USING DECISION MODELS METAMODEL FOR INFORMATION RETRIEVAL SABINA CRISTIANA MIHALACHE *

Transcription

1 ANALELE ŞTIINŢIFICE ALE UNIVERSITĂŢII ALEXANDRU IOAN CUZA DIN IAŞI Tomul LIV Ştiinţe Economice 2007 USING DECISION MODELS METAMODEL FOR INFORMATION RETRIEVAL SABINA CRISTIANA MIHALACHE * Abstract This paper tries to introduce the idea that the form of specialized knowledge relating to decision models, rules, strategies for an organization may be a way to organize information by attaching meaning to the existing formalized entities at the computer-based information system level. We realize an exemplification by using a rule in order to attach meaning to an existing class from the domain model in the form of a new property. The paper presents some technologies and tools that we used for the exemplified decision problem, some remarks and conclusions. We titled the article for information retrieval starting from the assumption that querying ontology after firing the rules of a general domain knowledge related to decision should provide answers that contain improved semantics. Key words: decision rules, ontology, OWL, RDF, Protégé, SPARQL. 1 Introduction This paper considers that in order to obtain information it is necessary to apply knowledge. Although we do not want to define or have a specific position on the subject of what knowledge signifies, we want to say that people develop knowledge when they are confronted to a specific situation, problem or decision. After the process of understanding and solving the problem they are capable to structure their experience in the form of IF- THEN-ELSE rules. In the decision modelling process a considerable amount of knowledge must be formalized. The decision-making process implies a particular domain knowledge which consists in the knowledge of acting under some constraints called decisions conditions. Although there are numerous attempts in formalizing the so called knowledge the computer still don t understand symbols as humans do. So, the attempt in providing useful information fails often because the computer can t assume what is useful for decisionmakers. Implementing models that can provide useful information in the decision-making process transforms in an analysis phase during the time of implementing computer-based information systems. The act of deciding belongs to the decision-maker. During the decision-making process a considerable amount of information is produced by applying knowledge. In this way, the decision models metamodel is useful in providing information. The information system at an organization level represents its logical metamodel. During time, technologies were invented in order to develop physical-logical metamodels for the subsystems that constitutes the information system. The principal problem consists in the existence of multiple physical subsystems resulted from the implementation of multiple technologies which determines serious limits in integrating information. * PhD student, Business Information Systems Department, Faculty of Economics and Business Administration, Alexandru Ioan Cuza University, Iasi, sabina.mihalache@gmail.com

2 48 SABINA CRISTIANA MIHALACHE The limits in integrating information signify for the decision modeling process the following problems: 1. data that is necessary in the decision-making process is changeable because its meaning is changed during time. In this case, it is necessary that the represented conceptual structures can be modified according to the decisional moment. A solution provided by us consists in using ontologies and inference rules; 2. data is heterogeneous because it is provided by different sources. In this case, it is necessary to describe data in a format that could permit the semantic interoperability. In the following we will imagine a scenario and we will sustain that the decision rules of acting must be implemented to provide better information. We will begin by presenting some considerations in defining knowledge and information that is needed in the decisionmaking process. 2 The Importance of Knowledge Application in Information Retrieval Obtaining information is realized by addressing queries. Usually the key words or the concepts that form a specific query are not stored by the existent data model. There are a lot of studies about conceptual queries. Besides the preoccupations related to fuzzy queries we must agree that a conceptual query contains key words that may be possible to obtain after applying some knowledge from a specific domain. That formalized domain contains entities that exist because they were previously specified. Formalizing domain knowledge relies on concepts and in representing the existential laws for the respective concepts. Knowledge in its pure sense refers to something that is always true or false or generally accepted axioms. The trap in considering particular knowledge as pure knowledge has lead to some misunderstandings and confusing terms especially in the informatics domain. The personal knowledge relating to an individual is a form of particular knowledge because what is generally accepted for an individual may not be general knowledge. Any person has principles, beliefs, feelings that form a specialized knowledge and a personal view of the world and a personal capacity of understanding messages. In the case of organizations there exists a particular form of knowledge at which its members must adhere and participate in creating value. A lot of theories exist in the domain and as long as organizations exist there will be an interest in organizing and managing knowledge. The most general form of knowledge at an organization level (although it is still particular knowledge) is related to organization s purposes, plans, objectives, strategies, business rules and so on. If we accept to consider this form of knowledge truth knowledge we may consider formalizing the plans, rules, objectives and strategies guide that guide the live of an organization. But the objectives and plans change in time and often in a rapid rhythm. So the constraints on the entities represented by the computer-based information system also change. The metamodells editing must be users-oriented to assure an adaptive characteristic to the computer-based information systems as in the non-computer based models the specialized users specifies formal and informal rules. So, we decided that this would have to be a prime characteristic to formalize business rules at a computer-based information system level: the business rules must be user-oriented which means that they would not have to be implemented on the data representation level. The place where the controls on data must be specified is presented in figure 1.

