Computing and Information Systems © University of Paisley 2006 Semantic Databases: An Information Flow (IF) and Formal Concept Analysis (FCA) Reinforced Information Bearing Capability (IBC) Model Yang Wang and Junkang Feng Database Research Group Capability (IBC) model (We will say more about it shortly) provides a new prospective to SDB, which both assures traditional requirements of design and brings up some philosophical and mathematical insights This would, therefore, promote SDB to be compatible with Knowledge base (KB) and hence to be a strong support for SW Semantic Database (SDB) seems hitherto somehow overlooked in the literature compared with its ‘big brother’, Semantic Web What are the hindrances to the development of SDB, which hence have to be taken into account as we observe, include information representation, knowledge management, meaning elicitation, constraints/regularity identification and formulation, and also partiality preservation We propose an architecture, which is a result of reinforcing the notion of the Information Bearing Capability (IBC) that we put forward elsewhere before by applying the theory of Information Flow (IF) and that of Formal Concept Analysis (FCA) We believe that this architecture should enable SDB to cover a number of these aspects, which build upon and go beyond the relational database (RDB) 1.1 A Short Review of Semantic Databases (SDB) INTRODUCTION Semantic Web (SW) is the supreme elegance of topics, which covers numerous fields, such as knowledge organization and management, network technology and even data modeling Comparing to this prosperous triumph, the seemingly evident lack of attention to Semantic Database (SDB) would appear rather peculiar Whereas it is well known that SDB aims at capturing, modeling and yielding meanings rather than raw data, we observe that the short of robust theoretical modeling foundation and guidance lies as a gulf before the ‘fortune’ In our opinion, if we want to achieve a satisfactory SDB, not only primary prerequisites such as capturing more semantics and constraints, but also profound concepts of information, representations and partiality, need to be addressed To get across this gulf, the foundation of this research is a series of theories (we refer to them as ‘SIT’, short for Semantic Information Theories) concerning semantic information and information flow including Dreske (1981), Devlin (1991), and in particular, Barwise and Seligman’s (1991) information channel theory (IF for short) We believe that an Information Flow (hereafter IF for short) and Formal Concept Analysis (FCA) reinforced Information Bearing A database system is a representation system, which should be able to reflect real objects in the circumstance being modeled The content of a database rests with what actually exists in the modeled domain while any change operates on this content should correspond with what happens to those real world objects Sustaining this tie is not easy as at the first glance Designing a data model that captures as much as meaning as the modeled domain is the solution of many researchers (Hammer and McLeod 1981, Jagannathan et.al, 1988, Tsur and Zamolo 1984) To this end, concepts around SDB came into the scene Bearing the goal of representing, describing and structuring more semantics and meanings than contemporary database (viz Relational Database) in mind, SDB needs to be closely related to the modeled domain Hammer addresses a number of criteria that should be enforced during SDM design (Hammer and McLeod 1981): • The constructs of the database model should provide for the explicit specification of a large portion of the meaning of a database So called semantic expressiveness is not sufficiently achieved by many current data modeling techniques, such as hierarchical, network, and relational models • A database model must support a relativist view of the meaning of a database, and allow the structure of a database to support alternative ways of looking at the same information Being capable of capturing more meaning requires never rigid definitions and distinctions between ‘entities’, ‘attributes’ and ‘association’ Computing and Information Systems ã â University of Paisley 2006 A database model must support the definition of schemata that are based on abstract entities This point, in fact, addresses that a database should have the mechanism to support possible semantic constraints In the related literature, there are mainly two most interesting streams identified by the authors in SDB modeling The first one is that some of the researchers are developing their SDM structure on the root of available modeling techniques Most related to this research, some systems are inheriting the basic modeling constructs of RDM’s apparatus, for example, Iris Data Model (Lyngback and Vianu 1987), Generic SDM (Chen and McLeod 1989) and SDB management System SIM (Boyed 2003) Meanwhile, Rishe and his group build up a Semantic Wrapper over RDB