Taghi Karimi / International Journal of Engineering Research and Applications (IJERA)       ISSN: 2248-9622 www.ijera.com ...
Taghi Karimi / International Journal of Engineering Research and Applications (IJERA)       ISSN: 2248-9622 www.ijera.com ...
Taghi Karimi / International Journal of Engineering Research and Applications (IJERA)       ISSN: 2248-9622 www.ijera.com ...
Taghi Karimi / International Journal of Engineering Research and Applications (IJERA)           ISSN: 2248-9622 www.ijera....
Taghi Karimi / International Journal of Engineering Research and Applications (IJERA)       ISSN: 2248-9622 www.ijera.com ...
Taghi Karimi / International Journal of Engineering Research and Applications (IJERA)         ISSN: 2248-9622 www.ijera.co...
Taghi Karimi / International Journal of Engineering Research and Applications (IJERA)       ISSN: 2248-9622 www.ijera.com ...
Taghi Karimi / International Journal of Engineering Research and Applications (IJERA)       ISSN: 2248-9622 www.ijera.com ...
Taghi Karimi / International Journal of Engineering Research and Applications (IJERA)       ISSN: 2248-9622 www.ijera.com ...
Upcoming SlideShare
Loading in …5
×

Dc32644652

422 views

Published on

IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com

Published in: Technology, Education
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
422
On SlideShare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
2
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Dc32644652

  1. 1. Taghi Karimi / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 2, March -April 2013, pp.644-652A Novel Expert System for Reasoning Based on Semantic Cancer Taghi Karimi Department of Mathematics, Payame Noor University, Tehran, IranAbstractThis paper describes a programmatic framework information between groups of people with differingfor representing, manipulating and reasoning with ontologies [5-9].geographic semantics. The framework enables Semantic issues pervade the creation, use and re-visualizing knowledge discovery, automating tool purposing of geographic information. In anselection for user defined geographic problem information economy we can identify the roles ofsolving, and evaluating semantic change in information producer and information consumer, andknowledge discovery environments. Methods, in some cases, in national mapping agencies fordata, and human experts (our resources) are example, datasets are often constructed incrementallydescribed using ontologies. An entity’s ontology by different groups of people with an implicit (butdescribes, where applicable: uses, inputs, outputs, not necessarily recorded) goal. The overall meaningand semantic changes. These ontological of the resulting information products are not alwaysdescriptions are manipulated by an expert system obvious to those outside that group, existing for theto select methods, data and human experts to solve most part in the creators‘ mental model. Whena specific user-defined problem; that is, a solving a problem, a user may gather geospatialsemantic description of the problem is compared information from a variety of sources without everto the services that each entity can provide to encountering an explicit statement about what theconstruct a graph of potential solutions. A data mean, or what they are (and are not) useful for.minimal spanning tree representing the optimal Without capturing the semantics of the data(least cost) solution is extracted from this graph, throughout the process of creation, the data may beand displayed in real-time. The semantic misunderstood, be used inappropriately, or not usedchange(s) that result from the interaction of data, at all when they could be. The consideration ofmethods and people contained within the resulting geospatial semantics needs to explicitly cater for thetree are determined via expressions of particular way in which geospatial tasks aretransformation semantics represented within the undertaken [10]. As a result, the underlyingJESS expert system shell. The resulting assumptions about methods used with data, and thedescription represents the formation history of roles played by human expertise need to beeach new information product (such as a map or represented in some fashion so that a meaningfuloverlay) and can be stored, indexed and searched association can be made between appropriateas required. Examples are presented to show (1) methods, people and data to solve a problem. It is notthe construction and visualization of information the role of this paper to present a definitive taxonomyproducts, (2) the reasoning capabilities of the of geographic operations or their semantics. To do sosystem to find alternative ways to produce would trivialize the difficulties of defininginformation products from a set of data methods geographic semanticsand expertise, given certain constraints and (3) therepresentation of the ensuing semantic changes by II. Background and Aimswhich an information product is synthesized. This paper presents a programmatic framework for representing, manipulating and I. Introduction reasoning with geographic semantics. In general The importance of semantics in geographic semantics refers to the study of the relations betweeninformation is well documented [1-4]. Semantics are symbols and what they represent [11]. In thea key component of interoperability between GIS; framework outlined in this paper, semantics have twothere are now robust