In relation to Accommodations, this charge includes an estimated amount to recover .. symbol or other proprietary designation of Agoda, its licensor or associated . You further grant Agoda the right to pursue at law any person or entity that. Definitional networks emphasize the subtype or is-a relation between a concept type . For comparison, the diagram on the right of Figure 5 is a discourse .. from and reason with information in the multiple languages of Wikipedia. predicate calculus with the symbol #self representing the current entity. DAFTAR SIMBOL Simbol Entity Relationship Diagram (ERD) No Simbol antar satu entitas dengan entitas lainnya 3 Atribut Atribut adalah karakteristik dari.
The generic terms sign or representation suffice for these, with the qualification that the terms are used equivocally up and down a full spectrum from the more abstract types to the more concrete tokens that are associated with each other.
More specifically, the linguistic turn in analytic philosophy begins with a focus on the syntactic character of the sentence, from which is abstracted its meaningful content, referred to as the corresponding proposition. A proposition is the content expressed by a sentence, held in a belief, or affirmed in an assertion or judgment.
Truthbearer is used by a number of writers to refer to any entity that can be judged true or false. The term truthbearer may be applied to propositions, sentences, statements, ideas, beliefs, and judgments. Some writers exclude one or more of these categories, or argue that some of them are true or false only in a derivative sense.
Other writers may add additional entities to the list. Truthbearers typically have two possible values, true or false. Fictional forms of expression are usually regarded as false if interpreted literally, but may be said to bear a species of truth if interpreted suitably.
Still other truthbearers may be judged true or false to a greater or lesser degree. Higher order signs[ edit ] As predicate terms, most discussions of truth allow for a number of phrases that are used to say in what ways signs or sentences or their abstract senses are regarded as true, either by themselves or in relation to other things. Theorists who admit the term call these phrases truth predicates.
A truth predicate that is used to ascribe truth to something, in and of itself, in effect treating truth as an intrinsic property of the thing, is called a one-place or monadic truth predicate.
Other forms of truth predicates may be used to say that something is true in relation to specified numbers and types of other things. These are called many-place or polyadic truth predicates. In ordinary parlance, the things that one says about a subject are expressed in predicates. If one says that a sentence is true, then one is predicating truth of that sentence.
Is this the same thing as asserting the sentence? This question serves as useful touchstone for sorting out some of the theories of truth.
Propositional attitudes[ edit ] What sort of name shall we give to verbs like 'believe' and 'wish' and so forth? I should be inclined to call them 'propositional verbs'. This is merely a suggested name for convenience, because they are verbs which have the form of relating an object to a proposition. As I have been explaining, that is not what they really do, but it is convenient to call them propositional verbs.
Of course you might call them 'attitudes', but I should not like that because it is a psychological term, and although all the instances in our experience are psychological, there is no reason to suppose that all the verbs I am talking of are psychological.
There is never any reason to suppose that sort of thing. What a proposition is, is one thing. How we feel about it, or how we regard it, is another. We can accept it, assert it, believe it, command it, contest it, declare it, deny it, doubt it, enjoin it, exclaim it, expect it, imagine it, intend it, know it, observe it, prove it, question it, suggest it, or wish it were so.
Different attitudes toward propositions are called propositional attitudes, and they are also discussed under the headings of intentionality and linguistic modality. The formal properties of verbs like assert, believe, command, consider, deny, doubt, hunt, imagine, judge, know, want, wish, and a host of others, are studied under these headings by linguists and logicians alike.
Many problematic situations in real life arise from the circumstance that many different propositions in many different modalities are in the air at once.
In order to compare propositions of different colors and flavors, as it were, we have no basis for comparison but to examine the underlying propositions themselves. Thus we are brought back to matters of language and logic.
Despite the name, propositional attitudes are not regarded as psychological attitudes proper, since the formal disciplines of linguistics and logic are concerned with nothing more concrete than what can be said in general about their formal properties and their patterns of interaction.
The variety of attitudes that a proposer can bear toward a single proposition is a critical factor in evaluating its truth. One topic of central concern is the relation between the modalities of assertion and belief, especially when viewed in the light of the proposer's intentions.