which produces set of SDB tools including Knowledge database tool, Knowledge base and Query Translator (http://n1.cs.fiu.edu/SemanticWrapper.ppt) The second is that some research shows that SDB is more likely linked to Ontology and Knowledge base (http://www.fmridc.org/f/fmridc/dmt/sdm.html) This would seem to orientate SDB to flourishing the development of SW Besides this, currently, research around SDB encounters numerous obstacles The bottleneck, as we have identified, resides in lack of certain infrastructure to retrieve semantics and formulate semantic constraints, not from traditional database point of view but follow vigorous guidance of Semantic Information Theory (SIT in short) We believe that by philosophically separating truly information from raw data, dually grasping semantic constraints and partially representing semantic information relation, an advance model of SDB can be achieved 1.2 IF and FCA Based IBC Prospect of SDB In 1998, we identified a research problem, namely the ‘information content’ of a formalized information system (Feng 1998) In that paper numerous works were cited and it was shown that the main cause of this problem seemed that information had been treated as ‘mystical liquid’ We then argued that the lack of clearly expressed and defined ‘information content’ of a conceptual data schema was responsible for many difficulties in data modeling and analysis as a process of inquiry, which is a basis for the design of an information system Then in 1999 we formulated a notion called ‘information bearing capability’ (IBC for short) by drawing on interdisciplinary views of information creation and transmission (Feng 1999) A four-facet principle currently elaborates this notion, which is concerned with a set of sufficient and necessary conditions for the IBC of an information system The conditions are: information content containment, distinguishability, accessibility and derivability (Feng 2005) The principle about IBC and their associated concepts that have been put forward in a series of research papers (such as Xu and Feng 2002, Feng and Hu 2002, Xu 2005, and Wang and Feng 2005a) may be seen as forming an innovative perspective for looking at information systems Now, IBC as a cornerstone is applied to a number of research problems that are being looked at by our group such as schema mapping, data exchanging and modeling The ideas around IBC however should be further developed and tested in real world applications To this end, it seems that the most appropriate tool to reason about and verify IBC would be IF combined with FCA We envisage that endeavor along this line will uplift the articulation of what might be called ‘the microscopic infrastructure’ of the IBC principle to an adaptable, adoptable and applicable level in SDB modeling This paper proceeds as follows In the next section, we highlight some aspects of SDB modeling that seem to have been overlooked in the light of SIT rooted IBC model Our approach of combined use of IF and FCA in the IBC model, which would, we believe, advance the state of the art of SDB, is introduced in section Following this, a conceptual picture of IF and FCA reinforced IBC model for SDB described and elucidated in section WHAT SHOULD A SDB REPRESENT AND PROVIDE? MODEL, As aforementioned, SDB is proposed in the literature to address those problems encountered in other forms of data modeling As summarized by Boyed (2003), there are several essential goals, which need to be sustained, during SDB development The SDB is a high-level semantics-based database description and structural formalism for databases (Hammer, 1981) Although attempting to capture all the semantics of the modeled domain is unattainable, SDB should endeavor to incorporate most of the semantics SDB advances RDB and other database models in terms of its real-world perception of the problems, different perspectives of queries, and most importantly its inheritance-based hierarchical modeling structure In addition to these known characteristics, following the insight of IBC based on SIT, we would propose more significant features for SDB Only when these features are delivered can we say that SDB is satisfiably achieved 2.1 Data, Information and Semantics Database is the vehicle for storing and providing information Without the guidance of interdisciplinary Computing and Information Systems © University of Paisley 2006 philosophical semantic information theory, it is not surprising that contemporary database modeling dose not separate data and truly information Notwithstanding modeling methods like RDB being many and varied, as far as SDB is concerned, it should broaden its edge to tackle the truth of data, information, meaning and semantics in order to capture semantics and to solve some difficult issues, for example, query answering, lossless transformation, etc In a typical contemporary database, ‘what you see is what you get’ is the prevailing feature Relation between data and