JP2007234008A5 - - Google Patents

Description

Method and system for determining and querying relevant sources of information and merging results from multiple content sources Related applications

  This patent application claims priority to US Provisional Patent Application No. 60 / 766,892, filed February 16, 2006, which is hereby incorporated by reference in its entirety.

Field of Invention

  The present invention generally relates to computerized techniques for information acquisition. More particularly, it relates to federated search and retrieval techniques that dynamically categorize queries to identify multiple content information sources accessible over the network, and from multiple information sources. Caching the returned results and further merging results from multiple sources based at least in part on source ratings, user ratings, business considerations and / or other factors.

Background of the Invention

  Inexpensive computer and networking technology has overloaded information by making large amounts of digital content available to Internet and mobile network users. As a result, users have access to much more information and entertainment than they can consistently and reliably locate with a large centralized public search engine.

  At the same time, considerable practical and commercial value is provided by text and data search technology, and the purpose of text and data search technology is generated and managed by a large Internet search engine available to the public. Identifying information that is most useful to the user within a given content collection, such as information.

  The resulting explosion and commercialization of information retrieval and retrieval technology is an increasing number of commercial data, media and text under intellectual property that is independently indexed and maintained by content sources Has produced a collection. These content information sources have limited economic incentives to make their digital content fully accessible for indexing by public search engines, and public search engines have access to the actual content. You get more economic benefits by contracting these sources with advertisers than by providing their users direct access.

  Most current search engines pre-index a collection of resources (eg, documents, images, websites) and collect in one or a group of computers to find content that satisfies the query in response to the query It is designed to inspect things and return an ordered list of possible matches as a result set to the user. Result item metadata indicating relevance ranking, meaning how close the content matches the query, may be returned explicitly or implicitly given in the order of the items in the result set Well, usually the most relevant item is at the top of the list. The ranking may be based on a numerical similarity score value or one of many possible measures previously calculated for the content by the content publisher and stored in the full text or database index Good.

  Search engine queries and indexing architectures are divided into at least three types: centralized indexing, meta search, and federated search engines. Each type is used to perform a search on a different type of content collection. For example, a centralized index makes it easy to search for the same type of content that is fully accessible, such as that found in a single enterprise content management system or the vast number of internet-enabled websites available to the public. Used to do.

  A meta search engine combines results from several external search engines or database indexes. Meta search engines have come colloquially to search for collections with similar text content collection indexes, such as multiple Internet search engines or bibliographic databases.

  A federated search also combines results from more than one search. Each search is typically performed against disparate content collections, such as those associated with different types of indexing engines. This searches, for example, mixed content from full text search engines and databases, different information resources such as from different file servers or different content types, or multiple sports sites including sports news, sports apparel and sports team products It sometimes requires access to collections with different ownership.

  To maximize the accuracy of a meta search or federated search, you should find the resource that gives the highest score for a meta-collection, not necessarily the one that gives the highest score for the individual collection in which they exist It is. For example, in a federated search for a combination of two different collections of sports and technical news, if the query includes the term “computer”, an incorrect implementation would be inappropriate for computer-related documents that appear in the sports collection. Will give weight. Since meta-collections are used to screen information from diverse collections, each with a different specialty or focus, the impact of this effect is substantial.

  In addition to conventional content access by stationary computers, there has been an explosion of Internet access using mobile computing devices such as laptops, personal digital assistants (PDAs), and mobile phones. This surge has significantly changed the nature of content access, and content publishers are reformatting and reorganizing their content for mobile access. While desktop computer users can comfortably search for information using multiple trials and browsing, mobile computing users can rely on small screens and input ergonomics, location specificity and their own mobility. Is generally limited. Because of these constraints, mobile computing users are less likely to want to receive all possible relevant results and more likely to want specific information immediately.

  This changing nature of content access is based on a new search and retrieval process that highlights the highest possible relevance in the first 5 to 10 entries of the result set, resulting in an information retrieval relevance value for recall. Play a big role in increasing. For the same reason, mobile users also require the shortest path to their desired content. Thus, rather than providing access to a list of content information sources, the search result item should allow the user to directly access the content item of interest.

  There are other challenges to the federated search function. Different sources of information index their content collections using different algorithms or by processing the same algorithm for different sections of text and / or metadata.

  Different sources may contain overlapping resource collections, which will result in the same content item appearing in the result set from both sources. Traditional deduplication algorithms remove all duplicates based on a metadata field value or set of field values. For example, a news source removes all content items that have the same headline, biline, and date values.

  Different sources contain similar content, but contain different depths of content (collection size) or change response characteristics (latency, uptime percentage). These variations can negatively impact the user experience by producing poor results or by not responding before the system or user perception timeout. Federated searches across multiple content information sources improve the opportunity for users to get some responses to their queries within a reasonable time frame.

  In addition, there may be wide variations in the relevance of the content collection to the query. Not all available content information sources contain collections that are sufficiently relevant to ensure that they are included in the meta-collection.

