Patent Application
20100030552
Embodiments of the inventive subject matter generally relate to the field of web environments, and more particularly to deriving ontology based on linguistics and community tag clouds.
The semantic web is an extension of the World Wide Web where the relationship (or semantics) between web information is well defined. The semantic web provides a method enabling machines to understand information in the same way that humans do, thus allowing machines to perform tasks such as finding and analyzing relevant data. The operation of the semantic web depends on the availability of an exhaustive description and classification (ontology) of various real worlds entities (e.g., people, places, objects, etc) and the relations between them.
In some embodiments, a method comprises receiving a tag cloud including tags that hyperlink to web content. The method can also comprise separating the tags into different linguistic categories, assigning a weight to each tag, and grouping the tags into clusters, wherein tags in a cluster are associated with a context. The method can also include determining one or more domains for the tag clusters, wherein a domain is a broadest class that defines one or more of the tags in a linguistic category, determining a hierarchy for the tags based on the weights of the tags, and identifying linguistic relationships between the tags. The method can also comprise determining properties associated with one or more of the tags and one or more of the domains, wherein the tag's properties are determined using linguistic analysis and storing the tags, the hierarchies, the linguistic relationships, and the properties.
The present embodiments may be better understood, and numerous objects, features, and advantages made apparent to those skilled in the art by referencing the accompanying drawings.
a) is a flow diagram illustrating operations for classifying and weighting tags from a tag cloud according to some embodiments of the invention.
b) is illustrates the concept of linguistic classification of tags according to some embodiments of the invention.
a) is a flow diagram illustrating operations for determining a general defining class (domain) for tag words within a linguistic bucket, according to some embodiments of the invention.
b) shows the tag classification and illustrates the noun clusters and noun domain.
a) illustrates an example sibling and parent linguistic hierarchy according to some embodiments of the invention.
b) is an example of a modified linguistic bucket depicting the elimination of tags present in the initial taxonomy according to some embodiments of the invention.
a) shows an example of tag statistics and the corresponding weight ratios for each tag.
b) illustrates an example of a weighted ontology tree according to some embodiments of the invention.
a) depicts a statistical analysis of the verbs and the associated domains according to some embodiments of the invention.
b) shows a relationship graph illustrating the relationship between domains.
The description that follows includes exemplary systems, methods, techniques, instruction sequences, and computer program products that embody techniques of the present inventive subject matter. However, it is understood that the described embodiments may be practiced without these specific details. In some instances, well-known instruction instances, protocols, structures, and techniques have not been shown in detail in order not to obfuscate the description.
Websites typically use tags and tag clouds to describe and locate web content. In describing content, users can browse through a website and “tag” content that appeals to them (e.g., web pages, pictures, video, etc). In some instances, users upload and tag their own content, so others can find it. To facilitate tagging, websites may provide users with a graphical user interface (GUI) through which they can apply tags to content. In some instances, users can apply multiple tags to the same content and post reviews of the content. Websites also use tag clouds to help users locate content. Tag clouds typically contain a set of related tags, where the tags can be text, such as keywords, that describe web content. Websites may present a tag cloud to help users find desired content, such as in response to a keyword search or other user inputs.
Although tagging can help users find content, there are some disadvantages associated with tagging. For example, tags are generally single words, as most tagging technologies do not allow multiple word (phrase) tags. Also, users cannot associate a context or description with the tags. For example, a user may tag a picture as “dog”. Alone, this tag (dog) could have a variety of meanings (e.g., animal, food, person, etc). Adding context to the tag (e.g., John's dog plays in the garden) could give users a better understanding of what to expect when they click on the tag. Additionally, different users can use the same tag with different meanings, thus making tags semantically imprecise. For example, a user interested in computers may search for content tagged “Apple” only to receive results related to the fruit. Current tagging technology also does not allow tags to be associated with their synonyms. For example, pictures tagged as “dog” will not show up when a user searches for content associated with the tag “puppy”. Therefore, as the tag space grows, the value of tags may diminish.
The Semantic web provides an efficient method to represent information by defining relationships (or semantics) between web information. The Semantic web provides a method enabling machines to understand information in the same way that humans do, thus allowing machines to perform tasks such as finding and analyzing relevant data. The information is stored in an ontology, which is a representation of the relationship between different entities (e.g., words in a language). From a tagging standpoint, ontology is useful because it associates tags with their synonyms, provides context and description for tags, and defines relationships with other tags. For example, a German shepherd can be classified as a type of dog and a type of animal, with attributes (e.g., eye color, fur color, etc.), and relationships (e.g., owned by a human, guards human's house, etc). This method of tag classification provides more contexts, description, and a better understanding than flat single-word tags that have no associated description.