For example, we frequently find ourselves faced with the question of whether a person's assertions conform to his or her beliefs. Discrepancies here can occur for many reasons, but when the departure of assertion from belief is intentional, we usually call that a lie.
This issue occurs mostly in databases for decision support systems, and software that queries such systems sometimes includes specific methods for handling this issue.
The second issue is a 'chasm trap'. A chasm trap occurs when a model suggests the existence of a relationship between entity types, but the pathway does not exist between certain entity occurrences.
For example, a Building has one-or-more Rooms, that hold zero-or-more Computers. One would expect to be able to query the model to see all the Computers in the Building. However, Computers not currently assigned to a Room because they are under repair or somewhere else are not shown on the list. Another relation between Building and Computers is needed to capture all the computers in the building.
This last modelling issue is the result of a failure to capture all the relationships that exist in the real world in the model. See Entity-Relationship Modelling 2 for details. Entity—relationships and semantic modeling[ edit ] Semantic model[ edit ] A semantic model is a model of concepts, it is sometimes called a "platform independent model".
It is an intensional model. At the latest since Carnapit is well known that: The first part comprises the embedding of a concept in the world of concepts as a whole, i. The second part establishes the referential meaning of the concept, i. Extension model[ edit ] An extensional model is one that maps to the elements of a particular methodology or technology, and is thus a "platform specific model". A relational graph Figure 4 contains three branching lines of identity, each of which corresponds to an existentially quantified variable in the algebraic notation.
The words and phrases attached to those lines correspond to the relations or predicates in the algebraic notation. With that correspondence, Figure 4 can be translated to the following formula in predicate calculus: As this formula illustrates, a relational graph can only represent two logical operators: The blank nodes in RDF correspond to existential quantifiers.
The problem of representing scope, which Peirce faced in his graphs ofalso plagued the early semantic networks used in artificial intelligence 80 years later. InPeirce made a simple, but brilliant discovery that solved all the problems at once: Then combinations of ovals with conjunction and the existential quantifier could express all the logical operators used in the algebraic notation Peirce At the left of Figure 5 is an existential graph for the sentence If a farmer owns a donkey, then he beats it.
The subgraph in the outer oval may be read If a farmer x owns a donkey y. The lines x and y are extended into the inner oval, which represents the consequent, then x beats y. Figure 5 may be translated to the following algebraic formula: For comparison, the diagram on the right of Figure 5 is a discourse representation structure DRSwhich Hans Kamp invented to represent natural language semantics.
Instead of nested ovals, Kamp used boxes linked by arrows; instead of lines of identity, he used variables. But the logical structures are formally equivalent: As an example that uses disjunction, Figure 6 shows an EG top and a DRS bottom for the sentence Either Jones owns a book on semantics, Smith owns a book on logic, or Cooper owns a book on unicorns. In both diagrams, the existential quantifiers for Jones, Smith, and Cooper are in the outer area, but the quantifiers for the three books are inside the alternatives.
Both diagrams can be mapped to exactly the same formula: At the top is the verb piqua stungfrom which the words that depend directly on the verb are hanging: Every word other than piqua is hanging below some word on which it depends.
Klein and Simmons adopted it for a machine translation system. The dependency theories have also been strongly influenced by case grammar Fillmorewhich provides a convenient set of labels for the arcs of the graphs Somers Figure 8 shows a conceptual dependency graph for the sentence A dog is greedily eating a bone.
He replaced the verb eat with one of his primitive acts ingest; he replaced adverbs like greedily with adjective forms like greedy; and he added the linked arrows marked with d for direction to show that the bone goes from some unspecified place into the dog the subscript 1 indicates that the bone went into the same dog that ingested it.
To learn or discover the larger structures automatically, case-based reasoning has been used to search for commonly occurring patterns among the lower-level conceptual dependencies Schank et al.
Even when those graphs have nodes marked with other logical operators, such as disjunction, negation, or the universal quantifier, they fail to express their scope correctly. During the s, various network notations were developed to represent the scope of logical operators.
The most successful approach was the method of adding explicit nodes to show propositions. Logical operators would connect the propositional nodes, and relations would either be attached to the propositional nodes or be nested inside them. In Figure 5, for example, the two EG ovals and the two DRS boxes represent propositions, each of which contains nested propositions. Each of the nodes labeled M1 through M5 represents a distinct proposition, whose relational content is attached to the propositional node.