Summary of the Invention

  Accordingly, one aspect of the present invention is to address one or more of the problems described above. In accordance with an exemplary embodiment of the present invention, in a computer-implemented method for processing a query, identifying a query from a user device and one or more relevant content information sources Categorizing the query to obtain a query result received in advance from one or more content information sources and storing it in a local cache if available, and one or more if not available Formatting the query according to one or more content information source specific query languages for the content information source and sending the formatted query for the one or more content information source to the one or more content information sources And caching results received from one or more content information sources Merging the results in response to the formatted query to one or more content information sources, and at least in part to one or more of users, information sources, content and / or distribution channel ranking factors Based on, merging the results, formatting the results for delivery to the user based on the device or requester characteristics, and returning the formatted results to the requester.

  According to another aspect of this exemplary embodiment of the invention, the method may further include: That is, the requesting device includes one or more of an internet enabled input device, an internet or audio enabled mobile device, an audio enabled input device, a computer, and a kiosk, and the content information source is associated with the content information source. Including one or more of: search engines, advertising engines, content distribution systems, and databases, one or more user ranking factors include user demographic characteristics, location, language, social networks and groups, and personalization characteristics One or more content ranking factors including at least one of page size, graphics, text elements, and text, and the one or more information source ranking factors are information sources Rating, trust One or more delivery channel ranking factors include business rules, business relationships, demographic selection, and marketing, including at least one or more of gender factors, latency factors, overall content relevance, and content size or coverage The query includes at least one or more of the purposes, and the query is categorized into one or more subjects or functional taxonomies or relevant categories in the controlled vocabulary, and the query is user-selected, device-specific and format-restricted And further comprising storing the results from each content information source in one or more caches, wherein the cache is query specific, the cache is information source specific, and the method Accesses one or more caches to retrieve existing results And the results are merged based at least in part on one or more of user, content, information source and / or distribution channel factors, wherein the one or more result ranking factors are source rating, metadata relevance Includes one or more of a factor, a similarity factor, and a ranking factor.

According to another aspect of this exemplary embodiment of the invention, the method may further include: That is, in each content source query result cache, one or more local ranking statistics are associated with each result item associated with one or more terms associated with the query and associated with metadata in the query context. It further includes calculating dynamically. The method further includes calculating at least one global statistic associated with one or more content items in the result set and calculating a normalization factor . The method further includes determining one or more relevance scores for result items from one or more content information sources according to at least one of global and / or local statistics. The method further includes normalizing the one or more relevance scores according to a normalization factor . The method further includes combining the results into a single result set based on the order determined by the normalization factor , and formatting the existing results obtained based on the one or more query context parameters.

  In accordance with an exemplary embodiment of the present invention, in a computer-implemented system for processing a query, to identify a receiving module that receives a query from a user device and one or more content information sources. A formatting module for formatting the query and one or more content information sources formatted according to a classification module for classifying the query and one or more content information sources specific to one or more content information sources A sending module for sending a query to one or more content information sources; a merge module for merging results in response to a formatted query from one or more content information sources based at least on one or more factors; Format results for delivery to user devices System comprising a results module for bets.

  In order to facilitate a thorough understanding of the present invention, reference is made to the accompanying drawings. These drawings should not be construed as limiting the invention, but are intended as exemplary only.

Detailed Description of Exemplary Embodiments

  Various embodiments of the present invention search in a manner that maximizes relevance of results while minimizing user-perceived latency and platform resources, including memory, processing and network requirements. It is aimed at returning results. These qualities are becoming increasingly important as the size of the result set and the number of sources increase.

  The following definitions are merely exemplary and are referred to herein to illustrate the various embodiments of the invention described below. The embodiments and scope of the invention are not limited by the definitions set forth below.

  Search engine: refers to a computer program that is designed to index information and store and retrieve information based on instructions from a user through a query. The process of performing individual searches on a single collection is called a search engine. The process of performing a search against multiple search engines and / or databases and combining the results is known as a federated or meta search engine.

  The method of ranking search results may include determining a relevance score for a resource (eg, resource, website, image) from a query perspective. A similarity score for the query may be calculated using attributes associated with the results and feature vectors characterizing the query words. Rank values may be assigned to results based on relevance scores, similarity scores, and / or other factors or criteria. Further, search results may be improved by adaptive ranking based on the user's previous behavior and resources returned from a text search engine or other content information source. More specifically, the user's previous behavior may be evaluated to determine a rate, and at that rate, adaptive correction may be applied to a given query.

  The result list is merged with the goal of placing the most relevant entries first for the convenience of the user. To reduce the computational overhead involved, the lists may not be merged based on a single entry-by-entry study. Rather, the lists may be merged based on examining a smaller number of entries from each list. A subset of entries may be selected from each list, and the lists may be merged according to these subsets rather than in a single entry evaluation per single list. The subset may be selected according to a technique that selects several items from a larger group. For example, the number n may be selected and the top n resources may be selected from each list. According to another example, the number may be chosen again and the merge algorithm may select n resources that are evenly spaced in each result list. According to yet another example, a number may be selected and n resources from each list may be selected randomly.

  A score value is determined for each entry in the various selected subsets. The score value may be a number that generally represents how close the entry matches the query, with a range of numbers indicating entries that are likely to be relevant to the user. A representative score for all score values is determined. The representative score may be an arithmetic average or a value proportional to the average for a set of score values.

