Generation of metadata tag descriptions

Abstract

Embodiments of the present disclosure relate to the generation of metadata tag descriptions. One or more techniques and / or systems for metadata tag evaluation are provided. For example, metadata tags associated with content can be identified (e.g., a social network post can be tagged with the hashtag #ML). A set of characters within the above content can be evaluated using a probability matrix and content to identify an extended metadata tag (e.g., an extended hashtag "machine learning"). Descriptive content associated with the extended metadata tag, such as a website, article, social network post, and / or other content, can be retrieved. A description of the metadata tag can be generated based on the descriptive content (e.g., a definition for machine learning). In this way, descriptions, related metadata tags, and / or supplemental content can be provided to users who are interested in learning about the metadata tags.

Description

[0001] This application is a divisional application of the invention patent application with application date of June 1, 2016, application number 201680032277.X, and invention name “Generation of metadata tag description”. Background Art

[0002] Many users may tag various content with metadata tags. In one example, a user may tag a vacation photo with the hashtag #myvacation. In another example, a user may tag a social network message about a baseball game with the hashtag #atthebaseballgame. Many times, metadata tags may be tagged with acronyms (e.g., #tgif for "thank goodness it's Friday") or newly created terms (e.g., #DWFS for the "Dancing with Friends show" that just premiered on television), which may not be recognizable to some users. Summary of the invention

[0003] This Summary is provided to introduce some concepts in a simplified form that are further described in the Detailed Description below. This Summary is not intended to identify key factors or essential features of the claimed subject matter, nor is it intended to limit the scope of the claimed subject matter.

[0004] Among other things, one or more systems and / or techniques for metadata tag evaluation are provided herein. In an example of metadata tag evaluation, a metadata tag associated with content is identified. The metadata tag includes a set of characters. The set of characters is evaluated using a probability matrix and the above content to identify an extended metadata tag. Descriptive content associated with the extended metadata tag is retrieved. A description of the metadata tag is generated based on the descriptive content. In one example, a query including the metadata tag is received. The extended metadata tag is identified as an extension of the metadata tag. The description of the metadata tag is provided as a query result of the query.

[0005] To achieve the aforementioned and related purposes, the following description and accompanying drawings set forth certain illustrative aspects and implementations. These are only a few of the various ways in which one or more aspects can be adopted. Other aspects, advantages and novel features of the present disclosure will become clear when considering the following detailed description in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Figure 1 is a flow chart illustrating an exemplary method of metadata tag evaluation.

[0007] Figure 2is a block diagram illustrating components of an exemplary system for metadata tag evaluation, wherein descriptions of metadata tags are generated.

[0008] Figure 3 is a block diagram illustrating components of an exemplary system for metadata tag evaluation, wherein descriptions of metadata tags are generated.

[0009] Figure 4 is a block diagram illustrating components of an exemplary system for metadata tag evaluation, wherein descriptions of metadata tags are provided as query results.

[0010] Figure 5 is an illustration of an exemplary computer-readable medium, which may include processor-executable instructions configured to implement one or more provisions set forth herein.

[0011] Figure 6 An exemplary computing environment is shown in which one or more of the provisions set forth herein may be implemented. DETAILED DESCRIPTION

[0012] The claimed subject matter is now described with reference to the accompanying drawings, wherein the same reference numerals are generally used to refer to the same elements throughout. In the following description, for the purpose of explanation, many specific details are set forth to provide an understanding of the claimed subject matter. However, it will be clear that the claimed subject matter can be practiced without these specific details. In other cases, structures and devices are shown in block diagram form for ease of description of the claimed subject matter.

[0013] One or more techniques and / or systems for metadata tag evaluation are provided herein. A user may wish to obtain a description of a metadata tag (e.g., a description of other non-self-identifiable topic tags corresponding to the acronym of "New TV Show Sitcom", such as a description of #NTVSC), supplementary content associated with the metadata tag (e.g., images and / or trailers of "New TV Show Sitcom"), and / or related metadata tags (e.g., #JohnDoe corresponding to an actor in "New TVShow Sitcom"). Therefore, as provided herein, a metadata tag evaluation component can evaluate a metadata tag to generate a description of the metadata tag, identify supplementary content of the metadata tag, and / or related metadata tags of the metadata tag. The metadata tag evaluation component can evaluate the metadata tag in an efficient manner, so that the description, supplementary content, and / or related metadata tags can be provided to the user based on the metadata tag evaluation. In one example, the metadata tag evaluation component can be hosted locally on the client device, and thus bandwidth utilization can be reduced and / or privacy of user information, such as social network posts, calendar entries, photos, messages, and / or other content (e.g., those contents that the user has given content for metadata tag evaluation). In another example, the metadata tag evaluation components may be hosted on a remote server and, thus, client-side memory and / or processor utilization may be mitigated. In one example, at least some of the metadata tag evaluation components may be hosted locally and at least some of the metadata tag evaluation components may be hosted remotely. It will be appreciated that a user may take affirmative action, such as providing selected consent, to allow access and / or use of content, information, data, etc. (e.g., social network posts, calendar entries, photos, messages, etc.), such as for purposes of evaluating metadata tags (e.g., in this case, after the user responds to a prompt regarding the collection and / or use of such information). For example, a user of a social network may specify, through social network account settings, that metadata tags (e.g., used by the user in a social network post) and / or other social network content may be used for purposes of generating descriptions of such metadata tags.

