Int. J. of Autonomous and Adaptive Communicaitons Systems (IJAACS), Vol. x, No. x, xxxx 1
Use Semantic Decision Tables to Improve Meaning Evolution Support Systems Yan Tang* and Robert Meersman Semantic Technology and Application Research Laboratory (STARLab), Department of Computer Science, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium Fax: +32 (0)2 629 38 19 E-mail:
[email protected] E-mail:
[email protected] ∗ Corresponding author Abstract: Meaning Evolution Support Systems have been recently introduced as a real-time, scalable, community-based cooperative systems to support the ontology evolution. In this paper, we intend to address the problems of accuracy and effectiveness by using Semantic Decision Tables. A Semantic Decision Table separates general decision rules from the processes, bootstraps policies and template dependencies in the whole system. Recently, DOGMA-MESS (“Developing Ontology Grounded Methodology and Applications” framework based “Meaning Evolution Support Systems”) is developed at VUB STARLab as a collection of meaning evolution support systems. We embed Semantic Decision Tables in DOGMA-MESS to illustrate our approach. Semantic Decision Tables play the roles in both top-down and bottom-up processes of the meaning evolution cycle. The decision rules that consist of templates dependency rules are mainly responsible for the top-down process execution. The bottom-up process execution relies on the ones that contain the concept lifting algorithms. Keywords: ontology, Meaning Evolution Support System, Semantic Decision Table Reference to this paper should be made as follows: Yan Tang and Robert Meersman (2009) ‘Use Semantic Decision Tables to Improve Meaning Evolution Support Systems’, Int. J. of Autonomous and Adaptive Communications Systems, in, Frode Eika Sandnes and Yan Zhang, (eds.) ISSN (Online): 1754-8640, ISSN (Print): 1754-8632
Biographical Notes: Yan Tang is a researcher at the Semantic Technology and Application Research Laboratory (STARLab), Department of Science, Free University of Brussels. Her research interests include semantic decision making, decision support and decision table, ontology based application architecture, business ontology and business rules. During her PhD period, she has published her work in several international journals, conference proceedings as well as book chapters, one of which has been edited by World Scientific.
c 2009 IJAACS Copyright
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Yan Tang and Robert Meersman Prof. Dr. Robert Meersman received his Ph.D in Mathematics at the Vrije Universiteit Brussel (VUB) in 1976. He founded the first InfoLab at University of Hasselt (Belgium, 1983-86), the second at University of Tilburg (The Netherlands, 1986-95) and VUB STARLab in 1995. He worked on the definition of the NIAM (now ORM) method as well as on its query and constraint languages (RIDL) and on the first tools for this methodology. He is now a full professor at VUB. His current research is focused on ontologies and their relationship and use in databases, semantic web and community-driven semantic design methodologies and tools.
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Introduction
Nowadays, a vast amount of ontology capturing methodologies and tools are available. In G´ omez-P´erez et al. (2004) and on the OnToWorld wiki websitea , a general survey on the ontology capturing methodologies is explored, such as the Gr¨ uninger and Fox method (Gr¨ uninger and Fox, 1995), the Uschold and King method (Uschold and King, 1995), Methontology (Fern´andez et al., 1997), CommonKADS (Schreiber et al., 1999), the linguistic based methodologies (e.g. Benjamin et al. (1994); Mizoguchi (2003)), Opjk methodology (Casanovas et al., 2007), and DOGMA methodology (Spyns et al., 2002, 2008; Jarrar and Meersman, 2008). DOGMA (Developing Ontology-Grounded Methods and Applications) is in general a framework for ontology capturing methodologies and applications. Amongst these mentioned methodologies, DOGMA methodology is designed partly based on the best practice of the other methodologies: 1) the Gr¨ uninger and Fox method for TOVE project uses the competency questions as a way to scope the domain of interests and conceptualization evaluation; 2) the Uschold and King method for Enterprise Ontology emphasizes the importance of incorporating brainstorming and defining/grouping terms in a natural language; 3) Methontology and CommonKADS focus on the structural knowledge management activities. Each activity produces a deliverable as the output; 4) the linguistic based methodologies ground the concepts on the basis of natural languages. Hence, it is necessary to use the natural language processing technologies while building multilingual ontologies from scratch; 5) the Opjk methodology adapts the argumentation method (so called “Diligent”). It underlines the socio aspect. Seeing the importance of community aspect in the notions of ontology (Gruber, 1993; Guarino and Poli, 1995), Semantic Web (Berners-Lee, 1999), Web 2.0 (Braun et al., 2007), and some socio aspect focused methodologies (e.g. Casanovas et al., 2007), the trends towards community impacts on ontology engineering result in a growing interest in community-grounded, knowledge-intensive methodology. DOGMA Meaning Evolution Support System (DOGA-MESS) is thus developed at the VUB STARLab (de Moor et al., 2006). As an extension to DOGMA, DOGMA-MESS is a machine-guided ontology versioning, merging and alignment system to support scalable ontology engineering. In practice, we observe that it is hard to do in an interorganizational setting, where there are many pre-existing organizational ontologies and rapidly evolving a http://ontoworld.org/wiki/Ontology
Engineering#Ontology Building Methodologies