information remains scrupulously bypassed For a long decade, data with its meaning is treated as information in the context of database (Checkland, 1981) A famous schema transformation approach, i.e., ‘information capacity’ (IC) (Miller 1993), straightly takes data instances of schemata as information Fusing Organizational Semiotics (OS) into database, ‘meaning is created from the information carried by signs’ (Mingers 1995) A veritably practical SDB should take the challenges that lie in several aspects around definitions of information, information content and meaning Some of my colleagues have provided an analysis about this (Wang and Feng 2005) Firstly, instances are not always faithful to their semantic types Traditionally, the schema of a database is thought to represent the type level of information while database instances fill into these type level classes whereby receive their semantics or meaning from the classes However, this view overlooks the facts that instances may not loyal to their respective semantic infrastructures These instances not represent any information that originated the types (Dretske 1991) Secondly, the meaning of data in the database is not necessarily to be part of their information content SDB should be able to use alternative ways to represent the same information Therefore, a data construct represents a piece of information only when the information content of the data construct includes that piece of information It is not convincing to use meaning as the criteria for the information content of a piece of data Finally, it is not adequate to take the ability of accommodating instances into the schema as the information capacity of data constructs in the database (Wang and Feng 2005) The fewer constraints being modeled, the less specific the instances are Hence, less information there is SDB modeling should take this point into consideration and facilitate it 2.2 Constraints and Representations No matter what form it is in; a database is after all need to represent objects and relations in the represented domain The modes of representation (Shimojima 1996) obey structural constraints that mirror the regularities that govern things going on in the represented domain Any representation involves certain kind of information flow Information flow results from the regularities in a distributed system (Barwise and Seligman 1997, P.8) Contemporary database like RDB limit themselves into a particular structure of constraints such as relational objects and associated relations SDB should go beyond these limits in the way of finding the best fit between the representing system and the represented domain Apart from this aspect, SDB should also ensure that its reasoning be consistent with the represented domain In other words, reasoning over constraints needs great care Wobcke (2000) identifies the differences between schema-based and information flow based reasoning The former is partly subjective and defeatable contrasting to the objectiveness and non-defeatability holding by the latter If given a fixed context by discarding all alternative situations, schema-based reasoning and information flow based reasoning are transferable Shimojima (1996) uses basic mathematical instruments to model constraints in order to perform a rigorous investigation on a wide range representation issues His research provides a sound theoretical foundation for developing our IBC model for SDB in virtue of inferential reasoning intimate to what happens in the domain to be modeled 2.3 Partiality Talking about semantics, it is evident to many researchers, especially those who are familiar with logics and linguistics, that there are ‘holes in reality’ (Duzi 2003) These holes reside in our abstract way of modeling particular dependency relations among real world objects Many attempts have been made to philosophically address such issues as Possible World Semantics and Situation Semantics As aforementioned, in database, there exit instances that not inherit semantics from its corresponding class types Following Duzi (2003), if we take these instances as the logical construction C (not unlike the notion of ‘concept’ of Dretske 1981) for the ‘mode of representation’, which is discussed in previous section, it should link the expression E and its denotation D Problems arise when we use empty concepts, the construction C will fail to achieve anything, not even any meaning As a result, the denotation D will fail to give any truth-value in an argument Computing and Information Systems © University of Paisley 2006 Macroscopically, it is necessary for SDB to be equipped with partial order to handle overall informational relationships Based on Dretske’s information flow (1991) and Barwise and Perry’s situation semantics (1993), Wobcke (2000) argues that using conditionals as basic appliance, people could evaluate the subjectiveness and intentionality of a collection of schemata The idea is to treat those conditionals as