Machine Learning Ontology


Instead of seeing each Machine Learning (ML) method as a "shiny new object", here is an attempt to create a unified picture. There is no consensus when it comes to an ontology for ML methods; organizational principles are simply ways to get our arms around knowledge so that we are not swamped by too many unconnected notions. In chapter 4 ("Modern" ML Method) of my upcoming book, "SYSTEMS Analytics", we develop the basic theory and algorithms for some key blocks in the diagram above. In ML practice, these ML methods are "wrapped" by "bootstrap" and "consensus" methods.

5 TEDTalks Every Entrepreneur Needs to Watch


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'Supergirl': Chyler Leigh, Floriana Lima Talk Impact Of Alex And Maggie's Relationship On Viewers

International Business Times

"Supergirl" stars Chyler Leigh and Floriana Lima revealed that they were pleasantly surprised by how significant Alex and Maggie's relationship has become to viewers who are struggling with their sexuality. "We definitely wanted [Alex and Maggie's relationship] to be a strong representation, and that's why we've thought so hard about it and wanted it to be beautifully done," Leigh continued. Although that could mean that one of them might pop the big question soon, Leigh's teaser could also suggest other things, like Maggie moving into Alex's place or Maggie finally introducing Alex to her family. "Supergirl" stars Chyler Leigh and Floriana Lima revealed that they are humbled by how Alex and Maggie's love story has been an inspiration to viewers who are struggling with their sexuality.

Ontology Re-Engineering: A Case Study from the Automotive Industry

AI Magazine

For over twenty-five years Ford Motor Company has been utilizing an AI-based system to manage process planning for vehicle assembly at its assembly plants around the world. The scope of the AI system, known originally as the Direct Labor Management System and now as the Global Study Process Allocation System (GSPAS), has increased over the years to include additional functionality on Ergonomics and Powertrain Assembly (Engines and Transmission plants). The knowledge about Ford's manufacturing processes is contained in an ontology originally developed using the KL-ONE representation language and methodology. In this article, we will discuss the process by which we re-engineered the existing GSPAS KL-ONE ontology and deployed semantic web technology in our application.

Ontologies: Practical Applications


NASA had an analogous problem, and they solved it with the practical application of data management best practices, which included the use of domain specific ontologies[3]. However, any enterprise information architecture intended to enable horizontal communication between disparate data sources, with related and/or potentially different domains (e.g., banking and insurance), must identify a methodology for rapidly merging, and extracting Key Data Elements (KDE) necessary for answering essential competency questions[5]. Whether it is an engine overheating or gasses reaching a dangerous level as identified by sensor data, network intrusion detection identified by real time network log monitoring, or social and news media feeds indicating a need for risk reduction procedures to be implemented, the organization that can quickly identify risk and/or opportunity will have a distinct advantage over their competitors. As described above, the practical application of ontologies range from NASA integrating data from multiple disparate systems that enables the rapid identification of system failures, to environmental monitoring for oil and gas operations through the Semantic Sensor Network (SSN)[1], to market volatility and risk management in the financial industry.

Why Ontologies?


Data Models will not contain vocabulary that defines the entire domain, but rather the data dictionary will contain information on the entities and attributes associated with a specific data element. Ontologies are used in many disciplines, but are most commonly associated with Artificial Intelligence (AI), and possibly Natural Language Processing (NLP) applications. However, a less likely application of ontologies, and an area of interest for me is with Data Integration and Knowledge Management systems. So, for Artificial Intelligence (AI) and Natural Language Processing (NLP) applications, ontologies are a critical component.

A.I. Has Grown Up and Left Home - Issue 8: Home - Nautilus

AITopics Original Links

Our approach to thinking, from the early days of the computer era, focused on the question of how to represent the knowledge about which thoughts are thought, and the rules that operate on that knowledge. By gathering together in a single virtual "space" all of the information and relationships relevant to a particular thought, the symbolic approach pursues what Daniel Dennett has called a "Cartesian theater"--a kind of home for consciousness and thinking. We know facts like, language processing occurs in Broca's area in the frontal lobe of the left hemisphere. Around 1960, linguistics pioneer Noam Chomsky made a bold argument: Forget about meaning, forget about thinking, just focus on syntax.

The Suggested Upper Merged Ontology (SUMO) - Ontology Portal

AITopics Original Links

Largest free, formal ontology available, with 25,000 terms and 80,000 axioms when all domain ontologies are combined. These consist of SUMO itself, the MId-Level Ontology (MILO), and ontologies of communications, countries and regions, distributed computing and user interfaces, economy, finance, automobiles and engineering components, Food, Dining, Sports, Shopping catalogs and Hotels, geography, government and Justice, language taxonomy, media and Music, Military (general, devices, processes, people), North American Industrial Classification System, people and their Emotions, physical elements, transnational issues, transportation and its Details, viruses, world airports A-K, world airports L-Z, weapons of mass destruction. See also a large amount of instance content from DBPedia about people and the YAGO, project which includes millions of facts from Wikipedia merged with SUMO, and an initial merge of the Mondial geographical data with SUMO. The Open Biomedical Ontologies are lightly mapped to SUMO.

Ontology Building: A Survey of Editing Tools

AITopics Original Links

Ontologies, however, are often able to provide an objective specification of domain information by representing a consensual agreement on the concepts and relations characterizing the way knowledge in that domain is expressed. The most prominent distinction is between the domain ontologies describing specific fields of endeavor, like medicine, and upper level ontologies describing the basic concepts and relationships invoked when information about any domain is expressed in natural language. The wide array of information residing on the Web has given ontology use an impetus, and ontology languages increasingly rely on W3C technologies like RDF Schema as a language layer, XML Schema for data typing, and RDF to assert data. This will likely involve identifying the domain's principal concrete concepts and their properties, identifying the relationships among the concepts, creating abstract concepts as organizing features, referencing or including supporting ontologies, distinguishing which concepts have instances, and applying other guidelines of your chosen methodology.