Use Semantic Decision Tables to Improve Meaning Evolution Support Systems collaborative requirements. Current ontology merging systemsb mainly focus on how to integrate different ontologies, such as in Madhavan et al. (2002). Researches concentrate on combining several (sub-) ontologies into one ontology by removing inconsistency and reducing conflicts among them. DOGM-MESS does not focus on how to solve these problems, but to gradually build interoperable ontologies amongst a large, knowledge-intensive community. One communicates with others’ needs, trying to find the overlapping interests, with which we make interorganizational ontologies. The core activity in meaning evolution support systems is to reach the final consensus on the conceptual definitions. How to manage the negotiation among the members (also called “domain experts”) of the community is crucial. In DOGMAMESS, the technology of meaning negotiation (de Moor, 2005) is thus integrated, which constructs the kernel of community management. However, the community behaviors are not thoroughly studied, which leads to the fact that the outcomes of the DOGMA-MESS processes often become “messy”. Therefore, a novel design by embedding Semantic Decision Tables (SDTs) in DOGMA-MESS process was proposed (Tang and Meersman, 2007 b). Semantic Decision Tables are used to capture the community’s behaviors at the macro level and guide the community at the micro level. Recently, we get increasing requirements of managing ontological structure at a high level, automatically checking the dependencies of different knowledge blocks, and the ability of the quick and accurate adaptation of the knowledge elicitation processes in DOGMA-MESS. These requirements become the challenges of this paper. Based on Tang and Meersman (2007 b), we enrich the model of DOGMA-MESS embedded with SDT in this paper. We focus on how SDTs are used in both bottomup and top-down processes of the meaning evolution cycle in DOGMA-MESS. The accuracy and effectiveness that SDTs can bring for the meaning evolution support systems in general are stressed. The remainder of this paper is structured as follows. In section 2, we present the background of the paper. We compare our work with the existing technologies in section 3. We design the model of DOGMA-MESS embedded with SDT in section 4. Different SDTs hold different semantically rich decision rules. The decision rules that consist of templates dependency rules are mainly responsible for the top-down process execution (section 4.1). The bottomup process execution relies on the ones that contain the selection algorithms, which can be evaluated (section 4.2). Section 5 contains the paper conclusion and the future work.
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Background
2.1
DOGMA
The research efforts on DOGMA (Developing Ontology-Grounded Methods and Applications, Jarrar and Meersman (2008); Spyns et al. (2002)) approach to ontology engineering have been performed at the VUB STARLab over ten years. It was b We
ing.
consider the ontology merging systems as a kind of systems in scalable ontology engineer-
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designed as a methodological framework inspired by the tried-and-tested principles of modeling conceptual databases. In the DOGMA framework one constructs (or converts) ontologies by the double articulation principle into two layers: 1) the lexon base layer that contains a vocabulary of simple facts called lexons, and 2) the commitment layer that formally defines rules and constraints by which an application (or “agent”) may make use of these lexons. A lexon is a quintuple hγ, t1, r1, r2, t2i, where γ is a context identifier. γ is assumed to point to a resource and serves to disambiguate the terms t1, t2 into the intended concepts. r1, r2, which are “meaningful” in this specific context γ, are the roles referring to the relationships that the concepts share with respect to one another. For example, a lexon hγ, Driver’s license, is issued to, has, Driveric indicates a fact that “a driver’s license is issued to a driver”, and “a driver has a driver’s license”. The linguistic nature of a lexon represents that a fundamental DOGMA characteristic is its grounding in the linguistic representation of knowledge. The community of domain experts chooses (or has to agree on) a given (natural) language, e.g. English, to store and present lexon terms and roles. A commitment corresponds to an explicit instance of an intentional logical theory interpretation of applications. It contains a set of rules in a given syntax, and describes a particular application view of reality, such as the use by the application of the (meta-) lexons in the lexon base. This describing process is also called ‘to commit ontologically’. The commitments need to be expressed in a commitment language that can be easily interpreted. Suppose we have a lexon hDriver’s license, is issued to, has, Driveri, which has the constraint as “one driver’s license is issued to at most one driver”. We apply the uniqueness constraints UNIQ on the lexon written as below: p1 = [Driver0 slicense, isissuedto, has, Driver] : U N IQ (p1) d
Just like the same database can be viewed and used by different database applications, the same lexon base can be queried, constrained and used by different ontology based application. The commitments interface the lexon base and different applications. They can be further modeled graphically with many popular modeling tools, such as Object Role Modeling (ORM, Halpin (2001)), Conceptual Graph (CG, Sowa (1984)) and Unified Modeling Language (UMLe ). Ontologies modeled in DOGMA can be further implemented in an ontology language, such as Web Ontology Language (OWLf ) and Resource Description Framework (Schema) (RDF(S)g ).