expressing constraints which are actually informational relations between facts and events of the kind that can be modeled using structures of situations (Wobcke 2000) The order of situations for the collection of constraints is in the form of partial order supporting subjective reasoning In certain circumstances, i.e., providing certain fixed context (situation), reasoning on this order is identical to the reasoning of information flow Also, in Duzi’s thesis (2001), she points out that information content inclusion relations (in relation to attributes) are of partial order Most specifically, she formalizes informational capability in a complete lattice based on the power set of the attributes in question Furthermore, it is interesting that this lattice is proved to be isomorphic to its substituting partial ordered set of equivalence classes Therefore, for the sake of manipulating informational scenarios, the need of supporting partial order of the IBC model both philosophically and mathematically should not be ignored Moreover, we believe that such a work would be aligned with issues in knowledge representation in the AI field ARCHITECTURE BASED UPON INFORMATION FLOW (IF) AND FORMAL CONCEPT ANALYSIS (FCA) The central idea of IBC is called the IBC principle This principle, is made up of conditions of information content containment, distinguishability, accessibility and derivability and it is put forward by Feng (2005) and his colleagues through a period of arduous work in the sense of drawing interdisciplinary views of information creation and transmission (Feng 1999, Xu and Feng 2002, Feng and Hu 2002, Xu 2005, and Wang and Feng 2005a) IF is first introduced into IBC for reasoning about and for verifying the principle (Wang and Feng 2005) As being successively compatible and content with the IBC, IF has become a headstone for further development and application of the IBC model For the purpose of elevating implementation, FCA is probed and found that it is adaptable, applicable and adoptable both theoretically and practically with IF 3.1 Channel-Theoretical Information Flow The Channel-theoretical Information Flow theory (IF) is a mathematical model of semantic information flow Information flow is possible due to the regularities among normally disparate components of a distributed system It is known that such a theory succeeds in capturing partial order of classifications (Kalfoglou and Schorlemmer 2005) that underlies the flow of information Sophisticated notions (we not go into details here) stemming from IF now have been formulated for explorations on semantic information and knowledge mapping and exchanging Kent (2002a, 2002b) exploits semantic integration of ontologies by extending a first order logic based approach (Kent 2000) which is also based on IF An information flow framework (IFF) has been advocated as a meta-level framework for organising the information that appears in digital libraries, distributed databases and ontologies (Kent 2001) From Kent’s work, Kalfoglou and Schorlemmer (2003a) develop an automated ontology mapping method in the field of knowledge sharing and cooperation IF and its surrounding concepts are also relevant to solving problems of semantic interoperability (Kalfoglou and Schorlemmer 2003b) Apart from this main stream of applications, IF supports various research efforts from defensible reasoning (Cavedon 1998); endoperspective formal model (Gunji et al 2004) to semiconcept and protoconcept graphs (Malik 2004) Besides the effective effort of using IF to represent, capture and model constraints for a given modelled domain, it is also observed that IF ‘was not developed as a tool to be used in real world reasoning’ (Devlin 1999) and we observe that it is on its own insufficient for describing domain information or knowledge To fill these gaps, Formal Concept Analysis (FCA) was proposed as a silver bullet 3.2 Formal Concept Analysis (FCA) FCA was developed by Rudolf Wille (Wille 1982) as a method for data analysis, information management, and knowledge representation (Priss 2005a) Presumably due to its applicable nature, it does not take long for FCA to become a common interest in many research communities, for example, social net work analysis (Freean and White 1993), linguistics (Priss 2005b), and software engineering (Fischer 1998, Eisenbarth et al 2001) As aforementioned, FCA provides solid foundations for not only information and knowledge retrieval by its underlying mathematical theory (Godin et al 1989, Kalfoglou et al 2004) but also for respective representations by concept lattice (Wille 1982, 1992, 1997b) along with concept graphs (Prediger and Wille 1999) We maintain that the use of FCA will supplement with IF in SDB modeling By using IF along, it would appear that the construction of an ‘information channel’ in many Computing and Information Systems © University of Paisley 2006 cases is difficult when applying IF to real information system problems To