Expert system

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Expert_system 

 

A Symbolics Lisp Machine: an early platform for expert systems.

In artificial intelligence, an expert system is a computer system emulating the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural code. The first expert systems were created in the 1970s and then proliferated in the 1980s. Expert systems were among the first truly successful forms of artificial intelligence (AI) software. An expert system is divided into two subsystems: the inference engine and the knowledge base. The knowledge base represents facts and rules. The inference engine applies the rules to the known facts to deduce new facts. Inference engines can also include explanation and debugging abilities.

History

Early development

Soon after the dawn of modern computers in the late 1940s – early 1950s, researchers started realizing the immense potential these machines had for modern society. One of the first challenges was to make such machine capable of “thinking” like humans. In particular, making these machines capable of making important decisions the way humans do. The medical / healthcare field presented the tantalizing challenge to enable these machines to make medical diagnostic decisions.

Thus, in the late 1950s, right after the information age had fully arrived, researchers started experimenting with the prospect of using computer technology to emulate human decision-making. For example, biomedical researchers started creating computer-aided systems for diagnostic applications in medicine and biology. These early diagnostic systems used patients’ symptoms and laboratory test results as inputs to generate a diagnostic outcome. These systems were often described as the early forms of expert systems. However, researchers had realized that there were significant limitations when using traditional methods such as flow-charts statistical pattern-matching, or probability theory.

Formal introduction & later developments

This previous situation gradually led to the development of expert systems, which used knowledge-based approaches. These expert systems in medicine were the MYCIN expert system, the INTERNIST-I expert system and later, in the middle of the 1980s, the CADUCEUS. 

Expert systems were formally introduced around 1965 by the Stanford Heuristic Programming Project led by Edward Feigenbaum, who is sometimes termed the "father of expert systems"; other key early contributors were Bruce Buchanan and Randall Davis. The Stanford researchers tried to identify domains where expertise was highly valued and complex, such as diagnosing infectious diseases (Mycin) and identifying unknown organic molecules (Dendral). The idea that "intelligent systems derive their power from the knowledge they possess rather than from the specific formalisms and inference schemes they use" – as Feigenbaum said – was at the time a significant step forward, since the past research had been focused on heuristic computational methods, culminating in attempts to develop very general-purpose problem solvers (foremostly the conjunct work of Allen Newell and Herbert Simon). Expert systems became some of the first truly successful forms of artificial intelligence (AI) software.

Research on expert systems was also active in France. While in the US the focus tended to be on rules-based systems, first on systems hard coded on top of LISP programming environments and then on expert system shells developed by vendors such as Intellicorp, in France research focused more on systems developed in Prolog. The advantage of expert system shells was that they were somewhat easier for nonprogrammers to use. The advantage of Prolog environments was that they were not focused only on if-then rules; Prolog environments provided a much better realization of a complete first order logic environment.

In the 1980s, expert systems proliferated. Universities offered expert system courses and two thirds of the Fortune 500 companies applied the technology in daily business activities. Interest was international with the Fifth Generation Computer Systems project in Japan and increased research funding in Europe.

In 1981, the first IBM PC, with the PC DOS operating system, was introduced. The imbalance between the high affordability of the relatively powerful chips in the PC, compared to the much more expensive cost of processing power in the mainframes that dominated the corporate IT world at the time, created a new type of architecture for corporate computing, termed the client-server model. Calculations and reasoning could be performed at a fraction of the price of a mainframe using a PC. This model also enabled business units to bypass corporate IT departments and directly build their own applications. As a result, client server had a tremendous impact on the expert systems market. Expert systems were already outliers in much of the business world, requiring new skills that many IT departments did not have and were not eager to develop. They were a natural fit for new PC-based shells that promised to put application development into the hands of end users and experts. Until then, the main development environment for expert systems had been high end Lisp machines from Xerox, Symbolics, and Texas Instruments. With the rise of the PC and client server computing, vendors such as Intellicorp and Inference Corporation shifted their priorities to developing PC based tools. Also, new vendors, often financed by venture capital (such as Aion Corporation, Neuron Data, Exsys, and many others), started appearing regularly.

The first expert system to be used in a design capacity for a large-scale product was the SID (Synthesis of Integral Design) software program, developed in 1982. Written in LISP, SID generated 93% of the VAX 9000 CPU logic gates. Input to the software was a set of rules created by several expert logic designers. SID expanded the rules and generated software logic synthesis routines many times the size of the rules themselves. Surprisingly, the combination of these rules resulted in an overall design that exceeded the capabilities of the experts themselves, and in many cases out-performed the human counterparts. While some rules contradicted others, top-level control parameters for speed and area provided the tie-breaker. The program was highly controversial, but used nevertheless due to project budget constraints. It was terminated by logic designers after the VAX 9000 project completion.

During the years before the middle of the 1970s, the expectations of what expert systems can accomplish in many fields tended to be extremely optimistic. At the beginning of these early studies, researchers were hoping to develop entirely automatic (i.e., completely computerized) expert systems. The expectations of people of what computers can do were frequently too idealistic. This situation radically changed after Richard M. Karp published his breakthrough paper: “Reducibility among Combinatorial Problems” in the early 1970s. Thanks to Karp's work it became clear that there are certain limitations and possibilities when one designs computer algorithms. His findings describe what computers can do and what they cannot do. Many of the computational problems related to this type of expert systems have certain pragmatic limitations. These findings laid down the groundwork that led to the next developments in the field. 

In the 1990s and beyond, the term expert system and the idea of a standalone AI system mostly dropped from the IT lexicon. There are two interpretations of this. One is that "expert systems failed": the IT world moved on because expert systems did not deliver on their over hyped promise. The other is the mirror opposite, that expert systems were simply victims of their success: as IT professionals grasped concepts such as rule engines, such tools migrated from being standalone tools for developing special purpose expert systems, to being one of many standard tools. Many of the leading major business application suite vendors (such as SAP, Siebel, and Oracle) integrated expert system abilities into their suite of products as a way of specifying business logic – rule engines are no longer simply for defining the rules an expert would use but for any type of complex, volatile, and critical business logic; they often go hand in hand with business process automation and integration environments.

Current approaches to expert systems

The limitations of the previous type of expert systems have urged researchers to develop new types of approaches. They have developed more efficient, flexible, and powerful approaches in order to simulate the human decision-making process. Some of the approaches that researchers have developed are based on new methods of artificial intelligence (AI), and in particular in machine learning and data mining approaches with a feedback mechanism. Recurrent neural networks often take advantage of such mechanisms. Related is the discussion on the disadvantages section.

Modern systems can incorporate new knowledge more easily and thus update themselves easily. Such systems can generalize from existing knowledge better and deal with vast amounts of complex data. Related is the subject of big data here. Sometimes these type of expert systems are called “intelligent systems.” 

Software architecture

Illustrating example of backward chaining from a 1990 Master's Thesis

An expert system is an example of a knowledge-based system. Expert systems were the first commercial systems to use a knowledge-based architecture. A knowledge-based system is essentially composed of two sub-systems: the knowledge base and the inference engine.