[0014] An example of metadata tag evaluation is given by Figure 1100 is shown. At 102, the method starts. At 104, a metadata tag associated with content can be identified, wherein the metadata tag includes a set of characters. The metadata tag can include a hashtag or any other descriptive identifier for the content. The content can include social network posts, microblog messages, images, videos, user comments, websites, articles, messages, and / or various other content that can be associated with metadata tags. In one example, a user can use the metadata tag #V4JIM to tag a social network post "Everyone should vote for Jim in the upcoming presidential election." However, many users may not understand the meaning of the metadata tag #V4JIM, and therefore it may be advantageous to evaluate the metadata tag #V4JIM to generate a description of the metadata tag #V4JIM.

[0015] At 106, the group of characters may be evaluated using a probability matrix and the content to identify an expanded metadata tag. The probability matrix may represent the likelihood / probability that a term or word beginning with characters within the group of characters will appear within the expansion of the metadata tag, and / or the likelihood / probability that a phrase including a term or word beginning with characters within the group of characters will appear within the expansion of the metadata tag. In one example, the probability matrix may be a customization of a word segmentation algorithm, where the likelihood / probability is limited to the content for the metadata tag (e.g., the probability may be based on text within a social network post, article, and / or various other content including the metadata tag), and / or where acronyms within the metadata tag are expanded based on identifying possible (e.g., most likely) expansions, where the identification of possible expansions is based on text within the content (e.g., the text "Everyone should vote for Jim in the upcoming presidential election"). The probability matrix may be populated based on text and / or features (e.g., features extracted from articles, images, videos, etc.) that appear in the content and / or other content associated with the metadata tag.

[0016] In one example, a first term may be identified for a first character "V" based on the first term having a first frequency probability above a threshold value (e.g., the highest frequency probability for "V") within a frequency probability matrix. For example, the term "vote" may be identified for the first character "V". A second term may be identified for a second character "4" based on the second term having a second frequency probability above a threshold value (e.g., the highest frequency probability for "4") within the frequency probability matrix. For example, the term "for" may be identified for the second character "4". A third term "Jim" may be identified using the probability matrix based on an evaluation of a third character "J", a fourth character "I", and a fifth character "M". A phrase "vote for Jim" including the first term, the second term, and the third term may be identified for inclusion in an expanded metadata tag based on the phrase having a phrase frequency probability above a phrase threshold value within the probability matrix, the phrase threshold value being, for example, a highest phrase frequency probability indicating a relatively high likelihood that the first term, the second term, and the third term occur together within an expansion of the metadata tag. It will be appreciated that a plurality of term candidates and / or phrase candidates may be identified and evaluated against the probability matrix to identify an expansion of the metadata tag. In this manner, the expanded metadata tag may be identified as "vote for Jim".

[0017] At 108, descriptive content associated with the expanded metadata tag "vote for Jim" can be retrieved. For example, Jim's Internet encyclopedia description, voting websites, Jim's social network profile, Jim's images, videos of Jim's political speeches, and / or various other descriptive content can be retrieved. At 110, a description of the metadata tag can be generated based on the descriptive content. In one example, a descriptive phrase can be extracted from the descriptive content, such as the descriptive phrase "Jim was born in Ohio in 1952, and is currently running for president of the United States" extracted from Jim's social network profile. In response to the descriptive phrase "Jim was born in Ohio in 1952, and is currently running for president of the United States" appearing above a threshold in at least some of the descriptive content (e.g., images, videos, voting websites, campaign emails, Jim's Internet encyclopedia description, etc.) and / or substantially matching these descriptive content above a threshold, the descriptive phrase can be included in the description of the metadata tag #V4JIM. For example, if the phrase "Jim was born in Ohio in 1952, and is currently running for president of the United States" and / or phrases determined to be sufficiently similar to that phrase (e.g., above a certain threshold, within some parameters (e.g., word count), etc.) appear a number of times exceeding a threshold in Jim's social network profile and / or other descriptive content (e.g., images, videos, online text related to Jim, etc.), then the descriptive phrase may be included in the description of the metadata tag #V4JIM.

[0018] In one example, based on the text and / or features of the descriptive content associated with the expanded metadata tag, a first description candidate (“Jim was born in Ohio, is 5 feet tall, and is running for class president”) and a second description candidate (e.g., “Jim was born in Alabama, is 6 feet tall, and is running for president”) can be generated as potential descriptions for the metadata tag. Because the descriptive candidates may not include cohesive or consistent descriptions, the descriptive candidates can be provided to users for evaluation (e.g., utilizing crowdsourcing techniques). A user selection of the first descriptive candidate or the second descriptive candidate can be received for use in generating a description for the metadata tag (e.g., based on the user selection, the first descriptive candidate or the second descriptive candidate is selected instead of the remaining descriptive candidates).

[0019] In one example, supplemental content corresponding to the expanded metadata tag may be identified. The supplemental content may include images (e.g., photos of Jim giving a speech), videos (e.g., videos of Jim in a parade), Jim's social network profile, keywords (e.g., campaign slogans that may be used by users to locate more information about Jim through a search interface), websites, app suggestions for apps downloadable from an app store (e.g., voting apps), advertisements (e.g., Jim's campaign ads), etc. In one example, the supplemental content may be identified as popular images, popular videos, popular articles, and / or any other popular content that may be referenced, for example, by social network content associated with the metadata tag (e.g., social network posts of images tagged with the metadata tag).