c In this paper, we do not focus on the discussion of the context identifier γ, which is omitted in other lexons, e.g. hγ, Driver’s license, is issued to, has, Driveri is thus written as hDriver’s license, is issued to, has, Driveri. d The syntax of the formalized commitment and the examples can be found at: http://www.starlab.vub.ac.be/website/SDT.commitment.example e UML is specified by OMG (Object Management Group), http://www.uml.org/ f http://www.w3.org/TR/owl-features/ g http://www.w3.org/TR/rdf-schema/
Use Semantic Decision Tables to Improve Meaning Evolution Support Systems 2.2
Meaning Evolution Support Systems
DOGMA-MESS (Meaning Evolution Support System, de Moor et al. (2006)) is to organize the process of interorganizational h ontology engineering. Its basic characteristic is to find the relevance of the concepts defined by different domain experts in order to capture the overlapping interests in the community. The goal is not to create a “complete” domain ontology that covers all the concept definitions in the domain, but a dynamically evolving “interorganizational” domain ontology. A generic model of interorganizational ontology engineering is presented in Fig. 1. An interorganizational ontology cannot be produced once, but needs to evolve over time. The process results in different versions - e.g. v 1 and v m in Fig. 1. Each version of the interorganizational ontology contains three layers: Organizational Ontology (OO), Lower Common Ontology (LCO) and Upper Common Ontology (UCO).
Figure 1
A Model of Interorganizaional Ontology Engineering, de Moor et al. (2006)
• Each domain has its own UCO, which contains the common concept type hierarchy and the domain canonical relations. For example, “employee”, “employer” and “personnel” are the common concept types in the domain of human resource management. The concept types “employee” and “employer” are the subtypes of “personnel” in the type hierarchy. The domain canonical relations are the relations that are common to the domain. For example, the ones between “employee” and “employer” can be “hire” or “fire”. • OO is created by individual domain experts based on the ontological structure at UCO level. Each OO within one ontology version is represented by one domain expert that may have different insights from the others’. h The name of “Interorganizational ontology” was coined by de Moor et al. (2006). The name “Inter-organizational” indicates that the ontology only contains the overlapping interests and is used between the organizations.
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Yan Tang and Robert Meersman • At LCO level, the concept definitions from different OO are aligned and merged. The overlapped domain interests are elicited. This process happens within a version. • When the interorganizational ontology evolves, all the definitions of the concepts at LCO level of version N are standardized and lifted to UCO level of version N+1.
2.3
Semantic Decision Tables
A Semantic Decision Table (SDT, Tang and Meersman (2008, 2007 a)) uses the tabular presentation as a decision table does. There are three basic elements in a decision table: the conditions, the actions (or decisions), and the rules that describe which actions might be taken based on the combination of the conditions. A condition is described by a condition stub and a condition entry. A condition stub contains a statement of a condition. Each condition entry indicates the relationship between the various conditions in the condition stub. An action (or decision) contains an action stub and an action entry. Each action stub has a statement of what action to be taken. The action entries specify whether (or in what order) the action is to be performed for the combination of the conditions that are actually met in the rule column. Table 1
An example of traditional decision tables (partial view)
Condition Bad weather It’s far from home Money in pocket Action Take a taxi back home Walk back home
1
2
3
Yes Yes Yes
Yes No Yes
No Yes Yes
*
* *
Table 1 is a simple, incomplete decision table with three conditions: “Bad weather”, “It’s far from home” and “money in pocket”; and two actions: “Take a taxi back home” and “Walk back home”. The condition “Bad weather” has two condition entries - “Yes (The weather is bad now)” and “No (The weather is not bad now)”. The rule column with ID ’1’ expresses a decision rule as “If the weather is bad, it’s far from home, and, there is money in pocket, then the decision is to take a taxi back home”. In the collaborative settings, one decision maker might misunderstand (or have his own comprehension of) the meaning of a decision item designated by others. For example, the condition “It’s far from home - Yes” in Table 1 can have different measures of distance. Does it mean that the distance is more than 1 km, or more than 3 km? A traditional decision table itself does not support the collaborative setting. In Tang and Meersman (2008, 2007 a), we have listed the following problems that occur when using traditional decision tables: 1) ambiguity in the