Once this ontology (classification) is generated, it can provide users with a better way to visualize the tag environment and describe how individual tags are related to one another. The ontology can also enable users to add description to their tags, thus making tags more understandable, informative (semantically rich), and easy to locate. Additionally, it also results in more precise and specific searches and captures the users' behavior, usage of words, etc. For example, consider two tags—one that reads “sunset at Pompano beach”, the other that reads “sunset at Miami beach”. A user may search for content with tags “sunset in Florida”. Using the ontology and the semantic web, the machine may identify that Pompano Beach and Miami Beach are both in Florida and hence display both results. However, the process of generating such a classification is very time consuming, requires people with a lot of programming expertise, and a variety of domain experts. Moreover, users tend to use colloquialisms and people's vocabulary changes over time.
Some embodiments of the inventive subject matter describe a method for automatically generating ontology from a tag cloud, thus enabling users to efficiently search, navigate, and tag content. Some embodiments of the inventive subject matter also describe a method for generating a concise ontology from a tag cloud, where the ontology matches current language trends. The discussion below describes the method for ontology generation and other important features in greater detail.
As shown in
The semantic domain analyzer 206 establishes a domain of the tag words. The domain refers to the largest category or broadest class that defines the other tag words. For example, the tag “Yorkshire” may refer to a dog, a place in the United Kingdom (U.K), or a pudding. The domain name helps clarify this ambiguity. If the domain is “animal”, Yorkshire refers to a dog, whereas if the domain is “place”, Yorkshire refers to a county in the U.K. Once the semantic domain analyzer 206 establishes the domain, it sends the weighted tags 204 to a taxonomy builder 208
The taxonomy builder 208 generates a classification tree from the weighted tags. In other words, the taxonomy builder converts tags from a tag cloud into a tree showing how different tags are related to other words. The taxonomy builder 208 generates taxonomy (i.e., the classification tree) for each of the noun tags and later combines the individual trees into a cumulative taxonomy. The cumulative taxonomy forms the input to a semantic analyzer 210.
The semantic analyzer 210 includes a taxonomy analyzer and pruner 212, a relationship analyzer 214, and an attribute analyzer 216. The taxonomy analyzer and pruner 212 appends a weight to each node in the classification tree. The analyzer uses any suitable algorithm to search through the specified website and determine the number of times the keyword (tag word) occurs in text, titles, and as tags, and calculates a tag-to-text ratio that acts as the node weight. The weights help determine classification levels. The lower the weight, the more general the class, the higher the weight, the more specific is the class. The relationship analyzer 214 derives relationships between different domains and between words in different domains. From a tag standpoint, it provides users with a better way to visualize the tag environment and also shows how different tags relate to each other. The attribute analyzer 216 determines attributes or defining properties and characteristics for the domains and classes. Determining the attributes provides the user with a better understanding of the tag and enables more precise and specific searches. Thus, the semantic analyzer 210 determines and characterizes useful information about the tag.
The ontology can include the tag classification, the weights associated with each node in the tree, the relationships between different nodes, and the attributes of the nodes. The ontology is stored in an ontology schema 218. The ontology schema 218 is stored in a repository (e.g., as a database on a storage device).
Thus to summarize, the various components of this system can mine the tag cloud and linguistically classify each of the tags in the tag cloud. The system can also analyze and cluster tags based on similar properties and word usages. Furthermore, the system can determine a hierarchy for each of the noun tags and determine a combined hierarchy for all the noun tags. The system can use the verb tags to identify potential relationships between the various tags and domains, while other linguistic analysis techniques are applied on the noun tags to determine their properties. The system can also store noun tags, their hierarchy, their relationships (or actions), and their defining attributes in a repository to enable users to perform efficient searches, navigation and tagging.
The procedure described above can be an iterative process. When a new tag cloud is found, the tag cloud linguistic analyzer 202 extracts and classifies tags based on their linguistic functionality. The semantic domain analyzer 206 clusters related nouns and determines the domain of the noun tags in the tag cloud. The system queries the ontology repository 218 to determine if the domain already exists. If the domain does not exist, a new one is created following the sequence of steps described above and the corresponding information is added to the repository. If the domain exists, information derived from the new tag cloud is added to the repository.
