How to Represent the World

As far as human knowledge about the world is commonly given in NL expressions and as far as universal ontology is a general science of the world, the examination of its impact on natural language science and technology is among the central topics of many academic workshops and conferences. Ontologists, knowledge engineers, lexicographers, lexical semanticists, and computer scientists are attempting to integrate top-level entity classes with language knowledge presented in extensive corpora and electronic lexical resources. Such a deep quest is mostly motivated by high application potential of reality-driven models of language for knowledge communication and management, information retrieval and extraction, information exchange in software and dialogue systems, all with an ultimate view to transform the World Wide Web into a machine-readable global language resource of world knowledge, the Onto-Semantic Web. One of the practical applications of integrative ontological framework is to discover the underlying mechanisms of representing and processing language content and meaning by cognitive agents, human and artificial. Specifically, to provide the formalized algorithms or rules, whereby machines could derive or attach significance (or signification) from coded signals, both natural signs obtained by sensors and linguistic symbols.

What Determines the World

The world or reality is the totality of things diversified into a multitude of collections of subworlds with the characteristic constraints and boundaries; all determined by the fundamental causal laws. Accordingly, the logic of reality is ultimately one; for there cannot be many logics of the universe. Although many personal perspectives allowed, and particular truths, special things and meanings, the global logic of reality is a single universal consistent system relied on a set of ontological classes and relationships, fundamental rules and laws. The universe is a deep, dark secret, mysterious and mystifying to human minds. And the fundamental challenge to the human mind is to provide a comprehensive account and model that explains in noncryprical terms how this unbounded environment changes and how its basic constituents causally related, and so forth. And this is all the legal responsibility of the UFO as a causal dynamic ontology, formulating the causal rules of world behavior as fundamental laws by establishing underlying regularities, uniformities, invariants, and correlations. So guiding science and technology, the task of dynamic ontology is to give us the overall structures, uniformities, patterns, laws, constraints, and invariants within which the many changes in the world take place, finding out what it is outside and inside, defining the natures and essences common to the denumerable multitude of individuals, by classifying the whole universe of things into the prime categories, classes, kinds and relationships.

The World Code

Formalizing the world in rigorous mathematical terms is no less significant than its fundamental understanding and modeling in terms of ontological constructs. Like black and white, opposite sexes or polarity signs, ontology and mathematics stand complementary to each other, making up the unique and unequaled knowledge domain or knowledge base, which involves two parts: • Ontological (real) mathematics, which defines the real significance for the mathematical entities, so studying the real status of mathematical objects, functions, and relationships in terms of ontological categories and rules. • Mathematical (formal) ontology, which defines the mathematical structures of the real world features, so concerned with a meaningful representation of the universe in terms of mathematical language. The combination of ontology and mathematics and substantial knowledge of sciences is likely the only one true road to reality understanding, modeling and representation. Ontology on its own can’t specify the fabric, design, architecture, and the laws of the universe. Nor theoretical physics with its conceptual tools and models: general relativity, quantum physics, Lagrangians, Hamiltonians, conservation laws, symmetry groups, quantum field theory, string and M theory, twistor theory, loop quantum gravity, the big bang, the standard model, or theory of everything material. Nor mathematics alone with its abstract tools, complex number calculus, differential calculus, differential geometry, analytical continuation, higher algebras, Fourier series and hyperfunctions is the real path to reality (Penrose, 2005).

How the World is Signified

How reality or the world with its parts and levels might be truly symbolized and represented by emerging semantic technology and knowledge systems appears the most challenging topic in the field of top ontology and ontology engineering. Along with causality, knowing the relationship of meaning makes all the difference in true representation of the real world features, in understanding (sensing, reading, or resolving) the real meaning values of world knowledge representation and reasoning. A formal account of meaning (or significance) is becoming a decisive issue in the whole matter of the Semantic Web promising machine-based processing by means of advanced information technologies. For without understanding the nature of meaning, its critical dimensions, mechanisms, and algorithms of representation in computable forms, the whole enterprise of semantic technology is an otiose undertaking and expensive academic mystification. As far as computing ontology is viewed as a semantic model where the relationships among resources are to be identified, differentiated, or processed by automated tools [SICoP, 2005], the above meaningful topics presuppose creating the standard ontology framework. As far as the emerging Semantic Web is the universal medium for the exchange of information across users, systems, applications, and networks, the unified frame ontology is the universal semantic platform for a uniform organization of all human knowledge.

How to Reason about the World

Along with substances of all kinds, states of all manner, and changes of all types and exemplifications, relationships of all sorts and instances appear to be among the prime constituents of the universe as a whole and its realms, regions, and domains as the world of nature, the universe of society, and the domain of minds. Hence, knowledge of relations, as the causeeffect relationship, constitutes the basic core of real knowledge and, consequently, the logical fundament for all basic kinds of reasoning about the world. Since all reasoning upon reality, its particular classes, parts and features, is eventually to be founded on the underlying relations of substances, states, changes, and analogies, as well as on the meta-relations of whole-part, comparison, contrast, identity, resemblance, and difference. To adequately represent and consistently reason about reality is vitally important not only for human beings but also for prospective intelligent machines driven by the ontological models of the world comprehending the logical models of possible worlds. A widely practiced logical tradition to represent the world in terms of abstract classes, properties, and relations or purely mathematical objects, functions, and relations looks to be a main conceptual obstacle to creating effective reasoning systems. Since the likewise artificial conceptualizations of the world are missing out the core of things, their nature and reality, providing the ontological ground and making true the truth. These would-be reasoning systems will not work effectively because of their built-in incapacity to work out any complex real problems or situations or challenges. Above all other things, such intelligent systems will be unable to see the difference of physical, mental, or social objects so that to recognize their attributes, qualities, properties, states, changes, and relations.