  All entries from all lists are merged or ranked based at least on the representative score for each list. Once each result list has an assigned representative score, each result list may be merged with other lists accordingly. For example, entries may be merged by selecting the list with the highest representative value (eg, the highest average score value). The first entry on the list that has not yet been selected is selected next. The representative value of the list is reduced by a fixed amount and the process is repeated until all entries are selected. If any representative value falls below zero after being subtracted, the representative value may be reset to its initial value or a predetermined value.

  According to another example, entries may be merged using a probabilistic approach, where each list has a probability value equal to the ratio of the representative value of that list to the total representative value of all lists. Assigned. Lists may be selected according to their probability values, making it easier to select lists with higher probability values. When a list is selected, the first entry on that list that is not yet selected is selected. This process is repeated and the total representative value is modified when all entries in the list are selected.

  Content source: refers to a publisher who has a collection of digital or non-digital content available over a network.

  (Content) source rating refers to a score used to measure the relative usefulness of a content source for a type of query. Content source ratings may be calculated by including content factors such as size or coverage, classification reliability, content quality, and / or other information that affects the relevance of the source results. Ratings may be affected by business relationships and usage patterns. For example, giving a selection of content source result items to select one source for other sources in the source library or to other source results in the combined result set Thus, the business relationship between the content information source and the seller may increase the rating of the information source. A source with a high percentage that returns no results has a lower rating than a source with a high percentage of queries that have return result items.

  Source ratings may also consider performance factors such as latency, response reliability and / or other criteria. For example, reliability refers to the percentage of information source uptime during an average one month period (or other period). Latency may be measured as the time for sending a query to a content information source by the query broker system, plus the return time of the result set from the information source to the query broker system. For example, the source rating may be calculated by combining an objective measure of the reliability and response latency of the source responding to the query with a subjective measure of content coverage. For example, an information source that is 99% available 24 hours a day has a higher source rating than an information source that is only available 93% of the day.

  In addition, using an adaptive process that relies on responses to previous queries, user selections or other dynamic events, including but not limited to queries or query sets, user traffic patterns, source responses, Source ratings may be updated in response to various factors, such as and / or advertising / marketing campaign considerations.

  Query: refers to a request that describes or identifies information or data sought by a user. The query may include various combinations of text, non-text, and / or user-selected categories. For example, a query can be a keyword (eg, term, phrase, natural language sentence), a non-text query (eg, an image or audio clip, and / or a numerical query such as an auction bid, purchase price or travel date), and And / or categories (eg, music genres such as rock, pop, or urban).

  Various combinations of query types and formats may be applied. For example, in the case of travel reservations, the query may include date ranges, departure and destination city pairs, and / or number of travelers. In the case of an audio file, the query may include an artist name, song title, etc., along with a language or musical phrase. In more complex scenarios, the query may be characterized with respect to stock quotes, stock price derivatives, signal patterns or isobaric lines.

  A user may send a query through a remote device such as a telephone, PDA and / or other mobile device. Further, the user may send the query using a computer or other communication device.

  Query Context: Demographic information such as user gender, age, and marital status, social networking information such as community, location, group membership, and / or other data received by search engines May be included. The query context may include other user specifics such as language selections, display selections, time / date data, and / or other information. The query context is the type of device (eg, mobile phone, laptop computer, PDA, game console, etc.), device settings / limitations (eg, size, graphics, audio, video, memory, etc.) ( Response display settings (such as font, color, etc.) may be included. The query context may include the user's current location and / or preferred location, which is used to select relevant search results for location related queries. For example, the user may search for nearby pizza shops. The search engine may automatically return a list of pizza restaurants that are closest to the user's current location.

  The query context is automatically obtained from the device and may be included in the query. In addition, the user may access a web page or other user interface to provide and / or update other data that will be included in the user selections, settings, and / or context.

  Stored queries: The user may pre-program frequent searches such as stock quotes, weather, updates about favorite celebrities without changing any underlying technology.

  Result item: The smallest piece of information. The result item is returned by the search engine and used to point to a specific document. The result item may include the location of the information resource and various other metadata values such as description, title and price.

  Result set: A list of result items returned from a search engine in response to a query.

  Categorization: is defined as the arrangement of entities in a potentially hierarchically organized group, such as taxonomies whose members have some similarity to each other. A categorization system involves the assignment of one or more group labels intended to represent the intellectual, functional or conceptual content of that resource to the resource. These labels are usually derived from a controlled vocabulary. A controlled vocabulary can be used in conjunction with an information acquisition system by identifying a set of authenticated terms or labels that can be used to normalize a set of terms (terminology) and properly cast a search query. Communication with these information acquisition systems.

  Taxonomy: may be a hierarchically arranged controlled vocabulary used to organize content in a collection. Internet search engines may have one or more related taxonomies to facilitate browsing searches for content collections. Web application developers and marketers may divide the organization of resources into two separate representations to satisfy the organizational use of different stakeholders. A single taxonomy node is called a taxon. Multiple taxons are called taxa.

  The reference taxonomy: may be a fine-grained, monotonically developing taxonomy used as a structure for human or mechanical classification of content items stored in a local collection.

