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Functional and semantic roles in a high-level knowledge representation language

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Abstract

We describe in this paper a formalization of the notion of “role” that involves a clear separation between two very different sorts of roles. Semantic roles, like student or customer, are seen as (pre-defined) transitory properties that can be associated with (usually animate) entities. From a formal point of view, they can be represented as standard concepts to be placed into a specific branch of a particular ontology; they formalize the static and classificatory aspects of the notion of role. Functional roles must be used, instead, to model those pervasive and dynamic situations corresponding to events, activities, circumstances etc. that are characterized by spatio-temporal references; see, e.g., “John is now acting as a student”. They denote the specific function with respect to the global meaning of an event/situation/activity... that is performed by the entities involved in this event/situation... and formalize the dynamic and relational aspects of the notion of role. A functional role of the subject/agent/actor/protagonist... type is used to associate “John” with the notion of student or customer (semantic roles) during a specific time interval. Formally, functional roles are expressed as primitive symbols like subject, object, source, beneficiary. Semantic and functional roles interact smoothly when they are used to deal with challenging knowledge representation problems like the so-called “counting problem”, or when we need to set-up powerful inference rules whose atoms can directly denote complex situations. In this paper, the differentiation between semantic and functional roles will be illustrated from an narrative knowledge representation language (NKRL) point of view. NKRL is a high-level conceptual tool used for the computer-usable representation and management of the inner meaning of syntactically complex and semantically rich multimedia information. But, as we will see, the importance of this distinction goes well beyond its usefulness in a specific NKRL context. In particular, the use of functional roles is of paramount importance for the set-up of those evolved n-ary forms of knowledge representation that allow us to get rid from the limitations in expressiveness proper to the standard (binary) solutions.

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Notes

  1. For example, Loebe (2007) and Mizoguchi et al. (2007, 2012) contest vigorously the possibility of any monolithic interpretation of the notion of role. Loebe differentiates between relational, processual and social roles—where relational and processual roles are then unified into abstract roles—and supplies formal models for the three. The role model of Mizoguchi and colleagues, called Hozo, is based on the use of four basic role-related categories: role concept, potential player, role-playing thing and role holder. These categories take into account both the static (role concept, role holder) and dynamic (potential player, role-playing thing) aspects of the role notion. Steimann (2000: 89, 94–98) introduces a separation between an IsA structured type hierarchy and a proper role hierarchy where, e.g., the roles Customer and Supplier are subsumed by Agent. In the same vein, Fan et al. (2001) assert that “...entities and roles are not related taxonomically” (2001: 30). Therefore, ROLES and ENTITIES are collected into two separate sub-hierarchies included within a global ontology having THING as top level concept. Two relations between roles and entities are introduced: played-by and purpose. The former is used to connect every instance of a role with an instance of an entity, while the latter represents a role that the entity is intended to play. With respect now to (Guarino 1992; Guarino and Welty 2000), his formalization of the notion of role is based on two principles: foundation (there cannot be a role of student without the presence of a school or university and of at least a subject matter) and anti-rigidity (the state of being a student is a transient one). Operationally, however, roles for Guarino are still traditional ontological entities (concepts) that can be inserted into an IsA hierarchical structure see, e.g., Fig. 1 in (Guarino 1992) and Figs. 1 and 2 in (Guarino and Welty 2000).

  2. According to the NKRL’s conventions, in this paper concepts are denoted in lower case and individuals (instances of concepts) in upper case. Moreover, the symbolic names of NKRL concepts and individuals always include at least one underscore symbol.

  3. The generalized predicates can correspond to the usual surface tensed/untensed verbs like press, move, exist, produce, but also to adjectives (“... worth several dollars...”, “...a dormant volcano...”), nouns (“...Jane’s amble along the park...”, “...a possible attack...) when they have a predicative function. In a structured/dynamic knowledge context, to each generalized predicate of the real world corresponds an elementary event in the digital world. Therefore, a situation like “The Control Room operator presses a button to start the auxiliary lubrication pump” implies the presence of two elementary events corresponding to the two verbs “press” and “start”. Situations like “Lucy was looking for a taxi” or “Peter lives presently in Paris” denote, in contrast, the presence of a unique elementary event—see in this context, for example, Matsuyoshi et al. (2010).

