Natural language processing methods and systems6950814Abstract Scheme for enriching an input network with knowledge from a fractal semantic knowledge network. The input network comprises objects and pointers between these objects, and the knowledge network comprises semantic units, and a plurality of Jani, whereby any of these Jani is associated with one or more of the semantic units such that the respective Janus is able to operate on one or more of the semantic units. The following steps are carried out: finding a counterpart element for an object or a pointer by looking for a semantic unit that is related to the object or the pointer; establishing a classification connection between the object or the pointer and its counterpart element; assigning the module that is associated with the counterpart element, if any, to the object or the pointer; examining the objects' or the pointers' neighborhoods in the input network by comparing them with the counterpart elements' neighborhoods in knowledge network to verify the classification connection. Claims 1. A computer implemented method for enriching an input network with knowledge from a fractal semantic knowledge network, wherein said input network comprises objects and pointers between said objects, and the knowledge network comprises semantic units and a plurality of modules arranged in a fractal semantic network, whereby any one of said modules is associated with one or more of said semantic units such that the respective module is able to operate on the one or more of said semantic units, said method comprising the steps of: Description CLAIM FOR PRIORITY
The construction of a fractal semantic network can be achieved as follows. The network is given by a list of semantic units 100, some of which are pointers (connection objects 104), as illustrated in FIGS. 2A and 2B. There might be different types of semantic units (as defined above) and pointers (similarity pointers 107, 109, functional pointers 108, 110, attribute pointers 111, and role pointers 112, also defined above). These pointers can be oriented upwards, downwards, or horizontally. The various semantic units 100 are interconnected in various ways. Some of the pointers (105) are hierarchical representing the multi-scale approach. Knowledge is presented in the knowledge database 11 as an associative network. A semantic unit may be linked by associative pointers to its associative semantic unit(s). These associative pointers reflect how one understands each semantic unit. It is important to note that these pointers can exist between any two semantic units. The pointers themselves are regarded as semantic units that can have pointers to other semantic units, reflecting the fact that something could act on the association between two semantic units rather than on the individual semantic units. Due to the fractal organization of the knowledge database, as outlined above, the otherwise very complex and rich structure of the world is significantly simplified. This also greatly simplifies the task of trying to understand the information conveyed in some input string 12 or input network 18, as will be addressed now. Using knowledge from the knowledge database 11 in order to enrich the information originally contained in an input network 18 can be done in a very efficient manner due to the fact that the network is fractal and thus self-similar across hierarchies. Using a local and iterative approach—as described hereinafter—one can use the knowledge contained in the knowledge database 11 to perform the understanding task of what is conveyed in the input string 12. The key is that this is done locally, making use of the fractal structure of the knowledge database 11, triggered by elements of the input network 18. The herein proposed schemes and systems are based on the above-described special model of textual information and natural language. According to this model, natural language as well as textual information consists of semantic units which are grouped at different levels of hierarchy and are all of a similar type. According to the present invention, it is advantageous to generate an input network 18 out of an input string 12 that also has a fractal semantic network structure like the knowledge database 11. Preferably, the knowledge database 11 and the input network 18 are all represented in what is called a higher-order fractal semantic network. The input network 18 of a speech input 12 or a question may be created by locating its semantic units, possible relations, possible attributes, and possible roles by using a parser that performs a deep syntactic and a shallow semantic analysis. The meaning understanding scheme presented herein yields a resulting semantic network (with a multitude of connections to the knowledge database) which can be used to generate a meaningful output 15, e.g., an answer or reaction. The presently described meaning understanding process bridges or merges two networks, the input network 18 and the knowledge database 11. The whole meaning understanding process is a process that is divided into an initialization phase and a subsequent iteration phase. The initialization may comprise two steps. According to the hereinafter described embodiments, the iteration also comprises two steps. Step one of the iteration is called Classification and is carried out by Classification Jani, while step two of the iteration is called Segmentation and is carried out by Segmentation Jani. There are different ways to initialize the process. One advantageous way of initialization is described in the