Ontologies, Semantic Web Languages, and an Example of ‘n-ary’ Approach Gian Piero ZARRI LaLIC, University Paris4/Sorbonne Maison de la Recherche – 28, rue Serpente – 75006 Paris – France
[email protected] http://www.lalic.paris4.sorbonne.fr/Zarri/home.html
ABSTRACT. Because they constitute “… a formal and explicit specification of a shared conceptualisation”, according to the well-known Gruber’s definition, ontologies represent one of the most interesting and potentially useful concepts introduced these last years in the Computer Science domain. Unfortunately, they are still far from being accepted and used in the day-to-day practice. This is due partly to some ‘intrinsic’ difficulties like those linked with the choice of the concepts, or with the incompatibilities among similar ontologies. We think, however, that ontologies are also facing problems linked with the choice of an inappropriate framework for their development. These last years, ‘construction of ontologies’ has become a sort of synonymous of ‘use of the W3C languages’ (RDF, OWL…): we will show in this paper that these languages are hampered by all sort of theoretical and practical limitations. Other, more powerful, alternatives could be taken into consideration.
1 Introduction Starting from the nineties, ontologies have emerged as an important research topic investigated by several research communities (Artificial Intelligence, Computational Linguistics, Cognitive Science, Data Engineering etc.), and potentially useful in defining standard for data exchange, information integration, and interoperability. A wellknown consensus definition [Gruber 1993] says that: “Ontologies represent a formal and explicit specification of a shared conceptualisation”. The word ‘shared’ is here particularly important: an ontology deals then with consensual knowledge, that is, this knowledge is not private to some individual, but must be accepted by a group. In spite of the indisputable attractiveness of this notion, and its success in the academic domain, ontologies seem still far from becoming a sort of ‘ordinary’ tool to be used in the day-to-day practice of Computer Science – even if Oracle (Oracle 11g RDF database), Amazon, Adobe and few other industrial groups have shown some interest in ontology-oriented, Semantic Web (W3C) supported tools like RDF. In a recent survey [Cardoso 2007], e.g., the Author remarks that: “… One surprising conclusion … is that ontologies being developed are much smaller in size that that can be ascertained from many research papers and conference keynotes and talks” – according to this survey, in fact, the ‘existing’ ontologies include on average less than 1,000 concepts, and many of them have less than 100 concepts. Another interesting remark of this same paper is that:
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“[the fact that] …the large majority of ontology developed are rather small … shows that the Semantic Web does not even need OWL and can achieve important objectives such as data-sharing and data-integration using just RDF alone” [Cardoso 2007: 88]. Taking into account the fact that OWL in its different versions is the ‘pièce de résistance’ of the W3C strategy for building up ontologies, we can wonder if the five to ten years of delay commonly anticipated before the ‘mainstream adoption’ of the (W3Cbased) ontological techniques are not, in fact, quite optimistic. There are, of course, ‘intrinsic’ reasons that explain (at least partly) these difficulties. Ontologies are difficult to set up for many reasons: e.g., the choice and the formalization of the ‘concepts’ is a very difficult task, and constructing ontologies by simple merging implies solving very difficult ‘alignment’ problems. We think, however, that at least part of these difficulties are also linked with the choice of an inappropriate theoretical framework for their development. These last years, in fact, ‘construction of ontologies’ has become a sort of synonymous of ‘use of the W3C languages’ (RDF, OWL…), i.e., there has been a sort of take over of this type of highly formalized approach on the overall ‘ontological’ domain. In the following, we will show that the W3C languages are hampered by many theoretical and practical limitations and that – at least for some domains – other, more powerful alternative can be used.
2 Problems affecting the W3C languages From a Knowledge Representation point of view, the main problem affecting the W3C languages concerns the fact that they are ‘binary’ languages – i.e., to define their properties, they make use only of relations taking two arguments– whilst, for many applications, an n-ary approach should be adopted see, to give only an example, the representation of generic situations in a legal reasoning context [Hoekstra et al. 2006]. Note that the argument often raised and stating that a representation using an n-ary relation can always be converted to a representation using a binary one, without loss of semantics is incorrect with respect to the last part of this sentence. In fact, it is true that, from a pure formal point of view, any n-ary relationship with n > 2 can always be reduced, in a very simple way, to a set of binary relationships; moreover, this sort of decomposition can also be important for many practical problems. However, this fact does not change at all the intrinsic, ‘semantic’ n-ary nature of a simple statement like “Mary hands the book over to Bill” that, to be understood, requires to be taken in its entirety. This means considering a semantic predicate of the GIVE type that introduces its three arguments, “Mary”, “Bill” and “book” through three semantic relationships (roles) like SUBJECT (or AGENT), BENEFICIARY and OBJECT, the whole n-ary construction being – this is the central point – necessarily managed as a coherent block at the same time. For the formal details see, e.g., [Zarri 2005a]. The impossibility of reducing n-ary to binary from a conceptual and semantic point of view has, as a practical consequence, the need of using specific n-ary (not W3C) tools for reasoning and inference when complex, ‘ontological’ problems must be dealt with in a not restricted way.