alleviate it, we envisage that ‘Conceptual Scaling’ techniques (Ganter and Wille 1989, Prediger and Stumme 1999)’, which are affinity with FCA, will be useful Furthermore, reasoning and inference over difference levels of a channel can be characterized by ‘Concept Graph’ (Prediger and Wille 1999) in the light of FCA-based ‘Concept Lattice’ (Wille 1982, Wille 1992, Wille 1997b) In other words, FCA provides the investigation with a basis for extraction, representation and demonstration of informational aspect of semantics, and at the same time IF-based techniques/methods can be charged with the task of information flow based reasoning As a result, the combined use of IF and FCA can shed some light on solving problems around the IBC within the context of SDB, which is also harmonious with knowledge discovery and representation 3.3 Prospect of Combined Use of IF and FCA The essential element of our IBC mode for SDB is the combined use of IF and FCA They provide vital insights for our SDB model The compatibility between them is crucial for any combined use We give reasons below for using IF theory and the theory of FCA in combination Firstly, both IF and FCA share the same origin, i.e., category theory with the means of Chu space (Gupta 1994, Barr 1996 and Pratt 1995) As Wolff (2000) observes, ‘it is really astonishing that these tools (IF and FCA) are not mutually taken into account in each other’s theory’ Priss (2005a) treats the ‘classifications’ in IF as a general sense of ‘concept lattices’ in FCA Following this line of thinking, secondly, nearly all fundamental concepts invented by both of IF and FCA can find counterparts in each other For example, the notions of ‘classifications’ in IF matches that of ‘formal context’ in FCA; ‘information channels’ in IF matches ‘scaled many-valued contexts’ in Conceptual Scaling (Ganter and Wille 1989, Ganter and Wille 1999) associated with FCA Other basic notions presented in IF, such as ‘state space’, ‘refinement of channels’, and ways of handling ‘vagueness’ are also delivered in FCA mathematically (Wolff 2000) Finally, IF bears epistemological resemblance to FCA To be explicit, starting from the same algebraic category, IF together with FCA aim at formulating and justifying ‘partial order’ that relies on agreed understanding of the existence of ‘duality’ between separated situations, which is exactly why information flow commences Combined use of IF and FCA is beneficial to constructing the IBC model of SDB SDB highly needs to capture more semantics In IF and FCA reinforced IBC model, FCA would serve as the linkage between IF reasoning and the modelled domain Due to the ‘non-directly-applicable’ nature of IF (Devlin 1999), applying it directly to modeling informational semantics proves to be problematic In contrast, a number of works stemming from FCA around knowledge discovery and information retrieval have been put forward For example, Stumme and his colleagues have encouraged the use of FCA in exploration and representation of implied information and facilitating the conversion of information into knowledge (Hereth et al 2000, Stumme et al 1998) We would use the ‘Conceptual Scaling’ techniques (Prediger and Stumme 1999, Prediger and Wille 1999) to combine FCA with IF reasoning because of FCA’s logical equivalence with ‘Information Channel’ The results of reasoning would be presented in Concept Graphs, which has advantages in representing semantics in partial order Also, a combined use of IF and FCA can satisfactorily model more semantic constraints identified by Hammer (1987) To tackle information flow, IF insists on analyzing relations between tokens and types According to the second principle of information flow, i.e., ‘information flow crucially involves both types and their particulars’ (Barwise and Seligman 1997, P.27) Originally and largely following Dretske (1981), we thought that semantics are presented on the type level which further provides the meanings to the tokens involved in information flow However, from the paper of Kalfoglou and Schorlemmer on IF-map (2003a), we find the important role of tokens, e.g., the same set of rivers and streams, played in determining semantics or constraints of the whole system in terms of semantic correspondences between the types We observe that in fact, Kalfoglou and Schorlemmer has employed primary thinking of FCA in exploring ‘intension’ and ‘extension’ of formal concepts within a given formal context That is from either set, i.e., intensions or extensions; we can define its counterpart in the context, and thus the formal concepts Therefore, using relations in tokens (extensions), we would gain relation of concepts and hence arrive at a set of constrains, which reflect a type of regularities of the whole system in the given context This is exactly how tokens take part in defining the semantics of a system, and in achieving semantic interoperability Further to this point, we envisage that duality held by both IF and