technical solutions to valuable and specific roles. Firstly, to determine theinteroperate geographic information in a syntactic most appropriate resources (method, data or humanand schematic sense (e.g. OGC, NSDI) but these fail expert) to use in concert to solve a geographicto take account of any sense of meaning associated problem, and secondly to act as a measure of changewith the information. describe how exchanging data in meaning when data are operated on by methodsbetween systems often fails due to confusion in the and human experts. Both of these roles are discussedmeaning of concepts. Such confusion, or semantic in detail in Section 3. The framework draws on aheterogeneity, significantly hinders collaboration if number of different research fields, specifically:groups cannot agree on a common lexicon for core geographical semantics [11-13]ontologiesconcepts. Semantic heterogeneity is also blamed for computational semantics, constraint-based reasoningthe inefficient exchange of geographic concepts and and expert systems and visualization to represent 644 | P a g e
  2. 2. Taghi Karimi / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 2, March -April 2013, pp.644-652aspects of these resources. The framework sets out to 1.1 Ontologysolve a multi-layered problem of visualizing In philosophy, Ontology is the ―study of the kinds ofknowledge discovery, automating tool selection for things that exist‖ In the artificial intelligenceuser defined geographic problem solving and community, ontology has one of two meanings, as aevaluating semantic change in knowledge discovery representation vocabulary, typically specialized toenvironments. The end goal of the framework is to some domain or subject matter and as a body ofassociate with geospatial information products the knowledge describing some domain using such adetails of their formation history and tools by which representation vocabulary [18]. The goal of sharingto browse, query and ultimately understand this knowledge can be accomplished by encoding domainformation history, thereby building a better knowledge using a standard vocabulary based on anunderstanding of meaning and appropriate use of the ontology. The framework described here utilizes bothinformation. definitions of ontology.The problem of semantic heterogeneity arises due tothe varying interpretations given to the terms used todescribe facts and concepts. Semantic heterogeneityexists in two forms, cognitive and naming. Cognitivesemantic heterogeneity results from no common baseof definitions between two (or more) groups. As anexample, think of these as groups of scientistsattempting to collaborate. If the two groups cannotagree on definitions for their core concepts thencollaboration between them will be problematic.Defining such points of agreement amounts toconstructing a shared ontology, or at the very least,points of overlap [12-17].Naming semantic heterogeneity occurs when thesame name is used for different concepts or differentnames are used for the same concept. It is notpossible to undertake any semantic analysis untilproblems of semantic heterogeneity are resolved.Ontologies, described below, are widelyrecommended as a means of rectifying semanticheterogeneity. The framework presented in this paper Figure 1 – Interrelationships between different types ofutilizes that work and other ontological research to ontology.solve the problem of semantic heterogeneity.The use of an expert system for automated reasoningfits well with the logical semantics utilized within theframework. The Java Expert System Shell (JESS) isused to express diverse semantic aspects aboutmethods, data, and human experts. JESS performsstring comparisons of resource attributes (parsedfrom ontologies) using backward chaining todetermine interconnections between resources.Backward chaining is a goal driven problem solvingmethodology, starting from the set of possiblesolutions and attempting to derive the problem. If theconditions for a rule to be satisfied are not foundwithin that rule, the engine searches for other rulesthat have the unsatisfied rule as their conclusion,establishing dependencies between rules. JESSfunctions as a mediator system, with a foundation Figure 2 – Information products arelayer where the methods, data, and human experts are derived from the interaction of entities.described (the domain ontology), a mediation layerwith a view of the system (the task ontology) and a The representation vocabulary embodies theuser interface layer (for receiving queries and conceptualizations that the terms in the vocabularydisplaying results). are intended to capture. Relationships described between conceptual elements in this ontology allow 645 | P a g e