following sections. Another approach is described towards the end of this description. During step 1 of the initialization, all objects of the input network 18 are regarded as unknown. They are thus attached via some initial classification connection to initial semantic units in the knowledge database 11 that are meant to be question marks, unspecified semantic units, or similar units that are within a category that is regarded as unknown. In other words, all objects may be connected to dummy semantic units. These kind of semantic units are herein referred to as Root Objects (root semantic units). A Root Object may have a Root Janus. In step 2 of the initialization some or all objects of the input network 18 inherit the Root Janus from the Root Object that they are attached to by their initial classification connection from step 1. Then for some or all of the objects the Root Jani get activated and refine the unspecified (initial) classification connections. An activated Root Janus tries to create one or several 'real' classification connections to semantic units in the knowledge database that are deemed to be related to the object of the input network 18, e.g., by analyzing word characteristics such as letter sequences or frequency spectra. This may include the matching of possibly misspelled words or typographical errors. Based on whether this 2nd step is successful, the confidence values Cx (see one of the later sections) of the classification connections may be adjusted accordingly to preliminary values (this is a heuristic process). For the purpose of the present description, it is assumed that there is an input network 18 that has several self-similar semantic units (objects) connected by pointers (see for example the input network 18 in FIG. 4A). Preferably, the input network 18 exhibits a hierarchical structure. The generation of such an input network 18 out of an input string 12, e.g. an unstructured sequence of words, is addressed in the co-pending patent application mentioned above, filed on the same day as the instant patent application. It is one goal of the present scheme to turn the input network 18 into a fractal semantic network structure that is comparable with the knowledge database's structure. In doing so, context is taken into account across a multitude of scales. The result is thus more reliable with respect to a correct understanding of the input's meaning than any process that uses just one level of hierarchy. During the initialization process described above, initial classification connections are established between objects of the input network 18 and semantic units of the knowledge database 11. An example of an input network 18 and a knowledge database 11 after completion of an initialization process is illustrated in FIG. 4A. The input network 18 (below the dashed line 45) comprises five objects 50.1-50.5. These objects are connected by pointers 51.1-51.4. During the initialization process described above, one compares the objects 50.1-50.5 and pointers 51.1-51.4 of the input network 18 with semantic units 40.1-40.5 and pointers 47.1-47.5 of the knowledge database 11 in order to find counterpart elements in this knowledge database 11. There are different ways of finding counterpart elements. One can, for example, do a simple string match where the string associated with an object 50.x or pointer 51.x in the input network 18 is compared to the strings associated with the semantic units 40.x or pointers 47.x in the knowledge database 11. Here is a simple example; assuming that the object 50.1 carries the string "car", a string matching algorithm is employed that searches the knowledge database 11 for strings that comprise the word "car". If the semantic unit 40.3 carries the name "car", then this particular semantic unit 40.3 is deemed to be a counterpart element. After such a counterpart element was found, a classification connection 42.1 is established between the object 50.1 and the semantic unit 40.3, as shown in FIG. 4C. Note that the simple string matching procedure can be generalized to a fuzzy string matching procedure, allowing to even compare word fragments or identify misspelled words. In the present example, just one counterpart element, namely 40.3, was found for 50.1. The object 50.2 has two counterpart elements 40.1 and 40.5 (cf. classification connections 42.2 and 42.3), the object 50.3 has no counterpart element, the object 50.4 has no counterpart element, and the object 50.5 has just one counterpart element 40.4 (cf. classification connection 42.4). Note that also one of the pointers of the input network 18, namely pointer 51.2 has a counterpart element 47.4 in the knowledge database 11. A corresponding classification connection 42.5 is thus established between pointer 51.2 and pointer 47.4. Instead of the string matching approach one can use a partial string matching approach, a fuzzy string matching approach, or the like. One can also employ an algorithm that analyzes word characteristics such as letter sequences or frequency spectra. Optionally, one can employ one or more dummy elements which are similar or the same as the Root Object in the knowledge database 11. One may establish classification connections from those objects in the input network 18 that do not have any counterpart elements in the knowledge database 11 to these dummy elements. This is not illustrated in any of the Figures. Referring now to FIGS. 4C-4F, the underlying principle of the iteration process is outlined. The first step of the iteration process is called the classification step. As mentioned