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Moreover, given the intrinsic ‘lack of expressiveness’ of the (binary) W3C languages, to base the central version of OWL, OWL DL, on Description Logics (DLs) has been probably an unfortunate idea. Already dismissed at the beginning of the nineties because of their inefficiency from a practical point of view, these logics have been resurrected in a SW context in name of a distorted interpretation of those ‘sound and clean semantics’ exigencies that lead inevitably to sacrifice representational sophistication to an (abstract) conception of computational tractability. Instead of the classical ‘rule/logic programming’ reasoning paradigm based on the so-called ‘resolution principle’, DLs make use in fact of inference by inheritance, a reasoning paradigm orthogonal with respect to the rule/logic programming paradigm and definitely less ‘expressive’ than this last one. Based then on the (weak) inheritance-based classification mechanism, the ‘native’ reasoning tools of DLs (of the OWL-like languages) are quite limited, and reduced in practice to offering some form of simple support like i) checking the consistency of classes/concepts (i.e., determining whether a class can have any instances), and ii) calculating the subsumption hierarchy (i.e., arranging the classes according to their generic/specific relationships). To do this, the W3C scholars have to their disposal several OWL-compatible reasoners like RACER, Pellet, FaCT++, etc. Giving, however, the impossibility of executing ‘interesting’ inferencing/reasoning operations without an adequate and ‘authentic’ rule system, several rule languages like RuleML, TRIPLE and SWRL – all based, roughly, on extensions of the inferential properties of Horn clauses and Datalog to deal with OWL-like data structures – have been proposed recently in a W3C context. The general impression is, however, that the W3C rule domain is still largely in its infancy and that the solutions proposed are, for the time being, quite limited with respect to the tasks they should carry out and particularly complicated to be used in practice. For example, SWRL – the “Semantic Web Rule Language” – ‘augments’ OWL by allowing a user to create ‘if-then’ rules written in terms of OWL classes, properties and individuals. Being based on a combination of OWL Lite and OWL DL sub-languages, it cannot support OWL Full and RDF/RDFS; moreover, given that it is not completely ‘decidable’ (more precisely, it is ‘semidecidable), SWRL rules are often written in a decidable subset like ‘DL-Safe SWRL’. The practical result is that, in this way, SWRL variables can only be bound to known individuals in a knowledge base (in an OWL ontology). To overcome all these limitations, the ‘normal’ strategy for executing the SWRL rules is then to make use of ‘external’ rule engines like Jess or Algernon. This can be done through, e.g., the SWRLJessTab, a plug-in of the Protégé-OWL plug-in, and after having previously downloaded separately the Jess rule engine. Note that Jess – a rule engine based on the Rete algorithm – is, basically, a re-implementation in Java of tools like CLIPS that go back to OPS5, the seventies and the expert systems era.
3 A quick outline of NKRL In the previous Section, we have mentioned a domain, legal reasoning, which cannot be dealt with correctly using the W3C languages. ‘Legal reasoning’ is only a sub-domain of
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a wider field, that of the so-called ‘nonfictional narratives’, that concerns ‘economically relevant multimedia information’ embodied into ‘documents’ like corporate documents, news stories, normative and legal texts, medical records, intelligence messages, actuality photos, material for eLearning etc. Dealing in a non-restricted way with this sort of information asks for the use of true n-ary languages like NKRL (Narrative Knowledge Representation Language) see, e.g., [Zarri 2003], [Zarri 2005], [Zarri 2008]. NKRL innovates with respect to the usual ontology paradigm by associating with the ontology of concepts (called HClass, Hierarchy of Classes, in NKRL) an ‘ontology of events’ (a hierarchy of templates, HTemp(lates)), i.e., a new sort of hierarchical structure where the nodes correspond to n-ary structures. Instead of using the traditional object (class, concept) – attribute – value organization, templates correspond in fact the combination of quadruples connecting together the symbolic name of the template, a predicate (BEHAVE, EXIST, EXPERIENCE, etc.), and the arguments of the predicate (a simple HClass concept, or of a structured association of concepts) introduced by named relations, the roles (SUBJ(ect), OBJ(ect), SOURCE, etc.). The quadruples have in common the ‘name’ and ‘predicate’ components. If we denote then with Li the generic symbolic label identifying a given template, with Pj the predicate used in the template, with Rk the generic role and with ak the corresponding argument, the NKRL core data structure for templates has the following general format: (Li (Pj (R1 a1) (R2 a2) … (Rn an))) .