[0020] In one example, second content including metadata tags may be retrieved. For example, articles tagged by a user with the #V4JIM metadata tag may be retrieved. The second content (such as the text of the article or the hashtag) may be evaluated to identify relevant metadata tags related to the metadata tag. For example, the hashtag #Jim4Prez used to tag the article may be identified as a relevant metadata tag.

[0021] In one example, the expanded metadata tag may be determined to correspond to spam or explicit content. For example, there may not actually be a Jim running for president, and a malicious source may be seeking campaign contributions to a fake campaign. Therefore, the metadata tag and / or description may be marked as spam and / or explicit content.

[0022] In this manner, extended metadata tags, descriptions, supplemental content, related metadata tags, spam, and / or explicit content may be identified based on an evaluation of the metadata tags. Such information may be provided to a user so that such a user may learn the definition of the metadata tags, explore supplemental content, learn related metadata tags, and / or receive warnings of spam and / or explicit content. For example, a query may be received that includes a metadata tag (e.g., a user may submit a query "what is #V4JIM"). An extended metadata tag "vote for Jim" may be identified as an extension of the metadata tag #V4JIM. Thus, extended metadata tags, descriptions, supplemental content, related metadata tags, warnings, etc. may be provided as query results for the query.

[0023] In one example, descriptions of metadata tags may be stored in a data structure (e.g., a database, etc.). Because metadata tags may have different meanings over time, multiple descriptions may be generated for the metadata tags, and the multiple descriptions may be stored in the data structure based on time identifiers corresponding to the following time ranges during which the descriptions were used to describe the metadata tags. For example, the metadata tag #XFL may represent the Xtreme Football League during a first time range (e.g., during 2001, which was the only year in which the XFL was in operation), and thus the extended metadata tag Xtreme Football League may be identified based on the metadata tag #XFL. An Xtreme Football League description may be generated for the metadata tag #XFL based on the Xtreme Football League descriptive content associated with the first time range. A first time identifier corresponding to the first time range in which the Xtreme Football League description was used to describe the metadata tag #XFL may be associated with the Xtreme Football League description. The Xtreme Football League description may be stored in the data structure based on the first time identifier. A second description of the metadata tag may be generated based on a second extended metadata tag and a second descriptive content associated with a second time range. For example, an Extreme Fishing League description may be generated for a metadata tag #XFL based on a second extended metadata tag Extreme Fishing League and Extreme Fishing League descriptive content associated with a second time range (e.g., a time range spanning from the 2011 season to the current season). A second time identifier corresponding to the second time range that the Extreme Fishing League description is used to describe the metadata tag #XFL may be associated with the Extreme Fishing League description. The Extreme Fishing League description may be stored within the data structure based on the second time identifier. In one example, various descriptions may be stored along a timeline (e.g., based on associated time identifiers), which may be presented to a user so that the user may better understand, utilize, and / or disambiguate such descriptions (e.g., how such descriptions evolve over time). It should be understood that any information associated with a metadata tag (e.g., not just a description) may similarly be associated with a time identifier (e.g., an extended metadata tag, supplemental content, related metadata tags, warnings, etc.).

[0024] It will be appreciated that the description of the metadata tag may be provided in a variety of ways (e.g., embedded within content tagged with the metadata tag; provided as a search result; etc.). In one example, when the metadata tag is used to tag content (e.g., when a user authored a social network post tagged with the metadata tag #XFL), the description may be provided with the content (e.g., an option to view the Extreme Fishing League description may be provided via a user interface element (such as a button) associated with the social network post; the Extreme Fishing League description may be inline with the metadata tag #XFL, such as displayed before the metadata tag #XFL (e.g., "Extreme Fishing League #XFL") or after the metadata tag #XFL (e.g., "XFL Extreme Fishing League"); the Extreme Fishing League description may be displayed as captions or a pop-up window when a user hovers a cursor over the metadata tag #XFL; etc.). At 112, the method ends.

[0025] Figure 2An example of a system 200 for metadata tag evaluation is shown. The system 200 includes a metadata tag evaluation component 204. The metadata tag evaluation component 204 can identify a metadata tag #TGIBF 222 associated with content, such as a social network post 202 related to "heading to a pub on Main street for drinks tonight." The metadata tag #TGIBF 222 can include a set of characters T, G, I, B, and F. The metadata tag evaluation component 204 can evaluate the set of characters T, G, I, B, and F using a probability matrix 206 to identify an expanded metadata tag 208. For example, the term "thank" may be identified as having a first probability within probability matrix 206 that is above a threshold for the character "T," the term "goodness" may be identified as having a second probability within probability matrix 206 that is above a threshold for the character "G," the term "it's" may be identified as having a third probability within probability matrix 206 that is above a threshold for the character "I," the term "blue" may be identified as having a fourth probability within probability matrix 206 that is above a threshold for the character "B," and the term "Friday" may be identified as having a fifth probability within probability matrix 206 that is above a threshold for the character "F." It will be appreciated that for a character, a variety of terms may be considered (e.g., the term "thank", the term "the", the term "taste", the term "towel", etc.), and the probability matrix 206 (which may be based on content such as text within the social network post 202) may be used to identify terms that have a relatively high probability of occurring frequently for the character and / or that have a relatively high probability of occurring frequently as part of a phrase that also has other terms identified for other characters within the group of characters (e.g., the term "thank" and the term "goodness" have a high probability of occurring together in the expansion of the metadata tag 222). In this manner, the probability matrix 206 may be utilized to identify the expanded metadata tag 208 for "thankgoodness it's blue Friday".