  Display Taxonomy: is a subset of the reference taxonomy and is used for display to individuals accessing the search engine. This display taxonomy is more variable than the reference taxonomy. The reason is that display taxonomy is used to highlight categories for individual browsing searches, and it is even more important for content source staff to highlight areas of content this week and different areas next week. This is because it may be decided. If the display taxonomy changes, the reference taxonomy is left unchanged to avoid reclassifying the entire content collection.

  Information Source Taxonomy: is a display taxonomy for content information sources accessible by the federated search engine. In a federated search system, there may be three levels of organization. For example, in addition to the display taxonomy that can be viewed by individuals and the reference taxonomy level for organizing the resources available in the meta-collection, the content source display taxonomy is used indirectly for user browsing. It may be possible.

  The present invention particularly relates to a method and system for an improved federated or meta search engine, wherein the improved federated or meta search engine categorizes queries and query contexts to provide a plurality of distributed heterogeneous content. Select the most relevant source from a set of sources and use the source, user, seller rating, and / or other factors to provide an ordered and combined set of search results to the user Generate with minimum latency.

  FIG. 1 is an exemplary diagram of a system 100 for federated search for multiple content information sources according to an embodiment of the present invention. The components of system 100 may be further duplicated, combined, and / or separated to support various applications of embodiments of the present invention. Additional elements may be implemented in the system to support various applications.

  System 100 is used to send a search query from user device 110 to federated search engine 122 to request a set of result items detailing the location of content resources that satisfy the user query. User device 110 may comprise a computer input device that includes a client or browser program 112 that allows a user to access the Internet. User device 110 may include mobile devices or other communication devices, including terminals such as kiosks or desktop computers. User device 110 may communicate via network 120, which may include the Internet or other network to federated search engine 122. The federated search engine 122 may operate in a client server, peer-to-peer, and / or other configuration.

  The search engine 122 includes a query broker system 130 with an information source selection, a result cache, and an associated merge program that merges information source specific result sets. Although shown as separate, the search engine 122 and query broker system 130 may be a single unit.

  The query broker system 130 may communicate to user devices (eg, user device 110) through the network 120 via a wired or wireless network connection, and content information sources (eg, content information sources 150a, 150b,... 150k, through the network 124). ... 150m), a database (as represented by database 170k), and / or other information sources 150m, such as "on-deck" content information sources, for example.

  The query broker system 130 may include various modules that perform functions related to searching, obtaining, and / or other processing. For example, query broker system 130 may include network connection 132, query processor 134, connector framework 136, merge process 138, result processor 140, result cache 142a ... 142n, and / or other modules. Various components of system 100 may be further duplicated, combined, and / or integrated to support various applications and platforms. In addition, modules, caches and other components may be implemented through multiple systems, platforms, applications, etc. Additional elements may be implemented in the system to support various applications.

  Network connectivity module 132 manages the balanced exchange of data through network 120, network 124, and / or other networks and communication portals.

  Query processor 134 dynamically categorizes queries. Embodiments of the present invention use a taxonomic structure that organizes content publishers, locations, content subjects or functions, and / or any other relevant content distinction, to allow user queries And / or perform dynamic classification of query states. The categorization function of the query processor 134 selects or identifies a subset of relevant content information sources from a library of information sources. By dynamically calculating a set of information sources relevant to the user's information request when a query is presented, embodiments of the present invention minimize the cost of acquiring irrelevant content Maximize precision.

  Query processor 134 reformats the query into a source-specific query language, and source connector framework 136 sends the reformatted query to the respective content information source. By reformatting the query into a source specific query language, more accurate results can be obtained more efficiently. In response, the source connector framework 136 receives a result set from each content information source.

  Content information sources 150a ... 150m use several modules to facilitate the acquisition of information from their content collection. The content information source communicates to the user device or other program through networks 160a ... 160m via a wired or wireless network connection. The search engine access modules 162a... 162m use a search engine owned indexing algorithm to parse the incoming query, match the query to the content index, and describe the description and location of the matching content item. Providing a result set containing such metadata. The indexes 164a ... 164m may include storage mechanisms as well as computer programs, which may contain metadata, text and / or other attributes from resources contained in the source content collection. . The stored content collection module 170a ... 170m includes resources, multimedia and / or other content that is indexed by a search engine, referenced by metadata, and accessible by the locations listed in the result set. May be.

  Content information source modules and other components may be implemented through multiple systems, platforms, applications, and the like. Additional elements may be implemented in the content source system to support various applications.

  The stored content collection includes data items such as collection items [170a-a ... 170a-e] and [170a-a, 170a-b, 170b-a, 170b-b, 170b-e]. Also good. For example, content items may appear in a collection, as in items [170a-a ... 170a-e]. However, content items may appear in more than one collection, as indicated by the overlap of content collections [170a-a, 170a-b] in collections 170a and 170b. In such a case, multiple references to the same content will appear in the result set of a federated search that calls both of these external engines. This situation exists when content resellers publish the same content to their respective members, which may include language, content ranking, organization, invoicing configuration and / or other considerations. The presentation to the user is changed due to various factors. The federated search engine may further preserve or remove duplicates in such a way as to generate a fair representation of multiple collections.