  4. Determiners (attributes) can be added to templates and predicative occurrences to introduce further details about the basic core, see Eq. (1), of their formal representation. In Table 1, several types of determiners are mentioned. The variables var2, var5 and var7 denote, e.g., determiners/attributes of the location type represented, in the predicative occurrences, by specific terms of the HClass concept location_ or by individuals derived from these terms. Modulators represent an important category of determiners/attributes that apply to a full, well-formed template or predicative occurrence to particularize its meaning. They are classed into three categories, temporal (begin, end, obs(erve)), deontic (oblig(ation), fac(ulty), interd(iction), perm(ission)) and modal (abs(olute), against, for, main, ment(al), wish, etc.), see Zarri (2009: 71–75). An example of use of the temporal modulator obs(erve) is reproduced in Table 4. A last category of attribute/determiners concerns the two operators date-1, date-2. They can only be associated with full predicative occurrences (e.g., the occurrence virt2.c32 in Table 1) and are used to materialize the temporal interval normally associated with the elementary event corresponding to the occurrence. A detailed description of the formal system used in NKRL for the representation and management of temporal information can be found, e.g., in Zarri (1998).

  5. The binding occurrence mechanism allows us to associate together, through reification operations and the use of connectivity phenomena operators, events (or even full narratives and scenarios) originally created as independent entities. Another second order NKRL mechanism permits to make reference to an elementary event (or full narrative), reified using its conceptual label, as an argument of another event. An example can correspond to an event \({{{\textsf {\textit{X}}}}}\) that denotes someone speaking about \({{{\textsf {\textit{Y}}}}}\), where \({{{\textsf {\textit{Y}}}}}\) is an elementary event or a logically coherent set of events. This relational device is called completive construction in NKRL, see Zarri (2009: 87–91, 2014a). The completive construction mechanism indicates clearly that all the second order features of NKRL, reification-based, correspond to powerful Higher Order Logic (HOL) structures. According to HOL, in fact, a predicate can take one or more other predicates as arguments, allowing quantification over super-predicate symbols (Enderton 2012).

  6. Note, in spite of this excursion in the Linguistics/Computational Linguistics field, that the work described in this paper is deeply rooted in the Artificial Intelligence and Knowledge Representation research. Like all the similar/related approaches described in Sect. 5.2 below it is based, in fact, on the hypothesis that is possible and reasonable to develop a formal notation for expressing “deep meaning” contents in a way totally independent from the search for an optimal form of correspondence with the surface, linguistic form these contents can assume in a particular natural language; see Zarri (1997) in this context and, for example, Jackendoff’s \(\theta \)-Criterion (1990) for a completely different approach.

  7. Bruce describes several deep cases systems typical of the state of research in knowledge representation and computational linguistics of the seventies. Among them, the original version of Fillmore’s case system includes eight relationships: Agent, Counter-Agent, Object, Result, Instrument, Source, Goal (defined here as “the place to which something moves”) and Experience. Simmon’s case system consists of seven relationships: Actant, Theme, Source, Goal (“place or state of the termination of the act”), Instrument, Locus, Time. A more articulate system is proposed by Grimes, who introduces a differentiation among Orientation Roles like Object, Source, Goal, Process Roles like Patient, Material, Result, Agentive Roles like Agent, Instrument, Force and the unique Benefactive Role, Benefactive. Spark Jones & Boguraev supplies a long list (28) of deep cases. These include, e.g., Activity, After, Agent, Before, Destination, Force (“The girl died from an accident”), Goal (“John went to town in order to buy a shirt”), Instrument, Location, Manner, Mental-Object, Object, Quantity, Reason (“John is afraid of being apprehended by the police”), Recipient, etc. In the Appendix B of his 1999 book, Sowa supplies his own list of thematic roles. They are for example: Agent, Beneficiary, Completion, Destination, Duration, Instrument, Location, Origin, Path (ex: “The pizza was shipped via Albany and Buffalo”), Patient, PointInTime, Recipient, Result, Start, Theme (“an essential participant that may be moved, said, or experienced, but is not structurally changed”), and so on.