[0026] The metadata tag evaluation component 204 can retrieve descriptive content, such as a set of descriptive content 212, from a descriptive content source 210. The descriptive content can include articles, Internet encyclopedia descriptions, social network posts, and / or various content that can describe the expanded metadata tag 208. The descriptive content can be evaluated to generate a description 214 for the metadata tag #TGIBF222. For example, a descriptive phrase (e.g., "the day before the big Seattle football team game. It was made official by declaring it ...") can be extracted from a website. Based on the descriptive phrase appearing above a threshold within the set of descriptive content 212 (e.g., within a threshold number or percentage of social network posts, websites, and / or other content), the descriptive phrase can be included in the description 214. The metadata tag evaluation component 204 can identify relevant metadata tags 216, such as #football, #Seattle, etc., based on the metadata tags used to mark the descriptive content within the set of descriptive content 212 (which can indicate that these metadata tags can be related to the metadata tag #TGIBF 222 and / or the extended metadata tags 208). The metadata tag evaluation component 204 can retrieve supplemental content 220 from the supplemental content source 218. For example, images, videos, social network profiles, application suggestions, advertisements, and / or other content related to the metadata tag #TGIBF 222 and / or the extended metadata tags 208 can be retrieved. In this manner, the metadata tag evaluation component 204 evaluates the metadata tag #TGIBF 222 to generate the description 214, identify relevant metadata tags 216, and / or retrieve supplemental content 220, which descriptions, tags, and / or content can be provided to users who are interested in learning more about the metadata tag #TGIBF 222.

[0027] Figure 3An example of a system 300 for metadata tag evaluation is shown. The system 300 includes a metadata tag evaluation component 304. The metadata tag evaluation component 304 can identify a metadata tag #DWFS 322 associated with content, such as a social network post 302 asking other users if they want to watch tonight's show. The metadata tag #DWFS 322 can include a set of characters D, W, F, and S. The metadata tag evaluation component 304 can evaluate the set of characters D, W, F, and S using a probability matrix 306 to identify an expanded metadata tag 308. For example, the term "dancing" can be identified as having a first probability within the probability matrix 306 above a threshold for the character "D", the term "with" can be identified as having a second probability within the probability matrix 306 above a threshold for the character "W", the term "friends" can be identified as having a third probability within the probability matrix 306 above a threshold for the character "F", and the term "show" can be identified as having a fourth probability within the probability matrix 306 above a threshold for the character "S". It will be appreciated that for a character, a variety of terms may be considered (e.g., the term "desk," the term "dog," the term "Dave," the term "dogwood," etc.), and that the probability matrix 306 (which may be based on content such as terms within the social network post 302) may be used to identify terms that have a relatively high probability of occurring frequently for the character and / or that have a relatively high probability of occurring frequently as part of a phrase that also has other terms identified for other characters within the group of characters (e.g., the term "dancing" and the term "show" have a high probability of occurring together in the expansion of the metadata tag 322). In this manner, the probability matrix 306 may be utilized to identify the expanded metadata tag 308 for the "Dancing with Friends show."

[0028] The metadata tag evaluation component 304 can retrieve descriptive content, such as a set of descriptive content 312, from a descriptive content source 310. The descriptive content can include articles, Internet encyclopedia descriptions, social network posts, television websites, photos, and / or various content that can describe the expanded metadata tag 308. The descriptive content can be evaluated to generate a description 314 for the metadata tag #DWFS 322. For example, a descriptive phrase (e.g., "Dancing with Friends is an adance competition show airing since 2008. It airs on...") can be extracted from a television website. Based on the descriptive phrase appearing above a threshold within the set of descriptive content 312 (e.g., within a threshold number or percentage of social network posts, websites, and / or other content), the descriptive phrase can be included in the description 314. The metadata tag evaluation component 304 can identify relevant metadata tags 316, such as #dancing, #channel2, etc., based on metadata tags used to mark descriptive content within the set of descriptive content 312 (which can indicate that these metadata tags can be related to the metadata tag #DWFS 322 and / or the extended metadata tags 308). The metadata tag evaluation component 304 can retrieve supplemental content 320 from a supplemental content source 318. For example, images, videos, social network profiles, application recommendations, advertisements, and / or other content related to the metadata tag #DWFS 322 and / or the extended metadata tags 308 can be retrieved. In this manner, the metadata tag evaluation component 304 evaluates the metadata tag #DWFS 322 to generate a description 314, identify relevant metadata tags 316, and / or retrieve supplemental content 320, which descriptions, tags, and / or content can be provided to users who are interested in learning more about the metadata tag #DWFS 322.

[0029] Figure 4 An example of a system 400 is shown for providing query results 418 including metadata tags 408 for a query 410. The system 400 includes a metadata tag evaluation component 402. The metadata tag evaluation component 402 can be associated with a metadata tag repository 404 in which descriptions of metadata tags, related metadata tags, and / or supplemental content related to metadata tags and / or extensions of metadata tags can be stored, indexed, and / or identified. The metadata tag evaluation component 402 can be associated with a search interface 406 (e.g., a web-based search engine interface) accessible to a user's client device. The user can submit a query 410 including the metadata tag #DWFS 408 within the search interface 406 to try to better understand the meaning of #DWFS.

