Here are publications arising from this AI3::: Adaptive Information site, as published by Semantics Press LLC.
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Combining Knowledge Graphs and Ontologies for Dynamic AppsAI3::: Adaptive Information, Semantics Press LLC2019, September, 7 pp. (2019/09/11)When used for KR, we can treat the terms 'knowledge graph' and 'ontology' as interchangeable. But ontologies also have a broader use as specifications for dynamic, ontology-driven applications, a distinction this article emphasizes.
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A Common Sense View of Knowledge GraphsAI3::: Adaptive Information, Semantics Press LLC2019, July, 12 pp. (2019/07/01)This article, based on a comprehensive history and definitions of the concept, provides a common-sense view of how to understand knowledge graphs.
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Desiderata for Knowledge GraphsAI3::: Adaptive Information, Semantics Press LLC2018, February, 3 pp. (2018/02/19)Knowledge graphs (aka 'ontologies') are all the rage, playing a central role in search services and virtual agents across the Web. As I argue in this article, knowledge graphs are still in their infancy. I present 9 capabilities I would like to see knowledge graphs fulfill as they mature.
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What is Representation?AI3::: Adaptive Information, Semantics Press LLC2017, November, 7 pp. (2017/11/22)Representations are signs and the means by which we point to, draw or direct attention to, or designate, denote or describe a particular object, entity, event, type or general. We draw on Charles S. Peirce to provide guidance as to how best characterize and use them for the purposes of knowledge representation.
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Hierarchies in Knowledge RepresentationAI3::: Adaptive Information, Semantics Press LLC2017, November, 7 pp. (2017/11/17)Structure needs to be a tangible part of thinking about a new knowledge representation (KR) installation, since many analytic choices need to be supported by the knowledge artifact. Different kinds of structure are best for different tools or kinds of analysis. The types of relations chosen for the artifact affects its structural aspects. These structures can be as simple and small as a few members in a list, to the entire knowledge graph fully linked to its internal and external knowledge sources.
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How I Interpret C.S. PeirceAI3::: Adaptive Information, Semantics Press LLC2017, September, 12 pp. (2017/09/20)In this article, I discuss the methods and approach -- the methodeutic -- that I use to interpret Charles Sanders Peirce's writings. First, I try to read as much by him and about his writings as I can. Second, I do not treat his writings as gospel. And, third, I try to view how to represent knowledge through the lens of Peirce's universal categories of Firstness, Secondness and Thirdness.
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Gold Standards in Enterprise Knowledge ProjectsAI3::: Adaptive Information, Semantics Press LLC2018, July, 7 pp. (2016/07/18)The most common scoring methods to gauge the “accuracy” of natural language or supervised machine learning analysis involves statistical tests based on the ideas of negatives and positives, true or false. Gold standards that contain errors make it difficult to test and refine existing IR and NLP algorithms. Knowledge-based artificial intelligence methods are geared to produce a wide and flexible range of reference sets that perform better than most other standards.
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A Foundational Mindset: Firstness, Secondness, ThirdnessAI3::: Adaptive Information, Semantics Press LLC2016, March, 12 pp. (2016/03/21)The triadic model of signs was built and argued by Peirce as the most primitive basis for applying logic suitable for the real world, with conditionals, continua and context. Truthfulness and verifiability of assertions is by nature variable. Because of the signs' groundings in logic, Peirce's three main forms of deductive, inductive and abductive logic also flow from the same approach and mindset. The data models and organizational schema underlying KR should be as close as possible to the logical ways the world is structured and perceived. Peirce's contributions make a notable difference in how knowledge representation efforts may move forward.
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How Fine Grained Can Entity Types Get?AI3::: Adaptive Information, Semantics Press LLC2016, March, 8 pp. (2016/03/08)Entities can be organized into natural classes (or types) that themselves form typologies, which are flexible structures for interoperating across disparate datasets and domains. What these structures are and how they are built is the focus of this article.
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A (Partial) Taxonomy of Machine Learning FeaturesAI3::: Adaptive Information, Semantics Press LLC2015, November, 14 pp. (2015/11/23)"Features" are perhaps the least discussed aspect of machine learning. This article investigates what features are and how to organize them from the perspective of text-oriented artificial intelligence. A better understanding of machine learning features for NLP tasks also helps promote how to design a platform for how to systematize the machine learning process.