  3. 3. Taghi Karimi / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 2, March -April 2013, pp.644-652for the production of rules governing how theseelements can be ―connected‖ or ―wired together‖ tosolve a geographic problem. In our case theseelements are methods, data and human experts, eachwith their own ontology. In the case of datasets, adomain ontology describes salient properties such aslocation, scale, date, format, etc., as currentlycaptured in meta-data descriptions (see Figure 1). Inthe case of methods, a domain ontology describes theservices a method provides in terms of atransformation from one semantic state to another. Inthe case of human experts the simplest representationis again a domain ontology that shows thecontribution that a human can provide in terms ofsteering or configuring methods and data. However,it should also be possible to represent a two-way flowof knowledge as the human learns from situationsand thereby expands the number of services they canprovide (we leave this issue for future work). Thesynthesis of a specific information product isspecified via a task ontology that must fuse togetherelements of the domain and application ontologies toattain its goal. An innovation of this framework is thedynamic construction of the solution network,analogous to the application ontology. In order forresources to be useful in solving a problem, their Figure 3 – A concrete example of the interaction ofontologies must also overlap. Ontology is a useful methods, data and human experts to produce anmetaphor for describing the genesis of the information product.information product. A body of knowledge described obtained during the creation of the product isusing the domain ontology is utilized in the initial captured within this layer. The capture of thisphase of setting up the expert system. A task semantic information describes the transformationsontology is created at the conclusion of the that the geospatial information undergoes, facilitatingautomated process specifically defining the concepts better understanding and providing a measure ofthat are available. An information product is derived repeatability of analysis, and improvingfrom the use of data extracted from databases and communication in the hope of promoting bestknowledge from human experts in methods, as shown practice in bringing geospatial information to bear.in figure 2. Egenhofer (2002) notes that the challenge remains ofBy forming a higher level ontology which describes how best to make these semantics available to thethe relationships between each of these resources it is user via a search interface. Pundt and Bishr, (2002)possible to describe appropriate interactions. As a outline a process in which a user searches for data tosimple example (figure 3), assume a user wishes to solve a problem. This search methodology is alsoevaluate landuse/landcover change over a period of applicable for the methods and human experts to betime. Classifying two LandsatTM images from 1990 used with the data. This solution fails when multipleand 2000 using reference data and expert knowledge, sources are available and nothing is known of theirthe user can compare the two resulting images to content, structure and semantics. The use of pre-produce a map of the area(s) which have changed defined ontologies aids users by reducing theduring that time. One interesting feature available search space. Ontological concepts relevantdemonstrated in this example is the ability of human to a problem domain are supplied to the user allowingexperts to gain experience through repeated exposure them to focus their query. A more advanced interfaceto similar situations. Even in this simple example a would take the user‘s query in their own terms andbasic semantic structure is being constructed and a map that to an underlying domain ontology (Bishr,lineage of the data can be determined. Arguably an 1998).additional intermediate data product exists betweenthe classifiers and the comparison; it has been As previously noted, the meaning of geospatialremoved for clarity. information is constructed, shaped and changed by1.2 Semantics the interaction of people and systems. SubsequentlyWhile the construction of the information product is the interaction of human experts, methods and dataimportant, a semantic layer sits above the operations needs to be carefully planned. A product created as aand information (figure 4). The geospatial knowledge result of these interactions is dependent on the ontology of the data and methods and the 646 | P a g e
  4. 4. Taghi Karimi / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 2, March -April 2013, pp.644-652 epistemologies and ontologies of the human experts. formal sense, so that we can describe the effects In light of this, the knowledge framework outlined deriving from their interaction. One strong caveat below focuses on each of the resources involved here is that our semantic description (described (data, methods and human experts) and the roles they below) does not claim to capture all senses of play in the evolution of a new information product. meaning attached to data, methods or people, and in In addition, the user‘s goal that produced the product, fact as a community of researchers we are still and any constraints placed on the process are learning about which facets of semantics are recorded to capture aspects of intention and situation important and how they might be described. It is not that also have an impact on meaning. This process currently possible to represent all aspects of meaning and the impact of constraint based searches are and knowledge within a computer, so we aim instead discussed in more detail in the following section. to provide descriptions that are rich enough to allow users to infer aspects of meaning that are important for specific tasks from the visualizations or reports that we can synthesize. In this sense our own descriptions of semantics play the role of a signifier—the focus is on conveying meaning to the reader rather than explicitly carrying intrinsic meaning per-se. The formalization of semantics based on ontologies and operated