  In accordance with an embodiment of the present invention, the source connector framework 136 receives results from individual content information sources (eg, search engines, databases, other data information sources, etc.) and sends the results to query / source specific Are further stored in the result cache 142a... 142n. The result cache includes a result set returned from the content information source in response to a particular query, such as, for example, a keyword, term set, hammed phrase, or category. The result cache may be time sensitive and may be such that the result becomes unusable after a predetermined period, such as a specific minute or hour, in order to keep the content fresh. A cache may have an associated unique cache key, which includes a source identifier, a query or category term, and / or other factors that facilitate reuse.

  A query specific cache (eg, result cache 142a ... 142n) stores results returned from content information sources 150a ... 150m and may store a merged result set for post processing in result processor 140. . Results processor 140 uses merge process 138 to edit and possibly cache the combined list to generate a single results list ranked for the user. Separate source-specific lists and combined lists may be reusable within a configurable time for responses to subsequent queries by the same or other users.

  When the time threshold has elapsed after most or all results have been received, or when other conditions are met, the merge process 138 differs in an order based on various factors. Merge result sets into a single list (or other format). For example, embodiments of the present invention may be directed to merging results based on source ratings after waiting 100 milliseconds for a source response. Further, the results may be ranked based on internal content relevance scores and / or other result specific criteria. For example, after individual results are received, the merge process 138 may merge source-specific results according to a merge algorithm or program. A merge algorithm or program can include result-based rankings such as relevance or accuracy, along with local ranking scores, source ordering values, source-specific general scores, and / or other source factors, and population Usage factors such as statistics, traffic patterns, user personalization and community values may be included. The merge process 138 may retain or remove duplicate results according to users, devices, and / or other selections or processes that may be applied to the results.

  FIG. 2 is an exemplary flowchart illustrating a method for query execution according to an embodiment of the present invention. The method of an embodiment of the present invention selects a relevant subset of content sources that may be available to a search engine, such as a federated search engine, and reformats the query in each subset. Send to source, receive and cache each result set, and merge result sets into a single combined result set.

  As shown in FIG. 2, in step 210, a query is received from the user. In step 222, queries are dynamically classified for one or more taxonomies that organize content source libraries, content subjects and functional aspects, and / or user and operator characteristics. In step 224, a subset of content information sources is identified from the information source library. In step 230, the result cache is checked for preexisting result sets. If there are no results in the cache for the query and query context, the query broker proceeds through steps 242, 244 and 246. In step 242, the query may be reformatted into an information source specific query language specific to a subset of the content information sources. In step 244, the reformatted query is sent to a content information source, such as a content provider, search engine, database, and / or other data information source. In step 246, the content is received from the content information source and stored in the local result cache. In step 250, the results from the content information sources are merged and further reformatted. In step 260, the results are returned for display to the user. Although the steps of FIG. 2 illustrate specific steps performed in a specific order, embodiments of the present invention add one or more steps to the process, thereby omitting steps in the process. It should be understood that and / or may be implemented by changing the order in which one or more steps are performed.

  Embodiments of the present invention perform dynamic categorization of user queries and / or query states against a pre-classified library of content information sources. In step 222, the query is categorized by the query processor 220 at runtime. For example, the user may search for a mobile phone game using the keyword “auto racing”. For example, at step 222, the query processor 220 classifies the query as a “mobile game” query, thereby identifying a set of mobile game content information sources. Furthermore, the granularity of the query category may be adjusted to make the search result more accurate. For example, the query may be a request for a game about auto racing. In this case, the query may be categorized as “mobile game” and “action”. Thus, the subset of content information sources is identified as being the most relevant to the combination of the two categories. Other variations may be applied.

  By dynamically calculating a set of information sources relevant to a user's information request when a query is presented, embodiments of the present invention maximize the relevance rate for the query. Furthermore, the amount of data transmitted over the network is minimized compared to other federated search techniques, thereby resulting in efficient bandwidth utilization. Furthermore, the topology of the federated search source selection mechanism easily supports a multi-tier hierarchy of search engines and meta search engines, thereby allowing any number of content collections, search engines, and / or other data information. Facilitates the expansion of the search system for sources.

  In step 224, a subset of content information sources is identified. The categorization function of the query processor 220 selects or identifies a subset of relevant content information sources from a library of potential information sources.

  In step 230, one or more result caches are checked. In accordance with an embodiment of the invention, the result cache is checked for previously returned results. Embodiments of the present invention may be directed to retrieving results for a query from a cache, thereby allowing reuse of results for identical and / or related queries from other users To. As a result, network transmission is minimized and the impact of network latency on the user is reduced. Thus, if it is determined that query results are already stored in the local internal or external cache, these results may be used directly to return to the user in step 250 or other search engines May be merged with the results from

  If there is no result in the cache, in step 242, the query may be reformatted into a source specific query language. For example, the query broker system may reformat the query into an information source specific query language for one or more content information sources. In step 244, the reformatted query is sent to the content information source.