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‘Natural Classes’ in the Knowledge WebAI3::: Adaptive Information, Semantics Press LLC2015, July, 6 pp. (2016/07/13)Pragmatic, effectively built ontologies compel the use of a realistic viewpoint for how classes should be bounded and organized. Science and technology are producing knowledge at unprecedented amounts, and realism is the best approach for testing the trueness of new assertions. We think realism is the most efficacious approach to ontology design.
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Logical Implications of InteroperabilityAI3::: Adaptive Information, Semantics Press LLC2015, June, 10 pp. (2015/06/08)Interoperability is a systemic activity for any organization, that spans from how we relate data and information to each other to how we organize, represent and publish that information. This article investigates the benefits from interoperability, and sets out first principles, guidelines and architectures for how to achieve it.
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A Primer on Knowledge StatisticsAI3::: Adaptive Information, Semantics Press LLC2015, May, 7 pp. (2015/05/18)Armed with four characterizations -- true positive, false positive, true negative, false negative -- we have the ability to calculate important statistical measures in knowledge management. Most of these measures have exact analogs in standard statistics, I attempt to explain the basis and the measures for these statistics in simple terms.
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Shaping Wikipedia into a Computable Knowledge BaseAI3::: Adaptive Information, Semantics Press LLC2015, March, 8 pp. (2015/03/31)Wikipedia is unparalleled as a resource for mining structure, concepts and entities. But Wikipedia is never itself used as a computable knowledge base. Why this is and how it can be remedied is the subject of this article.
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The Era of OpennessAI3::: Adaptive Information, Semantics Press LLC2015, January, 6 pp. (2015/01/12)"Openness" is totally remaking information technology and human interaction and commerce. The impacts on social norms and power and governance are just as profound. Though many innovations have uniquely shaped the course of human history, none appear to have matched the speed of penetration nor the impact of "openness".
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Knowledge-based Artificial IntelligenceAI3::: Adaptive Information, Semantics Press LLC2014, November, 10 pp. (2014/11/17)Knowledge bases are finally being effectively combined with AI, a dynamic synergy that is only now being recognized, let alone leveraged. Knowledge-based artificial intelligence, or KBAI, is the use of large statistical or knowledge bases to inform feature selection for machine-based learning algorithms used in AI. The use of knowledge bases to train the features of AI algorithms improves the accuracy, recall and precision of these methods.
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The Viking Algorithm for Connected NetworksAI3::: Adaptive Information, Semantics Press LLC2014, September, 8 pp. (2015/09/03This article shows that adding connections ("Big Structure") at Big Data scales can increase the value of enterprise information from one (ten) to three (thousands) orders of magnitude. The magnitude of the value scales linearly with each added structure (attribute). These value multipliers from adding Big Structure are a tremendously cost-effective addition to standard data wrangling efforts.
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Big Structure and Data InteroperabilityAI3::: Adaptive Information, Semantics Press LLC2014, August, 9 pp. (2014/08/18)Integration based on Big Structure surfaces all of the myriad aspects of semantic heterogeneities, exactly the kinds of issues that the semantic Web and semantic technologies were designed to address.
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What is Big Structure?AI3::: Adaptive Information, Semantics Press LLC2014, August, 7 pp. (2014/08/12)Big Structure is data relationships and context that can be combined into a coherent framework to enable dataset interoperability and understanding.
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Big Structure: At The Nexus of Knowledge Bases, the Semantic Web and Artificial IntelligenceAI3::: Adaptive Information, Semantics Press LLC2014, July, 8 pp. (2014/07/23)An approach using semantic technologies and artificial intelligence tools will begin to solve the data integration puzzle. By leveraging background knowledge, we will begin to extend into data interoperability. Purposeful attention to tooling and workflows geared to improve the mapping speed and efficiency by users will enable us to increase the stable of reference structures -- that is, Big Structure -- available for the next integration challenges.
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A Decade in the Trenches of the Semantic WebAI3::: Adaptive Information, Semantics Press LLC2014, July, 8 pp. (2014/07/14)Perhaps we have been in the wrong war for the wrong reasons. Linked data is certainly not an end and mostly appears to represent work, rather than innovation. The semantic Web is no longer the right war, either, because improvements there will not come so much from arguing semantic languages and paradigms. Learning how to master distributed data integration will teach the semantic Web much, and coupling artificial intelligence with knowledge bases will do much to improve the most labor-intensive stumbling blocks in the knowledge management workflow: mappings and transformations.