on using a language capable of representing relations provides for powerful semantic modelling (Kuhn, 2002). The framework, rules, and facts used in the Solution Synthesis Engine (see below) function in this way. Relationships are established between each of the entities, by calculating their membership within a set of objects capable of synthesizing a solution. We extend the approach of Kuhn by allowing the user to narrow a search for a solution based on the specificFigure 4 – Interaction of the semantic layer and semantic attributes of entities. Using the minimal operational layer. spanning tree produced from the solution synthesis it III. Knowledge framework is possible to retrace the steps of the process to The problem described in the introduction has calculate semantic change. As each fact is asserted it been implemented as three components. The first, contains information about the rule that created it (the and the simplest, is the task of visualizing the method) and the data and human experts that were network of interactions by which new information identified as resources required. If we are able to products are synthesized. The second, automating describe the change to the data (in terms of abstract the construction of such a network for a user-defined semantic properties) imbued by each of the processes task, is interdependent with the third, evaluating through which it passes, then it is possible to semantic change in a knowledge discovery represent the change between the start state and the environment, and both utilize functionality of the finish state by differencing the two. first. An examination of the abstract properties of data, methods and experts is followed by an Although the focus of our description is on explanation of these components and their inter- semantics, there are good reasons for including relationships. syntactic and schematic information about data and methods also, since methods generally are designed 1.3 Formal representation of components and to work in limited circumstances, using and changes producing very specific data types (pre-conditions This section explains how the abstract and post-conditions). Hence from a practical properties of data, methods and experts are perspective it makes sense to represent and reason represented, and then employed to track semantic with these aspects in addition to semantics, since they changes as information products are produced will limit which methods can be connected together utilizing tools described above. From the description and dictate where additional conversion methods are in Section 2 it should be evident that such changes required. Additional potentially useful properties are a consequence of the arrangement of data, arise when the computational and human computational methods and expert interaction applied infrastructure is distributed e.g. around a network. to data. At an abstract level above that of the data By encoding such properties we can extend our and methods used, we wish to represent some reasoning capabilities to address problems that arise characteristics of these three sets of components in a 647 | P a g e
  5. 5. Taghi Karimi / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 2, March -April 2013, pp.644-652when resources must be moved from one node to Using the notation above, our semantic descriptionanother to solve a problem. takes the form: IV. Description of data M : D p1 , p2 ,  , pn     D  p1 , p2 ,  , pn   Operation As mentioned in Section 2, datasets are , where Operation is a generic description of the roledescribed in general terms using a domain ontologydrawn from generic metadata descriptions. Existing or function the method provides, drawn from ametadata descriptions hold a wealth of such practical typology.information that can be readily associated with For example, a cartographic generalization methoddatasets; for example the FGDC (1998) defines a mix changes the scale at which a dataset is mostof semantic, syntactic and schematic metadata applicable, a supervised classifier transforms an arrayproperties. These include basic semantics (abstract of numbers into a set of categorical labels, anand purpose), syntactic (data model information, and extrapolation method might produce a map for nextprojection), and schematic (creator, theme, temporal year, based on maps of the past. Clearly, there areand spatial extents, uncertainty, quality and lineage). any number of key dimensions over which suchWe explicitly represent and reason with a subset of changes might be represented; the above examplesthese properties in the work described here and couldeasily expand to represent them all, or any other highlight spatial scale, conceptual ‘level’ (which at agiven metadata description that can be expressed basic syntactic level could be viewed simply as statistical scale) and temporal applicability, or simplysymbolically. Formally, we represent the set of nproperties of a dataset D as: D  p1 , p 2 ,  , p n  time. Others come to light following just a cursory exploration of GIS functionality: change in spatial(Gahegan, 1996). extents, e.g. windowing and buffering, change in uncertainty (very difficult in practice to quantify but V. Describing Methods easy to show in an abstract sense that there has been a While standards for metadata descriptions change).are already mature and suit our purposes,complementary mark-up languages for methods are Again, we have