further above, the process of Classification stands for the task of comparing one semantic unit to others. This is done in order to find comparable semantic units in the sense that they are alike, can perform similar tasks, have similar goals, are more general or more specific, are constituents or segments, or are in similar states, among other things. According to the present invention, the process of Classification is performed through an extensive local neighborhood analysis. This means that the degree of similarity of two semantic units is determined by the degree of similarity of their local neighborhoods with respect to the above comparison factors. As with determining the status of a semantic unit, when comparing semantic units it may not be enough to take into account the values of the attribute units of these semantic units. Instead, the topology of the network, i.e. the entire local neighborhood structures of the semantic units, may be considered. Therefore, an advantageous implementation of the process of Classification deals with the more general task of finding similar structures in the input network 18 and the knowledge database 18 and not just similar values of attribute units. Because of the self-similar structure of the networks 11 and 18, this Classification process can be implemented in a generic way, thus allowing the Classification Janus to be used throughout the entire network. During this classification step objects 50.x and pointers 51.x of the input network 18 inherit across all their classification connections 42.x the Classification Jani 43.x that are attached to the counterpart elements 40.x. That is, during this classification step one copies the Classification Jani 43.x that are associated with the counterpart elements 40.x, 47.x from these elements to the corresponding objects 50.x, 51.x in the input network 18. The Classification Jani 43.x are illustrated by little triangles. This classification step is illustrated in FIG. 4C. Note that the pointers 47.x may carry Classification Jani 43.x, too. In the present example, however, only the semantic units 40.x carry Classification Jani 43.x. In our example, the counterpart element 40.3 carries a Classification Janus 43.3. Since the counterpart element 40.3 is connected via the classification connection 42.1 to the object 50.1, the Classification Janus 43.3 is copied to this object 50.1. As shown in FIG. 4C, the object 50.1 now carries a copy of the Classification Janus 43.3. The counterpart element 40.6 is connected via the classification connection 42.4 to the object 50.5 and the Classification Janus 43.4 is copied to this object 50.5. The object 50.2 is an exception since this particular object is connected by two classification connections 42.2 and 42.3 to two counterpart elements 40.1 and 40.5. In such a case, the object 50.2 inherits the Classification Jani of all counterpart elements 40.1 and 40.5. That is, the two Classification Jani 43.1 and 43.5 are both copied to the object 50.2. After the Classification step is completed, an object 50.x, 51.x in the input network 18 may carry k Classification Jani 43.x, with k=0, 1, 2, . . . . Some or all of the Classification Jani 43.x perform—after having been triggered—local neighborhood analyses on the hierarchical level of their original locations in the knowledge database 11, i.e., they examine the local neighborhood structure in the knowledge database 11, such as roles or mandatory neighbors, and try to find similar structures in the neighborhood of the input network 18. Here they use the topology defined in the knowledge database 11 (by means of topology tables 32.x; 42.1) to determine the neighborhoods within which they conduct their analyses. FIG. 4B shows the topology table 42.1 of semantic unit 40.1. In addition, some or all of the Classification Jani 43.x may perform so-called upward segment neighborhood analyses in order to find neighbors which belong together because they are parts of entities on a higher scale. These upward neighbors may also represent more general descriptions of the semantic units in question. This helps to identify objects 50.x (or pointers 51.x) in the input network 18 that appear in multiple places. Overall successful upward segment neighborhood analysis gives an indication that the right meaning for the input constituents (objects 50.x and/or pointers 51.x) has been found in the knowledge database 11. Note that usually the objects 50.x of the input network 18 do not carry any Classification Jani, before the classification step is initiated. The objects 50.x may carry Root Jani though. Inheritance may also be interwoven with the classification step. For this purpose, objects 50.x and/or pointers 51.x may inherit attributes and/or relations from semantic units 40.x and/or pointers 47.x above it in hierarchy. The Classification Jani 43.x could for example examine the result(s) of an upward segment neighborhood analysis performed to find semantic units 40.x in the knowledge database 11 which belong to a segment on a higher level of hierarchy. For example, if an attribute is connected to a semantic unit 40.x, a descendant semantic unit, i.e., one that is below in hierarchy, is implicitly allowed to have the same attribute(s). This carries over through classification connections 42.x to objects 50.x and/or pointers 51.x in the input network 18. Let us assume that there is a semantic unit "car" and that this unit belongs to a semantic unit "vehicle" above in hierarchy. The semantic unit "vehicle" has the attribute "mobile". The semantic unit "car" now inherits