The knowledge base represents facts about the world. In early expert systems such as Mycin and Dendral, these facts were represented mainly as flat assertions about variables. In later expert systems developed with commercial shells, the knowledge base took on more structure and used concepts from object-oriented programming. The world was represented as classes, subclasses, and instances and assertions were replaced by values of object instances. The rules worked by querying and asserting values of the objects.

The inference engine is an automated reasoning system that evaluates the current state of the knowledge-base, applies relevant rules, and then asserts new knowledge into the knowledge base. The inference engine may also include abilities for explanation, so that it can explain to a user the chain of reasoning used to arrive at a particular conclusion by tracing back over the firing of rules that resulted in the assertion.

There are mainly two modes for an inference engine: forward chaining and backward chaining. The different approaches are dictated by whether the inference engine is being driven by the antecedent (left hand side) or the consequent (right hand side) of the rule. In forward chaining an antecedent fires and asserts the consequent. For example, consider the following rule:

$${\displaystyle R1:{\mathit {Man}}(x)\implies {\mathit {Mortal}}(x)}$$

A simple example of forward chaining would be to assert Man(Socrates) to the system and then trigger the inference engine. It would match R1 and assert Mortal(Socrates) into the knowledge base.

Backward chaining is a bit less straight forward. In backward chaining the system looks at possible conclusions and works backward to see if they might be true. So if the system was trying to determine if Mortal(Socrates) is true it would find R1 and query the knowledge base to see if Man(Socrates) is true. One of the early innovations of expert systems shells was to integrate inference engines with a user interface. This could be especially powerful with backward chaining. If the system needs to know a particular fact but does not, then it can simply generate an input screen and ask the user if the information is known. So in this example, it could use R1 to ask the user if Socrates was a Man and then use that new information accordingly.

The use of rules to explicitly represent knowledge also enabled explanation abilities. In the simple example above if the system had used R1 to assert that Socrates was Mortal and a user wished to understand why Socrates was mortal they could query the system and the system would look back at the rules which fired to cause the assertion and present those rules to the user as an explanation. In English, if the user asked "Why is Socrates Mortal?" the system would reply "Because all men are mortal and Socrates is a man". A significant area for research was the generation of explanations from the knowledge base in natural English rather than simply by showing the more formal but less intuitive rules.

As expert systems evolved, many new techniques were incorporated into various types of inference engines. Some of the most important of these were:

• Truth maintenance. These systems record the dependencies in a knowledge-base so that when facts are altered, dependent knowledge can be altered accordingly. For example, if the system learns that Socrates is no longer known to be a man it will revoke the assertion that Socrates is mortal.
• Hypothetical reasoning. In this, the knowledge base can be divided up into many possible views, a.k.a. worlds. This allows the inference engine to explore multiple possibilities in parallel. For example, the system may want to explore the consequences of both assertions, what will be true if Socrates is a Man and what will be true if he is not?
• Uncertainty systems. One of the first extensions of simply using rules to represent knowledge was also to associate a probability with each rule. So, not to assert that Socrates is mortal, but to assert Socrates may be mortal with some probability value. Simple probabilities were extended in some systems with sophisticated mechanisms for uncertain reasoning, such as Fuzzy logic, and combination of probabilities.
• Ontology classification. With the addition of object classes to the knowledge base, a new type of reasoning was possible. Along with reasoning simply about object values, the system could also reason about object structures. In this simple example, Man can represent an object class and R1 can be redefined as a rule that defines the class of all men. These types of special purpose inference engines are termed classifiers. Although they were not highly used in expert systems, classifiers are very powerful for unstructured volatile domains, and are a key technology for the Internet and the emerging Semantic Web.

Advantages

The goal of knowledge-based systems is to make the critical information required for the system to work explicit rather than implicit. In a traditional computer program the logic is embedded in code that can typically only be reviewed by an IT specialist. With an expert system the goal was to specify the rules in a format that was intuitive and easily understood, reviewed, and even edited by domain experts rather than IT experts. The benefits of this explicit knowledge representation were rapid development and ease of maintenance.

Ease of maintenance is the most obvious benefit. This was achieved in two ways. First, by removing the need to write conventional code, many of the normal problems that can be caused by even small changes to a system could be avoided with expert systems. Essentially, the logical flow of the program (at least at the highest level) was simply a given for the system, simply invoke the inference engine. This also was a reason for the second benefit: rapid prototyping. With an expert system shell it was possible to enter a few rules and have a prototype developed in days rather than the months or year typically associated with complex IT projects.

A claim for expert system shells that was often made was that they removed the need for trained programmers and that experts could develop systems themselves. In reality, this was seldom if ever true. While the rules for an expert system were more comprehensible than typical computer code, they still had a formal syntax where a misplaced comma or other character could cause havoc as with any other computer language. Also, as expert systems moved from prototypes in the lab to deployment in the business world, issues of integration and maintenance became far more critical. Inevitably demands to integrate with, and take advantage of, large legacy databases and systems arose. To accomplish this, integration required the same skills as any other type of system.

Disadvantages

The most common disadvantage cited for expert systems in the academic literature is the knowledge acquisition problem. Obtaining the time of domain experts for any software application is always difficult, but for expert systems it was especially difficult because the experts were by definition highly valued and in constant demand by the organization. As a result of this problem, a great deal of research in the later years of expert systems was focused on tools for knowledge acquisition, to help automate the process of designing, debugging, and maintaining rules defined by experts. However, when looking at the life-cycle of expert systems in actual use, other problems – essentially the same problems as those of any other large system – seem at least as critical as knowledge acquisition: integration, access to large databases, and performance.

Performance could be especially problematic because early expert systems were built using tools (such as earlier Lisp versions) that interpreted code expressions without first compiling them. This provided a powerful development environment, but with the drawback that it was virtually impossible to match the efficiency of the fastest compiled languages (such as C). System and database integration were difficult for early expert systems because the tools were mostly in languages and platforms that were neither familiar to nor welcome in most corporate IT environments – programming languages such as Lisp and Prolog, and hardware platforms such as Lisp machines and personal computers. As a result, much effort in the later stages of expert system tool development was focused on integrating with legacy environments such as COBOL and large database systems, and on porting to more standard platforms. These issues were resolved mainly by the client-server paradigm shift, as PCs were gradually accepted in the IT environment as a legitimate platform for serious business system development and as affordable minicomputer servers provided the processing power needed for AI applications.

Another major challenge of expert systems emerges when the size of the knowledge base increases. This causes the processing complexity to increase. For instance, when an expert system with 100 million rules was envisioned as the ultimate expert system, it became obvious that such system would be too complex and it would face too many computational problems. An inference engine would have to be able to process huge numbers of rules to reach a decision.

How to verify that decision rules are consistent with each other is also a challenge when there are too many rules. Usually such problem leads to a satisfiability (SAT) formulation. This is a well-known NP-complete problem Boolean satisfiability problem. If we assume only binary variables, say n of them, and then the corresponding search space is of size 2${\displaystyle ^{n}}$. Thus, the search space can grow exponentially.

There are also questions on how to prioritize the use of the rules in order to operate more efficiently, or how to resolve ambiguities (for instance, if there are too many else-if sub-structures within a single rule) and so on.

Other problems are related to the overfitting and overgeneralization effects when using known facts and trying to generalize to other cases not described explicitly in the knowledge base. Such problems exist with methods that employ machine learning approaches too.

