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Computer Science
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Ontology, metadata, and semiotics

Profile image of John SowaJohn Sowa

2000, … structures: Logical, linguistic, and computational issues

https://doi.org/10.1007/10722280_5
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Abstract

Abstract. The Internet is a giant semiotic system. It is a massive col-lection of Peirce's three kinds of signs: icons, which show the form of something; indices, which point to something; and symbols, which rep-resent something according to some convention. But current ...

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Metalanguage consists of signs that signify something about other signs, but what they signify depends on what relationships those signs have to each other, to the entities they represent, and to the agents who use those signs to communicate with other agents. Figure 1 shows the basic relationships in a meaning triangle (Ogden and Richards 1923). On the lower left is an icon that resembles a cat named Yojo. On the right is a printed symbol that represents his name. The cloud on the top gives an impression of the neural excitation induced by light rays bouncing off Yojo and his surroundings. That excitation, called a concept, is the mediator that relates the symbol to its object. Figure 1. The meaning triangle The triangle in Figure 1 has a long history. Aristotle distinguished objects, the words that refer to them, and the corresponding experiences in the psyché. Frege and Peirce adopted that three-way distinction from Aristotle and used it as the semantic foundation for their systems of logic. Frege's terms for the three vertices of the triangle were Zeichen (sign) for the symbol, Sinn (sense) for the concept, and Bedeutung (reference) for the object. As an example, Frege cited the terms morning star and evening star. Both terms refer to same object, the planet Venus, but their senses are very different: one means a star seen in the morning, and the other means a star seen in the evening. Following is Peirce's definition of sign:
Metalanguage consists of signs that signify something about other signs, but what they signify depends on what relationships those signs have to each other, to the entities they represent, and to the agents who use those signs to communicate with other agents. Figure 1 shows the basic relationships in a meaning triangle (Ogden and Richards 1923). On the lower left is an icon that resembles a cat named Yojo. On the right is a printed symbol that represents his name. The cloud on the top gives an impression of the neural excitation induced by light rays bouncing off Yojo and his surroundings. That excitation, called a concept, is the mediator that relates the symbol to its object. Figure 1. The meaning triangle The triangle in Figure 1 has a long history. Aristotle distinguished objects, the words that refer to them, and the corresponding experiences in the psyché. Frege and Peirce adopted that three-way distinction from Aristotle and used it as the semantic foundation for their systems of logic. Frege's terms for the three vertices of the triangle were Zeichen (sign) for the symbol, Sinn (sense) for the concept, and Bedeutung (reference) for the object. As an example, Frege cited the terms morning star and evening star. Both terms refer to same object, the planet Venus, but their senses are very different: one means a star seen in the morning, and the other means a star seen in the evening. Following is Peirce's definition of sign:
Aristotle observed that symbols could symbolize other symbols, as "written words are symbols of the spoken." Frege said that his logic could be used as a language to talk about the logic itself. But Peirce went further than either of hem in recognizing tha multiple triangles could be linked together in different ways by attaching a vertex of one to a vertex of another. By stacking another triangle on top, Figure 2 represents the conce the cloud that suggests the concept of Yojo to the symbol for the more elusive neural excita when some person recognizes tha ion. At the very top is a pt of representing an obj Yojo is being represen ect by a concept. The upper triangle relates [Cat: Yojo], which is a printable symbol cloud for the neural excitation that occurs ed by a printed symbol. Figure 2. Concept of representing an object by a concept