  In step 246, content is received from each content information source. Further, each content information source may predetermine scores, rankings, and / or other ratings for the content in their respective collections according to the query. Further, the result item may indicate an implicit ranking by being sent to the query broker system as an ordered result list. The query broker system 240 may receive results from individual content information sources (eg, search engines, databases, other data information sources, etc.) and further store the results in a local internal or external result cache. The local result cache may be identified by a query, a specific content information source, a group of information sources, a type of information source, and / or other categorization.

  In step 250, the content from the content information source is merged and further formatted. After individual results are received or when a time threshold has elapsed, the program may merge the source-specific results according to the merge algorithm or program, and the merge algorithm or program may have a local ranking score. Source ordering values, information source specific general scores, usage scores, user or merchant scores, and / or other factors. In step 250, the combined results list is edited to create a ranked single results list for the user. Separate source-specific lists and combined lists can be re-used within a configurable time to respond to subsequent queries by the same or other users. In addition, duplicate results may be retained or removed, and other selections may be applied to the results. The result may include a content information source reference with each result item to point to the content information source. For example, embodiments of the present invention may be directed to merging results into an order based on various factors, such as information source factors such as content quality and scale, content information source latency, and so on. It may include reliability, business relationships, externally determined quality ratings (eg, zaga rating, etc.), personal and community usage patterns, and / or other ratings and calculations. Further, the results may be ranked based on text and metadata relevance and / or may be ranked based on other result specific criteria.

For example, at least one global statistic associated with the content item in the result set may be calculated. This may include a score normalization factor consisting of a result item rank and an information source rating. In addition, content relevancy scores for result items from content information sources may be determined according to global statistics. In addition, the score may be normalized according to the order of the normalization factor for the meta-collection, the external similarity score, and the resulting meta-collection items returned from the content information source according to the source statistics.

  In step 260, the results are displayed to the user. When displaying results to the user, user device specific and / or user choices may be considered. For example, a mobile device may have a screen size limit, and the result item description or title may be shortened and / or not to accommodate the user's device and / or other selections. May be modified.

FIG. 3 is an exemplary flowchart illustrating a method for intelligent information source selection according to an embodiment of the present invention. Embodiments of the present invention may be directed to identifying a subset of relevant content information sources from search engine and database source libraries. The categorization process may analyze the query and its attributes to identify relevant subsets of content information sources. The query processor may use a categorization process to assign a query to the relevant taxon or taxa in the reference taxonomy and select an optimal set of related source taxa that uniquely identifies the content information source .

  In step 310, one or more query context attributes are identified. For example, attributes may include merchants, depth-first search channels, languages, countries, artists, titles, prices, and / or other metadata related to queries and / or users.

  In step 320, the computer program evaluates the query context attribute value. In step 322, a related reference taxonomy is selected in response to the depth-first search selection and other context parameter values. In step 330, the computer program determines whether the query is a set of terms or categories. A term may refer to a word, phrase, or the like. If so, in step 332, a term is assigned to the category in the associated reference taxonomy using a dynamic machine classification process. In step 340, the computer program determines whether the query is a category from the display taxonomy. If so, at step 342, the relevant category is identified in the reference taxonomy. In step 350, the selected reference taxonomy category is associated with an information source taxon or taxa associated with each selected content information source. In step 360, the query and query context values may be transformed to match the source metadata fields and values, which may include translation, user selection extraction, and the like. In step 370, query context attributes (eg, language, country, etc.) are matched to one or more source attribute, and the context attribute name is mapped to the source attribute name. In step 380, the source taxalist, matching metadata attribute names and values, and the transformed query are returned to the query broker system.

  FIG. 4 is an exemplary flowchart illustrating a method for accessing a result list, storing the result list, and merging the result list according to an embodiment of the present invention. As explained above, a subset of the information sources is identified and the related taxon is returned to the query broker system. In steps 410a ... 410m and 450, it is determined for each source and query whether an existing result set exists in the cache. If there are results, in step 455 the results are merged based on the incoming query context attribute values. In this step, the previously stored result is obtained from the result cache.

  If no result exists, in steps 420a ... 420m, the query is reformatted into an information source specific query language and sent to the respective content information source. In steps 430a ... 430m, the query broker system waits for results from each information source. A wait timeout or other predetermined condition may be implemented to ensure efficiency. Once the results are received, the results are stored in the result cache in steps 440a ... 440m. In step 450, it is determined whether all sources returned results or whether the timeout limit has expired. In step 455, all result items in the cached result set are merged into a single combined result set based on the incoming query context attribute values. The merge algorithm caches the merged list and creates a single ranked result list.

  As shown in step 460, the additional processing involves taking the top or next m items from the combined result set to create a user specific result page. In addition, the query broker system checks the results list for duplicates and groups them according to system and merchant choices and removes or retains them. This step may take into account query context attributes such as, but not limited to, device specific, user selection, and / or merchant restrictions in creating the results page. In step 465, the result page is sent to the user via a wired or wireless communication channel. In step 470, pointers are set to the remaining result items (in the (m + 1) th result item) in the combined result list.

  The merge process may calculate global statistics for each result item in each result set returned by the information source in response to the query. This global statistic is a function of two or more factors, which are expressed by a similarity score or ranking determined by the source, and result items for the query that are explicitly or implicitly included in the result item And external characteristics such as, but not limited to, source ratings, usage parameter values, user selection scores, or merchant selection values. FIG. 5 is an exemplary flowchart illustrating a merge process for representing external characteristics using rank order as a result item score and source rating according to an embodiment of the present invention.