this attribute since the car is mobile, too. This attribute "mobile" now carries over through a classification connection 42.x to respective object 50.x in the input network 18. In doing so, the input network 18 is enriched with knowledge from the knowledge database 11. Similarly, values of attributes may get inherited across hierarchies, where values are usually taken from the semantic unit closest to the one in question, as they can be regarded as more similar than units further away in hierarchy. For certain scaling or hierarchical connections 105 (is-in-particular connections or hierarchical similarity connections 107) roles or other closely linked neighbors can also be inherited. During this inheritance step, some or all of the objects 50.x and/or pointers 51.x in the input network 18 may inherit across some or all of their classification connections 42.x the Classification Jani 43.x that are attached to the counterpart elements 40.x and/or 47.x in the knowledge database 11. Note that the optional inheritance step and the classification step may at least to some extent be carried out concurrently. Continuing with our example, the Classification Janus 43.1 copied to the semantic unit 50.2 performs the neighborhood analysis as follows. First it examines the counterpart unit's topology table 42.1. The topology table 42.1 is illustrated in FIG. 4B. It finds that the semantic units 40.2 and 40.4 and the pointers 47.3 and 47.4, together with the semantic unit 46, form one local neighborhood, which happens to be the segment of the semantic unit 46. It now verifies whether these elements are counterparts of any of the elements of the input network 18. It finds that pointer 51.2 has counterpart pointer 47.4 through the classification connection 42.5. However, none of the other semantic units listed in the topology table are counterparts of elements of the input network 18. The search described in this paragraph is later referred to as the "bottom-up approach". Next, the semantic unit 46 is the segment that the semantic unit 40.1 belongs to, and the semantic unit 46 has a Classification Janus 43.6 attached. Therefore, this Classification Janus 43.6 is copied to the element 50.2 (cf. FIG. 4D). The Classification Janus 43.6 is triggered and performs a search later referred to as the "top-down approach". This is done as follows. First it finds that the element 50.1 is classified as semantic unit 40.3 (through the classification connection 42.1), which in turn is a special kind of semantic unit 40.2 (through the connection 47.1). Therefore, the semantic unit 40.2 is also (by inheritance) a counterpart of element 50.1. Then, it finds that the pointer 51.1 connects the objects 50.1 and 50.2. Thus, it creates a classification connection 42.7 between pointer 51.1 and pointer 47.3, the connection between the counterpart elements of 50.1 and 50.2. Finally, it finds that the object 50.3 is connected through the pointer 51.2 to the element 50.2, and the pointer 51.2 has counterpart pointer 47.4 through the classification connection 42.5. Therefore, it creates a classification connection 42.6 from the element 50.3 to the counterpart element 40.4. The Classification Janus 43.4 also performs a bottom-up search, which leads to the classification connection 42.9 between pointer 51.4 and pointer 47.2. Classification Jani 43.3 and 43.5 execute without any result. The result of this 1st iteration of the classification process is shown in FIG. 4D. Now that all Classification Jani 43.x have finished their task, the Segmentation Janus 46.4 gets triggered. Note that the Classification Jani 43.x could have performed most or all of their tasks in parallel. The Segmentation Janus 46.4 verifies that all semantic units of the segment 46, that is, the semantic units 40.1, 40.2, 40.4, 47.3, and 47.4, are counterparts of the elements in the input network 18. In addition, the connection structure among these input elements is the same as the connection structure among the semantic units of the segment 46. Therefore, the Segmentation Janus 46.4 creates a new segment 55 in the input network 18. Furthermore, it creates a classification connection 42.8 of the segment 55 to the segment 46. The result of this 1st iteration of the segmentation process is shown in FIG. 4E. Next follows the second iteration. The Classification Jani 43.2 and 43.6 and the Segmentation Janus 46.4 get copied through the newly created classification connections to the elements 50.3 and 55, respectively. This is illustrated in FIG. 4F. Some or all Classification Jani 43.x get triggered again. This time, the confidence values Cx for the classification connections 42.1, 42.2, 42.5, 42.6, 42.7, and 42.8 get increased because of the correctly classified segment structure. At the same time, the confidence value Cx for the classification connection 42.3 gets decreased because its neighborhood structure could not be classified correctly. It is now more likely that the semantic unit 40.1 is the correct counterpart of the element 50.2, while the semantic unit 40.5 is not. Note that if the semantic unit 40.4 has a topology table which includes the semantic unit 40.6 and the pointer 47.2, then the confidence values Cx that the elements 50.3, 50.5, and 51.4 are classified correctly are again increased, because the connection structure among these elements is the same as the connection structure among their counterpart semantic units in the knowledge database 11. The objects 50.x of the input network 18 can also be segmented if it is determined (e.g., by anaphora resolution) that they