  8. It can be interesting to note the similarity of Ceccato’s sfera nozionale with a recent popular tool developed in a MIT context like ConceptNet (Liu and Singh 2004; Speer and Havasi 2012, Web ref.9). ConceptNet is a semantic network whose nodes represent compound concepts: these are made up of words or short phrases in semi-structured English and of labeled relationships between them. The relationships (21, including IsA) are in the form of, e.g., CreatedBy, PartOf, UsedFor, PrerequisiteOf, DefinedAs, LocatedNear. (Recursive) compound concepts can be “[wake up in the morning] PrerequisiteOf [eat breakfast]”, “[kitchen table] UsedFor [eat breakfast]” “[chair] LocatedNear [kitchen table]” (Liu and Singh 2004: 213), see the sfera nozionale’s examples above. John Sowa (1984) had already noticed that Ceccato’s sfera nozionale was one of the first concrete examples of semantic network and that it predates the publication (1968) in abridged form of the Ross Quillian’s Ph.D. thesis (1966) that is conventionally assumed as the starting point of the semantic network developments.

References

  • Almeida JPA, Guizzardi G (2007) On the foundation for roles in RM-ODP, contributions from conceptual modelling. Workshops proceedings of the 11th international IEEE enterprise distributed object computing conference. Computer Society Press, Los Alamitos, pp 205–215

  • Bal M (1997) Narratology: introduction to the theory of narrative, 2nd edn. University Press, Toronto

    Google Scholar 

  • Barlatier P, Dapoigny R (2012) A type-theoretical approach for ontologies: the case of roles. Appl Ontol 7:311–356

    Google Scholar 

  • Bechhofer S, van Harmelen F, Hendler J, Horrocks I, McGuinness DL, Patel-Schneider PF, Stein LA (eds) (2004) OWL web ontology language reference, W3C recommendation 10 February 2004. http://www.w3.org/TR/owl-ref/. Accessed 2 July 2017

  • Berners-Lee T (2006) Linked data–design issues, W3C note 27 July 2006. http://www.w3.org/DesignIssues/LinkedData.html. Accessed 2 July 2017

  • Bertino E, Catania B, Zarri GP (2001) Intelligent database systems. Addison-Wesley and ACM Press, London

    MATH  Google Scholar 

  • Blomqvist E (2009) Semi-automatic ontology construction based on patterns, Ph.D. thesis. School of Engineering of the Linköping University, Linköping

  • Bruce B (1975) Case systems for natural language. Artif Intell 6:327–360

    Article  MATH  Google Scholar 

  • Cáceres C, Fernández A, Ossowski S, Vasirani M (2006) Agent-based service discovery for healthcare: an organizational approach. IEEE Intell Syst 21(6):11–20

    Article  Google Scholar 

  • Ceccato S (ed) (1961) Linguistic analysis and programming for mechanical translation (technical report RADC-TR-60-18). Feltrinelli, Milano

    Google Scholar 

  • Ceccato S (1964) Automatic translation of languages. Inf Storage Retr 2:105–158

    Article  MATH  Google Scholar 

  • Chaudhri VK, Farquhar A, Fikes R, Karp PD, Rice JP (1998) OKBC: a programmatic foundation for knowledge base interoperability. In: Mostow J, Rich C (eds) Proceedings of the 1998 national conference on artificial intelligence, AAAI/98. MIT Press/AAAI Press, Cambridge, pp 600–607

  • Clocksin WF, Mellish CS (1981) Programming in PROLOG. Springer, Berlin

    MATH  Google Scholar 

  • Davidson D (1967) The logical form of action sentences. In: Rescher N (ed) The logic of decision and action. University Press, Pittsburg, pp 81–95

