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Potential Uses in Depth

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Abstract

The three areas covered in depth in this chapter are workflows and business process management (BPM), semantic parsing, and robotics. The production and consumption of knowledge should warrant as much attention as do the actions or processes on the factory floor. Workflows are a visible gap in most knowledge management. A reason for the gap is that workflows and business processes intimately involve people. Shared communication is at the heart of workflow management, a reason why semantic technologies are essential to the task. In semantic parsing, a lexical theory needs to handle word senses, sentences and semantics, cross-language meanings, common-sense reasoning, and learning algorithms. A formal grammar provides a set of transition rules for evaluating tokens and a lexicon of types that can build up, or generate, representative language structures. We can map the compositional and semantic aspects of our language to the categorial perspectives of Peirce’s logic and semiosis, and then convert those formalisms to distributions over broad examples provided by KBpedia’s knowledge. Peircean ideas may contribute to part-of-speech tagging, machine learning implementations, and a dedicated Peircean grammar. Cognitive robots embrace the ideas of learning and planning and interacting with a dynamic world. Robotics is a critical potential testbed for Peircean ideas. Robots, to conduct actions, must resolve or ‘ground’ their reasoning symbols into indecomposable primitives. The implication is that higher order concepts are derivations in some manner of lower level concepts, which fits KBpedia and Peirce’s universal categories. Kinesthetic robots may also be essential to refine natural language understanding.

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Notes

  1. 1.

    BPM may also refer to business process modeling. We retain the management sense here, noting that the modeling part only comes after thinking through the management portion.

  2. 2.

    The exception to this observation is advanced manufacturing. Some of these businesses, inherently action oriented, have pioneered BPM’s related cousin of manufacturing process management. However, it is an open question whether manufacturing businesses are better at KM as well.

  3. 3.

    See, for example, the open-source Yaoqiang BPMN editor (http://bpmn.sourceforge.net/).

  4. 4.

    The word grammar is derived from a Greek word meaning “writing,” though at one time the knowledge of Latin grammar was viewed as endowing one with magical power, from which arose our word glamor [4].

  5. 5.

    In many areas of computational linguistics, care should be taken when comparing findings from the contributing disciplines.

  6. 6.

    This split somewhat reflects a similar one for discriminative versus generative machine learning models. Discriminative models, also called conditional models, are a class of models used in machine learning for modeling the dependence of unobserved (target) variables y on observed variables x. Example discriminative models include support vector models (SVM), conditional random fields (CRF), neural networks (xNN), linear regression, maximum entropy Markov, and random forests. Generative models use algorithms to try to reconstruct how the original data was generated, often through probabilistic means. Example models include hidden Markov models (HMM), naive Bayes, generative adversarial networks (GANs), Gaussian mixture model, and other types of the mixture model.

  7. 7.

    Nivre argues that a dependency grammar is not a grammar formalism, rather a specific way to describe the syntactic structure of a sentence [49].

  8. 8.

    See https://en.wikipedia.org/wiki/Deterministic_context-free_grammar

  9. 9.

    Also known as the Chomsky–Schützenberger hierarchy.

  10. 10.

    We talked of this simple data struct in Chap. 9.

  11. 11.

    For a sample detailed description see SLING, a frame-based semantic parser using a dependency grammar [50].

  12. 12.

    Montague’s contributions came to an untimely end when he was violently murdered at age 40.

  13. 13.

    This makes these grammars well suited to functional languages like Lisp.

  14. 14.

    Tokenizers and POS taggers, plus any reference tagsets employed, should be attentive to syntax that is declinable (noun, pronoun, verb, adverb). The indeclinable terms (proposition, conjunction, interjection, particles, modals) are less of a problem since only single terms are required. Declensions of tense, case, plurality, or gender are very important topics in some languages, though I do not speak further of it here.

  15. 15.

    By formal proposition I mean a sentence in the indicative mood, “for a proposition is equivalent to a sentence in the indicative mood” (1903, CP 2.315), for which Peirce was mostly concerned. Contrast this to the other moods (1893, CP 2.291) or “quasi-propositions,” see below.

  16. 16.

    A “selective” (1903, CP 4.408) is an indeterminant individual such as indicated by selective pronouns (any, every, all, no, none, whatever, whoever, everybody, anybody, nobody) or particular selectives (some, something, somebody, a, a certain, some or other, one) (1903, CP 2.289).

  17. 17.

    Peirce did not hold the common noun to be a universal part of speech (POS). He states, “I do not regard the common noun as an essentially necessary part of speech. Indeed, it is only fully developed as a separate part of speech in the Aryan languages and the Basque, −- possibly in some other out of the way tongues” (1904, CP 8.337).

  18. 18.

    Such as this, that, something, anything.

  19. 19.

    Peirce also termed this collateral experience, collateral information, and collateral acquaintance.

  20. 20.

    Of course, it is possible to write out full syllogisms in the confines of a single sentence, but most often arguments are made over multiple sentences.

  21. 21.

    One genesis of this grand synthesis is a 2010 paper by Coecke et al., “Mathematical Foundations for a Compositional Distributional Model of Meaning” [51], first unveiled in 2008 [52].

  22. 22.

    We are also missing a design or approach to compositionality.

  23. 23.

    Leiß’s publication is dated in 2009, but based on a conference paper presented in 2005.

  24. 24.

    Note that Peirce specifically excludes consideration of instinct in the scientific method and its quest for truth, since all assumptions should be open to question. Pragmatism, however, adds action and instinct to the equation.

  25. 25.

    Note that meaning has many connotations including existential, linguistic, philosophical, psychological, semiotic, of life, and others. Our use embraces all of these senses.

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    Michael K. Bergman

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Bergman, M.K. (2018). Potential Uses in Depth. In: A Knowledge Representation Practionary. Springer, Cham. https://doi.org/10.1007/978-3-319-98092-8_16

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