correspond to the same instance in the input string 12. The segment units (e.g., unit 55 in FIG. 4F) may have Classification Jani 43.x and Segmentation Jani 46.x attached to themselves. As mentioned above, the meaning understanding process, according to the present invention, then continues by iterating through the above-described classification step (1st step of the iteration) and segmentation step (2nd step of the iteration). That is, the segment's classification connections receive confidence values Cx which depend on the confidence values Cx of the classification connections of their constituents and vice versa. The constituents (e.g., semantic units 40.1, 40.2, 40.4, 47.3, and 47.4) of a segment (e.g., 46) may get re-classified depending on the segment that they belong to. Finally, segments may be grouped into segments on yet higher scales. The semantic units "car" and "airplane" belong to the segment "vehicle" and the segment "vehicle" may belong to a segment "man-made machine" on a higher scale, and so forth. A classification connection 42.x may receive a confidence value Cx which quantifies to what extent the object 50.x or 51.x of the input network 18 and the counterpart element 40.x or 47.x of the knowledge database 11 are deemed to be related, as schematically illustrated in FIG. 5. This value Cx may change throughout the whole classification step and/or the segmentation step and can be the same parameter as the weight factor on pointers, as described by the PCT Patent application WO 99/63455, International Application No.: PCT/IB99/00231, entitled "Processing of Textual Information and Automated Apprehension of Information". In the present embodiment, this confidence value Cx is incremented as the likelihood that the respective connection is a valid one increases. That is, the larger the confidence value Cx, the better the neighborhood match of the object 50.x, 51.x and counterpart element 40.x, 47.x is. The value Cx reaches a maximum if a perfect match is established. In an alternative implementation, the confidence value Cx may be decremented instead, so that it reaches a minimum if a perfect connection is found. The Classification Jani 43.x and/or the Segmentation Jani 46.x may be scheduled by a scheduler module. This scheduler module may follow certain rules. One such rule can for example define that those object of the input network 18 that represent verbs are processed with the highest priority, just to give one example. Another rule may define the order in which the various objects 50.x are processed. Yet another rule may define that filler-words, for instance, are not considered when looking for counterpart elements. Alternatively, the Jani receive importance values which are translated into relative execution time. A Classification Janus 43.x knows where it originally comes from. The Classification Janus 43.3 that is attached to the object 50.1 in the input network (cf. FIG. 4C, for example), remembers that its original is attached to the semantic unit 40.3. Furthermore, a Classification Janus 43.x is able to compare the new neighborhood in the input network 18 with the neighborhood in the knowledge database 11. This is done in order to verify whether the neighborhood is the same as the one in the knowledge database 11. If the neighborhood turns out to be the same, then it can be assumed that with a high likelihood the result of the classification was successful. That is, it is safe to assume that the respective classification connections between the objects 50.x in the input network 18 and the semantic units 40.x in the knowledge database 11 are valid, i.e., the Classification Janus 43.x was able to verify the arrangement. The respective classification connection 42.x gets a high confidence value Cx. Again, confidence values are assigned according to certain heuristics. As outlined, a Classification Janus 43.x is able to validate whether and to what extent a neighborhood in the input network 18 resembles a neighborhood in the knowledge database 11. If a classification connection 42.x is determined to be a good one, then the classification probability (confidence value Cx ) of the respective classification connection 42.x is updated. A generic description of the tasks of a Classification Janus 43.x can include the recognition of mandatory and optional roles during the classification step by exploring the local neighborhood of the object 50.x and pointer 51.x or semantic unit 40.x and pointer 47.x to which it is attached. Thus a Classification Janus 43.x needs some knowledge about the neighborhood of its semantic unit, which it obtains by examining the neighborhood. Other tasks of the Classification Jani 43.x may be comparing the input network neighborhoods with their knowledge database neighborhoods. The results of these tasks may be used to determine the confidence value Cx of the respective classification connection(s) 42.x. The Segmentation Janus 46.x may be designed to allow segmentation of semantic units and/or pointers, de-segmentation, fusion of two or more semantic units into one semantic unit, fission of a semantic unit into two or more parts, foundation of segments, boundary optimization with respect to segments, and so forth. In other words, a Segmentation Janus 46.x may be programmed to perform steps that facilitate the segmentation of semantic units according to similarities found during the Classification process. A Segmentation Janus 46.x may further be employed to create semantic units and/or pointers and to change values of attributes. According to one embodiment, the classification