[0030] The metadata tag evaluation component 402 can receive a query 410. The metadata tag evaluation component 402 can determine that the extended metadata tag "Dancing with Friends show" can be an extension of the metadata tag #DWFS 408. The metadata tag evaluation component 402 can retrieve (e.g., based on the extended metadata tag) a description 412 of the metadata tag 408, related metadata tags 414 related to the metadata tag 408, and / or supplemental content 416 for the metadata tag from the metadata tag repository 404. The metadata tag evaluation component 402 can provide search results 418 for the query 410 through the search interface 406. For example, the search results 418 can include the description 412 "dancing with Friends is a dance competition show airing since 2008. It airs on...", the related metadata tags #dancing and #channel2 414, and / or supplemental content 416 including images, videos, social network profiles, application suggestions, advertisements, and / or other content related to the metadata tag 408 #DFSFS.

[0031] According to one aspect of the present disclosure, a method for metadata tag evaluation is provided. The method includes identifying a metadata tag associated with content. The metadata tag may include a set of characters. The set of characters may be evaluated using a probability matrix and content to identify an extended metadata tag. Descriptive content associated with the extended metadata tag may be retrieved. A description of the metadata tag may be generated based on the descriptive content.

[0032] According to one aspect of the present disclosure, a system for metadata tag evaluation is provided. The system includes a metadata tag evaluation component. The metadata tag evaluation component can be configured to identify a metadata tag associated with content. The metadata tag can include a set of characters. The metadata tag evaluation component can be configured to evaluate the set of characters using a probability matrix and content to identify an extended metadata tag. The metadata tag evaluation component can be configured to retrieve descriptive content associated with the extended metadata tag. The metadata tag evaluation component can be configured to generate a description of the metadata tag based on the descriptive content.

[0033] According to one aspect of the present disclosure, a computer-readable medium including instructions is provided, which when executed perform a method for metadata tag evaluation. The computer-readable medium includes identifying a metadata tag associated with content. The metadata tag may include a set of characters. The set of characters may be evaluated using a probability matrix and content to identify an extended metadata tag. Descriptive content associated with the extended metadata tag may be retrieved. A description of the metadata tag may be generated based on the descriptive content. A query including the metadata tag may be received. The extended metadata tag may be identified as an extension of the metadata tag. The description of the metadata tag may be provided as a query result of the query.

[0034] According to one aspect of the present disclosure, a device for metadata tag evaluation is provided. A metadata tag associated with content can be identified by the device for metadata tag evaluation. The metadata tag can include a set of characters. The set of characters can be evaluated by the device for metadata tag evaluation using a probability matrix and content to identify an extended metadata tag. Descriptive content associated with the extended metadata tag can be retrieved by the device for metadata tag evaluation. A description of the metadata tag can be generated based on the descriptive content by the device for metadata tag evaluation.

[0035] According to one aspect of the present disclosure, a device for metadata tag evaluation is provided. A metadata tag associated with content can be identified by the device for metadata tag evaluation. The metadata tag can include a set of characters. The set of characters can be evaluated by the device for metadata tag evaluation using a probability matrix and content to identify an extended metadata tag. Descriptive content associated with the extended metadata tag can be retrieved by the device for metadata tag evaluation. A description of the metadata tag can be generated based on the descriptive content by the device for metadata tag evaluation. A query including the metadata tag can be received by the device for metadata tag evaluation. The extended metadata tag can be identified as an extension of the metadata tag by the device for metadata tag evaluation. The description of the metadata tag can be provided as a query result of the query by the device for metadata tag evaluation.

[0036] Yet another embodiment relates to a computer-readable medium including processor-executable instructions configured to implement one or more of the techniques presented herein. Example embodiments of a computer-readable medium or computer-readable device are described in Figure 5, where implementation 500 includes a computer-readable medium 508, such as a CD-R, DVD-R, flash memory drive, disk hard drive, etc., having computer-readable data 506 encoded thereon. The computer-readable data 506 (such as binary data including at least one of 0 or 1) in turn includes a set of computer instructions 504, which are configured to operate according to one or more principles set forth herein. In some embodiments, for example, the processor-executable computer instructions 504 are configured to perform method 502, such as Figure 1 In some embodiments, the processor executable instructions 504 are configured to implement a system such as Figure 2 At least some of the exemplary system 200 of Figure 3 At least some of the exemplary system 300, and / or Figure 4 At least some of the exemplary system 400 of the present invention. One of ordinary skill in the art may conceive of many such computer-readable media that are configured to operate according to the techniques presented herein.

[0037] Although the subject matter has been described in language specific to structural features and / or methodological acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing at least some of the claims.

[0038] As used in this application, the terms "component", "module", "system", "interface", etc. are generally intended to refer to a computer-related entity, which is hardware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, and / or a computer. As an illustration, both an application running on a controller and a controller can be a component. One or more components can reside within a process and / or execution thread, and a component can be located on one computer and / or distributed between two or more computers.

[0039] In addition, the claimed subject matter may be implemented as a method, apparatus or article of manufacture using standard programming and / or engineering techniques to produce software, firmware, hardware or any combination thereof to control a computer to implement the disclosed subject matter. The term "article of manufacture" as used herein is intended to encompass a computer program accessible from any computer-readable device, carrier or medium. Of course, many modifications may be made to this configuration without departing from the scope or spirit of the claimed subject matter.