    Google Scholar 

  • Dowty D (1989) On the Semantic content of the notion of ‘thematic role’. In: Chierchia G, Partee BH, Turner R (eds) Properties, types, and meaning, vol 2. Semantic issues. Kluwer, Dordrecht, pp 69–130

    Chapter  Google Scholar 

  • Dyer MG (1983) In-depth understanding. The MIT Press, Cambridge

    Google Scholar 

  • Enderton HB (2012) Second-order and higher-order logic. In: Zalta EN (ed) The stanford encyclopedia of philosophy, fall 2012 edition. http://plato.stanford.edu/archives/fall2012/entries/logic-higher-order/. Accessed 2 July 2017

  • Falbo RA, Barcellos MP, Nardi JC, Guizzardi G (2013) Organizing ontology design patterns as ontology pattern languages. In: Cimiano P, Corcho O, Presutti V, Hollink L, Rudolph S (eds) The semantic web: semantics and big data-proceedings of the 10th ESWC international conference (LNCS 7882). Springer, Berlin, pp 61–75

  • Fan J, Barker K, Clark P, Porter B (2001) Representing roles and purpose. In: Handschuh S, Dieng R, Staab S (eds) Proceedings of the K-CAP 2001 workshop on knowledge markup and semantic annotation. ACM, New York, pp 38–43

  • Fillmore CJ (1968) The case for case. In: Bach E, Harms RT (eds) Universals in linguistic theory. Holt, Rinehart and Winston, New York, pp 1–88

    Google Scholar 

  • Finlayson MA, Gervás P, Mueller E, Narayanan S, Winston P (eds) (2010) Computational models of narratives-papers from the AAAI 2010 fall symposium (technical report FS-10-04). AAAI Press, Menlo Park

    Google Scholar 

  • Freeman MW (1982) The QUA link. In: Schmolze JG, Brachman RJ (eds) Proceedings of the 1981 KL-ONE workshop (BBN Report n. 4842). Bolt Beranek and Newman, Cambridge, pp 54–64

  • Fukazawa Y, Naganuma T, Fujii K, Kurakake S (2006) Construction and use of role-ontology for task-based service navigation system. In: Cruz I, Decker S, Allemang D, Preist C, Schwabe D, Mika P, Uschold M, Aroyo L (eds) Proceedings of the 5th international semantic web conference, ISWC 2006 (LNCS vol. 4273). Springer, Berlin, pp 806–819

  • Garshol LM, Moore G (eds) (2008) Topic maps—data model (ISO/IEC JTC1/SC34 Document). http://www.isotopicmaps.org/sam/sam-model/2008-06-03/. Accessed 2 July 2017

  • Genilloud G, Wegmann A (2000) A new definition for the concept of role, and why it makes sense. In: Baclawski K, Kilov H (eds) Proceedings of the 2000 workshop on behavioral semantics-co-located with the OOPSLA 2000 conference on object-oriented programming, systems, languages, and applications. http://infoscience.epfl.ch/record/269/files/GenilloudW00a.pdf. Accessed 2 July 2017

  • Goldstein I, Papert S (1977) Artificial intelligence, language, and the study of knowledge. Cognit Sci 1:84–123

    Article  Google Scholar 

  • Guarino N (1992) Concepts, attributes and arbitrary relations: some linguistic and ontological criteria for structuring knowledge bases. Data Knowl Eng 8:249–261

    Article  Google Scholar 

  • Guarino N (2009) The ontological level: revisiting 30 years of knowledge representation. In: Borgida A, Chaudhri V, Giorgini P, Yu E (eds) Conceptual modelling: foundations and applications. Essays in honor of John Mylopoulos (LNCS vol. 5600). Springer, Berlin, pp 52–67

  • Guarino N, Welty C (2000) A formal ontology of properties. In: Dieng R, Corby O (eds) Knowledge acquisition, modeling, and management—proceedings of EKAW’2000 (LNCS vol. 1937). Springer, Berlin, pp 97–112