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Innovation, Information, Growth and WealthAI3::: Adaptive Information, Semantics Press LLC2014, June, 10 pp. (2014/06/15)Understanding the basis of growth, sustained over time, leading to greater wealth for individuals or nations, is the central question facing economics. This understanding is intimately related to the importance of information and innovation.
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Spring Dawns on Artificial IntelligenceAI3::: Adaptive Information, Semantics Press LLC2014, June, 7 pp. (2014/06/02)The Internet has changed the whole underlying substrate over which artificial intelligence ("AI") is taking place. This article covers eight major trends, including large statistical datasets and knowledge bases derived from the Web, that are fueling this new dawn in AI.
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The Age of the GraphAI3::: Adaptive Information, Semantics Press LLC2012, August, 8 pp. (2012/08/12)Virtually everywhere one looks we are in the midst of a transition for how we organize and manage information, indeed even relationships. There is a shared structure across all of these developments -- the graph. Graphs are proving to be the new universal paradigm for how we organize and manage information.
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The Rationale for Semantic TechnologiesAI3::: Adaptive Information, Semantics Press LLC2012, July, 9 pp. (2012/07/02)The beauty of semantic technologies – properly designed and deployed in a Web-oriented architecture – is that a thin interoperability layer may be placed over existing IT assets. The knowledge graph structure may be used to provide the semantic mappings between schema, while the Web service framework that is part of the WOA provides the source conversion to the canonical RDF data model.
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What is Structure?AI3::: Adaptive Information, Semantics Press LLC2012, May, 7 pp. (2012/05/28)As we see across examples from nature and life to language and all manner of communications, information is structure and structure is information.
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Give Me a Sign: What Do Things Mean on the Semantic Web?AI3::: Adaptive Information, Semantics Press LLC2012, January, 11 pp. (2012/01/24)The semantic Web's designers and early practitioners and advocates have been mired in a muddled, metaphysical argument of at least a decade over what URIs mean, what they reference, and what their actual true identity is.
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Making the Argument for Semantic TechnologiesAI3::: Adaptive Information, Semantics Press LLC2011, September, 5 pp. (2011/09/11)The essential thing to know about semantic technologies is that they are a conceptual and logical foundation to how information is modeled and interrelated. In these senses, semantic technologies are infrastructural and groundings, not applications per se.
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Leveraging Intangible Assets Using Semantic TechnologiesAI3::: Adaptive Information, Semantics Press LLC2011, May, 14 pp. (2011/05/10)With our transition to an information economy, we now see that intangible assets exceed the value of tangible ones. We see that the information component of these intangibles represent one-third to two-thirds of these intangibles. In other words, information makes up from 17% to more than one-third of an individual firm's value in modern economies.
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Ontology-Driven Apps Using Generic ApplicationsAI3::: Adaptive Information, Semantics Press LLC2011, March, 12 pp. (2011/03/7)A shift to generic applications driven by adaptive ontologies -- ODapps -- looks to shift the locus from software and programming to data and knowledge structures.
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What is a Reference Concept?AI3::: Adaptive Information, Semantics Press LLC2010, December, 6 pp. (2010/12/06)A reference concept is a fixed point in an information space. As one or more external sources link to these fixed points, it is possible to gather similar content together and to begin to organize the information space. Further, if the reference concept is itself part of a coherent structure, then additional value can be derived from these assignments, such as inference, consistence testing, and alignments.
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A New Methodology for Building Lightweight, Domain OntologiesAI3::: Adaptive Information, Semantics Press LLC2010, September, 11 pp. (2010/09/01)The development of ontologies goes by the names of ontology engineering or ontology building, and can also be investigated under the rubric of ontology learning. We prefer not to use the term ontology engineering, since it tends to convey a priesthood or specialized expertise in order to define or use them.
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A Brief Survey of Ontology Development MethodologiesAI3::: Adaptive Information, Semantics Press LLC2010, August, 6 pp. (2010/08/30)The development of ontologies goes by the names of ontology engineering or ontology building, and can also be investigated under the rubric of ontology learning. This paper summarizes key papers and links to this topic.
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The Sweet Compendium of Ontology Building ToolsAI3::: Adaptive Information, Semantics Press LLC2010, January, 11 pp. (2010/01/26)The article provides the largest and most comprehensive listing of ontology building tools available.
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Seven Pillars of the Open Semantic EnterpriseAI3::: Adaptive Information, Semantics Press LLC2010, January, 9 pp. (2010/01/12)By open semantic enterprise we mean an organization that uses the languages and standards of the semantic Web, including RDF, RDFS, OWL, SPARQL and others to integrate existing information assets, using the best practices of linked data and the open world assumption, and targeting knowledge management applications. It does so using some or all of the seven foundational pieces ("pillars") noted herein.