Natural Language Intelligences

Of all possible intelligent NL applications and semantic artifacts, a special value is today ascribed to building the question answering systems (Q&A) with broad and wide ontological learning (Onto Query Project, 2004), classified as open-domain Q&A knowledge systems [Question Answering, From Wikipedia, 2006]. This line of research is considered as upgrading of a traditional keyword query processing in database systems, as endowing the Web search engines with answering deduction capacities. Ideally, such a general-purpose Q&A agent should be able to cover questions (matters, subjects, topics, issues, themes) from any branch of knowledge and domain of interest by giving answers to any meaningful questions, like the Digital Aristotle, “an application that will encompass much of the world’s scientific knowledge and be capable of answering novel questions and advanced problemsolving” (Project Halo, 2004). The trade name of the Digital Aristotle was inspired by the scholar mostly admired for the depth and width of his perception, whose mind spread over ontology, physics, logics, epistemology, biology, zoology, medicine, psychology, literary theory, politics, and art.

What Organizes the World

Relationship as a primary constituent of reality setting a mutual order of things in the world commonly involves a large number of the terms or components or arguments that it interrelates. Realistic, actual, factual, existent, or real relationships consist of a multitude of elements, or parts, or terms, thus being polyadic, N-term connections. This implies that a true modeling of N-relational entities should consider their real nature, types, and properties (ontology) as well as the meanings of their correlatives (semantics) and formal attributes (mathematics). The first considers the reality of relations, telling us: • How they exist, intrinsically and inherently (in the very nature of things) or extrinsically and extraneously (as connections among things) • What kinds of relational species are there • How relationships are classified The second one indicates the primary meanings (definition or intension) with the connotative senses (extension) of the relations, or how they draw their meanings, from the correlative terms or from the relation itself. The third expresses the relationships in terms of mathematical sets, quantities, variables, functions, arguments, and values.

What Orders Reality

To model reality by mapping its content to computable forms, we need to know how to represent the first-class entities of any existence, relationships, the adhesive of the world. Both human and machine understanding of the universe, its parts and realms, consists in knowing the cardinal relationships and underlying rules and making valid inferences from them. The Semantic Web ontology is often identified with a schema defining relationships between different resources. For instance, the OWL markup language is supposed to specify the types of relationships represented in RDF language employing an XML vocabulary, with a view to determine the relationships (and hierarchies) among diverse Web data resources identified by URI. And the formal specification of relationships determines the meaning (semantics) of knowledge domains, and the universality and credibility of any ontology, its rigor, cogency, validity, and richness, come from the capacity to fully describe all the possible types of relationships in a domain of knowledge or practice. Without a systematic theory of relations, it hardly is possible to form a universal account (language or theory) describing reality, its entity classes, properties, individuals with their particular properties, on which human or machine reasoning has to take place.

What Makes Reality

The ultimate purpose of standard ontology is to describe and represent all the things in the world in comprehensive and consistent ways, whereby making the fundamental knowledge explicit to the formal reason of semantic systems and cognitive agents, natural, or artificial. To build such a formal universal framework capable of including the representation of anything, one can design a general system that includes a set-theoretic (logical) ontology constructed as a formal logical system composed of its objects-primitives (classes, individuals, and properties), logical syntax (notation techniques, formation and transformation rules), and formal semantics (model theory), as currently the Semantic Web formal languages and upper ontologies usually are constructing.

Ways to View the World

As a general science of the universe dealing with the most universal truths about all existence, an account of what there is in the world and the study of reality with its content, ontology has long had a critical import only for a small set of professionals. For the majority of researchers, not mentioning the non-committed people, it has been the realm of the occult, an esoteric domain of discourse, the region of most abstract reasoning, or the sphere of philosophical opinion. Today the field is rapidly increasing its audience and practical value. Maybe only a few people will call into question the importance of world models for such advanced knowledge domains as information sciences and computing technology. Increasingly, the universal science is identified with the life and soul of knowledge technologies and intelligent systems. The search for the world description standard as an exhaustive theoretical account and model of generic entities is becoming a research activity promising unprecedented advancements in the new cross area of science and technology. Particularly, this is important for creating knowledge systems of extraordinary performance, such as the emerging Semantic Web, implying the entire gamut of novel applications: knowledge management, intelligent databases, conceptual/semantic search and retrieval, software agents, speech and natural language understanding, e-commerce, and ubiquitous computing (Semantic Web Topic Hierarchy, 2007; Semantic Web Technology, 2007). Considering these design purposes, there are several technical requirements for building an ontology standard: expressivity, efficiency, scalability, compatibility, extensibility, and relative simplicity. But the most significant prerequisite appears to be its consistency with existent commonsense models of the world and scientific learning.