Another problem related to the knowledge base is how to make updates of its knowledge quickly and effectively. Also how to add a new piece of knowledge (i.e., where to add it among many rules) is challenging. Modern approaches that rely on machine learning methods are easier in this regard.

Because of the above challenges, it became clear that new approaches to AI were required instead of rule-based technologies. These new approaches are based on the use of machine learning techniques, along with the use of feedback mechanisms.

The key challenges that expert systems in medicine (if one considers computer-aided diagnostic systems as modern expert systems), and perhaps in other application domains, include issues related to aspects such as: big data, existing regulations, healthcare practice, various algorithmic issues, and system assessment. 

Applications

Hayes-Roth divides expert systems applications into 10 categories illustrated in the following table. The example applications were not in the original Hayes-Roth table, and some of them arose well afterward. Any application that is not footnoted is described in the Hayes-Roth book. Also, while these categories provide an intuitive framework to describe the space of expert systems applications, they are not rigid categories, and in some cases an application may show traits of more than one category.

Category	Problem addressed	Examples
Interpretation	Inferring situation descriptions from sensor data	Hearsay (speech recognition), PROSPECTOR
Prediction	Inferring likely consequences of given situations	Preterm Birth Risk Assessment
Diagnosis	Inferring system malfunctions from observables	CADUCEUS, MYCIN, PUFF, Mistral, Eydenet, Kaleidos
Design	Configuring objects under constraints	Dendral, Mortgage Loan Advisor, R1 (DEC VAX Configuration), SID (DEC VAX 9000 CPU)
Planning	Designing actions	Mission Planning for Autonomous Underwater Vehicle
Monitoring	Comparing observations to plan vulnerabilities	REACTOR
Debugging	Providing incremental solutions for complex problems	SAINT, MATHLAB, MACSYMA
Repair	Executing a plan to administer a prescribed remedy	Toxic Spill Crisis Management
Instruction	Diagnosing, assessing, and repairing student behavior	SMH.PAL, Intelligent Clinical Training, STEAMER
Control	Interpreting, predicting, repairing, and monitoring system behaviors	Real Time Process Control, Space Shuttle Mission Control

Hearsay was an early attempt at solving voice recognition through an expert systems approach. For the most part this category of expert systems was not all that successful. Hearsay and all interpretation systems are essentially pattern recognition systems—looking for patterns in noisy data. In the case of Hearsay recognizing phonemes in an audio stream. Other early examples were analyzing sonar data to detect Russian submarines. These kinds of systems proved much more amenable to a neural network AI solution than a rule-based approach.

CADUCEUS and MYCIN were medical diagnosis systems. The user describes their symptoms to the computer as they would to a doctor and the computer returns a medical diagnosis.

Dendral was a tool to study hypothesis formation in the identification of organic molecules. The general problem it solved—designing a solution given a set of constraints—was one of the most successful areas for early expert systems applied to business domains such as salespeople configuring Digital Equipment Corporation (DEC) VAX computers and mortgage loan application development.

SMH.PAL is an expert system for the assessment of students with multiple disabilities.

Mistral is an expert system to monitor dam safety, developed in the 1990s by Ismes (Italy). It gets data from an automatic monitoring system and performs a diagnosis of the state of the dam. Its first copy, installed in 1992 on the Ridracoli Dam (Italy), is still operational 24/7/365. It has been installed on several dams in Italy and abroad (e.g., Itaipu Dam in Brazil), and on landslide sites under the name of Eydenet, and on monuments under the name of Kaleidos. Mistral is a registered trade mark of CESI.

Semantic network

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Semantic_network 

 

Example of a semantic network

A semantic network, or frame network is a knowledge base that represents semantic relations between concepts in a network. This is often used as a form of knowledge representation. It is a directed or undirected graph consisting of vertices, which represent concepts, and edges, which represent semantic relations between concepts, mapping or connecting semantic fields. A semantic network may be instantiated as, for example, a graph database or a concept map.

Typical standardized semantic networks are expressed as semantic triples.

Semantic networks are used in natural language processing applications such as semantic parsing and word-sense disambiguation.

History

Examples of the use of semantic networks in logic, directed acyclic graphs as a mnemonic tool, dates back centuries. The earliest documented use being the Greek philosopher Porphyry's commentary on Aristotle's categories in the third century AD.

In computing history, "Semantic Nets" for the propositional calculus were first implemented for computers by Richard H. Richens of the Cambridge Language Research Unit in 1956 as an "interlingua" for machine translation of natural languages. Although the importance of this work and the CLRU was only belatedly realized.

Semantic networks were also independently implemented by Robert F. Simmons and Sheldon Klein, using the first order predicate calculus as a base, after being inspired by a demonstration of Victor Yngve. The "line of research was originated by the first President of the Association [Association for Computational Linguistics], Victor Yngve, who in 1960 had published descriptions of algorithms for using a phrase structure grammar to generate syntactically well-formed nonsense sentences. Sheldon Klein and I about 1962-1964 were fascinated by the technique and generalized it to a method for controlling the sense of what was generated by respecting the semantic dependencies of words as they occurred in text." Other researchers, most notably M. Ross Quillian and others at System Development Corporation helped contribute to their work in the early 1960s as part of the SYNTHEX project. It's from these publications at SDC that most modern derivatives of the term "semantic network" cite as their background. Later prominent works were done by Allan M. Collins and Quillian (e.g., Collins and Quillian; Collins and Loftus Quillian). Still later in 2006, Hermann Helbig fully described MultiNet.

In the late 1980s, two Netherlands universities, Groningen and Twente, jointly began a project called Knowledge Graphs, which are semantic networks but with the added constraint that edges are restricted to be from a limited set of possible relations, to facilitate algebras on the graph. In the subsequent decades, the distinction between semantic networks and knowledge graphs was blurred. In 2012, Google gave their knowledge graph the name Knowledge Graph. The Semantic Link Network was systematically studied as a social semantics networking method. Its basic model consists of semantic nodes, semantic links between nodes, and a semantic space that defines the semantics of nodes and links and reasoning rules on semantic links. The systematic theory and model was published in 2004. This research direction can trace to the definition of inheritance rules for efficient model retrieval in 1998 and the Active Document Framework ADF. Since 2003, research has developed toward social semantic networking. This work is a systematic innovation at the age of the World Wide Web and global social networking rather than an application or simple extension of the Semantic Net (Network). Its purpose and scope are different from that of the Semantic Net (or network). The rules for reasoning and evolution and automatic discovery of implicit links play an important role in the Semantic Link Network. Recently it has been developed to support Cyber-Physical-Social Intelligence. It was used for creating a general summarization method. The self-organised Semantic Link Network was integrated with a multi-dimensional category space to form a semantic space to support advanced applications with multi-dimensional abstractions and self-organised semantic links It has been verified that Semantic Link Network play an important role in understanding and representation through text summarisation applications. Semantic Link Network has been extended from cyberspace to cyber-physical-social space. Competition relation and symbiosis relation as well as their roles in evolving society were studied in the emerging topic: Cyber-Physical-Social Intelligence

More specialized forms of semantic networks has been created for specific use. For example, in 2008, Fawsy Bendeck's PhD thesis formalized the Semantic Similarity Network (SSN) that contains specialized relationships and propagation algorithms to simplify the semantic similarity representation and calculations.