[0040] Figure 6The following discussion provides a brief, general description of a suitable computing environment for implementing one or more specified embodiments set forth herein. Figure 6 The operating environment is only one example of a suitable operating environment and is not intended to suggest any limitation as to the scope of use or functionality of the operating environment. Example computing devices include, but are not limited to, personal computers, server computers, handheld or laptop devices, mobile devices (such as mobile phones, personal digital assistants (PDAs), media players, etc.), multiprocessor systems, consumer electronics, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like.

[0041] Although not required, embodiments are described in the general context of "computer-readable instructions" executed by one or more computing devices. Computer-readable instructions may be distributed via computer-readable media (as discussed below). Computer-readable instructions may be implemented as program modules that perform specific tasks or implement specific abstract data types, such as functions, objects, application programming interfaces (APIs), data structures, etc. Typically, the functionality of the computer-readable instructions may be combined or distributed as desired in various environments.

[0042] Figure 6 An example of a system 600 is shown that includes a computing device 612 configured to implement one or more embodiments provided herein. In one configuration, the computing device 612 includes at least one processing unit 616 and a memory 618. Depending on the exact configuration and type of computing device, the memory 618 may be volatile (e.g., such as RAM), non-volatile (such as ROM, flash memory, etc.), or some combination of the two. This configuration is Figure 6 It is shown by dashed line 614 in FIG.

[0043] In other embodiments, device 612 may include additional features and / or functionality. For example, device 612 may also include additional storage devices (e.g., removable and / or non-removable), including but not limited to magnetic storage devices, optical storage devices, etc. Such additional storage devices may be stored in the device 612. Figure 6 620. In one embodiment, computer readable instructions for implementing one or more embodiments provided herein may be in the storage device 620. The storage device 620 may also store other computer readable instructions for implementing an operating system, application programs, etc. For example, the computer readable instructions may be loaded into the memory 618 for execution by the processing unit 616.

[0044] The term "computer-readable medium" used herein includes computer storage media. Computer storage media include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions or other data. Memory 618 and storage device 620 are examples of computer storage media. Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other storage technology, CD-ROM, digital versatile disk (DVD) or other optical storage device, cassette, tape, disk storage device or other magnetic storage device, or any other medium that can be used to store the required information and can be accessed by device 612. However, computer storage media do not include propagation signals. On the contrary, computer storage media excludes propagation signals. Any such computer storage media can be part of device 612.

[0045] Device 612 may also include communication connection(s) 626 that allow device 612 to communicate with other devices. Communication connection 626 may include, but is not limited to, a modem, a network interface card (NIC), an integrated network interface, a radio frequency transmitter / receiver, an infrared port, a USB connection, or other interfaces for connecting computing device 612 to other computing devices. Communication connection(s) 626 may include wired connections or wireless connections. Communication connection(s) 626 may transmit and / or receive communication media.

[0046] The term "computer-readable medium" may include communication media. Communication media typically embodies computer-readable instructions or other data in a "modulated data signal" such as a carrier wave or other transport mechanism, and includes any information delivery media. The term "modulated data signal" may include a signal that has one or more of its characteristics set or changed in a manner that supports encoding information in the signal.

[0047] Device 612 may include (multiple) input devices 624, such as a keyboard, a mouse, a pen, a voice input device, a touch input device, an infrared camera, a video input device, and / or any other input device. Device 612 may also include (multiple) output devices 622, such as one or more displays, speakers, a printer, and / or any other output device. (Multiple) input devices 624 and (multiple) output devices 622 may be connected to device 612 via a wired connection, a wireless connection, or any combination thereof. In one embodiment, an input device or output device from another computing device may be used as the (multiple) input device 624 or (multiple) output device 622 of computing device 612.

[0048] The components of computing device 612 may be connected via various interconnects such as a bus. Such interconnects may include a peripheral component interconnect (PCI), such as PCI Express, a universal serial bus (USB), FireWire (IEEE 1394), an optical bus structure, etc. In another embodiment, the components of computing device 612 may be interconnected via a network. For example, memory 618 may consist of multiple physical memory units located at different physical locations interconnected by a network.

[0049] Those skilled in the art will recognize that storage devices for storing computer-readable instructions can be distributed across networks. For example, a computing device 630 accessible via network 628 can store computer-readable instructions for implementing one or more embodiments provided herein. Computing device 612 can access computing device 630 and download some or all of the computer-readable instructions for execution. Alternatively, computing device 612 can download fragments of computer-readable instructions as needed, or some instructions can be executed at computing device 612 and some instructions can be executed at computing device 630.

[0050] Various operations of the embodiments are provided herein. In one embodiment, one or more operations described may constitute computer-readable instructions stored on one or more computer-readable media, which, if executed by a computing device, will cause the computing device to perform the described operations. The order in which some or all operations are described should not be interpreted as implying that these operations must rely on the order. Those skilled in the art who benefit from this specification will understand alternative ordering. In addition, it should be understood that not all operations must be present in each embodiment provided herein. Moreover, it should be understood that in some embodiments, not all operations are required.

[0051] Furthermore, unless otherwise specified, "first," "second," etc. are not intended to imply a temporal aspect, a spatial aspect, an order, etc. Instead, these terms are merely used as identifiers, names, etc. of features, elements, belonging, etc. For example, a first object and a second object generally correspond to object A and object B, or two different objects, or two identical objects, or the same object.