connections 42.x never get removed. Instead, their confidence value Cx is set to a low value if the value is defined to increase if a classification gets better. Otherwise, i.e., if decrementing confidence values Cx are employed, the confidence value Cx of a bad connection is set to a high value. Likewise, one may suppress bad classification connections 42.x (e.g., those connections whose value Cx is above or below a threshold), or one can remove bad connections. Another embodiment—referred to as top-down segmentation—is addressed in this section. According to this embodiment, the Segmentation Jani 46.x may get activated when the semantic units of this segment are recognized as being ascendants of classified semantic units in the knowledge database 11 at a higher level of hierarchy. That is, the segmentation may take place at several levels. In this case, a new segment unit is created in the input network 18 and connected to the semantic units of the segment in the knowledge database 11 with a classification connection. In addition, all objects in the input network 18 that are classified as semantic units of the knowledge database 11 which are also ascendants of the same segment get grouped into the newly created segment in the input network 18. The confidence values Cx of the classification connection of the newly created segment may depend on the confidence values Cx of the classification connections of its constituents. This method can be optionally followed to higher scales through the hierarchy of the knowledge database 11 until the whole input network 18 is segmented into one or more segments at the highest level. The classification connections of segments at a higher level of hierarchy correspond to a more general meaning or context, while the classification connections of the individual objects in the input network 18 contain more specific information. Another embodiment—referred to as bottom-up segmentation—is addressed in the present section. If two or more objects of the input network 18 from different parts of the input string 12 have been classified and recognized as members of a segment in the knowledge database 18, then that segment's Segmentation Janus 46.x may be triggered in order to find other possible members that may have been classified within an acceptable semantical distance. To do this, one may check the topology tables that comprise information about a semantic unit's neighborhood. Again, a new segment unit is created in the input network 18 to represent all of these objects, and it is connected to the semantic units of the segment of the knowledge database 11 with a classification connection. This newly created segment in the input network 18 may for example represent the same instance in the input network 18, which is mentioned with different names in different places of the input string 12. If the input string 12 refers in one sentence to a "car" and in another sentence to a "sedan", then it is possible that both refer to the same instance. A newly created segment in the input network 18 may for example be called "vehicle". The bottom-up approach may co-exist with the top-down approach in the segmentation process since they may be favorable in different situations. Especially, for objects in the input network 18 that are 'far apart' but are still part of a larger segment, the bottom-up approach is probably more efficient. The above-mentioned scheduler module may have means to control whether and to what extent the bottom-up approach and the top-down approach co-exist. One may use an elimination method in order to search for a particular word (object) that is not mentioned directly but rather as an anaphor (i.e., by a co-reference) or as a metaphor (i.e., with transferred meaning). As an example consider "I", "intersection" and "sign" mentioned in the first part of an input string 12 (e.g. a story told). If an expression like "another car" appears in the input string 12, this means that in the previous part of the string 12 a car must be present, even though it has not been mentioned explicitly. The elimination method may now search through every previously mentioned noun in order to see which one of them can be used as a metaphor for car. This is again a neighborhood analysis. In the example the result is "I" as a metaphor for car. It is to be noted that some or all of the above processes lead to a logical segmentation. The result of the classification and segmentation processes may not necessarily be reflected in that any physical changes take place inside the input network and/or the knowledge database. The classification connections and like elements may be pointers in some table or memory space, for example. According to the present invention a plurality of local algorithms (Jani) are employed to perform the meaning understanding task. A Janus can be viewed as some sort of a software module or object that is programmed to perform the above-described tasks. The Jani are implemented as generically as possible, making use of the self-similar structure of the knowledge database across all levels of hierarchy. Jani are executed when triggered by the semantic units they are attached to, or when invoked by other Jani. The Jani described here are the Classification, Segmentation, and Root Janus, but it is conceivable to have many more Jani that perform a range of tasks, some more generic and some more specific. The important fact is that the network self-organizes with the help of the Jani on a local scale and not with the help of a global algorithm. All known "holistic" approaches