Basics of semantic networks

A semantic network is used when one has knowledge that is best understood as a set of concepts that are related to one another.

Most semantic networks are cognitively based. They also consist of arcs and nodes which can be organized into a taxonomic hierarchy. Semantic networks contributed ideas of spreading activation, inheritance, and nodes as proto-objects.

Examples

In Lisp

The following code shows an example of a semantic network in the Lisp programming language using an association list.

(setq *database*
'((canary  (is-a bird)
           (color yellow)
           (size small))
  (penguin (is-a bird)
           (movement swim))
  (bird    (is-a vertebrate)
           (has-part wings)
           (reproduction egg-laying))))

To extract all the information about the "canary" type, one would use the assoc function with a key of "canary".

WordNet

An example of a semantic network is WordNet, a lexical database of English. It groups English words into sets of synonyms called synsets, provides short, general definitions, and records the various semantic relations between these synonym sets. Some of the most common semantic relations defined are meronymy (A is a meronym of B if A is part of B), holonymy (B is a holonym of A if B contains A), hyponymy (or troponymy) (A is subordinate of B; A is kind of B), hypernymy (A is superordinate of B), synonymy (A denotes the same as B) and antonymy (A denotes the opposite of B).

WordNet properties have been studied from a network theory perspective and compared to other semantic networks created from Roget's Thesaurus and word association tasks. From this perspective the three of them are a small world structure.

Other examples

It is also possible to represent logical descriptions using semantic networks such as the existential graphs of Charles Sanders Peirce or the related conceptual graphs of John F. Sowa. These have expressive power equal to or exceeding standard first-order predicate logic. Unlike WordNet or other lexical or browsing networks, semantic networks using these representations can be used for reliable automated logical deduction. Some automated reasoners exploit the graph-theoretic features of the networks during processing.

Other examples of semantic networks are Gellish models. Gellish English with its Gellish English dictionary, is a formal language that is defined as a network of relations between concepts and names of concepts. Gellish English is a formal subset of natural English, just as Gellish Dutch is a formal subset of Dutch, whereas multiple languages share the same concepts. Other Gellish networks consist of knowledge models and information models that are expressed in the Gellish language. A Gellish network is a network of (binary) relations between things. Each relation in the network is an expression of a fact that is classified by a relation type. Each relation type itself is a concept that is defined in the Gellish language dictionary. Each related thing is either a concept or an individual thing that is classified by a concept. The definitions of concepts are created in the form of definition models (definition networks) that together form a Gellish Dictionary. A Gellish network can be documented in a Gellish database and is computer interpretable.

SciCrunch is a collaboratively edited knowledge base for scientific resources. It provides unambiguous identifiers (Research Resource IDentifiers or RRIDs) for software, lab tools etc. and it also provides options to create links between RRIDs and from communities.

Another example of semantic networks, based on category theory, is ologs. Here each type is an object, representing a set of things, and each arrow is a morphism, representing a function. Commutative diagrams also are prescribed to constrain the semantics.

In the social sciences people sometimes use the term semantic network to refer to co-occurrence networks. The basic idea is that words that co-occur in a unit of text, e.g. a sentence, are semantically related to one another. Ties based on co-occurrence can then be used to construct semantic networks.

Software tools

There are also elaborate types of semantic networks connected with corresponding sets of software tools used for lexical knowledge engineering, like the Semantic Network Processing System (SNePS) of Stuart C. Shapiro or the MultiNet paradigm of Hermann Helbig, especially suited for the semantic representation of natural language expressions and used in several NLP applications.

Semantic networks are used in specialized information retrieval tasks, such as plagiarism detection. They provide information on hierarchical relations in order to employ semantic compression to reduce language diversity and enable the system to match word meanings, independently from sets of words used.

The Knowledge Graph proposed by Google in 2012 is actually an application of semantic network in search engine.

Modeling multi-relational data like semantic networks in low-dimensional spaces through forms of embedding has benefits in expressing entity relationships as well as extracting relations from mediums like text. There are many approaches to learning these embeddings, notably using Bayesian clustering frameworks or energy-based frameworks, and more recently, TransE (NIPS 2013). Applications of embedding knowledge base data include Social network analysis and Relationship extraction.

 

Semantic technology

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Semantic_technology

 

Simplistic example of the sort of semantic net used in Semantic Web technology

The ultimate goal of semantic technology is to help machines understand data. To enable the encoding of semantics with the data, well-known technologies are RDF (Resource Description Framework) and OWL (Web Ontology Language). These technologies formally represent the meaning involved in information. For example, ontology can describe concepts, relationships between things, and categories of things. These embedded semantics with the data offer significant advantages such as reasoning over data and dealing with heterogeneous data sources.

Overview

In software, semantic technology encodes meanings separately from data and content files, and separately from application code. This enables machines as well as people to understand, share and reason with them at execution time. With semantic technologies, adding, changing and implementing new relationships or interconnecting programs in a different way can be just as simple as changing the external model that these programs share.

With traditional information technology, on the other hand, meanings and relationships must be predefined and "hard wired" into data formats and the application program code at design time. This means that when something changes, previously unexchanged information needs to be exchanged, or two programs need to interoperate in a new way, the humans must get involved.

Off-line, the parties must define and communicate between them the knowledge needed to make the change, and then recode the data structures and program logic to accommodate it, and then apply these changes to the database and the application. Then, and only then, can they implement the changes.

Semantic technologies are "meaning-centered". They include subjects but not limited to:

• encode/decode of semantic representation,
• knowledge graph embedding relationships,
• auto-recognition of topics and concepts,
• information and meaning extraction,
• semantic data integration, and
• taxonomies/classification.

Given a question, semantic technologies can directly search topics, concepts, associations that span a vast number of sources.

Semantic technologies provide an abstraction layer above existing IT technologies that enables bridging and interconnection of data, content, and processes. Second, from the portal perspective, semantic technologies can be thought of as a new level of depth that provides far more intelligent, capable, relevant, and responsive interaction than with information technologies alone.

 

Semantics

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Semantics 

Semantics
Linguistic Logical
Analysis Computational Lexical (lexis lexicology) Statistical Structural Prototype theory Force dynamics Unsolved linguistics problems Theory of descriptions
Computing
Types Action Algebraic Axiomatic Categorical Concurrency Denotational Game Operational Predicate transformational Theory Abstract interpretation Abstract semantic graph Semantic matching Analysis Latent Machine-learning Applications Semantic file system Semantic Web Semantic wiki
Language Linguistics

Semantics (from Ancient Greek: σημαντικός sēmantikós, "significant") is the study of meaning, reference, or truth. The term can be used to refer to subfields of several distinct disciplines, including philosophy, linguistics and computer science.

Linguistics

In linguistics, semantics is the subfield that studies meaning. Semantics can address meaning at the levels of words, phrases, sentences, or larger units of discourse. One of the crucial questions which unites different approaches to linguistic semantics is that of the relationship between form and meaning.