Aristotle observed that symbols could symbolize other symbols, as "written words are symbols of the spoken." Frege said that his logic could be used as a language to talk about the logic itself. But Peirce went further than either of hem in recognizing tha multiple triangles could be linked together in different ways by attaching a vertex of one to a vertex of another. By stacking another triangle on top, Figure 2 represents the conce the cloud that suggests the concept of Yojo to the symbol for the more elusive neural excita when some person recognizes tha ion. At the very top is a pt of representing an obj Yojo is being represen ect by a concept. The upper triangle relates [Cat: Yojo], which is a printable symbol cloud for the neural excitation that occurs ed by a printed symbol. Figure 2. Concept of representing an object by a concept
Figure 3. Object, name of object, symbol of name, and character string
Figure 3. Object, name of object, symbol of name, and character string
Table 1. Five semantic primitives The five primitives in Table | are available in every natural language and in every version of first- order logic. They are called semantic primitives because they go beyond syntactic relations between signs to semantic relations between signs and the world. Any notation that is capable of expressing these five primitives in all possible combinations must include all of FOL as a subset. As an example, the WHERE clause of the SQL query language can express each of these primitives and combine them in all possible ways; therefore, first-order logic is a subset of SQL. Different languages may use different notations for representing the five primitives:
Table 1. Five semantic primitives The five primitives in Table | are available in every natural language and in every version of first- order logic. They are called semantic primitives because they go beyond syntactic relations between signs to semantic relations between signs and the world. Any notation that is capable of expressing these five primitives in all possible combinations must include all of FOL as a subset. As an example, the WHERE clause of the SQL query language can express each of these primitives and combine them in all possible ways; therefore, first-order logic is a subset of SQL. Different languages may use different notations for representing the five primitives:
Figure 5. EG for Some human has two distinct human parents. The bar that represents (3x) in Figure 4 is connected to both copies of the HasParent relation in Figure 5. Two bars represent each of the human parents. If they were connected, they would represent the same individual; but to represent distinct individuals, the connection must be negated. In existential graphs, Peirce used an oval to indicate negation; in Figure 5, the oval negates part of the connecting bar. In the algebraic notation, Figure 5 would be represented by the following formula: EG and RDF notations. The symbol ~ in the formula represents conjunction, which is implicit in the | Figure 4 could be represented by a triple in RDF: the first human could be treated as an RDF resource, the HasParent relation as an RDF property type, and the second human as an RDF value. The existence of the human on the left would be indicated by the proposed RI var="x">, and the one on the right by <exists var="y">. DF quantifier <exists
Figure 5. EG for Some human has two distinct human parents. The bar that represents (3x) in Figure 4 is connected to both copies of the HasParent relation in Figure 5. Two bars represent each of the human parents. If they were connected, they would represent the same individual; but to represent distinct individuals, the connection must be negated. In existential graphs, Peirce used an oval to indicate negation; in Figure 5, the oval negates part of the connecting bar. In the algebraic notation, Figure 5 would be represented by the following formula: EG and RDF notations. The symbol ~ in the formula represents conjunction, which is implicit in the | Figure 4 could be represented by a triple in RDF: the first human could be treated as an RDF resource, the HasParent relation as an RDF property type, and the second human as an RDF value. The existence of the human on the left would be indicated by the proposed RI var="x">, and the one on the right by <exists var="y">. DF quantifier <exists
Figure 6. EG for Every human has two distinct human parents. As Table 2 illustrates, a pair of negations can represent either a universal quantifier or an implication. The EG in Figure 6 may be read in either way. If the two ovals are considered an implication, Figure 6 could be read /f there exists a human, then that human has a parent who is human and another parent who is human and the two parents are distinct. Another option is to read an existential quantifier nested between two ovals as the universal quantifier V, which is expressed by the English word every. Then Figure 6 could be read Every human has a parent who is human and another parent who is human and the two parents are distinct. By using the defined operators of Table 2, the formula could be rewritten in a form that shows the universal quantifier V and the implication D explicitly: Ontology, Metadata, and Semiotics