(1)
Templates represent formally generic classes of elementary events like ‘move a physical object’, ‘be present in a place’, ‘produce a service’, ‘send/receive a message’, etc. When a particular event pertaining to one of these general classes must be represented, the corresponding template is ‘instantiated’ to produce what, in the NKRL's jargon, is called a ‘predicative occurrence’, see [Zarri 2003]. To represent, e.g., a very simple narrative like: “British Telecom will offer its customers a pay-as-you-go (payg) Internet service in autumn 1998”, we must select firstly the template corresponding to ‘supply a service to someone’, represented in the upper part of Table 1. In a template, the arguments of the predicate (the ak terms in (1)) are represented by variables with associated constraints – which are expressed as concepts or combinations of concepts, i.e., using the terms of the HClass standard ‘ontology of concepts’. When deriving then a predicative occurrence like c1 in Table 1, the role fillers must conform to the constraints of the father-template. For example, BRITISH_TELECOM is an individual instance of the concept company_: this last is, in turn, a specialization of human_being_or_social_body. About 150 templates are permanently inserted into HTemp, which corresponds then to a ‘catalogue’ of narrative formal structures, ‘ready to use’ and very easy indeed to customize. Reasoning in NKRL ranges from the direct questioning of an NKRL knowledge base making use of ‘search patterns’ that try to unify the predicative occurrences of this base making use of subsumption-like operations – see [Zarri 2005b], [Zarri 2008] – to highlevel inference procedures requiring the utilization of complex inference engines. For example, the ‘transformation rules’ try to ‘adapt’ the query/queries (search patterns) that failed to the real contents of the knowledge base. The principle employed
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consists in automatically ‘transforming’ the original query (the antecedent) into one or more different queries (the consequent(s)) that are not strictly ‘equivalent’ but only ‘semantically close’ to the original one. A query posed, e.g., in terms of “searching for evidence of having lived in a given country” will then be replied in terms of “searching for evidence of an original school/university diploma delivered in that country”. Table 1: Deriving a predicative occurrence from a template. name: M ov e: Trans f erOf Serv ic e father: M ov e: Trans f erToSom eone position: 4. 24 NL description: ‘Transfer or Supply a Service to Someone’ M OVE
SUBJ OBJ [ SOURCE BENF [ M ODAL [ TOPI C [ CONTEXT {[ m odulat ors ] }
v ar1: [ v ar2] v ar3 v ar4: [ v ar5] ] v ar6: [ v ar7] v ar8] v ar9] v ar10]
v ar1, v ar4, v ar6 = hum an_being_or_s oc ial_body v ar2, v ar5, v ar7 = geographic al_loc at ion v ar3 = s erv ic e_ v ar8 = proc es s _, s ec t or_s pec if ic _ac t iv it y v ar9 = s ort al_c onc ept v ar10 = s it uat ion_ c 1)
M OVE
SUBJ OBJ BENF dat e-1: dat e-2:
BRI TI SH_TELECOM payg_int ernet _s erv ic e (SPECI F c us t om er_ BRI TI SH-TELECOM ) af t er-1-s ept em ber-1998
The ‘Hypothesis rules’ allow building up ‘common sense explications’ according to a number of pre-defined reasoning schemata. For example, from the information: “Pharmacopeia, an USA biotechnology company, has received 64,000,000 dollars from the German company Schering in an R&D activity context”, we are able to automatically construct a sort of ‘explication’ of this information by retrieving in the knowledge base information like: i) “Pharmacopeia and Schering have signed an agreement concerning the production by Pharmacopeia of a new compound” and ii) “in the framework of this agreement, Pharmacopeia has actually produced the compound”. The two ‘reasoning steps’ (condition schemata) at the origin of i) and ii) are automatically transformed in ‘standard’ search patterns by InferenceEngine during the execution of the rule. The two above modalities of inference can now be used in an ‘integrated’ way, see [Zarri 2005b], [Zarri 2008] for the details. ‘Integrated way’ means that: •
From a practical point of view, transformations can be used to find new answers when the search patterns derived directly from a condition schema of a hypothesis fail: a hypothesis deemed then to fail can continue successfully until its normal end.
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•
From a more general point of view, transformations can be used to modify, in an a priori unpredictable way, the reasoning steps (condition schemata) to be executed within a ‘hypothesis’ context. This is then equivalent to augment the possibility of discovering ‘implicit information’ within the knowledge base.