Theories in linguistic semantics

Formal semantics

Formal semantics seeks to identify domain-specific mental operations which speakers perform when they compute a sentence's meaning on the basis of its syntactic structure. Theories of formal semantics are typically floated on top of theories of syntax such as generative syntax or Combinatory categorial grammar and provide a model theory based on mathematical tools such as typed lambda calculi. The field's central ideas are rooted in early twentieth century philosophical logic as well as later ideas about linguistic syntax. It emerged as its own subfield in the 1970s after the pioneering work of Richard Montague and Barbara Partee and continues to be an active area of research.

Conceptual semantics

This theory is an effort to explain properties of argument structure. The assumption behind this theory is that syntactic properties of phrases reflect the meanings of the words that head them. With this theory, linguists can better deal with the fact that subtle differences in word meaning correlate with other differences in the syntactic structure that the word appears in. The way this is gone about is by looking at the internal structure of words. These small parts that make up the internal structure of words are termed semantic primitives.

Cognitive semantics

Cognitive semantics approaches meaning from the perspective of cognitive linguistics. In this framework, language is explained via general human cognitive abilities rather than a domain-specific language module. The techniques native to cognitive semantics are typically used in lexical studies such as those put forth by Leonard Talmy, George Lakoff, Dirk Geeraerts, and Bruce Wayne Hawkins. Some cognitive semantic frameworks, such as that developed by Talmy, take into account syntactic structures as well. Semantics, through modern researchers can be linked to the Wernicke's area of the brain and can be measured using the event-related potential (ERP). ERP is the rapid electrical response recorded with small disc electrodes which are placed on a person's scalp. 

Lexical semantics

A linguistic theory that investigates word meaning. This theory understands that the meaning of a word is fully reflected by its context. Here, the meaning of a word is constituted by its contextual relations. Therefore, a distinction between degrees of participation as well as modes of participation are made. In order to accomplish this distinction any part of a sentence that bears a meaning and combines with the meanings of other constituents is labeled as a semantic constituent. Semantic constituents that cannot be broken down into more elementary constituents are labeled minimal semantic constituents.

Cross-cultural semantics

Various fields or disciplines have long been contributing to cross-cultural semantics. Are words like love, truth, and hate universals? Is even the word sense – so central to semantics – a universal, or a concept entrenched in a long-standing but culture-specific tradition? These are the kind of crucial questions that are discussed in cross-cultural semantics. Translation theory, ethnolinguistics, linguistic anthropology and cultural linguistics specialize in the field of comparing, contrasting, and translating words, terms and meanings from one language to another (see Herder, W. von Humboldt, Boas, Sapir, and Whorf). But philosophy, sociology, and anthropology have long established traditions in contrasting the different nuances of the terms and concepts we use. And online encyclopaedias such as the Stanford encyclopedia of philosophy, Stanford Encyclopedia of Philosophy, and more and more Wikipedia itself have greatly facilitated the possibilities of comparing the background and usages of key cultural terms. In recent years the question of whether key terms are translatable or untranslatable has increasingly come to the fore of global discussions, especially since the publication of Barbara Cassin's Dictionary of Untranslatables: A Philosophical Lexicon, in 2014.

Computational semantics

Computational semantics is focused on the processing of linguistic meaning. In order to do this concrete algorithms and architectures are described. Within this framework the algorithms and architectures are also analyzed in terms of decidability, time/space complexity, data structures that they require and communication protocols.

Philosophy

Many of the formal approaches to semantics in mathematical logic and computer science originated in early twentieth century philosophy of language and philosophical logic. Initially, the most influential semantic theory stemmed from Gottlob Frege and Bertrand Russell. Frege and Russell are seen as the originators of a tradition in analytic philosophy to explain meaning compositionally via syntax and mathematical functionality. Ludwig Wittgenstein, a former student of Russell, is also seen as one of the seminal figures in the analytic tradition. All three of these early philosophers of language were concerned with how sentences expressed information in the form of propositions. They also dealt with the truth values or truth conditions a given sentence has in virtue of the proposition it expresses.

In present day philosophy, the term "semantics" is often used to refer to linguistic formal semantics, which bridges both linguistics and philosophy. There is also an active tradition of metasemantics, which studies the foundations of natural language semantics.

Computer science

In computer science, the term semantics refers to the meaning of language constructs, as opposed to their form (syntax). According to Euzenat, semantics "provides the rules for interpreting the syntax which do not provide the meaning directly but constrains the possible interpretations of what is declared."

Programming languages

The semantics of programming languages and other languages is an important issue and area of study in computer science. Like the syntax of a language, its semantics can be defined exactly.

For instance, the following statements use different syntaxes, but cause the same instructions to be executed, namely, perform an arithmetical addition of 'y' to 'x' and store the result in a variable called 'x':

Statement	Programming languages
x += y	C, C++, C#, Java, JavaScript, Python, Ruby, etc.
$x += $y	Perl, PHP
x := x + y	Ada, ALGOL, ALGOL 68, BCPL, Dylan, Eiffel, J, Modula-2, Oberon, OCaml, Object Pascal (Delphi), Pascal, SETL, Simula, Smalltalk, Standard ML, VHDL, etc.
MOV EAX,[y] ADD [x],EAX	Assembly languages: Intel 8086
ldr r2, [y] ldr r3, [x] add r3, r3, r2 str r3, [x]	Assembly languages: ARM
LET X = X + Y	BASIC: early
x = x + y	BASIC: most dialects; Fortran, MATLAB, Lua
Set x = x + y	Caché ObjectScript
ADD Y TO X.	ABAP
ADD Y TO X GIVING X	COBOL
set /a x=%x%+%y%	Batch
(incf x y)	Common Lisp
/x y x add def	PostScript
y @ x +!	Forth

Various ways have been developed to describe the semantics of programming languages formally, building on mathematical logic:

• Operational semantics: The meaning of a construct is specified by the computation it induces when it is executed on a machine. In particular, it is of interest how the effect of a computation is produced.
• Denotational semantics: Meanings are modelled by mathematical objects that represent the effect of executing the constructs. Thus only the effect is of interest, not how it is obtained.
• Axiomatic semantics: Specific properties of the effect of executing the constructs are expressed as assertions. Thus there may be aspects of the executions that are ignored.

Semantic models

The Semantic Web refers to the extension of the World Wide Web via embedding added semantic metadata, using semantic data modeling techniques such as Resource Description Framework (RDF) and Web Ontology Language (OWL). On the Semantic Web, terms such as semantic network and semantic data model are used to describe particular types of data model characterized by the use of directed graphs in which the vertices denote concepts or entities in the world and their properties, and the arcs denote relationships between them. These can formally be described as description logic concepts and roles, which correspond to OWL classes and properties.

Psychology

Semantic memory

In psychology, semantic memory is memory for meaning – in other words, the aspect of memory that preserves only the gist, the general significance, of remembered experience – while episodic memory is memory for the ephemeral details – the individual features, or the unique particulars of experience. The term 'episodic memory' was introduced by Tulving and Schacter in the context of 'declarative memory' which involved simple association of factual or objective information concerning its object. Word meaning is measured by the company they keep, i.e. the relationships among words themselves in a semantic network. The memories may be transferred intergenerationally or isolated in one generation due to a cultural disruption. Different generations may have different experiences at similar points in their own time-lines. This may then create a vertically heterogeneous semantic net for certain words in an otherwise homogeneous culture. In a network created by people analyzing their understanding of the word (such as Wordnet) the links and decomposition structures of the network are few in number and kind, and include part of, kind of, and similar links. In automated ontologies the links are computed vectors without explicit meaning. Various automated technologies are being developed to compute the meaning of words: latent semantic indexing and support vector machines as well as natural language processing, artificial neural networks and predicate calculus techniques.