  • Guizzardi G (2005) Ontological foundations for structural conceptual models, Ph.D. thesis. Centre for Telematics and Information Technology of the University of Twente, Enschede

  • Guizzardi G (2006) Agent roles, qua individuals and the counting problem. In: Garcia A, Choren R, Lucena C, Giorgini P, Holvoet T, Romanovsky A (eds) Software engineering for multi-agent systems IV, research issues and practical applications (LNCS vol. 3914). Springer, Berlin, pp 143–160

  • Gupta A (1980) The logic of common nouns: an investigation in quantified modal logic. Yale University Press, New Haven

    MATH  Google Scholar 

  • Heath T, Bizer C (2011) Linked data: evolving the web into a global data space. Morgan & Claypool, San Rafael

    Google Scholar 

  • Hitzler P, Krötzsch M, Parsia B, Patel-Schneider PF, Rudolph S (eds) (2009) OWL 2 web ontology language primer, W3C recommendation 27 October 2009. http://www.w3.org/TR/owl2-primer/. Accessed 2 July 2017

  • Jackendoff R (1990) Semantic structures. The MIT Press, Cambridge

    Google Scholar 

  • Kim J (1996) Events as property exemplifications. In: Casati R, Varzi AC (eds) Events (international research library of philosophy, 15). Dartmouth Publishing, Aldershot, pp 117–135

    Google Scholar 

  • Kolodner JL (1984) Retrieval and organizational strategies in conceptual memory: a computer model. Lawrence Erlbaum, Hillsdale

    Google Scholar 

  • Kozaki K, Sunagawa E, Kitamura Y, Mizoguchi, R (2007) Role representation model using OWL and SWRL. In: Proceedings of the 2nd workshop on roles and relationships in object oriented programming, multiagent systems and ontologies-co-located with ECOOP 2007. http://www.ei.sanken.osaka-u.ac.jp/pub/kozaki/Role07kozaki.pdf. Accessed 2 July 2017

  • Lenat DB, Guha RV, Pittman K, Pratt D, Shepherd M (1990) CYC: toward programs with common sense. Commun ACM 33(8):30–49

    Article  Google Scholar 

  • Lenat DB, Witbrock M, Baxter D, Blackstone E, Deaton C, Schneider D, Scott J, Shepard B (2010) Harnessing cyc to answer clinical researchers’ ad hoc queries. AI Mag 31(3):13–32

    Article  Google Scholar 

  • Liu H, Singh P (2004) ConceptNet-a practical commonsense reasoning tool-kit. BT Technol J 22(4):211–226

    Article  Google Scholar 

  • Loebe F (2007) Abstract vs. social roles-towards a general theoretical account of roles. J Appl Ontol 2:127–158

    Google Scholar 

  • Mani I, Pustejovsky J (2004) Temporal discourse models for narrative structure. In: Webber B, Byron D (eds) Proceedings of the ACL workshop on discourse annotation. Association for Computational Linguistics (ACL), Stroudsburg, pp 57–64

  • Masolo C, Borgo S, Gangemi A, Guarino N, Oltramari A (2003) WonderWeb deliverable D18 (IST project 2001-33052). Laboratory for Applied Ontology, Trento. http://www.loa.istc.cnr.it/old/Papers/D18.pdf. Accessed 2 July 2017

  • Masolo C, Vieu L, Bottazzi E, Catenacci C, Ferrario R, Gangemi A, Guarino N (2004) Social roles and their descriptions. In: Dubois D, Welty C, Williams MA (eds) Proceedings of the 9th international conference on knowledge representation and reasoning, KR2004. AAAI Press, Menlo Park, pp 267–277

  • Masolo C, Guizzardi G, Vieu L, Bottazzi E, Ferrario R (2005) Relational roles and qua-individuals. In: Boella G, Odell J, van der Torre L, Verhagen H (eds) Roles, an interdisciplinary perspective-papers from the AAAI 2005 fall symposium (technical report FS-05-08). AAAI Press, Menlo Park, pp 103–112