FCA enables us to support alternative ways for the user to view even the same information in SDB Start with the relations that reside in types and we would end up with relation of tokens and vice versa Therefore, depending on what aim we want to achieve, we could selectively take either tokens or types as our starting point in different analysis Explicitly, if we want to solve the semantic interoperability problem, as Kalfoglou and Schorlemmer did, we shall investigate tokens- Computing and Information Systems © University of Paisley 2006 determined relations in order to achieve the relations on types On the other hand, if we want to find out why and how data constructs represents (or conveys) the information about a given semantic relation (i.e., a relation between some real world objects), in most cases, we will take the semantics on types of this structure as a foundation OUTLINE OF IF AND FCA REINFORCED IBC MODEL FOR SDB Based on previous sections, we can now start describing the IF and FCA reinforced IBC model designed for SDB We will begin with data schemata as we believe that original databases and schemata is too valuable to be retained (Figure 1) The original database schema together with a serial of dependencies held by the schema would be analyzed by using IF and FCA This analysis needs to be assisted by obtained initiative business constraints e.g stake holder views, presented in the format of scales, so that subjectiveness is preserved at this early stage The construction of ‘information channel’ of IF will benefit from the technique of ‘conceptual scaling’ of FCA The output of investigation is a conceptual space which contains all the constraints (semantics) captured by every information channel This space is called by us as the ‘kernel of IBC’ When the user puts a query for a piece of information to this kernel, if there is no direct answer, an inference will be carried out by means of a set of ‘information content inference rules’ (Feng and Hu 2002) Then, final results are added into a separate conceptual space following the decision of the user Connected with knowledge representation and management, the consequent results could be transformed using XML-extended Information Flow Framework (IFF) (http://www.ontologos.org/IFF/The %20IFF%20Language.html) language Figure Overall Picture of IF and FCA Reinforced IBC model There are two most important parts of this model which show in two boxes in Figure To clarify what actually happens inside of them, we will use two more diagrams In Figure 2, there is a detailed process for arriving at the kernel of IBC Both primary database schemata and instances are translated into many-valued context by FCA Then, two scaling processes are performed Computing and Information Systems © University of Paisley 2006 The first one called ‘conceptual scaling’ It is based on the idea that embedded structural constraints are used as scales to construct corresponding IF channels The many-valued context will then become single-valued context as a result Following this, using dependencies that are determined by business rules as the other scales, another scaling, i.e., the ‘relational scaling’, will be accomplished by a final lattice layout also with a crowd of information channels The ultimate results are sets of ‘IF’ theories derived from all of the channels This is what we want to model as the system regularities Figure How to Achieve Kernel of IBC In addition, another significant part in our model is inference on information content (Figure 3) The information content based inference rules are put forward by Feng and Hu (Feng and Hu 2002) Furthermore, through two MSc projects (Wang 2005 and Xu 2005), we found that these inference rules can be justified by theory of IF In the future, we will generalize these verifications by not only IF but also FCA Computing and Information Systems © University of Paisley 2006 Figure Information Inference Rules (IIR) CONLUSIONS Cavedon, L (1998) Default Reasoning as Situated Monotonic Inference, Minds and Machines Checkland, P (1981) Systems Thinking, Systems Practice Chichester, UK: Wiley Chen, I A and McLeod, D (1989) Derived Data Update in Semantic Databases, Proceedings of the fifteenth international conference on Very large data bases, p.225-235, July, Amsterdam, The Netherlands Devlin, K (1991) Logic and Information, Cambridge Devlin, K (2001) Introduction to Channel Theory, ESSLLI 2001, Helsinki, Finland Dretske, F (1981) Knowledge and the Flow of Information, Basil Blackwell, Oxford Duží, M (2001) Logical Foundations of Conceptual Modelling In VŠB-TU Ostrava Duží, M (2003) Do we have to deal with partiality? In Miscellania Logica, vol Tom V, 45-76 Eisenbarth, T., Koschke, R., & Simon, D (2001) Feature-driven Program Understanding using Concept Analysis of Execution Trace In Proceedings of the Ninth International Workshop This paper represents our first step towards satisfactorily modeling a SDB by means of an IF and FCA