After all individual results have been received, the query broker system includes a general score (such as n _j ) and a result specific relevance score (such as m _j ). The results may be merged according to For example, in step 520, the source taxa is obtained using the taxa identifier stored in the query context. In step 530, for each information source, from the attribute values stored in each source taxon (e.g., such as n _i) information source score is calculated. In steps 540a ... 540m, for each information source, the result item is obtained from the associated cache. In step 545a... 545m, an item score (such as m _j ) is determined for each information source result item, and in step 550a... 550m (eg score _ij = f (n _i , m _j Reranking scores (such as) are calculated. As indicated by step 560, the query broker may edit the merged list using score _ij to create a single results list ranked for the user.

FIG. 6 is an exemplary diagram for re-ranking result items from a result set returned by multiple sources in accordance with an embodiment of the present invention. In this example, information source A may have an information source rating n _A that is 80 and information source B may have an information source rating n B that is 50, as shown in 600a and 600b, respectively. . Local statistics for each item in each result set are calculated as a function of item order, m _Aj and m _Bj , as indicated by 610a and 610b. As indicated by 640a and 640b, the global statistic, re-ranking score _ij is calculated as a function of the result item rank m _ij and the source rating n _i , so the score _ij for each result item is multiplied by the source rating. The product of the reverse rank for each result item. As indicated by 650, the combined result set includes items from information source A and items from information source B, by sorting each result item by their associated global score _ij . Is arranged.

  As explained above, each content source (eg, search engine, database, etc.) may determine a score for the content in its respective collection according to the query. The connector framework may receive results from individual search engines, calculate a local ranking score for each item, and store the results in a source-specific cache, where the results are the respective rankings and / or other scores. May be included. After all individual results have been received, the query broker system may merge the results according to a merge algorithm when the time threshold elapses or other preconditions are met. The merge algorithm may take into account local ranking scores, general scores specific to the source, and / or other factors and conditions.

  In accordance with embodiments of the present invention, the systems and processes described in the present invention are connected over a network as a standalone application or as a group operating in client-server mode on any general purpose or dedicated computing device. May be implemented on several general purpose or special purpose computing devices. In accordance with other embodiments of the present invention, a computer usable and writable medium having a plurality of computer readable program codes stored therein is provided to implement the process of the present invention. Is done. The process and system of the present invention is a Windows® operating system or a Unix®-based operating system (eg, a Hewlett-Packard® or Red Hat Linux® Unix-based operating system). May be implemented in various operating systems, such as various versions of AS / 400-based operating systems. For example, the computer-usable and writable medium may be comprised of a CD ROM, floppy disk, hard disk, or any other computer-usable medium. One or more of the components of the system embodying the invention may include computer readable program code in the form of functional instructions stored on a computer usable medium. When these components are installed in the system, the functions described by these components may be executed by the system. The computer readable program code for the present invention may be sold in conjunction with other computer readable program software. Also, only some of the components may be provided in the computer readable code.

  Further, various entities and combinations of entities may use a computer to implement components that perform the functions described above. According to embodiments of the present invention, the computer may be a standard computer including input devices, output devices, processor devices, and data storage devices. According to other embodiments of the present invention, the various components may be computers of different departments within the same company or entity. Other computer configurations may also be used. In accordance with other embodiments of the present invention, the various components may be a company or another entity such as a limited company. Other embodiments may be used in accordance with applicable laws and regulations.

  In accordance with certain embodiments of the present invention, the system may include software system components. The system may operate on a network and may be connected to other systems that share a common database and a common server running additional data or application services. Other hardware configurations may be provided.

  Other embodiments, uses and advantages of the invention will be apparent to those skilled in the art in view of the specification and practice of the invention disclosed herein. Specifications and examples should be considered exemplary only. The intended scope of the invention is limited only by the appended claims.

  Although the invention has been particularly shown and described within the framework of processing claims, it will be understood that variations and modifications can be effected by a person skilled in the art without departing from the scope of the invention. Moreover, those skilled in the art will appreciate that such processes and systems need not be limited to the specific embodiments described herein.

FIG. 1 is an exemplary diagram of a federated search query system for multiple content information sources, in accordance with an embodiment of the present invention. FIG. 2 is an exemplary flowchart illustrating a method for query execution according to an embodiment of the present invention. FIG. 3 is an exemplary flowchart illustrating a method for intelligent information source selection according to an embodiment of the present invention. FIG. 4 is an exemplary flowchart illustrating a method for accessing a result list, storing the result list, and merging, according to an embodiment of the present invention. FIG. 5 is an exemplary flowchart illustrating a merging process according to an embodiment of the present invention. FIG. 6 is an exemplary illustration of a re-ranking result in accordance with an embodiment of the present invention.