At the user's end, the system prompts the user for preference information. This can include a broad category (e.g., dog) or a narrower category (e.g., Yorkshire terrier) to help the ontology generator 222 identify the domain and generate the appropriate classification tree. The system also prompts the user for a pruning threshold. The ontology pruner 220 and the ontology generator 222 work together to eliminate all the nodes below the pruning threshold. The user interface (e.g., website GUI) may display an initial ontology with weights to help the user better select a threshold, provide a description, and let the user enter weights by trial and error, etc. Additionally, more advanced users may also have the option of selecting an ontology display language 224(e.g., RDF).
Any one or more of the components described in
The servers 308 and the clients 304 are connected to a communication network 314. The communication network 314 can include any technology suitable for passing communication between the clients and servers (e.g., Ethernet, 802.11n, SONET, etc.). Moreover, the communication network 314 can be part of other networks, such as cellular telephone networks, public-switched telephone networks (PSTN), cable television networks, etc. Additionally, the server 308 and clients 304 can be any suitable computing devices capable of executing software in accordance with the embodiments described herein.
The described embodiments may be provided as a computer program product, or software, that may include a machine-readable medium having stored thereon instructions, which may be used to program a computer system (or other electronic device(s)) to perform a process according to embodiments, whether presently described or not, since every conceivable variation is not enumerated herein. A machine-readable medium includes any mechanism for storing or transmitting information in a form (e.g., software, processing application) readable by a machine (e.g., a computer). The machine-readable medium may include, but is not limited to, magnetic storage medium (e.g., floppy diskette); optical storage medium (e.g., CD-ROM); magneto-optical storage medium; read only memory (ROM); random access memory (RAM); erasable programmable memory (e.g., EPROM and EEPROM); flash memory; or other types of medium suitable for storing electronic instructions. In addition, embodiments may be embodied in an electrical, optical, acoustical or other form of propagated signal (e.g., carrier waves, infrared signals, digital signals, etc.), or wireline, wireless, or other communications medium.
a) is a flow diagram illustrating operations for classifying and weighting tags from a tag cloud according to some embodiments of the invention. The tag cloud in
At block 402, the tag cloud linguistics analyzer 202 receives tags from a tag cloud 201. In some instances, the user can specify the tag cloud to be used as an input. In other instances, the user can specify the website or the server (e.g., website URL) from which the tag cloud linguistics analyzer 202 can retrieve the tag cloud. After the tag cloud linguistics analyzer 202 extracts tags from the tag cloud, the flow continues at block 404.
At block 404, the tag cloud linguistics analyzer 202 performs a linguistic classification on the tags. The tags are separated into sets (or buckets) of nouns, verbs, adjectives, synonyms, derived words, and translated words.
At block 406, the tag cloud linguistics analyzer 202 calculates the tag to text ratio for the noun and verb spaces. The tag cloud linguistics analyzer 202 can use any suitable algorithm to parse through all the tags and text associated with the specified website. The tag cloud linguistics analyzer 202 can determine the number of times the tag keyword occurs in the website's text and the number of times the keyword is used as a tag. With these statistics, the tag cloud linguistics analyzer 202 calculates the tag to text ratio (i.e., the ratio of the number of times the keyword is used as a tag to the number of times the keyword appears in the text), uses these values as initial weights, and appends these weights to the tags. The flow continues at block 408.
At block 408, the tag cloud linguistics analyzer 202 applies the weights to the tags in the linguistic buckets creating a weighted tag list 204. The weights indicate the significance and relevance of the keyword when used as a tag. In other words, the higher the weight the more significant (and rare) is the tag word. For example, the tag “animal” is a common word and hence occurs more frequently in text as compared to the tag “Yorkshire terrier”. Therefore, “Yorkshire terrier” will have a higher tag to text ratio weight as compared to “animal”. This indicates that “Yorkshire terrier” is more significant and provides more meaning and description as compared to “animal”. After the tag cloud linguistics analyzer 202 weights the tags, the flow ends.
A semantic domain analyzer 206 can further analyze the weighted tags in the linguistic buckets to determine the tag cloud domains, as described below.
a) is a flow diagram illustrating operations for determining a general defining class (domain) for tag words within a linguistic bucket, according to some embodiments of the invention. The following discussion will describe the flow 500 with reference to the architectural diagram of
At block 502, a semantic domain analyzer 206 receives a weighted tag list 204 from the tag cloud linguistics analyzer 202. The tag cloud linguistics analyzer 202 determines the tag to text ratio, appends this information to each of the tags extracted from the tag cloud, and sends this information to the semantic domain analyzer 206. The semantic domain analyzer 206 groups related tags and determines the domain of the related tags. The domain refers to the largest category or the broadest class, which defines the other tags in the linguistic bucket. The flow continues at block 604.