Figure 6. EG for Every human has two distinct human parents. As Table 2 illustrates, a pair of negations can represent either a universal quantifier or an implication. The EG in Figure 6 may be read in either way. If the two ovals are considered an implication, Figure 6 could be read /f there exists a human, then that human has a parent who is human and another parent who is human and the two parents are distinct. Another option is to read an existential quantifier nested between two ovals as the universal quantifier V, which is expressed by the English word every. Then Figure 6 could be read Every human has a parent who is human and another parent who is human and the two parents are distinct. By using the defined operators of Table 2, the formula could be rewritten in a form that shows the universal quantifier V and the implication D explicitly: Ontology, Metadata, and Semiotics
Figure 7. CG for If there is a human, then he or she has two distinct human parents.
Figure 7. CG for If there is a human, then he or she has two distinct human parents.
Figure 8. CG for Every human has two distinct human parents. Figure 8 could be represented in RDF with the proposed <foral1> quantifier, but CGs also support other generalized quantifiers that have not yet been considered for RDF. As an example, Figure 9 simplifies Figure 8 by introducing the generic plural symbol {*} to represent a set and the number 2 to represent its count or cardinality. The resulting CG can be mapped to the following formula:
Figure 8. CG for Every human has two distinct human parents. Figure 8 could be represented in RDF with the proposed <foral1> quantifier, but CGs also support other generalized quantifiers that have not yet been considered for RDF. As an example, Figure 9 simplifies Figure 8 by introducing the generic plural symbol {*} to represent a set and the number 2 to represent its count or cardinality. The resulting CG can be mapped to the following formula:
Figure 11. Definition of the HasParent relation. form dyadic relations by combining with concept types that represent roles. Figure 11 shows how the HasParent relation is defined in terms of the relation Has and the role type Parent.
Figure 11. Definition of the HasParent relation. form dyadic relations by combining with concept types that represent roles. Figure 11 shows how the HasParent relation is defined in terms of the relation Has and the role type Parent.
Although the Chases relation allows shorter graphs and formulas than the concept [Chase], it introduces other complexities into the ontology. A general representation for tenses and modality, fo: example, would require a proliferation of relation types, such as HasChased, Wil1Chase, and MustHaveBeenChasing., Furthermore, the dyadic relation chases (x, y) makes no provision for attaching adverbs and other modifiers to the verb. Figure 13 takes advantage of the more general representation to define the concept type Chase in terms of a graph for an animate agent (parameter #1) that is following a mobile entity (parameter #2) in a rapid manner. Figure 13. Definition of the concept type Chase Figure 13 is only a partial definition because it represents a necessary, but not a sufficient condition. Runners in a race, for example, might be following one another rapidly, but only because they are pursuing a common goal. A more complete definition must include the purpose, which might be different for different senses of the word chase. Figure 14 defines one sense, called ChaseHunt, in which the purpose of the agent is to catch the mobile entity that is being chased.
Although the Chases relation allows shorter graphs and formulas than the concept [Chase], it introduces other complexities into the ontology. A general representation for tenses and modality, fo: example, would require a proliferation of relation types, such as HasChased, Wil1Chase, and MustHaveBeenChasing., Furthermore, the dyadic relation chases (x, y) makes no provision for attaching adverbs and other modifiers to the verb. Figure 13 takes advantage of the more general representation to define the concept type Chase in terms of a graph for an animate agent (parameter #1) that is following a mobile entity (parameter #2) in a rapid manner. Figure 13. Definition of the concept type Chase Figure 13 is only a partial definition because it represents a necessary, but not a sufficient condition. Runners in a race, for example, might be following one another rapidly, but only because they are pursuing a common goal. A more complete definition must include the purpose, which might be different for different senses of the word chase. Figure 14 defines one sense, called ChaseHunt, in which the purpose of the agent is to catch the mobile entity that is being chased.