Use Semantic Decision Tables to Improve Meaning Evolution Support Systems information representation of the condition stubs or action stubs, 2) conceptual duplication amongst the conditions, 3) uncertainty in the condition entries, and 4) difficulties in managing large tables. The notion of Semantic Decision Table (SDT, Tang and Meersman (2007 a)) was introduced to tackle the mentioned problems. What makes an SDT different from a traditional decision table is its semantics. Unlike traditional decision tables, the concepts, variables and decision rules are explicitly defined. A decision group shares the most important decision knowledge within a group decision making session. SDTs are modeled in DOGMA (section 2.1). Accordingly, an SDT contains a set of SDT lexons, SDT commitments and a specific decision task. The question on how to construct an SDT within a decision group is answered in our recent publications (Tang, 2007; Tang and Meersman, 2008, 2007 a). Although an SDT contains SDT lexons and SDT commitments, SDT itself is not an ontology. It is because, in many cases, one SDT is used for one specific decision task. The SDT commitments can contain both static, ontological axioms and temporal, changeable rules. Also note that the usage of SDTs is not restricted to a specific system, such as DOGMA-MESS in this paper. Instead, we use DOGMAMESS as an example to demonstrate how SDTs can improve community-grounded systems in group decision making domain, e.g. collaborative human resource management.
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Related Work
Consensual knowledge base introduced in Euzenat (1995), meeting techniques applied in Casanovas et al. (2007), cooperative domain ontology studied in Aschoff et al. (2004), “Diligent” in Opjk methodology (Casanovas et al., 2007) are promising related work in building consensus on ontology level. However, authors in de Moor et al. (2006) discuss that those methodologies are lack of community consideration although those methodologies work out some basic principles for building ontological consensus. DOGMA-MESS focuses on the community aspects of scalable ontology engineering, and provides a “fat” community grounded model. With the help of SDT, DOGMA-MESS is enhanced with the study of community behaviors at a more detailed level. In particular in the EC Prolix projecti , we use SDT in DOGMA-MESS for the following reasons: • The tabular reports generated based on SDT commitments, in general, are extremely convenient and user-friendly for non-technical domain experts. • SDTs are used to constrain the dependencies between the templates. The accuracy of the system is thus improved. • SDTs are used to capture the behaviors of the community, manage and guide the community systematically and automatically. Therefore, the effectiveness of the system increases. i The objective of PROLIX is to align learning with business processes in order to enable organizations to faster improve the competencies of their employees according to continuous changes of business requirements. URL: http://www.Prolixproject.org/
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Yan Tang and Robert Meersman • The flexibility at the system management level increases because the knowledge engineers can create different algorithms and decision rules based on their needs.
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Embed Semantic Decision Tables in a Meaning Evolution Support System
Recently, modeling layered ontologies has been studied so far. The scalable ontology model we describe focuses on neither the typology of ontology nor the construction of layered ontologies. Instead, we focus on the idea of how to gradually build ontologies within layered ontologies. Based on the work in (Zhao and Meersman, 2005; de Moor et al., 2006), we model a scalable ontology into four layers: Top Level Ontology (TLO), Upper Common Ontology (UCO), Lower Common Ontology (LCO) and Organizational/Topical Ontology (OTO). In (Zhao and Meersman, 2005), topical ontology structure for scalable ontology engineering is introduced to represent the knowledge structure of the domain experts (the stakeholders of a community) by involving different view points of analysis and modeling. Later on, the interorganizational ontology model is designed to match the requirements for meaning evolution (de Moor et al., 2006). We integrate SDT into the topical ontology model and interorganizational ontology model (Fig. 1), the result of which is illustrated in Fig. 2. The dotted lines with arrows in Fig. 2 indicate the specialization dependencies between the ontologies of different levels. Comparing to Fig. 1, Fig. 2 contains an extra ontological level - the level of Top Level Ontology (TLO)j , which defines the abstract concept types, such as ‘Actor’, ‘Object’, ‘Process’ and ‘Quality’. Conceptualization at this level is not allowed to be changed. The relations between these concept types fall into two categories: i) the hierarchical relations reflected by the type hierarchical construct. This kind of relations is also called subsumption ontological roles (e.g. “subtype of” relationship in Sowa (2000)). ii) Other Core Canonical Relations, such as “partof” merelogical relation in Guarino and Poli (1995), “property-of” relation and “equivalent” relation. Another difference is the OTO Level. In Fig. 1, the lowest level is OO (organizational ontology) level. In Fig. 2, the lowest level is OTO (Organizational and Topical Ontology) level, which includes OO level. OTO seek to represent systematically the knowledge structure the domain experts has on the given themes (or tasks) individually. A Topical Ontology “lays foundation for application (or task) specific ontologies and conceptual models its semantic space covers multiple subjects and dynamic evolution of the core concepts within a topic” (Zhao and Meersman, 2005). The concepts within a topic represent the terminology of the application structure, assumption and framework. Within a version, every domain expert (or every enterprise-wise stakeholder group) is responsible to build his own OTO based on the ontology models in UCO. j It was called “MO (Meta Ontology)” level in the old papers, e.g. de Moor et al. (2006); Tang and Meersman (2008). However, we have debated whether to name it as MO level or not in the OTM’07 conferences (http://www.cs.rmit.edu.au/fedconf/). As the structures at this level do not necessarily model the ontology itself, we conclude that it is better to call it TLO.