We will limit ourselves to comment informally an example developed in a recent ‘defense’ application see, e.g., [Zarri 2005b]. Let us suppose that, as one of the answers to a question concerning generic kidnapping events, we have retrieved the information: “Lieven de la Paille and Eric Drum have been kidnapped by a group of people on June 13, 1999”. We want now to use a hypothesis like that of Table 2 to verify if this kidnapping is, in reality, a specific ‘ransom kidnapping’. Activating InferenceEngine will give rise to a failure because of the impossibility of satisfying directly the ‘intermediate’ steps Cond1, Cond2 and Cond3 of the rule, i.e., of founding direct matches of the search patterns derived from these condition schemata with information in the base. Table 2: Inference steps for a ‘ransom kidnapping’ hypothesis (Cond1) (Cond2) (Cond3) (Cond4)
The kidnappers are part of a separatist movement or of a terrorist organization. This separatist movement or terrorist organization currently practices ransom kidnapping of particular categories of people. In particular, executives or assimilated categories are concerned (other rules deal with civil servants, servicemen, members of the clergy etc.). It can be proved that the kidnapped is really a businessperson or assimilated.
If we allow now the use of transformations in a hypothesis context, this means to make use of a ‘ransom kidnapping’ hypothesis having a format potentially equivalent to that of Table 3. For example, the proof that the kidnappers are part of a terrorist group or separatist organization can now be obtained indirectly, transformation T3, by checking whether they are members of a specific subset of this group or organization. We can see, in particular, that a whole family of transformations corresponds to the condition schema Cond2 of the original hypothesis. They represent variants of this general idea: the separatist movement or the terrorist organization, or some group or persons affiliated with them, have requested/received money for the ransom of the kidnapped. Note that transformation T2 implies only a single ‘transformed schema’ (consequent) schema, whereas all the residual transformations of the ‘family’ are ‘multi-consequent’.
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Conclusion
In this paper, after having recalled that the usual W3C languages are hampered by many theoretical and practical limitations, we have supplied some details about NKRL (Narrative Knowledge Representation Language), a fully implemented knowledge representation and inferencing system especially created for an ‘intelligent’ exploitation of
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narrative knowledge. The main innovation introduced by NKRL is the addition of an nary ‘ontology of events’ to the usual (binary) ‘ontology of concepts’. Table 3. Table 2 rule with transformations concerning the intermediary inference steps (Cond1) The kidnappers are part of a separatist movement or of a terrorist organization. – (Rule T3, Consequent1) Try to verify whether a given separatist movement or terrorist organization is in strict control of a specific sub-group and, in this case, – (Rule T3, Consequent2) check if the kidnappers are members of this sub-group. We will then assimilate the kidnappers to ‘members’ of the movement or organization. (Cond2) This movement or organization practices ransom kidnapping of given categories of people. – (Rule T2, Consequent) The family of the kidnapped has received a ransom request from the separatist movement or terrorist organization. – (Rule T4, Consequent1) The family of the kidnapped has received a ransom request from a group or an individual person, and – (Rule T4, Consequent2) this second group or individual person is part of the separatist movement or terrorist organization. – (Rule T5, Consequent1) Try to verify if a particular sub-group of the separatist movement or terrorist organization exists, and – (Rule T5, Consequent2) check whether this particular sub-group practices ransom kidnapping of particular categories of people. – … (Cond3) In particular, executives or assimilated categories are concerned. – (Rule T0, Consequent1) In a ‘ransom kidnapping’ context, we can check whether the kidnapped person has a strict kinship relationship with a second person, and – (Rule T0, Consequent2) (in the same context) check if this second person is a businessperson or assimilated. (Cond4) It can be proved that the kidnapped person is really an executive or assimilated. – (Rule T6, Consequent) In a ‘ransom kidnapping’ context, ‘personalities’ like consultants, physicians, journalists etc. can be assimilated to businesspersons.
References [Cardoso 2007] Cardoso, J. (2007). The Semantic Web Vision : Where are We ? IEEE Intelligent Systems 22(5): 84-88. [Gruber 1993] Gruber, T.R. (1993). A Translation Approach to Portable Ontology Specifications. Knowledge Acquisition 5: 199-220. [Hoekstra et al. 2006] Hoekstra, R., Liem, J., Bredeweg, B., and Breuker, J. (2006). Requirements for Representing Situations. In: Proceedings of the OWLED*06 Workshop on OWL: Experiences and Directions (CEUR-WS.org/Vol-216). Aachen: Sun SITE Central Europe. [Zarri 2003] Zarri, G.P. (2003). A Conceptual Model for Representing Narratives. In: Innovations in Knowledge Engineering. Adelaide: Advanced Knowledge International. [Zarri 2005a] Zarri, G.P. (2005). An n-ary Language for Representing Narrative Information on the Web. In: SWAP 2005, Semantic Web Applications and Perspectives – Proceedings of the 2nd Italian Semantic Web Workshop (CEUR-WS.org/Vol-166). Aachen: Sun SITE Central Europe. [Zarri 2005b] Zarri, G.P. (2005). Integrating the Two Main Inference Modes of NKRL, Transformations and Hypotheses. Journal on Data Semantics (JoDS) 4: 304-340. [Zarri 2008] Zarri, G.P. (2008, in press). Representation and Management of Narrative Information. London: Springer.