Ideasthesia

Ideasthesia is a psychological phenomenon in which activation of concepts evokes sensory experiences. For example, in synesthesia, activation of a concept of a letter (e.g., that of the letter A) evokes sensory-like experiences (e.g., of red color).

Psychosemantics

In the 1960s, psychosemantic studies became popular after Charles E. Osgood's massive cross-cultural studies using his semantic differential (SD) method that used thousands of nouns and adjective bipolar scales. A specific form of the SD, Projective Semantics method uses only most common and neutral nouns that correspond to the 7 groups (factors) of adjective-scales most consistently found in cross-cultural studies (Evaluation, Potency, Activity as found by Osgood, and Reality, Organization, Complexity, Limitation as found in other studies). In this method, seven groups of bipolar adjective scales corresponded to seven types of nouns so the method was thought to have the object-scale symmetry (OSS) between the scales and nouns for evaluation using these scales. For example, the nouns corresponding to the listed 7 factors would be: Beauty, Power, Motion, Life, Work, Chaos, Law. Beauty was expected to be assessed unequivocally as "very good" on adjectives of Evaluation-related scales, Life as "very real" on Reality-related scales, etc. However, deviations in this symmetric and very basic matrix might show underlying biases of two types: scales-related bias and objects-related bias. This OSS design meant to increase the sensitivity of the SD method to any semantic biases in responses of people within the same culture and educational background.

Prototype theory

Another set of concepts related to fuzziness in semantics is based on prototypes. The work of Eleanor Rosch in the 1970s led to a view that natural categories are not characterizable in terms of necessary and sufficient conditions, but are graded (fuzzy at their boundaries) and inconsistent as to the status of their constituent members. One may compare it with Jung's archetype, though the concept of archetype sticks to static concept. Some post-structuralists are against the fixed or static meaning of the words. Derrida, following Nietzsche, talked about slippages in fixed meanings.

Systems of categories are not objectively out there in the world but are rooted in people's experience. These categories evolve as learned concepts of the world – meaning is not an objective truth, but a subjective construct, learned from experience, and language arises out of the "grounding of our conceptual systems in shared embodiment and bodily experience". A corollary of this is that the conceptual categories (i.e. the lexicon) will not be identical for different cultures, or indeed, for every individual in the same culture. This leads to another debate.

General semantics

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/General_semantics

General semantics is concerned with how events translate to perceptions, how they are further modified by the names and labels we apply to them, and how we might gain a measure of control over our own responses, cognitive, emotional, and behavioral. It can serve as an antidote to certain kinds of delusional thought patterns in which necessarily incomplete and possibly warped mental constructs are projected onto the world and treated as reality itself. After partial launches under the names human engineering and humanology, Polish-American originator Alfred Korzybski (1879–1950) fully launched the program as general semantics in 1933 with the publication of Science and Sanity: An Introduction to Non-Aristotelian Systems and General Semantics.

In Science and Sanity, general semantics is presented as both a theoretical and a practical system whose adoption can reliably alter human behavior in the direction of greater sanity. In the 1947 preface to the third edition of Science and Sanity, Korzybski wrote: "We need not blind ourselves with the old dogma that 'human nature cannot be changed', for we find that it can be changed." However, in the opinion of a majority of psychiatrists, the tenets and practices of general semantics are not an effective way of treating patients with psychological or mental illnesses. While Korzybski considered his program to be empirically based and to strictly follow the scientific method, general semantics has been described as veering into the domain of pseudoscience.

Starting around 1940, university English professor S. I. Hayakawa (1906–1992), speech professor Wendell Johnson, speech professor Irving J. Lee, and others assembled elements of general semantics into a package suitable for incorporation into mainstream communications curricula. The Institute of General Semantics, which Korzybski and co-workers founded in 1938, continues today. General semantics as a movement has waned considerably since the 1950s, although many of its ideas live on in other movements, such as neuro-linguistic programming and rational emotive behavior therapy.

Overview

"Identification" and "the silent level"

In the 1946 "Silent and Verbal Levels" diagram, the arrows and boxes denote ordered stages in human neuro-evaluative processing that happens in an instant. Although newer knowledge in biology has more sharply defined what the text in these 1946 boxes labels "electro-colloidal", the diagram remains, as Korzybski wrote in his last published paper in 1950, "satisfactory for our purpose of explaining briefly the most general and important points". General semantics postulates that most people "identify," or fail to differentiate the serial stages or "levels" within their own neuro-evaluative processing. "Most people," Korzybski wrote, "identify in value levels I, II, III, and IV and react as if our verbalizations about the first three levels were 'it.' Whatever we may say something 'is' obviously is not the 'something' on the silent levels."

Institute of General Semantics "Silent and Verbal Levels" diagram, circa 1946

By making it a 'mental' habit to find and keep one's bearings among the ordered stages, general semantics training seeks to sharpen internal orientation much as a GPS device may sharpen external orientation. Once trained, general semanticists affirm, a person will act, respond, and make decisions more appropriate to any given set of happenings. Although producing saliva constitutes an appropriate response when lemon juice drips onto the tongue, a person has inappropriately identified when an imagined lemon or the word "l–e–m–o–n" triggers a salivation response.

"Once we differentiate, differentiation becomes the denial of identity," Korzybski wrote in Science and Sanity. "Once we discriminate among the objective and verbal levels, we learn 'silence' on the unspeakable objective levels, and so introduce a most beneficial neurological 'delay'—engage the cortex to perform its natural function." British-American philosopher Max Black, an influential critic of general semantics, called this neurological delay the "central aim" of general semantics training, "so that in responding to verbal or nonverbal stimuli, we are aware of what it is that we are doing".

In the 21st century, the physiology underlying identification and the neurological delay is thought to involve autoassociative memory, a neural mechanism crucial to intelligence. Briefly explained, autoassociative memory retrieves previously stored representations that most closely conform to any current incoming pattern (level II in the general semantics diagram) arriving from the senses. According to the memory-prediction model for intelligence, if the stored representations resolve the arriving patterns, this constitutes "understanding", and brain activity shifts from evaluation to triggering motor responses. When the retrieved representations do not sufficiently resolve newly arrived patterns, evaluating persists, engaging higher layers of the cortex in an ongoing pursuit of resolution. The additional time required for signals to travel up and down the cortical hierarchy constitutes what general semantics calls a "beneficial neurological delay".

Abstracting and consciousness of abstracting

Identification prevents what general semantics seeks to promote: the additional cortical processing experienced as a delay. Korzybski called his remedy for identification "consciousness of abstracting." The term "abstracting" occurs ubiquitously in Science and Sanity. Korzybski's use of the term is somewhat unusual and requires study to understand his meaning. He discussed the problem of identification in terms of "confusions of orders of abstractions" and "lack of consciousness of abstracting". To be conscious of abstracting is to differentiate among the "levels" described above; levels II–IV being abstractions of level I (whatever level I "is"—all we really get are abstractions). The techniques Korzybski prescribed to help a person develop consciousness of abstracting he called "extensional devices".