    Google Scholar 

  • Masolo C, Vieu L, Kitamura Y, Kozaki K, Mizoguchi R (2011) The counting problem in the light of role kinds. In: Davis E, Doherty P, Erdem E (eds) Proceedings of commonsense-2011, the tenth international symposium on logical formalization on commonsense reasoning (AAAI Spring symposium, technical report SS-11-06). AAAI Press, Menlo Park, pp 76–82

  • Matsuyoshi S, Eguchi M, Sao C, Murakami K, Inui K, Matsumoto Y (2010) Annotating event mentions in text with modality, focus, and source information. In: Calzolari N, Choukri K, Maegaard B, Mariani J, Odijk J, Piperidis S, Rosner M, Tapias D (eds) Proceedings of the international conference on language resources and evaluation, LREC 2010. European Language Resources Association (ELRA), Paris, pp 1456–1463

  • Mizoguchi R, Sunagawa E, Kozaki K, Kitamura Y (2007) A model of roles within an ontology development Tool: Hozo. J Appl Ontol 2:159–179

    Google Scholar 

  • Mizoguchi R, Kozaki K, Kitamura Y (2012) Ontological analyses of roles. In: Ganzha M, Maciaszek LA, Paprzycki M (eds) Proceedings of the federated conference on computer science and information systems, FedCSIS 2012. IEEE Xplore, New York, pp 489–496

  • Mizoguchi R, Galton A, Kitamura Y, Kozaki K (2015) Families of roles: a new theory of occurrent-dependent roles. J Appl Ontol 10:367–399

    Article  Google Scholar 

  • Mueller ET (2013) Computational models of narrative. Sprache und Datenverarbeitung Int J Lang Data Process SDV 37(1–2):11–39

    Google Scholar 

  • Niles I, Pease A (2001) Towards a standard upper ontology. In: Welty C, Smith B (eds) Proceedings of the 2nd international conference on formal ontology in information systems, FOIS-2001. IOS Press, Amsterdam, pp 2–9

  • Noy FN, Fergerson RW, Musen MA (2000) The knowledge model of Protégé-2000: combining interoperability and flexibility. In: Dieng R, Corby O (eds) Knowledge acquisition, modeling, and management—proceedings of EKAW’2000 (LNCS vol. 1937). Berlin, Springer, pp 17–32

  • Noy FN, Rector A (eds) Hayes P, Welty C (contributors) (2006) Defining N-ary relations on the semantic web, W3C working group note 12 April 2006. http://www.w3.org/TR/2006/NOTE-swbp-n-aryRelations-20060412/. Accessed 2 July 2017

  • Palmer M, Gildea G, Xue N (2010) Semantic role labeling. Morgan and Claypool, San Rafael

    Book  Google Scholar 

  • Pepper S (2010) Topic maps. In: Bates MJ (ed) Encyclopedia of library and information sciences, 3rd edn. Taylor & Francis, Abingdon, pp 5247–5260

    Google Scholar 

  • Pustejovsky J, Ingria R, Saurí R, Castaño J, Littman J, Gaizauskas R, Setzer A, Katz G, Mani I (2005) The specification language TimeML. In: Mani I, Pustejovsky J, Gaizauskas R (eds) The language of time: a reader. University Press, Oxford, pp 545–558

    Google Scholar 

  • Quillian MR (1966) Semantic memory, Ph.D. dissertation. Carnegie Institute of Technology, Pittsburgh. An abridged version has been published. In: Minsky M (ed) (1968) Semantic information processing. The MIT Press, Cambridge, pp 227–270

    Google Scholar 

  • Reynolds D (ed) (2014) The organization ontology, W3C recommendation,16 Jan 2014. http://www.w3.org/TR/vocab-org/. Accessed 2 July 2017

  • Rosner M, Somers H (1980) Case in linguistics and cognitive science (ISSCO working paper 40). ISSCO, Geneva