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The Open World Assumption: Elephant in the RoomAI3::: Adaptive Information, Semantics Press LLC2009, December, 9 pp. (2009/12/21)In speaking of the semantic Web, it is not infrequent that the open world assumption (OWA) gets mentioned. What this post argues is that this somewhat obscure concept may hold within it the key as to why there have been decades of too-frequent failures in the enterprise in business intelligence, data warehousing, data integration and federation, and knowledge management.
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Advantages and Myths of RDFAI3::: Adaptive Information, Semantics Press LLC2009, April, 11 pp. (2009/04/08)Because RDF is simultaneously a framework, data model and basis for building more complex vocabularies, it is both simple and complex at the same time.
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What is Linked Data?AI3::: Adaptive Information, Semantics Press LLC2008, June, 6 pp. (2008/06/28)Linked Data is a set of best practices for publishing and deploying instance and class data using the RDF data model, naming the data objects using uniform resource identifiers (URIs), and exposing the data for access via the HTTP protocol, while emphasizing data interconnections, interrelationships and context useful to both humans and machine agents.
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Information is the Basis for Economic GrowthAI3::: Adaptive Information, Semantics Press LLC2007, August, 6 pp. (2007/08/23)The information by which the means to produce and disseminate information itself is changing and growing. This is an infrastructural innovation that applies multiplier benefits upon the standard multiplier benefit of information. In other words, innovation in the basis of information use and dissemination itself is disruptive. Over history, writing systems, paper, the printing press, mass paper, and electronic information have all had such multiplier effects.
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An Intrepid Guide to OntologiesAI3::: Adaptive Information, Semantics Press LLC2007, May, 17 pp. (2007/05/16)It is not unrealistic to also seek "naturalness" in the organization of our knowledge domains, to seek "naturalness" in the organization of their underlying ontologies. Like natural systems in biology, this naturalness should emerge from the shared understandings and perceptions of the domain's participants.
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Did You Blink? The Structured Web Just ArrivedAI3::: Adaptive Information, Semantics Press LLC2007, April, 10 pp. (2007/04/02)DBpedia is the first and largest source of structured data on the Internet covering topics of general knowledge. You may have not yet heard of DBpedia, but you will. Its name derives from its springboard in Wikipedia.
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Sources and Classification of Semantic HeterogeneitiesAI3::: Adaptive Information, Semantics Press LLC2006, June, 5 pp. (2008/06/06)Semantic mediation — that is, resolving semantic heterogeneities — must address more than 40 discrete categories of potential mismatches from units of measure, terminology, language, and many others. These sources may derive from structure, domain, data or language. Earlier postings in this recent series traced the progress in climbing the data federation pyramid.
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Why Are $800 Billion in Document Assets Wasted Annually?AI3::: Adaptive Information, Semantics Press LLC2006, April, 27 pp. (2006/04/06)It is a tragedy of no small import when $800 billion in readily available savings from creating, using and sharing documents is wasted in the United States each year. How can waste of such magnitude – literally equivalent to almost 8% of gross domestic product or more than 40% of what the nation spends on health care – occur right before our noses? And how can this waste occur so silently, so insidiously, and so ubiquitously that none of us can see it?
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Untapped Assets: The $3 Trillion Value of U.S. Enterprise DocumentsAI3::: Adaptive Information, Semantics Press LLC2005, July, 41 pp. (2005/07/20)Today, in the advanced knowledge economy of the United States, the information contained within documents represents about a third of total gross domestic product, or an amount of about $3.3 trillion annually. Yet our understanding of the value of documents and the means to manage them is abysmal. These failures impact enterprises of all sizes from the standpoints of revenues, profitability and reputation. Continued national productivity growth — and thus the wealth of all citizens — depends critically on understanding and managing these document values. As this white paper describes, the lack of a compelling and demonstrable common understanding of the importance of documents is in itself a major factor limiting available productivity benefits. There is an old Chinese saying that roughly translated is “what cannot be measured, cannot be improved.” Many corporate officers may believe this to be the case for document creation and productivity, but, as this paper shows, in fact many of these document issues can be measured. To wit, some 25% of all of the annual trillions of dollar spent on document creation costs lend themselves to actionable improvements.