3 Using decision models metamodel for information retrieval 49 Controls specification on the concepts represented in ontology The place where the queries are specified Firing rule knowledge Editing controls Firing rule Attached meaning (not data, but meaning) Ontology Instanciating individuals Organizing data Fig. 1 The place where the control on concepts is specified The business rules guide the business decisions so we must track the conclusion that the decisions formalized models aren t part of the data level organization unless they contain procedural knowledge contained by formulas and sort of calculus performed on data which must be implemented as procedures on the data constructs. The business rules, plans and objectives are best represented by IF THEN ELSE rules so this would have to be the metamodel used in representation, but again not on the data level because it would be called control constructs. The metamodel must be represented on the concepts and the metamodel editing must be performed by the user. A concept is better suited to a class of objects that contains instances; it describes an entity someone would say; a concept is a combination of instances that characterizes a specified entity. 3 Reaching Semantics through Representing Rules In the following we will present a problem and a possible conceptualization to this problem to sustain our ideas. The activities that must be realized in information integration are: 1. describing the different sources of data in a object-atribute-value representation in order to be transformed in an ontology format; 2. building different ontologies corresponding to different sources of data; 3. mapping the local ontologies corresponding to a domain ontology. In order to provide information there must be a mapping between different data sources stored usually in relational databases. Considering the case of a simple decision anyone can assume that the data sources are heterogeneous. If we take as example the case of deciding if an asset is impaired, information provided by the organization s database isn t sufficient. We must take into account the market value for the asset or the so called capitalized value or utility value. Assuming that for finding this value someone would choose the Internet, the data found for the respective value would have to be saved in a file format that other program should understand. For an application the term understanding means read and perform a predefined function, calculus or execute a procedure on that data.

4 50 SABINA CRISTIANA MIHALACHE For that data to be combined with data derived from a database they must share a common ontology. Usually for data extracted from a database it is recommended the RDF [W3C, 2004] format which is a description in the form of triples, each consisting of a subject, a predicate and an object. We exemplified this problem by using a database in PostgreSQL which stores data about fixed assets and by using an.xml file as we would save data referring to the capitalized value from a web site. The database and the.xml file are represented in figure 2. Fig. 2 The two different sources for data To transform the two different formats in which we have stored data about assets we used D2RQ [Bizer, 2006] which is a declarative language to describe mappings between relational database schema and OWL/RDFS ontologies. The mappings allow RDF applications to access the content of huge, non-rdf databases using Semantic Web query languages like SPARQL. The mapping is actually quite simple to make and implies two command lines in the MS-DOS prompt presented in the listing from figure 3. generate-mapping -o baza.n3 -d org.postgresql.driver -u postgres -p sabina jdbc:postgresql://localhost/mijlocfix dump-rdf -m baza.n3 > baza.rdf Fig. 3 The comand lines for mapping the relational database schema and for generating the RDF format The first command line generate a map in n3 format by connecting trough the database driver to the database using a login name and a password. The second one transforms the format n3 in a RDF file. By doing this we obtained two separate files but both in the RDF format. After that we combined the two files simply by eliminating the redundant triples. The standard language recommended to build ontology is OWL [W3C, 2004 ] and there are a lot of ontology editors but the most known tool is Protégé Editor which is opensource (there is a big community who uses this tool to develop ontologies). In order to transform the file from RDF format to the OWL format we used SWOOP which is another open-source tool that supports the RDF format but it doesn t have so many functionalities as Protégé has. We simply saved the file in the owl format. Finally we were being able to see the database relational schema as ontology. We can observe that the ontology refers to a superclass called THING which belongs to Protégé and that we have a single class whose name corresponds to table s name from relational database schema; the class has properties whose names are the fields names and individuals and their names corresponds to table s tuples. We can say in this moment that we managed to combine two different data sources that share the ontology defined by the table source data. In this moment it is good to ask ourselves if we have represented concepts. And a good answer is: not so much according to our above definition. We have data stored in individuals that have properties that belong to a class so we may say that the model could present the capacity to recognize and classify, but not to evaluate because the data represented has no attached meaning.

