, in order to improve their formalization beyond the ontoepistemic properties. The results are presented along definitions from the original FOAF Relationship Vocabulary in Table 1. We have also determined which of these properties are symmetric, asymmetric and irreflexive, and compared the results to the Relationship Vocabulary definitions. Irreflexivity is implied by asymmetry, but there are also relations that are symmetric but also irreflexive (e.g. engagedTo). We did not assigned irreflexivity or reflexivity to properties, which we considered questionable (e.g. it is psychological question, whether one can be enemyOf himself and it depends on application, whether one hasMet himself). The table has the following columns:

It might be interesting to ask which relations are irreflexive. Irreflexivity is implied by asymmetry so we may conclude that all relations that are asymmetric are also irreflexive. For other relations the question is puzzling and in many cases difficult to answer from the point of view of formal ontology. It may belong to psychology or other disciplines and is matter of an interpretation. Can a person be influenced by itself? Can a person lose contact with itself? Can a person be employed by itself in true sense of term employment? Because these questions do not have definite answers we did not include irreflexivity in the Table 1.

For obvious reasons, relations that are not ontoepistemic (e.g., parentOf) should not be subproperties of knowsOf: (64) ¬ OE ( ϕ ) → ¬ ∀ x ∀ y ( ϕ ( x , y ) → k O ( x , y ) )

7.1. The DBpedia and schema.org HR case

The most used HR vocabulary is actually a general vocabulary, the DBpedia ontology,25

We have also applied our ontoepistemic property to DBpedia (and schema.org, see below) vocabulary (see again Table 2). The practical impact of the property becomes evident as soon as we would like to use HR relations to reconstruct social networks of public persons. For example, Lloyd Osborne was influenced by Robert Louis Stevenson, spouse of Fanny Stevenson, who is a relative of Lloyd Osborne. In principle, we cannot be sure that R.L. Stevenson was aware of Osborne being a relative of his as well, or that he influenced him. Osborne on its turn was probably aware of the influence of R.L. Stevenson’s, but still we cannot infer he was aware of being a relative of his. On the contrary, in a regular social network analysis scenario, we would easily flatten these relations, assuming the Stevensons and Osborne form a social subgraph.

The benefits of a foundational axiomatization can be described empirically for human relationships. When considering an axiomatization for large, crowdsourced, and partly uncontrolled data, new interesting problems appear which show the importance of ontological and logical methods to improve data quality, but also pose a challenge for the sustainability of data quality assumptions.

DBpedia is quite interesting for ontology design because its ontology schema is partly misaligned with the real usage in data. In practice, the domain and range axioms declared in the schema are not based on the actual distribution of types of entities referred to in the triples, either because of data dirtiness, or because of missing data analysis. For example, the relation almaMater is declared to have domain Person and range EducationalInstitution, and it results to be used in 64928 triples; however, 87 of them are actually used with range Person, data that should be probably cleaned up. On the other hand, the appointer relation is declared with range Person, but no domain is provided, although all its 1506 triples have a Person in their domain position.

Even worse, in some cases misalignment with data generates ontological conflicts that in accurate logical representations would generate also logical inconsistencies. For example, the associatedBand relation, declared with no specific domain, and range Band, is consequently used in 64193 triples with instances of the class Band (a subclass of Organisation) in the range, but in 31187 triples with instances of the class MusicalArtist (a subclass of Person). This is not just dirty data (about 2/3 against about 1/3), but an inaccuracy in creating the class taxonomy or relation axioms. The DBpedia ontology schema does not define disjointness axioms extensively, but if these data and schema are used in a more rigorous context, and experts would intuitively make Person and Organisation disjoint classes, all musical artists declared in the range of associateBand would be inferred to be also bands. Now, since the two superclasses are disjoint, the consequence would be a massive inconsistency in the ontology.

A different problem can be seen at the schema level. If the DBpedia ontology schema is aligned to DOLCE,27

A recent result by Paulheim and Gangemi (2015) on cleaning up DBpedia ontology and data after the linking to DOLCE-Zero (a simplified version of DOLCE + DnS, downloadable from http://www.ontologydesignpatterms.org/ont/dul/D0.owl) proves the practical feasibility as well as the substantial gain when using foundational axioms in order to improve linked open data: more than 3 million triples have been found inconsistent, anti-patterns emerged, and design solutions have been suggested. The trade-off between advantages of foundational axioms, and the difficulty of enforcing all potential distinctions, is also discussed.

Schema.org is also a source of human relations, and it is becoming a de facto ontology standard for search engine optimization, being maintained and enforced by Google, Yahoo, Microsoft, and Yandex. There is no easy detection of the size of available data for schema.org, but it is interesting to report on what HR are currently included in it, as shown in Table 3.28

Besides HR fragments of general ontologies like DBpedia and schema.org, several domain-oriented vocabularies also focus on HR relations. From the of approx. 2800 vocabulary entities listed in LOV,29

In Table 4 we can see that the most massively used HR vocabulary at the domain level is GNDO, the German National Library ontology. Since library linked data presumably cover human relationships of authors (or other encyclopedically relevant persons), this use of HR vocabulary is mostly complementary to the use of FOAF/RV, which is typically used by ‘ordinary’ persons in their personal profiles.

Additionally, we also examined in detail all the LOV vocabularies that had been arranged, by the LOV curators, into a ‘cluster’ named ‘People’. There are 14 vocabularies aside FOAF and RV. Of these, 8 contain entities with some relevance to the HR topic. The vocabularies are (from this restricted point of view) reviewed below as roughly ordered from those pre-dominantly focused on officially recognised relationships, through those dealing with mere ‘knowing’, to ontologies that focus on modelling inner feelings of persons. Note that GNDO is not member of this cluster as its coverage is much broader; there is however the Agrelon ontology coming from the same provider (German National Library).

Bio ontology30

PoderVocab32

VCard ontology33

Agrelon ontology34

Gen ontology35

Online presence ontology37

Appearances ontology39

Emoca ontology40