At block 504, the semantic domain analyzer 206 uses a thesaurus and clusters the tags in the noun set. The thesaurus can be a visual thesaurus (e.g., Visuwords™) or any suitable dictionary (e.g., an online dictionary, such as Merriam-Webster Online). The semantic domain analyzer 206 can cluster the tags in the noun bucket by semantic affinity. This means that the semantic domain analyzer 206 takes into account common properties of the tags in the noun bucket and determines noun tags that can be used in a similar context.
At block 506, the semantic domain analyzer 206 establishes the domain for each tag cluster. Tags with lower weights are more likely to be super classes and therefore are the best candidates to be the domain name. This is because the more abstract the class is, the fewer users will use it as a tag. For example, people are more likely to tag a picture of a Yorkshire terrier as “YorkshireTerrier” than as “animal”. By establishing the domain, the exact meaning of words that are part of the domain can be determined. For example, referring to
A taxonomy builder 208 can further analyze the weighted tags 204 in the linguistic buckets to determine an initial taxonomy, as described below.
At block 602, a taxonomy builder 208 determines an initial taxonomy for the noun tag. Typically, the taxonomy builder 208 starts with the noun tag with the highest weight (tag to text ratio as calculated by the tag cloud linguistic analyzer). The tags with a higher weight are more semantically significant. This implies the higher or more abstract the class, the less it will be used as a tag. Users generally make an effort not to use general words while tagging content so that others can easily find it. In some embodiments, the taxonomy builder 208 interfaces with a linguistic thesaurus to determine data indicating taxonomy for the specified noun tag. Taxonomy is a semantic classification. In other words, it is a group of words that show how the tag relates to other words in a language.
At block 604, the taxonomy builder 208 determines the hierarchical level of the tag in the initial taxonomy (
At block 606, the taxonomy builder accesses the linguistic buckets (see
At block 608, the taxonomy builder determines whether there are any more tags in the noun bucket. The taxonomy builder can use a linguistic thesaurus to determine the sibling and parent hierarchy associated with each of the noun tags in the linguistic bucket. The taxonomy builder can use any suitable algorithm to evaluate each of these hierarchy trees and combine them into a single taxonomy, which relates all the noun tags in the linguistic bucket.
A taxonomy analyzer 210 then determines the statistics of the nodes in the taxonomy to create a weighted taxonomy tree as described below.
At block 802, the taxonomy analyzer 210 determines the statistics of the tag. This means that the taxonomy analyzer determines the number of times the tag word occurs in the text, in the title, and as a tag. To get accurate statistics, the taxonomy analyzer 210 includes the title as an implied tag. This is because people generally treat titles as tags and often do not use nouns in the title as tag words.
At block 804, the taxonomy analyzer 210 determines the tag to text ratio weight. To calculate this weight, the taxonomy analyzer makes use of the statistics of the tag in the text, tags, and title. The title is considered to be an implied tag. The taxonomy analyzer 210 calculates the final weight ratio as (Tag+Title)/Text. The weight ratios associated with the tags are indicative of how understandable or semantically unambiguous the tags are. In
At block 806, the taxonomy analyzer 210 appends these weights to the ontology tree obtained from the taxonomy builder. As described previously, the taxonomy builder 208 generates taxonomy (tree structure showing how words relate to each other) for each noun tag and creates a cumulative tree structure depicting relationships between all the noun tags. The taxonomy analyzer 210 then calculates a weight for each of the component nouns in the tree structure and appends or associates the weights with the corresponding noun tags.
After the taxonomy analyzer 210 stores the weighted ontology is stored in a repository, a relationship analyzer 214 determines the relationship between words in the tag cloud.