Use Semantic Decision Tables to Improve Meaning Evolution Support Systems
Figure 2 Interorganizational ontology engineering model with the integration of Semantic Decision Tables
How does DOGMA-MESS execute the model in Fig. 2? Suppose we have some top level models at TLO level designed by the knowledge engineer. The whole process in Fig. 2 starts with creating the templates at UCO level based on the models at LCO level. These templates at UCO level form the first version of the interorganizational ontology (ontology version V1). Then, these templates are delivered to the domain experts from different enterprises. When they receive the templates, they start to create the ontologies at OTO level. After a time period, the system collects the newly introduced concepts, selects a few and lifts them from OTO level to LCO level. When the lifting process is finished, the core domain experts empty the concept set at LCO level by standardizing and formalizing them. Then, the core domain experts merge them at UCO level. A new ontology version V2 is created. As the starting point of creating the ontology V3, the domain experts introduce new concepts based on the updated templates at UCO level, and so forth. By executing the model visualized in Fig. 2 recursively, the ontology evolves. 4.1
Top Down: Use Semantic Decision Tables to Mange Templates Dependencies
We use SDT (section 2.3) to manage the dependencies between different templates at UCO level (Fig. 2). In DB theory, several classic templates dependencies are defined in Sadri and Ullman (1982). In the most general sense, dependencies are possible constrained relations. Among all kinds of templates dependencies, multivalued dependencies, subset dependencies, join dependencies, mutual dependencies,
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and generalized mutual dependencies are the mostly used. Except the multivalued dependencies, the others are also called “functional dependency”. We carefully reuse the above notions for ontology engineering. The dependencies in Sadri and Ullman (1982) are the constraints at the instance level. Note that the definitions of “instance” in DB theory and in ontology engineering are disparate. In DB theory, a record in a table is called an instance. Suppose that we have a table column called “vehicle” with two record “bus” and “TOYOTA VIP-234”. These two records are both called the instances of “bus”. In ontology engineering, “bus” is a type at the conceptual level. “TOYOTA VIP-234”, which is a car with a plate license, points to a unique object in the UoD. Hence, “TOYOTA VIP-234”, in ontology engineering, is an instance at the instance/application level. In this paper, we follow the definition of “instance” in ontology engineering. We use SDT to manage the dependencies at both the instance level and the conceptual level. In the following subsections, different kinds of dependencies are explained. 4.1.1
Multivalued Dependencies
The multivalued dependencies are used to constrain the generating process of the templates. For example, we have two templates that are relevant to the concept “course” (Fig. 3).
Figure 3
Two templates in Conceptual Graph at UCO level
If we apply the multivalued dependency of “course” on these two templates, and if a course type (e.g. “Java course”) is introduced by using one template in Fig. 3, “Java course” will be automatically added to another template as a subtype of “Course”. The constraint is stored as the following SDT commitment: (P 1 = [T eacher, teach, , Course], P 2 = [T eaching, teach, , Course]) : M U LT IV AL DEP (P 1(Course), p2(Course)). Seeing that the same templates can sometime be applied to different contexts k in an ontology in DOGMA-MESS, we stress that to use the multivalued dependencies is necessary. 4.1.2
Subset Dependencies
The subset dependencies is similar to the is-a subsumption relationship in ontology engineering. We say that concept A is the subset of concept B when the subtypes and instances of A belong to the set of concept B. For example, we have two templates: one is related to the concept “Teacher” and the other is related to the concept “Lecturer” (Fig. 4). In the context of “university”, “Lecturer” is a subtype of “Teacher”. k For
example, the members of “course” in the context of “university” are different from the ones of “middle school”.