[0052] In addition, "exemplary" is used herein to mean used as an example, instance, illustration, etc., and is not necessarily advantageous. As used herein, "or" is intended to mean an inclusive "or" rather than an exclusive "or". In addition, unless otherwise specified or the context clearly points to the singular form, the "one" and "an" used in this application are generally interpreted as meaning "one or more". Moreover, at least one of A and B, etc. generally means A or B, and / or A and B. In addition, to the extent that "including", "having", "having", "having" and / or their various variations are used in specific embodiments or claims, these terms are intended to be inclusive in a manner similar to the term "comprising".

[0053] In addition, although the present disclosure has been shown and described with respect to one or more implementations, equivalent changes and modifications will occur to those skilled in the art based on the reading and understanding of this specification and the accompanying drawings. The present disclosure includes all such modifications and changes and is limited only by the scope of the appended claims. In particular, with respect to the various functions performed by the above-mentioned components (e.g., elements, resources, etc.), unless otherwise specified, the terms used to describe such components are intended to correspond to any component (e.g., functionally equivalent) that performs the specified functions of the described components, although these components are not structurally equivalent to the disclosed structures. In addition, although specific features of the present disclosure may have been disclosed with respect to only one of several implementations, such features may be combined with one or more other features of other implementations as long as it may be desirable and advantageous for any given or specific application.

Claims

1. A method for improving computer search results by extending a computer search index using generated metadata tag descriptions, the method comprising: obtaining, at a computing device, first content, the first content comprising a first metadata tag, the first metadata tag comprising a plurality of text characters; determining, using the computing device, based on a first probability matrix, that a word or phrase corresponds to a set of one or more characters of the first metadata tag; generating, with the computing device, an expanded version of the first metadata tag by replacing the set of one or more characters of the first metadata tag with the word or phrase determined to correspond to the set of one or more characters; retrieving, at the computing device, descriptive content that provides additional information related to the expanded first metadata tag, the descriptive content being different from the first content; as well as At the computing device, generating a first description for the first metadata tag based on the retrieved descriptive content; wherein each individual set of the one or more characters is less than all characters in the plurality of text characters included in the first metadata tag; wherein for each of the sets of the one or more characters of the first metadata tag, the probability matrix includes a probability that each of a plurality of different words or phrases corresponds to the each of the sets of the one or more characters; and Wherein the probabilities contained in the first probability matrix depend at least in part on the first content.

2. The method according to claim 1, further comprising: receiving a query including the first metadata tag; identifying the extended first metadata tag as an extension of the first metadata tag; as well as The first description for the first metadata tag is provided as a query result for the query.

3. The method according to claim 1, further comprising: retrieving second content including the first metadata tag and the second metadata tag; as well as Based on the second content, the second metadata tag is identified as being related to the first metadata tag.

4. The method according to claim 3, further comprising: receiving a query including the first metadata tag; identifying the extended first metadata tag as an extension of the first metadata tag; as well as The first description and metadata tag suggestions for the first metadata tag are provided as query results for the query, the metadata tag suggestions including the second metadata tag.

5. The method of claim 1, wherein determining that a word or phrase corresponds to the set of one or more characters comprises: identifying a first word or phrase based on a first frequency probability for a first word or phrase of a first set of one or more characters within the probability matrix being above a threshold; identifying a second word or phrase based on a second frequency probability within the probability matrix for a second word or phrase of a second set of one or more characters being above the threshold; as well as The first word or phrase and the second word or phrase are identified as included in the expanded first metadata tag based on a longer phrase including the first word or phrase and the second word or phrase having a phrase frequency probability above a phrase threshold within the probability matrix.

6. The method according to claim 1, wherein generating the first description comprises: extracting a first descriptive phrase from the retrieved descriptive content; as well as In response to the first descriptive phrase appearing above a threshold within the retrieved descriptive content, the first descriptive phrase is included within the first description.

7. The method according to claim 1, wherein generating the first description comprises: generating a first description candidate and a second description candidate for the first metadata tag based on the retrieved descriptive content; as well as A user selection of the first description candidate or the second description candidate is received for use in generating the first description for the metadata tag.

8. The method according to claim 1, further comprising: In response to determining that the expanded first metadata tag corresponds to at least one of spam or explicit content, at least one of the first metadata tag or the first description is marked as at least one of spam or explicit content.

9. The method according to claim 1, further comprising: associating a first time identifier with the first description, the first time identifier corresponding to a first time range, the first description describing the first metadata tag during the first time range; storing the first description in a data structure based on the first time identifier; generating a second description for the first metadata tag based on a second extended version of the first metadata tag and second descriptive content associated with a second time range; associating a second time identifier with the second description, the second time identifier corresponding to the second time range, the second description describing the first metadata tag during the second time range; as well as Based on the second time identifier, the second description is stored within the data structure.

10. The method according to claim 1, further comprising: identifying social media content that includes both the first metadata tag and a reference to supplemental content external to the social media content; receiving a search query including the first metadata tag; and Both the first description and the supplemental content for the first metadata tag are provided as search results for the query.