  • Schank RC (1973) Identification of conceptualizations underlying natural language. In: Schank RC, Colby KM (eds) Computer models of thought and language. W.H. Freeman and Co., San Francisco, pp 187–247

    Google Scholar 

  • Schank RC, Abelson RP (1977) Scripts, plans, goals and understanding: an inquiry into human knowledge structures. Lawrence Erlbaum, Oxford

    MATH  Google Scholar 

  • Shapiro SC (1979) The SNePS semantic network processing system. In: Findler NV (ed) Associative networks: representation and use of knowledge by computers. Academic Press, New York, pp 179–203

    Chapter  Google Scholar 

  • Shotton D, Peroni S (2013) PRO, the publishing roles ontology-creative commons, 1.5.3 Version. http://www.sparontologies.net/ontologies/pro/source.html. Accessed 2 July 2017

  • Soon KH (2013) Representing roles in formalizing domain ontology for land administration. Proceedings of LADM2013: 5th FIG international land administration domain model workshop. International Federation of Surveyors (FIG), Copenhagen, pp 203–222

  • Sowa JF (1984) Conceptual structures: information processing in mind and machine. Addison-Wesley, Reading

  • Sowa JF (1999) Knowledge representation: logical, philosophical, and computational foundations. Brooks Cole Publishing Co, Pacific Grove

    Google Scholar 

  • Spärck Jones K, Boguraev B (1987) A note on a study of cases. Comput Linguist 13:65–68

    Google Scholar 

  • Spear AD (2006) Ontology for the twenty first century: an introduction with recommendations. Institute for formal ontology and medical information science, Saarbrücken

    Google Scholar 

  • Speer R, Havasi C (2012) Representing general relational knowledge in ConceptNet 5. Proceedings of the 8th conference on language resources and evaluation (LREC’12). European Language Resources Association (ELRA), Paris, pp 3679–3686

  • Steimann F (2000) On the representation of roles in object-oriented and conceptual modeling. Data Knowl Eng 35:83–106

    Article  MATH  Google Scholar 

  • Stojanovic N, Abecker A, Etzion O, Paschke A (eds) (2009) Proceedings of the 2009 AAAI spring symposium on intelligent complex event processing (technical report SS-09-05). AAAI Press, Menlo Park

  • Swoyer C, Orilia F (2014) Properties. In: Zalta EN (ed) The stanford encyclopedia of philosophy, fall 2014 edition. http://plato.stanford.edu/archives/fall2014/entries/properties/. Accessed 2 July 2017

  • Trame J, Kessler C, Kuhn W (2013) Linked data and time-modeling researcher life lines by events. In: Tenbrink T, Stell J, Galton A, Wood Z (eds) Proceedings of the 11th international conference on spatial information theory, COSIT 2013 (LNCS vol. 8116). Springer, Berlin, pp 205–223

  • Van Valin RD (1999) Generalized semantic roles and the syntax-semantics interface. In: Corblin F, Dobrovie-Sorin C, Marandin JM (eds) Empirical issues in formal syntax and semantics 2. Thesus, The Hague, pp 373–389

    Google Scholar 

  • Vieu L, Borgo S, Masolo C (2008) Artefacts and roles: modeling strategies in a multiplicative ontology. In: Eschenbach C, Grüninger M (eds) Proceedings of the 5th international conference on formal ontology in information systems-FOIS 2008. IOS Press, Amsterdam, pp 121–134

  • Westermann U, Jain R (2006) Events in multimedia electronic chronicles (E-Chronicles). Int J Semant Web Inf Syst 2(2):1–23

    Article  Google Scholar 

  • Wieringa R, de Jonge W, Spruit P (1995) Using dynamic classes and role classes to model object migration. Theory Pract Object Syst 1:61–83

    Google Scholar 

  • Woods WA (1975) What’s in a link: foundations for semantic networks. In: Bobrow DG, Collins AM (eds) Representation and understanding: studies in cognitive science. Academic Press, New York, pp 35–82