5 Using decision models metamodel for information retrieval 51 In order to evaluate this meaning W3C recommends SWRL Semantic Web Rule Language which permits editing rules. SWRL permits only editing rules and not firing on them (capability that would permit adding a property value to an individual and in this way reaching the model s semantics). Under the umbrella of the rule engines are a lot of tools but because Protégé is Java based the engine recommended is Jess. The users community developed also a JessTab Plugin which is available with the last version of Protégé. Jess is a rule engine that derives from CLIPS and uses Horn clauses to represent the rules. The users community also use bridges to map SWRL Rules into Jess rules but because this is not a trivial task we managed to do it without using a bridge. So we wanted to attach the meaning that for an asset to have the capitalized value under the net accounting value is to be impaired. The used rule is presented in figure 4. (defrule depreciere?f <- (object (is-a vocab0:postgres_mijlocfix) (OBJECT?obj) (vocab:mijlocfix_valcapitalizata?k) (vocab:mijlocfix_valoarecontabilaneta?c&:(<?k?c))) => (slot-set?f depreciere "da")) Fig. 4 The impaired rule expressed in Jess Once the relevant OWL concepts and SWRL rule have been represented in Jess, the Jess execution engine can perform inference. As rules fire, new Jess facts are inserted into the fact base. Those facts are then used in further inference. When the inference process completes, these facts can then be transformed into OWL knowledge, a process that is the inverse of the mapping mechanism. After firing the presented rule the facts stored in Jess are the same but they have an additional slot named depreciere if the condition specified in the rule is true. In our case, only one fact presents this aspect and we present the fact in figure 5. There exists a slot named depreciere that we defined in OWL ontology as a property of vocab0:mijlocfix_nrinventar with the accepted values da and nu. The slot does not belong to the ontology provided by relational database schema. The slot was defined by us and its value is attached to the individuals only if the rule proves to be true. Fig. 5 The fact that contains the additional slot depreciere What we deliberately omitted to say is that we used the D2RQ tool especially because it provides a way to visualize the individuals from ontology in a browser, so in order for a user to extract useful information it is sufficient to access the n3 format of the ontology in a browser. D2R Server address is and it permits viewing data in n3 and in the RDF format and more important querying the ontology by using SPARQL. Protégé doesn t permits saving the owl format back into n3 format once the OWL ontology has individuals defined by using rules. In this way the information retrieval process is improved by the attached properties resulted from the fired rules.

6 52 SABINA CRISTIANA MIHALACHE 4 Conclusions Better decisions means improving the information provided. We tried in this paper to outline that the knowledge of acting from the decision models must be implemented to improve information, to actually provide better information to the user. Using the decision models for problem-solving task proved to be a failure in the past. It concluded in very static models, non-adaptive ones, with no utility for the user because they captured a kind of model that impose performing an action by the decision maker in the form of transferring knowledge from the model to human being. We do not try to argue that this is the best model that we want to recommend but in the moment the technology and the technology s producers aren t too concept-oriented in the business domain although there is a clear whish to solve the problem of interoperability. Only for our little example we used no more than six tools to manage to transmit a simple message and we are not saying that this is a sure way to represent meaning. The tools that we used have a lot of facilities that we maybe omitted to consider. We may consider using ontologies in portal-based applications, in client-server architecture by sharing information described by an ontology domain. We provided this model oriented somehow on decisions due to the last standardization efforts in the field of information integration and due to the fact that we study decisions models as a special concerning in this moment. References Bizer, C., D2RQ V0.5 - Treating Non-RDF Databases as Virtual RDF Graphs, at accessed 31 May 2007; Google, Semantic Web Ontology Editor, at accessed 31 May 2007; Protégé, The Protégé Ontology Editor and Knowledge Acquisition System from Stanford University, at accessed 31 May W3C, Resource Description Framework (RDF), at accessed 31 May W3C, Web Ontology Language (OWL), at accessed 31 May 2007;