Extensional devices

Satisfactory accounts of general semantics extensional devices can be found easily. This article seeks to explain briefly only the "indexing" devices. Suppose you teach in a school or university. Students enter your classroom on the first day of a new term, and, if you identify these new students to a memory association retrieved by your brain, you under-engage your powers of observation and your cortex. Indexing makes explicit a differentiating of students_{this term} from students_{prior terms}. You survey the new students, and indexing explicitly differentiates student₁ from student₂ from student₃, etc. Suppose you recognize one student—call her Anna—from a prior course in which Anna either excelled or did poorly. Again, you escape identification by your indexed awareness that Anna_{this term, this course} is different from Anna_{that term, that course}. Not identifying, you both expand and sharpen your apprehension of "students" with an awareness rooted in fresh silent-level observations.

Language as a core concern

Autoassociative memory in the memory-prediction model describes neural operations in mammalian brains generally. A special circumstance for humans arises with the introduction of language components, both as fresh stimuli and as stored representations. Language considerations figure prominently in general semantics, and three language and communications specialists who embraced general semantics, university professors and authors Hayakawa, Wendell Johnson and Neil Postman, played major roles in framing general semantics, especially for non-readers of Science and Sanity.

The science

Many recognized specialists in the knowledge areas where Korzybski claimed to have anchored general semantics—biology, epistemology, mathematics, neurology, physics, psychiatry, etc.—supported his work in his lifetime, including Cassius J. Keyser, C. B. Bridges, W. E. Ritter, P. W. Bridgman, G. E. Coghill, William Alanson White, Clarence B. Farrar, David Fairchild, and Erich Kähler. Korzybski wrote in the preface to the third edition of Science and Sanity (1947) that general semantics "turned out to be an empirical natural science".

But the type of existence, if any, of universals and abstract objects is an issue of serious debate within metaphysical philosophy. So Black summed up general semantics as "some hypothetical neurology fortified with dogmatic metaphysics". And in 1952, two years after Korzybski died, American skeptic Martin Gardner wrote, "[Korzybski's] work moves into the realm of cultism and pseudo-science."

Former Institute of General Semantics executive director Steve Stockdale has compared GS to yoga. "First, I'd say that there is little if any benefit to be gained by just knowing something about general semantics. The benefits come from maintaining an awareness of the principles and attitudes that are derived from GS and applying them as they are needed. You can sort of compare general semantics to yoga in that respect... knowing about yoga is okay, but to benefit from yoga you have to do yoga." Similarly, Kenneth Burke explains Korzybski's kind of semantics contrasting it, in A Grammar of Motives, with a kind of Burkean poetry by saying "Semantics is essentially scientist, an approach to language in terms of knowledge, whereas poetic forms are kinds of action".

History

Early attempts at validation

The First American Congress for General Semantics convened in March 1935 at the Central Washington College of Education in Ellensburg, Washington. In introductory remarks to the participants, Korzybski said:

General semantics formulates a new experimental branch of natural science, underlying an empirical theory of human evaluations and orientations and involving a definite neurological mechanism, present in all humans. It discovers direct neurological methods for the stimulation of the activities of the human cerebral cortex and the direct introduction of beneficial neurological 'inhibition'....

He added that general semantics "will be judged by experimentation". One paper presented at the congress reported dramatic score improvements for college sophomores on standardized intelligence tests after six weeks of training by methods prescribed in Chapter 29 of Science and Sanity.

Interpretation as semantics

General semantics accumulated only a few early experimental validations. In 1938, economist and writer Stuart Chase praised and popularized Korzybski in The Tyranny of Words. Chase called Korzybski "a pioneer" and described Science and Sanity as "formulating a genuine science of communication. The term which is coming into use to cover such studies is 'semantics,' matters having to do with signification or meaning." Because Korzybski, in Science and Sanity, had articulated his program using "semantic" as a standalone qualifier on hundreds of pages in constructions like "semantic factors," "semantic disturbances," and especially "semantic reactions," to label the general semantics program "semantics" amounted to only a convenient shorthand.

Hayakawa read The Tyranny of Words, then Science and Sanity, and in 1939 he attended a Korzybski-led workshop conducted at the newly organized Institute of General Semantics in Chicago. In the introduction to his own Language in Action, a 1941 Book of the Month Club selection, Hayakawa wrote, "[Korzybski's] principles have in one way or another influenced almost every page of this book...." But, Hayakawa followed Chase's lead in interpreting general semantics as making communication its defining concern. When Hayakawa co-founded the Society for General Semantics and its publication ETC: A Review of General Semantics in 1943—he would continue to edit ETC. until 1970—Korzybski and his followers at the Institute of General Semantics began to complain that Hayakawa had wrongly coopted general semantics. In 1985, Hayakawa gave this defense to an interviewer: "I wanted to treat general semantics as a subject, in the same sense that there's a scientific concept known as gravitation, which is independent of Isaac Newton. So after a while, you don't talk about Newton anymore; you talk about gravitation. You talk about semantics and not Korzybskian semantics."

Lowered sights

The regimen in the Institute's seminars, greatly expanded as team-taught seminar-workshops starting in 1944, continued to develop following the prescriptions laid down in Chapter XXIX of Science and Sanity. The structural differential, patented by Korzybski in the 1920s, remained among the chief training aids to help students reach "the silent level," a prerequisite for achieving "neurological delay". Innovations in the seminar-workshops included a new "neuro-relaxation" component, led by dancer and Institute editorial secretary Charlotte Schuchardt (1909–2002).

But although many people were introduced to general semantics—perhaps the majority through Hayakawa's more limited 'semantics'—superficial lip service seemed more common than the deep internalization that Korzybski and his co-workers at the Institute aimed for. Marjorie Kendig (1892–1981), probably Korzybski's closest co-worker, director of the Institute after his death, and editor of his posthumously published Collected Writings: 1920–1950, wrote in 1968:

I would guess that I have known about 30 individuals who have in some degree adequately, by my standards, mastered this highly general, very simple, very difficult system of orientation and method of evaluating—reversing as it must all our cultural conditioning, neurological canalization, etc.... To me the great error Korzybski made—and I carried on, financial necessity—and for which we pay the price today in many criticisms, consisted in not restricting ourselves to training very thoroughly a very few people who would be competent to utilize the discipline in various fields and to train others. We should have done this before encouraging anyone to popularize or spread the word (horrid phrase) in societies for general semantics, by talking about general semantics instead of learning, using, etc. the methodology to change our essential epistemological assumptions, premises, etc. (unconscious or conscious), i.e. the un-learning basic to learning to learn.

Yes, large numbers of people do enjoy making a philosophy of general semantics. This saves them the pain of rigorous training so simple and general and limited that it seems obvious when said, yet so difficult.