At block 1002, the relationship analyzer 214 analyzes the relationship between a verb in the verb linguistic bucket (shown in
At block 1004, the relationship analyzer 214 determines verb relationships between tags. In some instances, the relationship analyzer 214 can interface with a linguistic dictionary to determine the verb's usage with other tags. For example, to identify the relationship between “yorkie”, “walk”, and “beach”, the relationship analyzer 214 can send “walk” to the linguistic dictionary. The linguistic dictionary can return possible usages of the verb “walk” including the fact that “walk” can have a transitive (verbs with a subject and objects) and an intransitive usage (verbs with a subject but no objects). In some instances, the relationship analyzer 214 can use a suitable algorithm to parse through the dictionary results to determine verb relationships. Thus, for the intransitive verb's usage, the relationship analyzer 214 determines that “yorkie walks on the beach”. For the transitive verb's usage, the relationship analyzer 214 determines that “people walk yorkies on the beach”.
At block 1006, the relationship analyzer 214 determines whether there are any more verbs in the verb bucket (refer
At block 1008, the relationship analyzer 214 determines supplement verbs identifying the actions performed by the noun tag. Because people generally do not use verbs as tag words, the linguistic verb bucket includes very few verb relationships. To determine other relationships between the noun tags and to create a more exhaustive ontology definition, the relationship analyzer 214 can perform a linguistic analysis on the noun tags. The relationship analyzer can receive information about the noun tags from the linguistic thesaurus and use a suitable algorithm to parse through this information and determine supplement relationship verbs.
At block 1010, the relationship analyzer 214 weights the verbs according to their occurrence. In some instances, the relationship analyzer 214 can include a ranking algorithm to rank verbs according to their occurrence. For example, verbs that appear in the statistical analysis and in the dictionary are ranked “1”, thus denoting that the verbs are valid actions and used most frequently by people (e.g., walk). Verbs that appear only in the statistical analysis (
At block 1012, the relationship analyzer 214 adds the verbs to the ontology. As described previously, the relationship analyzer categorizes the verbs based on their occurrence in statistical analysis and in the dictionary. In some instances, all the verbs from the statistical analysis and the dictionary are included in the ontology. In some embodiments, only the most relevant verbs are added to the ontology definition. For example, verbs that show up in statistical analysis and in the dictionary (rank 1 verb) are valid actions and are popular with the tag users. Hence, these verbs must definitely be a part of the ontology. Thus, the relationship analyzer 214 determines the relationship between ontology classes based on reality usage (i.e., how people use words) and linguistic usage (i.e., how words are defined). After the relationship analyzer 214 appends the specified verbs to the ontology, the flow ends.
At block 1302, the attribute analyzer 216 determines supplement information about the various ontology classes. In some instances, the attribute analyzer can connect to an online dictionary or use a local dictionary to determine various supplement information associated with the ontology classes. The attribute analyzer can also use any suitable algorithm to parse through the related results. For example, results for a “Yorkshire terrier” may include information such as “a terrier with wiry hair”, “small rough coated terrier of British origin”, and so on. After the attribute analyzer 216 receives the supplement information related to the nodes (ontology classes), the flow continues at block 1304.
At block 1304, the attribute analyzer 216 parses this supplement information into a syntax structure tree (e.g., Noam Chomsky's tree). A syntax structure tree includes a set of precise rules (grammar) which govern the structure of the language and the creation of sentences. These rules can include classification of words into parts of speech, connecting the parts of speech, etc. A syntax structure tree illustrates a hierarchical relationship between different parts of the sentence. For example, a sentence can include a subject (i.e., noun phrase) and a predicate (i.e., verb phrase). The noun phrase can further include an article (e.g., a, an, the), nouns, prepositional phrases (describing the noun), adjectives, etc. Similarly, the verb phrase can include the verb (i.e., noun's action) and a noun phrase. The syntax structure tree can also account for combined sentences by identifying conjunctions (e.g., and) joining two or more sentences. The attribute analyzer 216 can use these rules, to classify the supplement information into different components and finally determine the attributes for the ontology classes. The flow continues at bock 1306.
At bock 1306, the attribute analyzer 216 compares the supplement information with the syntax structure tree's rules and determines the different components of the sentence. The attribute analyzer can identify noun phrases (e.g., English origin), verb phrases (e.g., resembles a lamb), prepositional phrases (e.g., with a silky blue gray coat), and adjective phrases (e.g., medium sized terrier). After the attribute analyzer 216 determines the supplemental information's different parts of speech, the flow continues at block 1308.
At block 1308, the attribute analyzer 216 determines ontology class attributes. The attribute analyzer can process the different parts of speech (e.g., noun phrase, verb phrase, etc) and extract the attributes for the ontology class. In some instances, the attribute analyzer can parse the sentences' components through another syntax structure tree to determine the attributes. For example, consider the prepositional phrase “with a silky blue gray coat”. The attribute analyzer can process this information to determine a preposition (i.e., with), an article (i.e., a), and the ontology class attributes (i.e., silky blue gray coat). After the attribute analyzer 216 determines the ontology class attributes, the flow continues at block 1310.