reinforced IBC Three more criteria, i.e., extracting information, modeling semantic constraints and also partially representing information flow, have been proposed in addition to traditional SDB requirements The overall idea of the IBC model for SDB is shown with diagrams that heavily draw on concepts from both IF and FCA This attempt seems worthwhile for the development of SDB, and it is also compatible with most modern knowledge management systems, and therefore relevant to the area of semantic web References Barr, M (1996) The Chu construction Theory and Applications of Categories, 2(2):17–35 Barwise, J and Seligman, J (1997) Information Flow: the Logic of Distributed Systems, Cambridge University Press, Cambridge Barwise, J and Perry J (1983) Situations and Attitudes, Cambridge, Mass.: Bradford-MIT Boyed, S (2003) A Semantic Database Management System: SIM The University of Texas at Austin, Department of Computer Sciences Technical Report CS-TR-03-43 Computing and Information Systems © University of Paisley 2006 on Program Comprehension International Conference on Software Maintenance Feng, J (1998) The "Information Content’ Problem of a Conceptual Data Schema, SYSTEMIST, Vol.20, No.4, pages 221-233, November 1998 ISSN: 0961-8309 Feng, J (1999) An Information and Meaning Oriented Approach to the Construction of a Conceptual Data Schema, PhD Thesis, University of Paisley, UK Feng, J and Hu, W (2002) Some considerations for a semantic analysis of conceptual data schemata, in Systems Theory and Practice in the Knowledge Age (G Ragsdell, D West J Wilby, eds.), Kluwer Academic/Plenum Publishers, New York Feng, J (2005) Conditions for Information Bearing Capability, Computing and Information Systems Technical Reports No 28, University of Paisley, ISSN 1461-6122 Fischer, B (1998) Specification-Based Browsing of Software Component Libraries Proc Automated Software Engineering, Hawaii, 246254 fMRIDC, The Semantic Database Model, http://www.fmridc.org/f/fmridc/dmt/sdm.html Ganter, B and Wille, R (1999) Formal Concept Analysis: mathematical foundations Springer ISBN: 3-540-62771-5 Godin, R., Gecsei, J., & Pichet, C (1989) Design of Browsing Interface for Information Retrieval In N J Belkin, & C J van Rijsbergen (Eds.), Proc SIGIR ’89, 32-39 Gupta, V (1994) Chu Spaces: A Model of Concurrency PhD thesis, Stanford University, 1994 Hammer, M., McLeod, D (1981) Database Description with SDM: A Semantic Database Model, ACM Trans Database Syst (3): 351386 Hereth, J., Stumme, G., Wille, R and Wille, U (2000) Conceptual Knowledge Discovery in Data Analysis In B Ganter, & G Mineau (Eds.), Conceptual Structures: Logical, Linguistic and Computational Issues LNAI 1867 Berlin: Springer, 421-437 Information Flow Framework (IFF) Language, http://www.ontologos.org/IFF/The%20IFF %20Language.html Jagannathan, D., Guck, R L., Fritchman, B L., Thompson, J P., Tolbert, D M (1988) SIM A Database System Based on the Semantic Data Model SIGMOD Conference: 46-55 Kalfoglou, Y and Schorlemmer, M (2003a) IFMap: an Ontology Mapping Method based onIinformation Flow Theory Journal on Data Semantics, 1(1):98–127 Kalfoglou, Y and Schorlemmer, M (2003b) Using Information Flow Theory to Enable Semantic Interoperability, In Proceedings of the 6th Catalan Conference on Artificial Intelligence (CCIA '03), Palma de Mallorca, Spain, October 2003 Kalfoglou, Y., Dasmahapatra, S., & Chen-Burger, Y (2004) FCA in Knowledge Technologies: Experiences and Opportunities In P Eklund (Ed.), Concept Lattices: Second International Conference on Formal Concept Analysis, LNCS 2961 Berlin: Springer, 252-260 Kalfoglou, Y., Schorlemmer, M (2005) Using Formal Concept Analysis and Information Flow for Modeling and Sharing Common Semantics: lessons learnt and emergent issues, In Proceedings of the 13th International Conference on Conceptual Structures (ICCS2005), Kassel, Germany, July 2005 Kent, R E (2000) The Information Flow Foundation for Conceptual Knowledge Organization In: Dynamism and Stability in Knowledge Organization Proceedings of the Sixth International ISKO Conference Advances in Knowledge Organization 111–117 Ergon Verlag, Würzburg Kent, R E (2001) The Information Flow Framework Starter document for IEEE P1600.1, the IEEE Standard Upper Ontology working Group, http://suo.ieee.org/IFF/ Kent, R E (2002a.) 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Using Formal Concept Analysis and Information Flow for Modeling and Sharing Common Semantics: lessons learnt and emergent issues, In Proceedings of the 13th International Conference on Conceptual... applicable and adoptable both theoretically and practically with IF 3.1 Channel-Theoretical Information Flow The Channel-theoretical Information Flow theory (IF) is a mathematical model of semantic information. .. transmission (Feng 1999, Xu and Feng 2002, Feng and Hu 2002, Xu 2005, and Wang and Feng 2005a) IF is first introduced into IBC for reasoning about and for verifying the principle (Wang and Feng 2005) As