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Computers figuring out what words mean from PhysOrg.com
The Internet got smarter this week with the release of a semantic map that teaches computers the meanings behind words -- and gives the machines a vocabulary far larger than that of a typical US college graduate.
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Depending on your terminological tradition, objects have "instance variables," "members," "fields," "slots," "associations" or "references." But what matters isn't the terminology, but the semantics and mathematics.
In terms of structure and mathematics, objects are nodes (vetices, points) in a directed graph, and the references objects make to each other are directed arcs (edges) in the graph. The transitive closure of all the objects reachable by following the references from some root object is commonly called the object graph of the root object. Any object can be considered as the "root" of an object graph—although each object considered as a root node results in a different object graph.
This is one reason you typically don't find either a tree data structure or a graph data structure provided as a Collection class in a typical class library: An object already is a tree or graph node, and by defining the object's instance variables and methods, its class defines the name and semantics of the "arcs" that can be navigated from a node of that type.
Objects are graph-theoretic, in the same sense that relational databases are set-theoretic. Set theory is a subset of graph theory—which is why object-relational mapping is non-trivial.
But what is the semantics of a variable? In other words, what is the meaning of the fact that a particular variable (or any reference by name to an object) has any particular value?
Predicates
A named object reference, just like an arc in a graph, represents (or "models") an arity-2 predicate, where the two nodes are the arguments of the predicate. The existence of the arc connecting the two graph nodes is, by convention, interpreted as an assertion that the predicate represented ("modelled") by the arc that connects the two nodes is true.
For example, a variable named "parent" would normally be used to assert that whatever object is the value of that variable is the "parent" of some other object (or represents/models the parent of whatever entity is being modelled by that object). More formally, if there is an object A and an object B, and object A has an instance variable named "parent" whose value is object B, then that situation is normally intended to represent (model) the fact that the predicate hasParent(objectA, objectB) is true.
The name of a variable (or of any named reference to a value) should indicate the semantic role played by the variable—in other words, it should name the predicate modelled by the reference. Programmers often fail to do this, preferring instead to give names to variables based on the type of the values to be held by the variables. But that's a fundamental mistake: It's much easier to infer the type of a value from its semantic role, than it is to infer the semantic role from the type. And the semantic role played by variables is far more important to the meaning of code than is the type of their values.
Language designers typically have been making an analogous mistake, focusing consiberable energy and effort on type constraint systems for variables, while almost completely ignoring the core semantics of variables (or other named references to values.) Fortunately, that is beginning to change.
In addition to the semantic role played by a variable, the following aspects of (or perspectives on) the semantics of a reference to an object (value) are also of fundamental importance:
Xerox Rolls Out Semantics-Based Search from PhysOrg.com
(AP) -- Xerox Corp. says its new search engine based on semantics will analyze the meaning behind questions and documents to help researchers find information more quickly.
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In the article Web Services 'Wizard' May Help Computers Do People's Work, Scientist Says, ScienceDaily reports:
Many web services exist today to allow people to find information on websites. But machines can't negotiate with other machines or engage in rudimentary deductive reasoning, as Petrie envisions. Computers use the standard Web Services Description Language (WSDL) put forth by W3C, a consortium established by web inventor Tim Berners-Lee, to understand input messages and deliver output messages in the same language.
But even if a standard way of describing computer services exists on the web, each description uses different terms. A person planning a trip can go to an airline website and find flight information. But machines charged with the same task fail. "Flight" may be abbreviated as "flt" on one carrier's website but as "flite" on another's.
"A human can look at those two words and know that must be the same thing, but a machine has no idea," Petrie says. That makes it difficult for web services to search multiple sites, retrieve desired information and present a menu of options.
Existing web services lack a common understanding of terms. The way to fix this, Petrie says, is to develop formal semantics, or logical expressions from which meaning can be inferred. The challenge is that some formalisms are expressive but too hard to compute, while others are easy to compute but insufficiently expressive.
Why is Business Semantics the New Hot Topic?
Quote:
Q: Can we talk more about ontologies? What is an ontology and why is it useful?
McComb: The most commonly quoted definition (from Tom Gruber of Stanford) of an ontology is "a specification of a conceptualization." Or, to put that slightly differently, an ontology is a formal way to organize knowledge and terms. Typically ontologies are represented as graphical relationships or networks, as opposed to taxonomies which are usually represented hierarchically.
As to why they are useful? Well, in any system (be it computer-based or in the world at large), we need to have agreement on the meaning or terms and their interrelationships in order to share understanding. In the IT world, software and software agents act upon these meanings, so there need to be specific "commitments" to the meanings in the system. Paraphrasing Gruber again, we build software and agents that commit to ontologies, and we design ontologies so we can share knowledge among the agents. The value (to answer the original question) then comes from the ability to share and re-use knowledge between agents in the system.
In Start Making Sense: Get From Data To Semantic Integration, author Neil Raden (founder and president of consulting firm Hired Brains) makes the following points:
What if we had a different kind of metadata that would let more users go further on their own with BI [Business Intelligence] and productive data analysis? Semantic integration technology, also known as "ontology," is the next step in rationalizing information integration in and beyond organizations. Semantic integration is the application of Semantic Web concepts developed by Tim Berners-Lee, director of the World Wide Web Consortium (W3C). Berners-Lee and the W3C define the Semantic Web as a framework that "allows data to be shared and reused across application, enterprise and community boundaries." Semantic integration raises the level of abstraction so people and systems can focus on meaning and relationships.
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A rich semantic ontology can serve as the abstraction layer, not just between BI tools and the data warehouse, but between everything. For an enterprise, semantic integration can become the orchestration point, resource broker and domain adviser. Physical location and data structures become irrelevant; data stewards are free to tune and modify their resources as they see fit. Operational and analytic processes are freed of the burden of tracking data.
With composite application development based on service-oriented architecture (SOA) on the rise, consider one last critical reason to do something about semantic integration. The composite idea is to assemble and reassemble, in something close to real time, applications based on business needs from standards-based services. In other words, we'll be relying on machines to make inferences about information because there won't be time to program everything.