Use Semantic Decision Tables to Improve Meaning Evolution Support Systems
Figure 4
One template relevant to ’Teacher’ and the other to ’Lecturer’
We store this subset dependency in SDT commitments in two ways: 1) to create a new lexon constrained with a subset constraint; 2) to write a subset constraint directly between these two templates. The SDT commitments are shown as follows: P 3 = [Lecturer, isa, , T eacher] : SU BSET (P 3(Lecturer), P 3(T eacher)).//method1 (P 1 = [T eacher, teach, , Course], P 4 = [Lecturer, hasID, , P ersonnelID]) : SU BSET (P 4(Lecturer), P 1(T eacher)).//method2 SUBSET(P4(Lecturer), P1(Teacher)) (in method 2) records the subsumption relationship between “Lecturer” and “Teacher” defined in lexon P4 and P1. A “Teacher” is a super type of “Lecturer”. SUBSET() constraint can also be applied in the same lexon, e.g. in method 1. Both methods are equivalently used. 4.1.3
Join Dependencies
The join dependencies are the constraints while joining two templates. We often use them together with other constraints, such as subset and equal. Take the same example as in section 4.1.2 (Fig. 4), we write the following SDT commitment to indicate that “Teacher” is joined with “Lecturer”. (P 1 = [T eacher, teach, , Course], P 4 = [Lecturer, hasID, , P ersonnelID]) : JOIN DEP (P 1(T eacher), P 4(Lecturer)), SU BSET (P 1(Lecturer), P 3(T eacher)). JOIN DEP(P1(Teacher), P4(Lecturer)) means that the concepts “Teacher” and “Lecturer” defined in lexons P1 and P4 have a join dependency. In this example, the join dependency is used together with a subset constraint - “Lecturer” is a subset of “Teacher”. Thus, when we join “Lecturer” into “Teacher”, the structure of “Lecturer” is not changed. Fig. 5 shows the result of applying this SDT commitment in the system. If we apply a join dependency combined with an equal constraint, then the templates of both concepts should be updated.
Figure 5
the result of applying join dependency to the two templates in Fig. 4.
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4.1.4
Other Dependencies
Other dependencies have been studied so far, e.g. the constraints of information modeling and relational database in Halpin (2001). We mostly use the following constraints: • Uniqueness. The uniqueness constraint and how to write its commitment are discussed early in section 2.1. • Mandatory. A role is mandatory “if and only if, for all states of the database, the role must be played by every member of the population of its object type” (Halpin, 2001, pp. 166). For example, we want to have a fact that “a lecturer must have a Personnel ID”. We then apply a mandatory constraint on the lexon hLecturer, has ID, , Personnel IDi written as: P 4 = [Lecturer, hasID, , P ersonnelID] : M AN D(P 4(hasID)). • Equality. The equality constraint discussed in (Halpin, 2001, pp. 231) is very similar to the multivalued dependency we deal with in section 4.1.1. A big difference between them is that an equality constraint is applied to two concepts with different names, while a multivalued dependency deals with two concepts with the same name. For example, the following SDT commitment means that “Course” and “Lecture” are equivalent. (P 1 = [T eacher, teach, , Course], P 5 = [T eacher, teach, , Lecture]) : EQU AL(P 1(Course), P 5(Lecture)). • Exclusion. Type A and type B are mutually exclusive if and only if they don’t share any members. This dependency constraint is checked when a new concept type is introduced as a member of Type A or B. If a domain expert tries to add the same concept type to another type (B or A), he would violate this constraint. Suppose we don’t allow a teacher to be a student at the same time. We shall apply the dependency of exclusion to “Teacher” and “Student” written in the following SDT commitment: (P 1 = [T eacher, teach, , Course]), P 6 = [Student, learn, , Course]) : ORl (P 1(T eacher), P 6(Student)). 4.1.5
Tabular reports generated by SDT in the DOGMA-MESS Top Down Process
Early in this section, we have discussed different kinds of template dependencies and how to write the SDT commitments respectively. In each DOGMA-MESS top down process iteration, SDT is used not only to constrain the templates dependencies, but also to generate tabular reports. Once a domain expert introduces a new concept, the system checks the dependencies stored in the SDT commitments. A tabular report, which is generated when a domain expert tries to add new concepts, stores the dependencies information of every new concept. Table 2 is a simple example generated when a domain expert add “Trainer” as a subtype of “Lecturer”, l OR
is derived from logical disjunction operator - or. For the exclusive-or in logical theory can be considered equivalently to the mutual exclusion in set theory.