employ a global algorithm rather than a plurality of local algorithms to perform a meaning understanding task. This global algorithm has to be designed very carefully to include all possible cases and exceptions. Because this is almost impossible, most of the "holistic" approaches have failed, and only some of them have achieved minor successes. A system in accordance with the present invention may comprise a processing unit 13, as illustrated in FIG. 1. This unit 13 may have one CPU or multiple CPUs. The Jani are executed in parallel on the CPU(s). Alternatively, each Janus may be its own processor which requires a completely different hardware architecture. The advantage of the methods described and claimed herein over others is that the meaning understanding task consists of a relatively small number (small compared to the size of the knowledge database) of different Jani. These Jani perform their actions on a local scale, as opposed to a global algorithm acting on the entire network. Most of these Janus actions can even be performed in parallel. In addition, not all of the Jani have to complete their actions successfully to achieve an overall successful understanding of the meaning of the input string. Thus, the present invention is computationally feasible and terminates successfully under almost all circumstances and conditions. As described above, the meaning understanding task according to the present invention is done by several Janus actions. According to this model, meaning understanding is performed in two steps which are repeated iteratively until the system 10 converges towards an output 15. This output 15 can be the meaning of the input string 12 or even some action or reaction. The two iteration steps incorporate the above-described Jani, the Classification Jani and the Segmentation Jani. In addition, a scheduling mechanism (e.g., implemented as a software module) may be employed to control the scheduling of the Jani. In the embodiments described so far, a Root Janus acts as the basic or starting Janus of the system and is connected to the Root Object of the knowledge database 11. The Root Janus' main objectives may be to create string matches between objects 50.x and/or pointers 51.x of the input network 18 and semantic units 40.x and/or pointers 47.x of the knowledge database 11, collect knowledge database information, clone (copy) the Classification Jani into the input network 18, and finally trigger the cloned (copied) Classification Jani in the input network 18. In this particular way, the Root Janus performs the first steps of cross-linking the knowledge database 11 with the input network 18, i.e., the initialization process of the two-step iteration process. By this first cross-linking the objects 50.x and/or pointers 51.x of the input network 18 inherit more specific Classification Jani 43.x from the semantic units 40.x and/or pointers 47.x in the knowledge database 11 that are in a more specific neighborhood or context. From there on these Classification Jani 43.x take over and classify and restructure the input network 18 and possibly trigger Segmentation Jani 46.x that create new units (e.g., unit 53 in FIG. 4D) in the input network 18 by grouping/segmenting existing ones, as described further above in connection with the segmentation process. Instead of using a Root Object and Root Jani, one can use any other approach which allows to establish at least one initial connection between one object 50.x or 51.x of the input network 18 and one of the semantic units 40.x or pointers 47.x of the knowledge database 11. Such a connection can for example be randomly established. If an initial connection is established to one particular semantic unit 40.x or pointer 47.x of the knowledge database 1 the Classification Janus 43.x attached to this semantic unit 40.x or pointer 47.x is cloned (copied) to the respective object or pointer. Then, the classification process is started, as described above. The present invention can also be used for data mining purposes. The inventive approach allows to extract meaning from the textual information conveyed in an input network 18 and can process huge amounts of information. It can determine relationships and trends that were previously invisible or unclear. The inventive approach allows to automatically apprehend meaning of an input network 18 with a previously unmatched quality. It has been described, how the concept of a self-organizing fractal semantic network can be applied to the problems of Natural Language Understanding. In the present context, input strings 12 (e.g., texts) are transformed into initial input networks 18. Structuring and connecting these input networks 18 to knowledge databases 11 with the help of the Classification and Segmentation methods described above then accomplishes the task of understanding these input strings 12. Although illustrative embodiments of the present invention have been described herein with reference to the accompanying drawings, it is to be understood that the invention is not limited to those precise embodiments, and that various other changes and modifications may be affected therein by one skilled in the art without departing from the scope or spirit of the invention. If not otherwise stated herein, it is to be assumed that all patents, patent applications, patent publications and other publications (including web-based publications) mentioned and cited herein are hereby fully incorporated by reference herein as if set forth in their entirety herein.
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