Figure 14. Definition of the concept type ChaseHunt
Figure 14. Definition of the concept type ChaseHunt
In Figure 14, the purpose relation (Purp) links the action to a situation that would occur upon the successful completion of the chase. According to Peirce, purpose is a triadic relation, of which two arguments are shown explicitly in Figure 14. The implicit third argument is the agent of Chase, whose intention is to bring about the desired situation. That situation is nested inside a context box because its intentional status is different from the context of the act of chasing. If the chase is unsuccessful, the act of catching might never occur. Figure 15 defines another concept type ChaseAway, in which the agent's purpose is not to catch the mobile entity, but to cause it to leave its current location. Figure 15. Definition of the concept type ChaseAway
In Figure 14, the purpose relation (Purp) links the action to a situation that would occur upon the successful completion of the chase. According to Peirce, purpose is a triadic relation, of which two arguments are shown explicitly in Figure 14. The implicit third argument is the agent of Chase, whose intention is to bring about the desired situation. That situation is nested inside a context box because its intentional status is different from the context of the act of chasing. If the chase is unsuccessful, the act of catching might never occur. Figure 15 defines another concept type ChaseAway, in which the agent's purpose is not to catch the mobile entity, but to cause it to leave its current location. Figure 15. Definition of the concept type ChaseAway
These sentences show how indexicals are used to make context-dependent references. When new en ities are first mentioned, they are introduced with the indefinite article, as in the phrases a situation, a cat named Yojo, and a mouse. The two middle sentences refer to the mouse with the de of finite article the and to the cat with the name Yojo or the pronoun he. In the last sentence, the head the mouse, which had not been mentioned explicitly, is marked with the definite article because the introduction of the mouse implicitly introduces all of its expected parts. In Figure 16, the indexicals are marked with the # symbol: the pronoun he is represented as #he, and the definite article the is represented with the # symbol by itself. Figure 16. Nested situations with unresolved indexicals
These sentences show how indexicals are used to make context-dependent references. When new en ities are first mentioned, they are introduced with the indefinite article, as in the phrases a situation, a cat named Yojo, and a mouse. The two middle sentences refer to the mouse with the de of finite article the and to the cat with the name Yojo or the pronoun he. In the last sentence, the head the mouse, which had not been mentioned explicitly, is marked with the definite article because the introduction of the mouse implicitly introduces all of its expected parts. In Figure 16, the indexicals are marked with the # symbol: the pronoun he is represented as #he, and the definite article the is represented with the # symbol by itself. Figure 16. Nested situations with unresolved indexicals
Figure 17. Nested situations with indexicals resolved
Figure 17. Nested situations with indexicals resolved
No single system discussed in this paper incorporates all the features desired in a KE system, but the critical research has been done, and the remaining work requires more development effort than pure research. Figure 18 shows the flow of information from documents to logic and then to documents or to various computational representations. The dotted arrow from documents to controlled languages requires human assistance. The solid arrows represent fully automated translations that have been implemented in one or more systems. Figure 18. Flow of information from documents to computer representations
No single system discussed in this paper incorporates all the features desired in a KE system, but the critical research has been done, and the remaining work requires more development effort than pure research. Figure 18 shows the flow of information from documents to logic and then to documents or to various computational representations. The dotted arrow from documents to controlled languages requires human assistance. The solid arrows represent fully automated translations that have been implemented in one or more systems. Figure 18. Flow of information from documents to computer representations