chosen not to restrict ourselves to astill in their infancy. It is straightforward to represent specific set of properties, but rather to remain flexiblethe signature of a method in terms of the format of in representing those that are important to specificdata entering and leaving the method, and knowing application areas or communities. We note that asthat a method requires data to be in a certain format Web Services become more established in the GISwill cause the system to search for and insert arena, such an enhanced description of methods willconversion methods automatically where they are be a vital component in identifying potentially usefulrequired. So, for example, if a coverage must be functionality.converted from raster format to vector format beforeit can be used as input to a surface flow accumulation VI. Describing Peoplemethod, then the system can insert appropriate data Operations may require additionalconversion methods into the evolving query tree to configuration or expertise in order to carry out theirconnect to appropriate data resources that would task. People use their expertise to interact with dataotherwise not be compatible. Similarly, if an image and methods in many ways, such as gathering,classification method requires data at a nominal scale creating and interpreting data, configuring methodsof 1:100,000 or a pixel size of 30m, any data at finer and interpreting results. These activities are typicallyscales might be generalized to meet this requirement structured around well-defined tasks where theprior to use. Although such descriptions have great desired outcome is known, although as in the case ofpractical benefit, they say nothing about the role the knowledge discovery, they may sometimes be moremethod plays or the transformation it imparts to the speculative in nature. In our work we have cast thedata; in short they do not enable any kind of semantic various skills that experts possess in terms of theirassessment to be made. ability to help achieve some desired goal. This, in A useful approach to representing what GIS turn, can be re-expressed as their suitability tomethods do, in a conceptual sense, centers on a oversee the processing of some dataset by sometypology (e.g. Albrecht‘s 20 universal GIS operators, method, either by configuring parameters, supplying1994). Here, we extend this idea to address a number judgment or even performing the task explicitly. Forof different abstract properties of a dataset, in terms example, an image interpretation method may requireof how the method invoked changes these properties identification of training examples that in turn(Pascoe & Penny, 1995; Gahegan, 1996). In a necessitate local field knowledge; such knowledgegeneral sense, the transformation performed by a can also be specified as a context of applicabilitymethod (M) can be represented by pre-conditions and using the time, space, scale and theme parameterspost-conditions, as is common practice with interface that are also used to describe datasets. As such, aspecification and design in software engineering. given expert may be able to play a number of roles 648 | P a g e
  6. 6. Taghi Karimi / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 2, March -April 2013, pp.644-652that are required by the operations described above, and a standard proposal for syntactical exchangewith each role described as: notations (http://www.ontoknowledge.org/oil/).E :    p1 , p2 ,  , pn  , meaning that  Operation 1.5 Solution Synthesis Engineexpert E can provide the necessary knowledge to The automated tool selection process orperform Operation within the context of p1…, pn. So solution synthesis is more complex relying on domainto continue the example of image interpretation, p1…, ontologies of the methods, data and human expertspn might represent (say) floristic mapping of Western (resources) that are usable to solve a problem. TheAustralia, at a scale of 1:100,000 in the present day. task of automated tool selection can be divided into a number of phases. First is the user‘s specification ofAt the less abstract schematic level, location the problem, either using a list of ontologicalparameters can also be used to express the need to keywords or in their own terms which are mapped tomove people to different locations in order to conduct an underlying ontology. Second ontologies ofan analysis, or to bring data and methods distributed methods, data and human experts need to bethroughout cyberspace to the physical location of a processed to determine which resources overlap withperson. the problem ontology. Third, a description of the user‘s problem and any associated constraints isAnother possibility here, that we have not yet parsed into an expert system to define rules thatimplemented, is to acknowledge that a person‘s describe the problem. Finally networks of resourcesability to perform a task can increase as a result of that satisfy the rules need to be selected andexperience. So it should be possible for a system to displayed.keep track of how much experience an expert hasaccrued by working in a specific context (described Defining a complete set of characteristic attributes foras p1…, pn). (In this case the expert expression real world entities (such as data, methods and humanwould also require an experience or suitability score experts) is difficult due to problems selectingas described for constraint management described attributes