For M2, the experiencer is Bob, the verb is believe, and the theme is a proposition M3. For M3, the agent Agnt is some entity B1, which is a member of the class Dog, the verb is eat, and the patient Ptnt is an entity B2, which is a member of the class Bone.
As Figure 9 illustrates, propositions may be used at the metalevel to make statements about other propositions: Conceptual graphs Sowaare a variety of propositional semantic networks in which the relations are nested inside the propositional nodes. The more subtle differences are in the range of quantification and the point where the quantification occurs. Donkeywhich is restricted to the type or sort Farmer or Donkey.
In the CG, the arcs with arrows indicate the argument of the relations numbers are used to distinguish the arcs for relations with more than two arguments.Entity Relationship Diagram (ERD) Tutorial - Part 2
A conceptual graph that corresponds to Figure 9 Figures 8 and 10 both represent the sentence Sue thinks that Bob believes that a dog is eating a bone. The SNePS proposition M1 corresponds to the entire CG in Figure 11; M2 corresponds to the concept box that contains the CG for the nested proposition Bob believes that a dog is eating a bone; and M3 corresponds to the concept box that contains the CG for the more deeply nested proposition A dog is eating a bone.
Data model - Wikipedia
Each concept box in a CG could be considered a separate proposition node that could be translated to a complete sentence by itself. Sue] expresses the sentence There exists a person named Sue. By such methods, it is possible to translate propositions expressed in SNePS or CGs to equivalent propositions in the other notation. For most sentences, the translations are nearly one-to-one, but sentences that take advantage of special features in one notation may require a more roundabout paraphrase when translated to the other.
Different versions of propositional semantic networks have different syntactic mechanisms for associating the relational content with the propositional nodes, but formal translation rules can be defined for mapping one version to another.
Peirce, Sowa, and Kamp used strictly nested propositional enclosures with variables or lines to show coreferences between different enclosures. Gary Hendrixdeveloped a third option: Implicational Networks An implicational network is a special case of a propositional semantic network in which the primary relation is implication.
Other relations may be nested inside the propositional nodes, but they are ignored by the inference procedures. Depending on the interpretation, such networks may be called belief networks, causal networks, Bayesian networks, or truth-maintenance systems.
Sometimes the same graph can be used with any or all of these interpretations. Figure 12 shows possible causes for slippery grass: If it is the rainy season, the arrow marked T implies that it recently rained; if not, the arrow marked F implies that the sprinkler is in use. For boxes with only one outgoing arrow, the truth of the first proposition implies the truth of the second, but falsity of the first makes no prediction about the second.
An implicational network for reasoning about wet grass Suppose someone walking across a lawn slips on the grass. Figure 12 represents the kind of background knowledge that the victim might use to reason about the cause. A likely cause of slippery grass is that the grass is wet. It could be wet because either the sprinkler had been in use or it had recently rained.
If it is the rainy season, the sprinkler would not be in use. Therefore, it must have rained. The kind of reasoning described in the previous paragraph can be performed by various AI systems. Chuck Rieger developed a version of causal networks, which he used for analyzing problem descriptions in English and translating them to a network that could support metalevel reasoning.
Judea Pearl, who has developed techniques for applying statistics and probability to AI, introduced belief networks, which are causal networks whose links are labeled with probabilities.
Different methods of reasoning can be applied to the same basic graph, such as Figure Nodes of the graph may be annotated with truth values, probabilities, or fuzzy values. Methods of logical inference are used in truth-maintenance systems Doyle ; de Kleer A TMS would start at nodes whose truth values are known and propagate them throughout the network. For the case of the person who slipped on the grass, it would start with the value T for the fact that the grass is slippery and work backwards.
Alternatively, a TMS could start with the fact that it is now the rainy season and work forwards. By combinations of forward and backward reasoning, a TMS propagates truth values to nodes whose truth value is unknown. Besides deducing new information, a TMS can be used to verify consistency, search for contradictions, or find locations where the expected implications do not hold.