At block 1310, the attribute analyzer 216 stores this information into an ontology repository 218. The repository supports ontology definition and inferencing capabilities. The ontology data repository can be realized in different schema and implementations (e.g., RDF table). The repository can be on a centralized server or local to a particular machine. This repository contains different domains, their sub classes, attributes, and/or properties defining the various classes, and the relationship between different domains and classes. The information stored in the repository can be used to generate a more user-friendly ontology tree according to the user's preferences (e.g. domain name). After the ontology (domain and sub class) information has been stored in the repository, the flow ends thus converting the initial tag cloud into a well-defined descriptive tag classification.
The process of determining an exhaustive ontology is an iterative process. The sequence of operations described parses through one tag cloud at a time. Once the ontology information for the specified tag cloud is determined and stored in the repository, the system searches for another tag cloud. In some instances, users may also choose to halt the process of ontology generation. In other instances, users may have the option of specifying a tag cloud (e.g., tag cloud URL) or a website, which contains tag clouds. The new tag cloud is sent to the tag cloud linguistics analyzer 202, where the tags are extracted and grouped into linguistic elements. The semantic domain analyzer 204 clusters related nouns and determines the domain of the noun tags in the tag cloud. The system queries the ontology repository 218 (database) to determine if the ontology class (domain) already exists. If the ontology class does not exist, a new one is created following the sequence described in
At block 1402, an application receives a request to display tag ontology. The application could be a web browser or any other software, which supports presenting tag clouds and tag ontology. In some instances, users can request tag ontology by clicking on a link or a graphical user interface (GUI) component (e.g., button) on the application. In some instances, the website may automatically generate an ontology request when a user clicks on a link to a website. The application can open up a GUI in the form of a pop-up window or a sidebar on the web browser. The flow continues at block 1404, where the application prompts the users to enter their preferences (e.g., ontology class).
At block 1404, the client application prompts users to specify ontology parameters including a tagging threshold, ontology class, ontology language, etc. The tagging threshold is important in pruning the ontology and displaying a concise or verbose ontology depending on the user's specifications.
At block 1406, the client application connects to the server to send the user's preference information. The client can send a file (e.g., an XML file) including the user specified ontology class, pruning threshold, ontology language, and other user preferences (e.g., include ontology attributes, etc). The flow continues at block 1410, where the server can locate the appropriate ontology tree, prune it according to the user-defined threshold, and interface with the client's application program to display the ontology.
At block 1408, an ontology generator 222 locates and retrieves the specified ontology classes, sub classes, attributes, and other relationships from the ontology repository. The ontology generator 222 can use the information specified by the user to retrieve relevant data from the ontology schema 218 and construct the ontology.
At block 1410, the ontology pruner 220 uses the user specified tagging threshold to prune the ontology. The ontology pruner 220 can eliminate all the ontology classes with a weight lower than the tagging threshold. The higher the threshold, the more concise is the ontology. Similarly, the lower the threshold, the more descriptive is the displayed ontology.
At block 1412, the ontology generator 222 converts the ontology into the user specified ontology language 224. Different ontology languages have different features, machine interpretability, etc (e.g., OWL is a stronger language than RDF). Ontology languages 224 may also have different versions, which may or may not be compatible with each other. Different users may be familiar with different ontology languages and may want to view the ontology in a specific language. The ontology generator 222 can generate the ontology in a suitable user specified language. The ontology generator 222 can interface with the application on the client 104 via the network 114 to display the desired ontology. The flow then comes to an end.
While the embodiments are described with reference to various implementations and exploitations, it is understood that these embodiments are illustrative and that the scope of the inventive subject matter is not limited to them. In general, techniques for deriving a linguistic based ontology from tag clouds are described herein and may be implemented with facilities consistent with any hardware system or hardware systems. Many variations, modifications, additions, and improvements are possible.
Plural instances may be provided for components, operations, or structures described herein as a single instance. Finally, boundaries between various components, operations, and data stores are somewhat arbitrary, and particular operations are illustrated in the context of specific illustrative configurations. Other allocations of functionality are envisioned; and may fall within the scope of the inventive subject matter. In general, structures and functionality presented as separate components in the exemplary configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements may fall within the scope of the inventive subject matter.