Use Semantic Decision Tables to Improve Meaning Evolution Support Systems and “Tutor” as a subtype of “Teacher”. As there is a subset dependency between “Lecturer” and “Teacher” (“Lecturer” is a subset of “Teacher”, section 4.1.2), the subtypes of “Lecturer”, e.g. “Trainer”, are automatically added as a subtype of “Teacher” (the column named “Trainer”, Table 2). The reason is explained in the column named “1Lecturer” in Table 2: “Lecturer” is the subset of “Teacher”. If the domain expert tries to add “Apprentice” as a subtype of both “Teacher” and “Student”, it cannot be added because “Teacher” and “Student” have exclusion dependency (see the SDT commitment of exclusion in section 4.1.4). Thus, the add action of “Apprentice” is denied (the column named “Apprentice”, Table 2), the reason of which is explained in the columns named “2Teacher” in Table 2. Table 2
A table that shows validity of introduced concepts
SUBSET EXCLUSION Add Add to others Add conflicted Add Action Denied
Trainer Lecturer
Tutor Teacher
* *1 Teacher
*
Apprentice Teacher,Student
1Lecturer Teacher
2Teacher Student
*2
Table 2 shows a very simple example that the DOGMA-MESS top down processes can benefit from SDT. In the next subsection, how to use SDT to guide the concepts elicitation in a bottom up process is discussed. 4.2
Bottom Up: Use Semantic Decision Tables to Guide the Concepts Elicitation
Within an ontology version, the system needs to select a few concepts amongst the ones at OTO level. We use Semantic Decision Tables (SDT) to store the lifting algorithms and manage the process. Figure 6
The concept ’Teacher’ is designed at UCO level
Figure 7 level
A new relevant concept Patience is introduced by a domain expert at OTO
Let us first look at a simple lifting algorithm. When we lift a concept from OTO level to LCO level, we need to choose some concepts at OTO level. Let Sc be the concept set at OTO level, and let S1 be the resulting lifted concept set at LCO level. In order to compute this process automatically, we hereby introduce two important condition stubs used to form SDT condition lexons - the relevance score Rc and the interest point Ip . A concept Ci at OTO level is considered as a relevant candidate concept when it gets certain amount of relevance score Rc . Rc is set zero when a new concept is defined at the first time. It increases when the concept is defined in other organizational ontologies designed by different domain experts. For example, if we
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get the concept “skill of safety” defined within the same context from two different organizational ontologies, the Rc of this concept is increased by one. Interest point Ip starts from zero. Ip is assigned to an existing concept at UCO level. It increases by one when a new concept, which is connected to this existing concept, is introduced. For example, we have the concept definition of “Teacher” at UCO level (Fig. 6). When a domain expert adds a new relevant concept “Patience” to “Teacher” at OTO level (Fig. 7), Ip of “Teacher” is increased by one. Ip reflects the focusing interests of the stakeholder community. We consider the concept “Patience” as a candidate concept that will be lifted to UCO level only when Ip of its connected concept “Teacher” meets a certain threshold value. When a new concept at OTO level is connected to more than one concept at UCO level, we choose the biggest number of Ip . Accordingly, we formalize a lift rule into the SDT commitments as illustrated in Table 3. Table 3 language
An example of SDT commitments and their explanations in the natural
ID 1
Commitment (P1 = [concept, has, is of, Ip], P2 = [Ip, is of, has, concept]) : UNIQ (P1, P2).
2
(P3 = [concept , has, is of, Rc], P4 = [Rc, is of, has, concept]) : UNIQ (P3, P4).
3
(P5 = [concept, has, is of, Rc], P6 = [concept, has, is of, Ip], P7 = [concept, is lifted to, contain, UCO level]) : IMP(AND(P5(Rc) ≥ T1, P6(Ip)≥ T2), P7).
Verbalization Each concept has at most one interest point. And each interest point is of at most one concept. Each concept has at most one relevance score. And each relevance score is of at most one concept. The fact that a concept is lifted to UCO level depends on two conditions: 1. whether its relevance score is more than T1 or not; And 2. Whether its interest point is more than T2 or not.
Commitment 1 in Table 3 uses the uniqueness constraint (‘UNIQ’) to express the one-to-one relationship between ‘concept’ and ‘interest point’. So does commitment 2. Commitment 3 uses the propositional connectives ‘IMP’ (the implication connective) and ‘AND’ (the conjunction connective) to express that: a candidate concept can be lifted to UCO level if and only if its relevance point and interest point meet their threshold (‘T1’ and ‘T2’). Based on Table 3, two concepts at the OTO level - ‘Patience’m and ‘Oral comprehension’, which are considered as two candidate concepts, are analyzed in Table 4. Table 4 contains the decision whether the concepts ‘Patience’ and ‘Oral comprehension’ at OTO level can be lifted to LCO level or not. The tabular report is automatically generated by the SDT plug-in in the DOGMA-MESS tooln . As the relevance score (20) of the concept ‘Oral comprehension’ doesn’t reach the threshold (25), it is kept at OTO level for next MESS iterations. m Its
concept is given by Fig. 4. DOGMA-MESS tool currently developed in STARLab is a web portal to assist domain experts to design ontologies: http://www.dogma-mess.org/ n The
Use Semantic Decision Tables to Improve Meaning Evolution Support Systems Table 4 A tabular report generated based on the SDT, which contains the commitments in Table 3 (T1=25, T2=25) Super Type Relevance Score Relevant concept at UCO Interest Point ... Action/Decision Keep for next MESS iteration Lift to LCO
Patience N/A 30 Teacher 30 ...