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Some Considerations Regarding the Usefulness of Applying Semiotics in Web Ontologies
Daniela Popescul

The current paper aims to study Web ontologies from a semiotic perspective. On the one hand, the study of signs is constantly used lately in the analysis of the social life, of organisations and informational systems. On the other hand, Web ontologies may be seen as systems of signs, wishing to catch the reality of a certain field by identifying fundamental categories of identities and the relations between them. Their intention is to describe their significance as accurately as possible, so as to ensure the best communication between computing systems. Since any act of communication includes and involves signs and codes (Fiske, J., quoted in Borţun, 2006), in the current paper we bring to discussion the opportunity of using semiotics, as the science that studies signs, in the study of the Web ontologies

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Metadata Pragmatics: Toward a Unified Semiotic Framework
Timothy W . Elfenbein

2013

With the increasing focus on data sharing in the sciences and information organization in networked information-spaces, metadata has become a prominent area of research and activity. Scholars in library and information science and science studies have distinct approaches to describing and understanding metadata. This thesis reviews the accounts of metadata given by these two fields and then takes preliminary steps toward a unified analytic framework for research, based in Peircean semiotics as it has been developed within semiotic anthropology.

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THE SEMANTIC + PRAGMATIC WEB = THE SEMIOTIC WEB
Ahti Pietarinen

A desideratum for the semiotic web is proposed. It combines both the recent semantic and the pragmatic web initiatives but surpasses them by Peirce’s pragmatic approach to inquiry. I address some conceptual shortcomings of the semantic and pragmatic web proposals, and suggest that in semiotic web, one can operationalise basic ideas in an effective way. A multi-agent systems is proposed that goes beyond both semantic and pragmatic notions by employing a logic of questioning and answering and takes agents as roles in a dialogical semiotic inquiry of all signs mediated in the universe of the web. The outcome is that in the web realm, semantic and pragmatic elements are not separated and that their merger has significant emergent properties matching Peirce’s sign theory.

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Semiotics for Ontologies and Knowledge Representation
Violeta Damjanovic

2005

The Semantic Web implies the comprehensive use of the main semiotic postulates on the Web. One of the most important facts about human understanding comes from the area of linguistics and semiotics. The semiotic techniques, such as syntagm and paradigm are explained through the examples in learning environment, in the context of logical understanding and knowledge representation on the Web.

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A survey of Peirce Semiotics Ontology for Artificial Intelligence, Nested Graphic Model for knowledge representation.
Yi-An Sun

Abstract (survey paper): In this paper I would review computer scientist John Sowa’s Semiotics ontology application in AI modeling. To begin with the survey of semiotics theory and definition of symbol, communication and epistemology of semiotic in conceptual structure. Than, turn to Sowa’s Nested Graphic Model in knowledge representation. Because Semiotics is the study of signification in the wide sense. This means that semiotics is concerned with significations which are not verbally conveyed, such as texts, graphics, other visual signs, or symbolic logic. Thus Semiotics is a systematic science for AI field which search to establish general rules and invariants. The purpose of this paper is to analyze the differences of meaning, to explore their implications for web-based metadata, and to show how the methods of logic and ontology can be used to define, relate, and translate signs from one vocabulary to another. Among the methods discussed in this paper are Peirce's systems of logic, ontology, and semiotics, which are presented in more detail in the book Knowledge Representation by Sowa (2000). Keyword: 1.Nested Graphic Model(NGM) 2.Peirce Semiotics 3. knowledge representation 4.Artificial Intelligence 5. John Sowa

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Related topics

  • Semiotics
  • Computer Science
  • Information Retrieval
  • Computational Linguistics
  • Human Animation
  • Controlled Natural Language

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    Matchmaking Framework for Mathematical Web Services
    Julian Padget

    Journal of Grid Computing, 2006

    Service discovery and matchmaking in a distributed environment has been an active research issue for some time now. Previous work on matchmaking has typically presented the problem and service descriptions as free or structured (marked-up) text, so that keyword searches, tree-matching or simple constraint solving are sufficient to identify matches. In this paper, we discuss the problem of matchmaking for mathematical services, where the semantics play a critical role in determining the applicability or otherwise of a service and for which we use OpenMath descriptions of pre-and post-conditions. We describe a matchmaking architecture supporting the use of match plug-ins and describe five kinds of plug-in that we have developed to date: (i) A basic structural match, (ii) a syntax and ontology match, (iii) a value substitution match, (iv) an algebraic equivalence match and (v) a decomposition match. The matchmaker uses the individual match scores from the plug-ins to compute a ranking by applicability of the services. We consider the effect of pre-and post-conditions of mathematical service descriptions on matching, and how and why to reduce queries into Disjunctive Normal Form (DNF) before matching. A case study demonstrates in detail how the matching process works for all four algorithms.

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    Towards Machine Learning on the Semantic Web
    Volker Tresp

    Lecture Notes in Computer Science, 2008

    In this paper we explore some of the opportunities and challenges for machine learning on the Semantic Web. The Semantic Web provides standardized formats for the representation of both data and ontological background knowledge. Semantic Web standards are used to describe meta data but also have great potential as a general data format for data communication and data integration. Within a broad range of possible applications machine learning will play an increasingly important role: Machine learning solutions have been developed to support the management of ontologies, for the semi-automatic annotation of unstructured data, and to integrate semantic information into web mining. Machine learning will increasingly be employed to analyze distributed data sources described in Semantic Web formats and to support approximate Semantic Web reasoning and querying. In this paper we discuss existing and future applications of machine learning on the Semantic Web with a strong focus on learning algorithms that are suitable for the relational character of the Semantic Web's data structure. We discuss some of the particular aspects of learning that we expect will be of relevance for the Semantic Web such as scalability, missing and contradicting data, and the potential to integrate ontological background knowledge. In addition we review some of the work on the learning of ontologies and on the population of ontologies, mostly in the context of textual data.