Successors at the Institute of General Semantics continued for many years along the founders' path. Stuart Mayper (1916–1997), who studied under Karl Popper, introduced Popper's principle of falsifiability into the seminar-workshops he led at the Institute starting in 1977. More modest pronouncements gradually replaced Korzybski's claims that general semantics can change human nature and introduce an era of universal human agreement. In 2000, Robert Pula (1928–2004), whose roles at the Institute over three decades included Institute director, editor-in-chief of the Institute's General Semantics Bulletin, and leader of the seminar-workshops, characterized Korzybski's legacy as a "contribution toward the improvement of human evaluating, to the amelioration of human woe...."

Hayakawa died in 1992. The Society for General Semantics merged into the Institute of General Semantics in 2003. In 2007, Martin Levinson, president of the Institute's Board of Trustees, teamed with Paul D. Johnston, executive director of the Society at the date of the merger, to teach general semantics with a light-hearted Practical Fairy Tales for Everyday Living. The Institute currently offers no training workshops.

Other institutions supporting or promoting general semantics in the 21st century include the New York Society for General Semantics, the European Society for General Semantics, the Australian General Semantics Society, and the Balvant Parekh Centre for General Semantics and Other Human Sciences (Baroda, India).

The major premises

• Non-Aristotelianism: While Aristotle wrote that a true definition gives the essence of the thing (defined in Greek to ti ên einai, literally "the what it was to be"), general semantics denies the existence of such an 'essence'. In this, general semantics purports to represent an evolution in human evaluative orientation. In general semantics, it is always possible to give a description of empirical facts, but such descriptions remain just that—descriptions—which necessarily leave out many aspects of the objective, microscopic, and submicroscopic events they describe. According to general semantics, language, natural or otherwise (including the language called 'mathematics') can be used to describe the taste of an orange, but one cannot give the taste of the orange using language alone. According to general semantics, the content of all knowledge is structure, so that language (in general) and science and mathematics (in particular) can provide people with a structural 'map' of empirical facts, but there can be no 'identity', only structural similarity, between the language (map) and the empirical facts as lived through and observed by people as humans-in-environments (including doctrinal and linguistic environments).
• Time binding: The human ability to pass information and knowledge from one generation to the next. Korzybski claimed this to be a unique capacity, separating people from animals. This distinctly human ability for one generation to start where a previous generation left off, is a consequence of the uniquely human ability to move to higher and higher levels of abstraction without limit. Animals may have multiple levels of abstraction, but their abstractions must stop at some finite upper limit; this is not so for humans: humans can have 'knowledge about knowledge', 'knowledge about knowledge about knowledge', etc., without any upper limit. Animals possess knowledge, but each generation of animals does things pretty much in the same way as the previous generation, limited by their neurology and genetic makeup. For example, at one time most human societies were hunter-gatherers, but now more advanced means of food production (growing, raising, or buying) predominate. Except for some insects (for example, ants), all animals are still hunter-gatherer species, even though many have existed longer than the human species. For this reason, animals are regarded in general semantics as space-binders (doing space-binding), and plants, which are usually stationary, as energy-binders (doing energy-binding).
• Non-elementalism and non-additivity: The refusal to separate verbally what cannot be separated empirically, and the refusal to regard such verbal splits as evidence that the 'things' that are verbally split bear an additive relation to one another. For example, space-time cannot empirically be split into 'space' + 'time', a conscious organism (including humans) cannot be split into 'body' + 'mind', etc., therefore, people should never speak of 'space' and 'time' or 'mind' and 'body' in isolation, but always use the terms space-time or mind-body (or other organism-as-a-whole terms).
• Infinite-valued determinism: General semantics regards the problem of 'indeterminism vs. determinism' as the failure of pre-modern epistemologies to formulate the issue properly as the failure to consider or include all factors relevant to a particular prediction, and failure to adjust our languages and linguistic structures to empirical facts. General semantics resolves the issue in favor of determinism of a special kind called 'infinite-valued' determinism which always allows for the possibility that relevant 'causal' factors may be 'left out' at any given date, resulting in, if the issue is not understood at that date, 'indeterminism', which simply indicates that our ability to predict events has broken down, not that the world is 'indeterministic'. General semantics considers all human behavior (including all human decisions) as, in principle, fully determined once all relevant doctrinal and linguistic factors are included in the analysis, regarding theories of 'free will' as failing to include the doctrinal and linguistic environments as environments in the analysis of human behavior.

Connections to other disciplines

The influence of Ludwig Wittgenstein and the Vienna Circle, and of early operationalists and pragmatists such as Charles Sanders Peirce, is particularly clear in the foundational ideas of general semantics. Korzybski himself acknowledged many of these influences.

The concept of "silence on the objective level"—attributed to Korzybski and his insistence on consciousness of abstracting—are parallel to some of the central ideas in Zen Buddhism. Although Korzybski never acknowledged any influence from this quarter, he formulated general semantics during the same years that the first popularizations of Zen were becoming part of the intellectual currency of educated speakers of English. On the other hand, later Zen-popularizer Alan Watts was influenced by ideas from general semantics.

General semantics has survived most profoundly in the cognitive therapies that emerged in the 1950s and 1960s. Albert Ellis (1913–2007), who developed rational emotive behavior therapy, acknowledged influence from general semantics and delivered the Alfred Korzybski Memorial Lecture in 1991. The Bruges (Belgium) center for solution-focused brief therapy operates under the name Korzybski Institute Training and Research Center. George Kelly, creator of personal construct psychology, was influenced by general semantics. Fritz Perls and Paul Goodman, founders of Gestalt therapy are said to have been influenced by Korzybski Wendell Johnson wrote "People in Quandaries: The Semantics of Personal Adjustment" in 1946, which stands as the first attempt to form a therapy from general semantics.

Ray Solomonoff (1926–2009) was influenced by Korzybski. Solomonoff was the inventor of algorithmic probability, and founder of algorithmic information theory (a.k.a. Kolmogorov complexity).

Another scientist influenced by Korzybski (verbal testimony) is Paul Vitanyi (born 1944), a scientist in the theory of computation.

During the 1940s, 1950s, and 1960s, general semantics entered the idiom of science fiction. Notable examples include the works of A. E. van Vogt, The World of Null-A and its sequels. General semantics appear also in Robert A. Heinlein's work, especially Gulf. Bernard Wolfe drew on general semantics in his 1952 science fiction novel Limbo. Frank Herbert's novels Dune and Whipping Star are also indebted to general semantics. The ideas of general semantics became a sufficiently important part of the shared intellectual toolkit of genre science fiction to merit parody by Damon Knight and others; they have since shown a tendency to reappear in the work of more recent writers such as Samuel R. Delany, Suzette Haden Elgin and Robert Anton Wilson. In 2008, John Wright extended van Vogt's Null-A series with Null-A Continuum. William Burroughs references Korzybski's time binding principle in his essay The Electronic Revolution, and elsewhere. Henry Beam Piper explicitly mentioned general semantics in Murder in the Gunroom, and its principles, such as awareness of the limitations of knowledge, are apparent in his later work. A fictional rendition of the Institute of General Semantics appears in the 1965 French science fiction film, Alphaville, directed by Jean-Luc Godard.

Neil Postman, founder of New York University's media ecology program in 1971, edited ETC.: A Review of General Semantics from 1976 to 1986. Postman's student Lance Strate, a co-founder of the Media Ecology Association, served as executive director of the Institute of General Semantics from 2007 to 2010.