When contradictions are found, the structure of the network may be modified by adding or deleting nodes; the result is a kind of nonmonotonic reasoning called belief revision. Forward and backward reasoning methods can also be adapted to computing probabiliies, since truth can be considered a probability of 1 and falsity as 0. But the continuous range of probabilities [0. The most detailed study of probabilistic reasoning in causal or belief networks has been done by Pearl Pearl analyzed various techniques for applying Bayesian statistics to derive a causal network from observed data and to reason about it.
In both the logic-based and the statistical systems, the information used to derive the implications is ignored by the inference procedures. Doyle developed the first TMS by extracting a subgraph of implications from the rules of an expert system. Similar techniques can be applied to other propositional networks to derive an implicational subgraph that could be analyzed by logical or statistical methods. Although implicational networks emphasize implication, they are capable of expressing all the Boolean connectives by allowing a conjunction of inputs to a propositional node and a disjunction of outputs.
Gerhard Gentzen showed that a collection of implications in that form could express all of propositional logic. The generalized rule of modus ponens states that when every one of the antecedents is true, at least one of the consequents must be true. In effect, the commas in the antecedent have the effect of and operators, and the commas in the consequent have the effect of or operators.
That option, called Horn-clause logic, is widely used for rule-based systems. To support full propositional logic, later versions of TMS allow multiple or operators in the consequent. Executable Networks Executable semantic networks contain mechanisms that can cause some change to the network itself.
The executable mechanisms distinguish them from networks that are static data structures, which can only change through the action of programs external to the net itself. Three kinds of mechanisms are commonly used with executable semantic networks: Message passing networks can pass data from one node to another. For some networks, the data may consist of a single bit, called a marker, token, or trigger; for others, it may be a numeric weight or an arbitrarily large message.
Attached procedures are programs contained in or associated with a node that perform some kind of action or computation on data at that node or some nearby node. Graph transformations combine graphs, modify them, or break them into smaller graphs. In typical theorem provers, such transformations are carried out by a program external to the graphs.
When they are triggered by the graphs themselves, they behave like chemical reactions that combine molecules or break them apart. These three mechanisms can be combined in various ways. Messages passed from node to node may be processed by procedures attached to those nodes, and graph transformations may also be triggered by messages that appear at some of the nodes.
An important class of executable networks was inspired by the work of the psychologist Otto Selz, who was dissatisfied with the undirected associationist theories that were then current. As an alternative, Selz proposed schematic anticipation as a goal-directed method of focusing the thought processes on the task of filling empty slots in a pattern or schema. Figure 13 is an example of a schema that Selz asked his test subjects to complete while he recorded their verbal protocols.
This task is actually more difficult in German than in English: The simplest networks with attached procedures are dataflow graphs, which contain passive nodes that hold data and active nodes that take data from input nodes and send results to output nodes.
Figure 14 shows a dataflow graph with boxes for the passive nodes and diamonds for the active nodes. A dataflow graph For numeric computations, dataflow graphs have little advantage over the algebraic notation used in common programming languages.
Figure 14, for example, would correspond to an assignment statement of the following form: When dataflow graphs are supplemented with a graphic method for specifying conditions, such as if-then-else, and a way of defining recursive functions, they can form a complete programming language, similar to functional programming languages such as Scheme and ML. Petri nets, first introduced by Carl Adam Petriare the most widely-used formalism that combines marker passing with procedures.
Like dataflow diagrams, Petri nets have passive nodes, called places, and active nodes, called transitions. In addition, they have a set of rules for marking places with dots, called tokens, and for executing or firing the transitions. To illustrate the flow of tokens, Figure 15 shows a Petri net for a bus stop where three tokens represent people waiting and one token represents an arriving bus. Petri net for a bus stop At the upper left of Figure 15, each of the three tokens represents one person waiting at the bus stop.
The token at the upper right represents an arriving bus. The transition labeled Bus stops represents an event that fires by removing the token from the arriving place and putting a token in the waiting place. When the bus is waiting, the transition labeled One person gets on bus is enabled because it has at least one token in both of its input places.
It fires by first removing one token from both of its input places and putting one token in both of its output places including the Bus waiting place from which one token had just been removed.