that accurately describe the entity. Bishr‘sbelow in section 3.3). We could then represent a solution of using cognitive semantics to solve thisfeedback from the analysis exercise to the user, problem, by referring to entities based on theirmodifying their experience score. function, is implemented in this framework. Methods utilize data or are utilized by human experts and are1.4 Visualization of knowledge discovery subject to conditions regarding their use such as data The visualization of the knowledge discovery format, scale or a level of human knowledge. Theprocess utilizes a self organising graph package rules describe the requirements of the methods (‗if‘)(TouchGraph) written in Java. TouchGraph enables and the output(s) of the methods (‗then‘). Data andusers to interactively construct an ontology utilizing human experts, specified by facts, are arguably moreconcepts (visually represented as shapes) and passive and the rules of methods are applied to or byrelationships (represented as links between shapes). them respectively. A set of properties governing howEach of the concepts and relationships can have rules may use them are defined for data, (e.g. format,associated descriptions that give more details for each spatial, and temporal extents) and human expertsof the entity types (data, methods, and people). A (e.g. roles and abilities) using an XML schema andsample of the visualization environment is shown parsed into facts.below in figure 5.Touchgraph supports serialization allowing thedevelopment of the information product to berecorded and shared among collaborators.Serialization is the process of storing and convertingan object into a form that can be readily reused ortransported. For example, an ontology can beserialized and transported over the Internet. At theother end, deserialization reconstructs the object fromthe input stream. Information products describedusing this tool are stored as DAML+OIL objects sothat the interrelationships between concepts can bedescribed semantically. The DAML+OIL architecturewas chosen as the goal of the DARPA Agent MarkupLanguage component (DAML) is to capture termmeanings, and thereby providing a Web ontologylanguage. The Ontology Interchange Language (OIL)contains formal semantics and efficient reasoningsupport, epistemological rich modeling primitives, Figure 5 – A sample of the visualization environment. 649 | P a g e
  7. 7. Taghi Karimi / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 2, March -April 2013, pp.644-652The first stage of the solution synthesis is the user The compare rule (Table 1) has dependencies onspecification of the problem using concepts and rules that collect data sources (used for comparisons)keywords derived from a problem ontology. The and the rules that accomplish those comparisonsproblem ontology, derived from the methods, data (intersection and union). If each of these rules can beand human expert ontologies, consist of concepts satisfied on the ―if‖ side of the clause, then the resultsdescribing the intended uses of each of the resources. of the comparison rules are stored, together with theThis limitation was introduced to ensure the data sources that were used in the comparison and theframework had access to the necessary entities to products of the comparison. The results of the rulesolve a user‘s problem. A more advanced version of ―firing‖ are stored in a list that will be used to form athe problem specification is proposed which uses minimal spanning tree for graphing [10-30].natural language parsing to allow the user to specify aproblem. This query would then be mapped to the As the engine runs, each of the rules ―needed‖ areproblem ontology allowing the user to use their own satisfied using backward chaining, the goal issemantics instead of being governed by those of the fulfilled, and a network of resources is constructed.system. As each rule fires and populates the network a set of criteria is added to a JESS fact describing each of theThe second stage of the solution synthesis process user criteria that limits the network. Each of theseparses the rules and facts describing relationships criteria is used to create a minimal spanning tree ofbetween data, methods, and human experts. The operations. User criteria are initially based upon theJESS rule, compare (Table 1), illustrates the key spatial concepts of identity, location, direction,interaction between the rule (or method) distance, magnitude, scale, time availability,requirements and the facts (data and human experts). operation time, and semantic change.Sections of the rule not essential for illustrating itsfunction have been removed. It is important to note Users specify the initial constraints, via the userthat these rules do not perform the operations interface (figure 6) prior to the automated selection ofdescribed rather they mimic the semantic change that tools. As an example, a satellite image is required forwould accompany such an operation. The future an interpretation task, but the only available data iswork section outlines the goal of running this system 30 days old and data from the next orbit over thein tandem with a codeless programming environment region will not be available for another 8 hours. Is itto run the selected toolset automatically. ―better‖ to wait for that data to become available or is Table 1 - JESS Sample code it more crucial to achieve a solution in a shorter time(1) defrule compare ;; compare two data sets using potentially out of date data? It is possible that(2) (need-comparison_result $?) the user will request a set of limiting conditions that(3) (datasource_a ?srcA) are too strict to permit a solution. In these cases all(4) (datasource_b ?srcB) possible solutions will be displayed allowing the user(5) intersection_result <- (intersect ?srcA ?srcB) to modify their constraints. The user specified(6) union_result <- (union ?srcA ?srcB) constraints are used to prune the network of resources(7) => ;; THEN constructed (i.e. all possible solutions to the problem)(8) (assert (comparison_result (inputA ?srcA) to a minimal spanning tree which is the solution that(inputB ?srcB) (intersect ?intersection_result) (union satisfies all of the user‘s constraints.?union_result) ;; ―perform‖ the operation VII. ResultsWith all of the resource rules defined, the missing This section presents the results of the framework‘slink is the problem to be solved using these rules. Theproblem ontology is parsed into JESS to create a setof facts. These facts form the ―goal‖ rule whichmirrors the user‘s problem specification. Each of thefacts in the ‗if‘ component of the goal rule are in theform ‗need-method_x‘. The JESS engine now has therequisite components for tool selection.Utilizing backward-chaining JESS searches for ruleswhich satisfy the left hand side (LHS) of the rule. Inthe case of dependencies (rules preceded by ―need-‖)JESS searches for rules that satisfy the ―need-‖request and runs them prior to running the rulegenerating the request. The compare rule (above)runs only when a previous rule requires acomparison_result fact to be asserted in order for thatrule to be completed. Figure 6 – Interface showing constraint selection. 650 | P a g e
  8. 8. Taghi Karimi / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 2, March -April 2013, pp.644-652solution synthesis and representation of semantic IX. Conclusionschange. The results of the knowledge discovery This paper outlined a framework forvisualization are implicit in this discussion as that representing, manipulating and reasoning withcomponent is used for the display of the minimal geographic semantics. The framework enablesspanning tree. visualizing knowledge discovery, automating tool selection for user defined geographic problemA sample problem, finding a home location with asunset view is used to demonstrate the solutionsynthesis. In order to solve this problem, raster(DEM) and vector (road network) data needs to beintegrated. A raster overlay, using map algebra,followed by buffer operations is required to findsuitable locations, from height, slope and aspect data.The raster data of potential sites needs to beconverted to a vector layer to enable a bufferingoperation with vector road data. Finally a viewshedanalysis is performed to determine how much of thelandscape is visible from candidate sites.The problem specification was simplified by hard-coding the user requirements into a set of facts loadedfrom an XML file. The user‘s problem specificationwas reduced to selecting pre-defined problems from amenu.A user constraint of scale was set to ensure that dataused by the methods in the framework was at aconsistent scale and appropriate data layers wereselected based on their metadata and format. With the Figure 7 – Diagram showing optimal path derived fromuser requirements parsed into JESS and a problem thinned networkselected, the solution engine selected the methods,data and human experts required to solve the solving, and evaluating semantic change inproblem. The solution engine constructed a set of all knowledge discovery environments. A minimalpossible combinations and then determined the spanning tree representing the optimal (least cost)shortest path by summing the weighted constraints solution was extracted from this graph, and can bespecified by the user. Utilizing the abstract notation displayed in real-time. The semantic change(s) thatfrom above, with methods specifying change thus: result from the interaction of data, methods and M 1 : D p1 , p 2 ,  , p n  Operation D  p1 , p 2 , the n formation history of each new information ,p  people contained within the resulting tree represents  , the user weights were included and summed for all product (such as a map or overlay) and can be stored,modified data sets: indexed and searched as required. D1 u1 p1 , u 2 p 2 ,, u n p n ,... Dn u1 p1 , u 2 p 2 ,, u n p n  X. Acknowledgements. As a result of this process the solution set is pruned Our thanks go to Sachin Oswal, who helpeduntil only the optimal solution remains (based on user with the customization of the TouchGraph conceptconstraints). visualization tool used here. This work is partly funded by NSF grants: ITR (BCS)-0219025 and ITR VIII. Future Work Geosciences Network (GEON). The ultimate goal of this project is tointegrate the problem solving environment with the Referencescodeless programming environment GEOVISTA [1] Abel, D.J., Taylor, K., Ackland, R., andStudio currently under development at Pennsylvania Hungerford, S. 1998, An Exploration of GISState University. The possibility of supplying data to Architectures for Internet Environments.the framework and determining the types of questions Computers, Environment and Urban Systems.which could be answered with it is also an interesting 22(1) pp 7 -23.problem. [2] Albrecht, J., 1994. Universal elementary GISA final goal is the use of natural language parsing of tasks- beyond low-level commands. In Waugh Tthe user‘s problem specification. C and Healey R G (eds) Sixth International Symposium on Spatial Data Handling : 209-22. 651 | P a g e