In addition, though not directly published on this site, much of the background material was for my 2018 book:
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A Knowledge Representation Practionary: Guidelines Based on Charles Sanders PeirceSpringer International Publishing, 462 pp. (2018)[Author's full book preview] Download PDF
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PrefaceA Knowledge Representation Practionary: Guidelines Based on Charles Sanders PeirceSpringer International Publishing, viii-xiii (2018)[Author's preview chapter] Download PDF
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1. IntroductionA Knowledge Representation Practionary: Guidelines Based on Charles Sanders PeirceSpringer International Publishing, 1-14 (2018)[Author's preview chapter] Download PDF
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2. Information, Knowledge, RepresentationA Knowledge Representation Practionary: Guidelines Based on Charles Sanders PeirceSpringer International Publishing, 15-42 (2018)[Author's preview chapter] Download PDF
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3. The SituationA Knowledge Representation Practionary: Guidelines Based on Charles Sanders PeirceSpringer International Publishing, 45-64 (2018)[Author's preview chapter] Download PDF
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4. The OpportunityA Knowledge Representation Practionary: Guidelines Based on Charles Sanders PeirceSpringer International Publishing, 65-84 (2018)[Author's preview chapter] Download PDF
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5. The PreceptsA Knowledge Representation Practionary: Guidelines Based on Charles Sanders PeirceSpringer International Publishing, 85-104 (2018)[Author's preview chapter] Download PDF
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6. The Universal CategoriesA Knowledge Representation Practionary: Guidelines Based on Charles Sanders PeirceSpringer International Publishing, 107-127 (2018)[Author's preview chapter] Download PDF
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7. A KR TerminologyA Knowledge Representation Practionary: Guidelines Based on Charles Sanders PeirceSpringer International Publishing, 129-149 (2018)[Author's preview chapter] Download PDF
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8. KR Vocabulary and LanguagesA Knowledge Representation Practionary: Guidelines Based on Charles Sanders PeirceSpringer International Publishing, 151-180 (2018)[Author's preview chapter] Download PDF
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9. Keeping the Design OpenA Knowledge Representation Practionary: Guidelines Based on Charles Sanders PeirceSpringer International Publishing, 183-205 (2018)[Author's preview chapter] Download PDF
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10. Modular, Expandable TypologiesA Knowledge Representation Practionary: Guidelines Based on Charles Sanders PeirceSpringer International Publishing, 207-226 (2018)[Author's preview chapter] Download PDF
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11. Knowledge Graphs and BasesA Knowledge Representation Practionary: Guidelines Based on Charles Sanders PeirceSpringer International Publishing, 227-247 (2018)[Author's preview chapter] Download PDF
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12. Platforms and Knowledge ManagementA Knowledge Representation Practionary: Guidelines Based on Charles Sanders PeirceSpringer International Publishing, 251-272 (2018)[Author's preview chapter] Download PDF
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13. Building Out The SystemA Knowledge Representation Practionary: Guidelines Based on Charles Sanders PeirceSpringer International Publishing, 273-294 (2018)[Author's preview chapter] Download PDF
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14. Testing and Best PracticesA Knowledge Representation Practionary: Guidelines Based on Charles Sanders PeirceSpringer International Publishing, 295-316 (2018)[Author's preview chapter] Download PDF
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15. Potential Uses in BreadthA Knowledge Representation Practionary: Guidelines Based on Charles Sanders PeirceSpringer International Publishing, 319-341 (2018)[Author's preview chapter] Download PDF
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16. Potential Uses in DepthA Knowledge Representation Practionary: Guidelines Based on Charles Sanders PeirceSpringer International Publishing, 343-369 (2018)[Author's preview chapter] Download PDF
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17. ConclusionA Knowledge Representation Practionary: Guidelines Based on Charles Sanders PeirceSpringer International Publishing, 371-380 (2018)[Author's preview chapter] Download PDF
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Appendix A: Perspectives on PeirceA Knowledge Representation Practionary: Guidelines Based on Charles Sanders PeirceSpringer International Publishing, 381-407 (2018)[Author's preview chapter] Download PDF
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Appendix B: The KBpedia ResourceA Knowledge Representation Practionary: Guidelines Based on Charles Sanders PeirceSpringer International Publishing, 409-419 (2018)[Author's preview chapter] Download PDF
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Appendix C: KBpedia Feature PossibilitiesA Knowledge Representation Practionary: Guidelines Based on Charles Sanders PeirceSpringer International Publishing, 421-434 (2018)[Author's preview chapter] Download PDF