"An ontology defines the terms used to describe and represent an area of knowledge. … Ontologies include computer-usable definitions of basic concepts in the domain and the relationships among them."
– from OWL Web Ontology Language Use Cases and Requirements. W3C Recommendation (10 February 2004). Jeff Heflin, editor.
The usage of open standards for data and knowledge models eliminates proprietary vendor lock-in. This builds the basis for a wide range of applications, starting from semantic search and text mining, ranging to data integration and data analytics. By that means, knowledge models become actionable and can help to find answers in unstructured content, trigger alerts or to make better decisions. SKOS is relatively easy to learn and can produce massive input to make machine learning tasks more precise.
Most of the machine learning algorithms were developed to solve a well-known problem in AI, which is called the'Knowledge Acquisition Bottleneck'. It deals with the question how subject matter experts (SMEs) can be enabled to work together with data scientists on knowledge models in an efficient and sustainable way (See also: Taxonomies and Ontologies – The Yin and Yang of Knowledge Modelling). Machine learning algorithms learn from data, and by that, successful implementations are obviously strongly related to data quality and the approaches taken to encode the semantics (meaning) of data. Facing the'Knowledge Acquisition Bottleneck' also means that experts' knowledge is recognized as an essential asset of any organization.
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.
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. 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. 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), to market volatility and risk management in the financial industry.
Most of us spend a good portion their time searching for "answers", "results" using wellknown search systems like Google, Altavista, Bing... Let's imagine one person having mentally "digested" relevant (controlled) Taxonomies being at our side while you get back thousands of results from an answering machine in answer to your latest question. In this practical and fast running demonstrator (***) a couple of taxonomies (Thesauri MESH, STW, EUROVOC and REEGLE) but also one OWL ontology (GENEO) can be selected to be used as "wises" for filtering out (controlling) domain relevant search results. Taxonomies (but also and expecially Ontologies) can be efficiently used as "domain filters" to control and re-rank search results on large document corpora.
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 ISO and the White House Roundtables definition on data quality have some subtle differences. The ISO provides a semantic definition to Data Quality which serves as the metadata requirement. According to Liu and Ram's "A semiotic Framework for Analyzing Data Provenance Research", the word provenance used in the context of data has different meanings for different people. A lot of the discussions around data quality and data discoverability have revolved around metadata and something called ontologies.
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The Cognonto demo is powered by an extensive knowledge graph called the KBpedia Knowledge Graph, as organized according to the KBpedia Knowledge Ontology (KKO). The KBpedia Knowledge Graph is a structure of more than 39,000 reference concepts linked to 6 major knowledge bases and 20 popular ontologies in use across the Web. It is for these reasons that we developed an extensive knowledge graph building process that includes a series of tests that are run every time that the knowledge graph get modified. The process of checking if external concepts linked to the KBpedia Knowledge Graph satisfies the structure is the same.