  9. 9. Taghi Karimi / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 2, March -April 2013, pp.644-652[3] Bishr, Y., 1997. Semantic aspects of interoperable Information Technology. Springer Verlag, pp. GIS. Ph.D Dissertation Thesis, Enschede, The 139-170. Netherlands, 154 pp. [17] Guarino, N., 1997b. Understanding , building and[4] Bishr, Y., 1998. Overcoming the semantic and using ontologies. International Journal of Human- other barriers to GIS interoperability. Computer Studies, 46: 293-310. International Journal of Geographical [18] Hakimpour, F. and Timpf, S., 2002. A Step towards Information Science, 12(4): 299-314. GeoData Integration using Formal Ontologies. In:[5] Brodaric, B. and Gahegan, M., 2002. M. Ruiz, M. Gould and J. Ramon (Editors), 5th Distinguishing Instances and Evidence of AGILE Conference on Geographic Information Geographical Concepts for Geospatial Database Science. Universitat de les Illes Balears, Palma de Design. In: M.J. Egenhofer and D.M. Mark Mallorca, Spain, pp. 5. (Editors), GIScience 2002. Lecture Notes in [19] Honda, K. and Mizoguchi, F., 1995. Constraint- Computing Science 2478. Springer-Verlag, pp. 22- based approach for automatic spatial layout 37. planning. 11th conference on Artificial Intelligence[6] Chandrasekaran, B., Josephson, J.R. and Benjamins, for Applications, Los Angeles, CA. p38. V.R., 1997. Ontology of Tasks and Methods, [20] Kokla, M. and Kavouras, M., 2002. Theories of AAAI Spring Symposium. Concepts in Resolving Semantic Heterogeneities,[7] Egenhofer, M., 2002. Toward the semantic 5th AGILE Conference on Geographic Information geospatial web, Tenth ACM International Science, Palma, Spain, pp. 2. Symposium on Advances in Geographic [21] Kuhn, W., 2002. Modeling the Semantics of Information Systems. ACM Press, New York, Geographic Categories through Conceptual NY, USA, McLean, Virginia, USA, pp. 1-4. Integration. In: M.J. Egenhofer and D.M. Mark[8] Fabrikant, S.I. and Buttenfield, B.P., 2001. (Editors), GIScience 2002. Lecture Notes in Formalizing Semantic Spaces for Information Computer Science. Springer-Verlag. Access. Annals of the Association of American [22] MacEachren, A.M., in press. An evolving Geographers, 91(2): 263-280. cognitive-semiotic approach to geographic[9] Federal Geographic Data Committee. FGDC- visualization and knowledge construction. STD-001-1998. Content standard for digital Information Design Journal. geospatial metadata (revised June 1998). Federal [23] Mark, D., Egenhofer, M., Hirtle, S. and Smith, B., Geographic Data Committee. Washington, D.C. 2002. Ontological Foundations for Geographic[10] Fonseca, F.T., 2001. Ontology-Driven Geographic Information Science. UCGIS Emerging Resource Information Systems. Doctor of Philosophy Theme. Thesis, The University of Maine, 131 pp. [24] Pascoe R.T and Penny J.P. (1995) Constructing[11] Fonseca, F.T., Egenhofer, M.J., Jr., C.A.D. and interfaces between (and within) geographical Borges, K.A.V., 2000. Ontologies and knowledge information systems. International Journal of sharing in urban GIS. Computers, Environment Geographical Information Systems, 9:p275. and Urban Systems, 24: 251-271. [25] Pundt, H. and Bishr, Y., 2002. Domain ontologies[12] Fonseca, F.T. and Egenhofer, M.J., 1999. for data sharing–an example from environmental Ontology-Driven Geographic Information monitoring using field GIS. Computers & Systems. In: C.B. Medeiros (Editor), 7th ACM Geosciences, 28: 95-102. Symposium on Advances in Geographic [26] Smith, B. and Mark, D.M., 2001. Geographical Information Systems, Kansas City, MO, pp. 7. categories: an ontological investigation.[13] Gahegan, M., Takatsuka, M., Wheeler, M. and International Journal of Geographical Hardisty, F., 2002. Introducing GeoVISTA Information Science, 15(7): 591-612. Studio: an integrated suite of visualization and [27] Sotnykova, A., 2001. Design and Implementation computational methods for exploration and of Federation of Spatio-Temporal Databases: knowledge construction in geography. Computers, Methods and Tools, Centre de Recherche Public - Environment and Urban Systems, 26: 267-292. Henri Tudor and Laboratoire de Bases de Donnees[14] Gahegan, M. N. (1999). Characterizing the Database Laboratory. semantic content of geographic data, models, and [28] Sowa, J. F., 2000, Knowledge Representation: systems. In Interoperating Geographic Logical, Philosophical and Computational Information Systems (Eds. Goodchild, M.F., Foundations (USA: Brooks/Cole). Egenhofer, M. J. Fegeas, R. and Kottman, C. A.). [29] Turner, M. and Fauconnier, G., 1998. Conceptual Boston: Kluwer Academic Publishers, pp. 71-84. Integration Networks. Cognitive Science, 22(2):[15] Gahegan, M. N. (1996). Specifying the 133-187. transformations within and between geographic [30] Visser, U., Stuckenschmidt, H., Schuster, G. and data models. Transactions in GIS, Vol. 1, No. 2, Vogele, T., 2002. Ontologies for geographic pp. 137-152. information processing. Computers &[16] Guarino, N., 1997a. Semantic Matching: Formal Geosciences, 28: 103-117. Ontological Distinctions for Information Organization, Extraction, and Integration. In: M.T. Pazienza (Editor), Information Extraction: A Multidisciplinary Approach to an Emerging 652 | P a g e

×