11. One or more computing devices for improving computer search results by extending a computer search index using the generated metadata tag description, the one or more computing devices generally comprising: one or more processing units; as well as a computer-readable memory comprising computer-executable instructions that, when executed by the one or more processing units, cause the one or more computing devices to: Obtaining first content, the first content comprising a first metadata tag, the first metadata tag comprising a plurality of text characters; determining, based on a first probability matrix, that a word or phrase corresponds to a set of one or more characters of the first metadata tag; generating an expanded version of the first metadata tag by replacing the set of one or more characters of the first metadata tag with the word or phrase determined to correspond to the set of one or more characters; retrieving descriptive content that provides additional information related to the expanded first metadata tag, the descriptive content being different from the first content; as well as generating a first description for the first metadata tag based on the retrieved descriptive content; wherein each individual set of the one or more characters is less than all characters in the plurality of text characters included in the first metadata tag; wherein for each of the sets of the one or more characters of the first metadata tag, the probability matrix includes a probability that each of a plurality of different words or phrases corresponds to the each of the sets of the one or more characters; and Wherein the probabilities contained in the first probability matrix depend at least in part on the first content.

12. The one or more computing devices of claim 11, wherein the computer-readable memory comprises further computer-executable instructions that, when executed by the one or more processing units, cause the one or more computing devices to: associating a first time identifier with the first description, the first time identifier corresponding to a first time range, the first description describing the first metadata tag during the first time range; generating a second description for the first metadata tag associated with a second time range, the second description being different from the first description and based on second descriptive content different from the first descriptive content; as well as A second time identifier is associated with the second description, the second time identifier corresponding to the second time range, the second description describing the first metadata tag during the second time range.

13. The one or more computing devices of claim 11, wherein the computer-readable memory comprises further computer-executable instructions that, when executed by the one or more processing units, cause the one or more computing devices to: identifying social media content that includes both the first metadata tag and a reference to supplemental content external to the social media content; receiving a search query including the first metadata tag; and Both the first description and the supplemental content for the first metadata tag are provided as search results for the query.

14. The one or more computing devices of claim 11, wherein the computer-readable memory comprises further computer-executable instructions that, when executed by the one or more processing units, cause the one or more computing devices to: receiving a query including the first metadata tag; identifying the extended first metadata tag as an extension of the first metadata tag; as well as The first description for the first metadata tag is provided as a query result for the query.

15. The one or more computing devices of claim 11, wherein the computer-readable memory comprises further computer-executable instructions that, when executed by the one or more processing units, cause the one or more computing devices to: retrieving second content including the first metadata tag and the second metadata tag; Based on the second content, identifying the second metadata tag as being related to the first metadata tag; receiving a query including the first metadata tag; identifying the extended first metadata tag as an extension of the first metadata tag; as well as The first description and metadata tag suggestions for the first metadata tag are provided as query results for the query, the metadata tag suggestions including the second metadata tag.

16. The one or more computing devices of claim 11, wherein the computer-readable memory comprises further computer-executable instructions that, when executed by the one or more processing units, cause the one or more computing devices to: identifying a first word or phrase based on a first frequency probability for a first word or phrase of a first set of one or more characters within the probability matrix being above a threshold; identifying a second word or phrase based on a second frequency probability within the probability matrix for a second word or phrase of a second set of one or more characters being above the threshold; as well as The first word or phrase and the second word or phrase are identified as included in the expanded first metadata tag based on a longer phrase including the first word or phrase and the second word or phrase having a phrase frequency probability above a phrase threshold within the probability matrix.

17. The one or more computing devices of claim 11, wherein the computer-readable memory comprises further computer-executable instructions that, when executed by the one or more processing units, cause the one or more computing devices to: extracting a first descriptive phrase from the retrieved descriptive content; and In response to the first descriptive phrase appearing above a threshold within the retrieved descriptive content, the first descriptive phrase is included within the first description.

18. A computer-readable storage medium comprising computer-executable instructions which, when executed, cause a computing device to improve computer search results by extending a computer search index with generated metadata tag descriptions by performing the following steps, the steps comprising: Obtaining first content, the first content comprising a first metadata tag, the first metadata tag comprising a plurality of text characters; determining, based on a first probability matrix, that a word or phrase corresponds to a set of one or more characters of the first metadata tag; generating an expanded version of the first metadata tag by replacing the set of one or more characters of the first metadata tag with the word or phrase determined to correspond to the set of one or more characters; retrieving descriptive content that provides additional information related to the expanded first metadata tag, the descriptive content being different from the first content; as well as generating a first description for the first metadata tag based on the retrieved descriptive content; wherein each individual set of the one or more characters is less than all characters in the plurality of text characters included in the first metadata tag; wherein for each of the sets of the one or more characters of the first metadata tag, the probability matrix includes a probability that each of a plurality of different words or phrases corresponds to the each of the sets of the one or more characters; and Wherein the probabilities contained in the first probability matrix depend at least in part on the first content.

19. The computer-readable medium of claim 18, comprising further computer-executable instructions which, when executed, further cause the computing device to perform the following steps, comprising: retrieving second content including the first metadata tag and the second metadata tag; Based on the second content, identifying the second metadata tag as being related to the first metadata tag receiving a search query including the first metadata tag; as well as Both the first description for the first metadata tag and the second metadata tag are provided as a result for the search, the second metadata tag being provided as a suggestion.

20. The computer-readable medium of claim 18, comprising further computer-executable instructions which, when executed, further cause the computing device to perform the following steps, comprising: associating a first time identifier with the first description, the first time identifier corresponding to a first time range, the first description describing the first metadata tag during the first time range; generating a second description for the first metadata tag associated with a second time range, the second description being different from the first description and based on second descriptive content different from the first descriptive content; as well as A second time identifier is associated with the second description, the second time identifier corresponding to the second time range, the second description describing the first metadata tag during the second time range.

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