Oral comprehension Competence 20 Teacher 30 ...
... ... ... ... ... ...
* *
The resulting concept set is then provided to the core domain experts, who are responsible for standardizing the concepts and merging them at UCO level. As the concepts are defined and visualized in the Conceptual Graph models, the core domain experts can use many available conceptual graph matching manners, such as in Myaeng and Lopez-Lopez (1992), to merge the new concepts automatically into the existing concepts. During this merging phase, some extra links between new concepts and existing concepts need to be constructed. For example, conceptually equivalent concepts need to be linked with the “equivalent” canonical relation. The merging process results in several reorganized conceptual graphs at UCO level.
4.2.1
Process Evaluation Using SDT
Table 3 is as an example of a lifting rule. In practice, users are free to choose their preferred lifting rules. Furthermore, the algorithm in SDT can be evaluated. Let Ncan be the number of the candidate concepts, which resides at OTO level. And let Nsel be the number of selected concepts that are lifted. The concept selection rate O is defined as: O = Nsel /N can . The selection rate O can be studied by setting up different values of T1 and T2 (Table 5).
Table 5 project
A result of the selection rate O in tabular report taking from the PROLIX
T1 T2 O
Exp 1 8 15 25%
Exp 2 20 15 12%
Exp 3 37 15 12%
Exp 4 44 15 10%
Exp 5 56 15 0%
In practice, core domain experts need to adjust the parameters T1 and T2 based on real situation. For example, they may set T1 and T2 higher when there are a lot of concept candidates and they don’t have a lot of time to standardize them. An advantage of using the generated tabular reports (e.g. Table 5) is to help the core domain experts to determine which values that they should assign to T1 and T2.
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Yan Tang and Robert Meersman Conclusion and Future Work
We have discussed using Semantic Decision Tables (SDTs) in community grounded, knowledge intensive Meaning Evolution Support Systems (in particular, DOGMAMESS) for ontology evolution. More explicitly, SDTs were used in both bottom-up and top-down processes of the meaning evolution cycle in DOGMA-MESS. In the top-down process, the decision rules of SDTs are used to constrain the dependencies between the templates at the Upper Common Ontology level and check the validity of the concepts. We specified seven categories of templates dependencies that are mostly used. They are multivalued dependencies, subset dependencies, join dependencies, uniqueness dependencies, mandatory dependencies, equality dependencies and exclusion dependencies. With SDTs, the accuracy of the concepts provided by the domain experts gets improved. In the bottom-up process, the decision rules of SDTs are utilized to draw the decisions whether the concepts are lifted or not. The concepts created by the domain experts are no longer selected manually, instead, the decisions are executed automatically in the processes. The behaviors of the community are captured, managed and guided by SDTs. Hence, the system effectiveness is enhanced. In addition, we emphasize the importance of capturing the community’s behavior and guide it respectively. The community’s behaviors are coded as the parameters in the lifting algorithm stored in SDTs. The algorithms can be modified by simply writing SDT commitments. Thus, the flexibility increases. After the execution of each process (no matter whether it is the top-down process or the bottom-up process), a tabular report is generated based on SDTs. We consider such a tabular report as a complementary mechanism for non-technical users. A big disadvantage is the complexity. The knowledge engineers need to know how to construct SDTs. However, it is rather easy for the experts if they already know how to model ontologies in DOGMA. For the notion of SDT is as well modeled in the DOGMA framework. We have developed a tool to support constructing and visualizing SDTs. The current version supports modeling some specific commitment types. A web portal to support DOGMA-MESS methodology has been developed de Moor et al., 2006. In this paper, we focus on the system management while introducing new concepts that matter the overlapped interests of the community. In practice, we observe that the concepts in an ontology can be obsolete after a long time period. Therefore, we need to update the ontology by modifying (e.g. deleting, replacing with others, and redefining) the concepts. A future work is to use SDTs in this kind of modifying processes.
Acknowledgements The research is partly supported by the EC Prolix project.
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