    Chapter  Google Scholar 

  • Zarri GP (1997) NKRL, a knowledge representation tool for encoding the ‘meaning’ of complex narrative texts. Nat Lang Eng Special Issue on Knowledge Representation for Natural Language Processing in Implemented Systems 3:231–253

    Google Scholar 

  • Zarri GP (1998) Representation of temporal knowledge in events: the formalism, and its potential for legal narratives. In: Martino AA, Nissan E (eds) Special issue on formal models of legal time: law, computers and artificial intelligence, information and communications technology law, vol 7, pp 213–241

  • Zarri GP (2005) Integrating the two main inference modes of NKRL, transformations and hypotheses. J Data Semant 4:304–340

    MATH  Google Scholar 

  • Zarri GP (2009) Representation and management of narrative information, theoretical principles and implementation. Springer, London. https://link.springer.com/book/10.1007%2F978-1-84800-078-0. Accessed 2 July 2017

  • Zarri GP (2011a) Differentiating between “functional” and “semantic” roles in a high-level conceptual data modeling language. In: Murray RC, McCarthy PM (eds) Proceedings of the 24th international Florida AI research society conference, FLAIRS-24. AAAI Press, Menlo Park, pp 75–80

  • Zarri GP (2011b) Knowledge representation and inference techniques to improve the management of gas and oil facilities. Knowl Based Syst 24:989–1003

    Article  Google Scholar 

  • Zarri GP (2013) Advanced computational reasoning based on the NKRL conceptual model. Expert Syst Appl 40:2872–2888

    Article  Google Scholar 

  • Zarri GP (2014a) Conceptual and content-based annotation of (multimedia) documents. Multimed Tools Appl 72:2359–2391

    Article  Google Scholar 

  • Zarri GP (2014b) Sentiments analysis at conceptual level making use of the narrative knowledge representation language. In: Hussain A, Cambria E, Schuller B, Howard N (eds) Special issue on affective neural networks and cognitive learning systems for big data analysis, neural networks (NEUNET), vol 58, pp 82–97

  • Zarri GP (2015) A structured and in-depth representation of the semantic content of elementary and complex events. Int J Metadata Semant Ontol 10:12–27

    Article  Google Scholar 

  • Web ref.1: http://www.cyc.com/platform/opencyc (accessed 2 July 2017)

  • Web ref.2: http://www.ilc.cnr.it/EAGLES96/EAGLESLE.PDF (accessed 2 July 2017)

  • Web ref.3: http://ontologydesignpatterns.org/wiki/Ontology:DOLCE%2BDnS_Ultralite (accessed 2 July 2017)

  • Web ref.4: http://linkeddatacatalog.dws.informatik.uni-mannheim.de/state/ (accessed 2 July 2017)

  • Web ref.5: http://ontologydesignpatterns.org/wiki/Ontology_Design_Patterns_._org_%28ODP%29 (accessed 2 July 2017)

  • Web ref.6: http://schema.org/ (accessed 2 July 2017)

  • Web ref.7: https://schema.org/docs/full.html (accessed 2 July 2017)

  • Web ref.8: https://lists.w3.org/Archives/Public/public-vocabs/2014Oct/0170.html (accessed 2 July 2017)

  • Web ref.9: http://conceptnet5.media.mit.edu/ (accessed 2 July 2017)

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Acknowledgements

I gratefully acknowledge the two unknown referees who reviewed a first version of this paper for their useful suggestions that helped me to improve considerably the technical quality and the legibility of my text.

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  1. STIH Laboratory, Sorbonne University, Maison de la Recherche 28, rue Serpente, 75006, Paris, France

    Gian Piero Zarri

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  1. Gian Piero Zarri
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Correspondence to Gian Piero Zarri.

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Zarri, G.P. Functional and semantic roles in a high-level knowledge representation language. Artif Intell Rev 51, 537–575 (2019). https://doi.org/10.1007/s10462-017-9571-5

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