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    Systematized analysis using data mining’s methodology on the topic of regional industrial symbiosis and its networks
    Erika D. Uršič

    Research in Social Change

    The aim of this paper is the analysis and a presentatition of texts and terminological ontology of scientific and professional publications in the field of regional Industrial Symbiosis and Industrial Symbiotic Networks. With computer-aided text analysis it is possible to have a comprehensive overview of the publications of works in this field and a more detailed presentation of the most fruitful concepts of publications.

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    Cooperative Discovery of Interesting Action Rules
    Agnieszka Dardzinska

    Lecture Notes in Computer Science, 2006

    There are two aspects of interestingness of rules, objective and subjective measures ([7], [1], [15], [16]. Objective measures are datadriven and domain-independent. Generally, they evaluate the rules based on their quality and similarity between them. Subjective measures are user-driven, domain-dependent, and include unexpectedness, novelty and actionability [7], [1], [15], [16]. Liu [7] defines a rule as actionable one, if user can do an action to his/her advantage based on that rule. In [12] it was assumed that actionability has to be expressed in terms of changes in values of certain attributes which are used in an information system. They introduced a new class of rules (called action rules) which are constructed from certain pairs of association rules extracted from the same information system. Conceptually similar definition of an action rule was proposed independently by [4]. Action rules have been investigated further in [14], [13], [11], and [18]. In order to construct action rules it is required that attributes in a database are divided into two groups: stable and flexible. Flexible attributes are used in a decision rule as a tool for making hints to a user what changes within some of their values are needed to reclassify a group of objects from one decision class into another one. In this paper, we give a strategy for constructing all action rules from a given information system and show that action rules constructed by system DEAR, presented in [13], cover only a small part of all action rules. Clearly, we are not interested in all action rules as we are not interested in extracting all possible rules from an information system. Classical strategies like See5, LERS, CART , Rosetta, W eka are discovering rules which classification part is either the shortest or close to the shortest. This approach is basically ruling out all other classification rules unless their are surprising rules [17]. In this paper, we introduce the notion of a cost of an action rule and define interesting action rules as rules of the smallest cost. We give a strategy showing how interesting action rules can be generated from action rules discovered system DEAR.

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    On the unification power of models
    Jean Bezivin

    Software & Systems Modeling, 2005

    In November 2000, the OMG made public the MDA™ initiative, a particular variant of a new global trend called MDE (Model Driven Engineering). The basic ideas of MDA are germane to many other approaches such as generative programming, domain specific languages, model-integrated computing, generic model management, software factories, etc. MDA may be defined as the realization of MDE principles around a set of OMG standards like MOF, XMI, OCL, UML, CWM, SPEM, etc. MDE is presently making several promises about the potential benefits that could be reaped from a move from codecentric to model-based practices. When we observe these claims, we may wonder when they may be satisfied: on the short, medium or long term or even never perhaps for some of them. This paper tries to propose a vision of the development of MDE based on some lessons learnt in the past 30 years in the development of object technology. The main message is that a basic principle ("Everything is an object") was most helpful in driving the technology in the direction of simplicity, generality and power of integration. Similarly in MDE, the basic principle that "Everything is a model" has many interesting properties, among others the capacity to generate a realistic research agenda. We postulate here that two core relations (representation and conformance) are associated to this principle, as inheritance and instantiation were associated to the object unification principle in the class-based languages of the 80's. We suggest that this may be most useful in understanding many questions about MDE in general and the MDA approach in particular. We provide some illustrative examples. The personal position taken in this paper would be useful if it could generate a critical debate on the research directions in MDE.

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