Claims (59)

In a computer-implemented method for processing a query,
Receiving a query from a user device;
Categorizing the query to identify one or more content information sources;
Formatting the query according to one or more content information source specifics to the one or more content information sources;
Sending the formatted query to the one or more content information sources for the one or more content information sources;
Merging results in response to the formatted query from the one or more content information sources based at least in part on one or more factors;
Formatting the result for delivery to the user device.   The method of claim 1, wherein the one or more factors include one or more global and / or local factors.   The method of claim 1, wherein the user device comprises one or more of an Internet enabled input device, an Internet or audio enabled mobile device, an audio enabled input device, a computer, and a kiosk.   The method of claim 1, wherein the one or more content information sources include one or more of a search engine, an advertisement engine, and an access interface to a database associated with the content information source.   The method of claim 1, wherein the one or more factors include at least one or more of: edit rating, response reliability, response latency, content relevance, and content size or coverage.   The one or more factors include at least one or more of user selection, usage statistics, query frequency, category frequency, seller selection, recommendation statistics, user-created ratings, and / or business relationships. The method described. The one or more factors may include at least one or more of statistics associated with text or non- text analysis of the result item, data or text mining analysis , data or text clustering, and / or non- text pattern analysis. The method of claim 1 comprising:   The method of claim 1, wherein the one or more factors include at least one or more of statistics associated with device specific and / or format specifications.   The method of claim 1, wherein the one or more factors include at least one or more of user demographic characteristics, location, language, social network, social group, and personalization characteristics.   The method of claim 1, wherein the one or more factors include at least one of page size, graphics, text elements, and text.   The method of claim 1, wherein the one or more factors include at least one or more of a source rating, a reliability factor, and a latency factor.   The method of claim 1, wherein the one or more factors include at least one or more of business rules, business relationships, demographic selection, and marketing purposes.   The method of claim 1, wherein the one or more factors include at least one of a local ranking score, a source ordering value, a source-specific general score, and a source factor.   The one or more factors include at least one or more of results-based ranking, relevance, accuracy, usage factors, and the usage factors include demographics, traffic patterns, user personalization, and community values. The method of claim 1, comprising one or more of:   The method of claim 1, wherein the query is classified into categories in one or more taxonomy or controlled vocabulary. The method of claim 1, wherein the results are merged based at least in part on one or more of text relevance, user selection, ordering, relevance or similarity score, and / or result item factors.   The result item factor includes one or more of information source rating, metadata relevance factor, similarity factor, ranking factor, seller selection, usage pattern, location, device specification, query frequency, and / or category frequency. The method of claim 16 comprising.   At each content source, one or more local ranking statistics for each result item associated with one or more terms associated with the query and associated with metadata in the query context that is responsive to the query. The method of claim 1 further comprising the step of dynamically calculating. Calculating at least one global statistic associated with one or more content items in the result set;
The method of claim 1, further comprising calculating a normalization factor .   The method of claim 19, further comprising determining one or more relevance scores for the result item from the one or more content information sources according to the at least one global and / or local statistics. The method of claim 19, further comprising normalizing the one or more relevance scores according to the normalization factor . 20. The method of claim 19, further comprising combining the results into a single result set based on the ordering determined by the normalization factor .   The method of claim 1, further comprising storing results from each content information source in one or more caches. Accessing the one or more caches to obtain existing results;
24. The method of claim 23, further comprising: formatting the obtained existing result based on one or more query context parameters.   24. The method of claim 23, wherein accessing the one or more caches mitigates one or more information source operations.   The method of claim 1, wherein categorizing the query occurs dynamically when the query is received.   The method of claim 1, further comprising identifying one or more duplicate results.   28. The method of claim 27, further comprising removing the one or more duplicate results according to one or more of user selection, device selection, and merchant selection.   28. The method of claim 27, further comprising maintaining the one or more duplicate results according to one or more of user selection, device selection, and merchant selection.   A computer readable medium comprising code for performing the operations of the method of claim 1. In a computer-implemented system for processing queries,
A receiving module for receiving a query from a user device;
A categorization module for categorizing the query to identify one or more content information sources;
A formatting module that formats the query according to one or more content information source specifics to the one or more content information sources;
A transmission module for transmitting the formatted query to the one or more content information sources to the one or more content information sources;
A merge module that merges results in response to the formatted query from the one or more content information sources based at least on one or more factors;
A result module that formats the result for delivery to the user device.   32. The system of claim 31, wherein the one or more factors include one or more global and / or local factors.   32. The system of claim 31, wherein the user device comprises one or more of an internet enabled input device, an internet or voice enabled mobile device, a voice enabled input device, a computer, and a kiosk.   32. The system of claim 31, wherein the one or more content information sources includes one or more of a search engine, an advertisement engine, and an access interface to a database associated with the content information source.   32. The system of claim 31, wherein the one or more factors include at least one or more of edit rating, response reliability, response latency, content relevance, and content size or coverage.   32. The one or more factors include at least one or more of user selection, usage statistics, query frequency, category frequency, seller selection, recommendation statistics, user-created ratings, and / or business relationships. The described system. 32. The one or more factors include at least one or more of text or non- text analysis of the result item, data or text mining analysis , data or text clustering, and / or non- text pattern analysis. System.   32. The system of claim 31, wherein the one or more factors include at least one or more of device specific and / or statistics associated with a format specification.   32. The system of claim 31, wherein the one or more factors include at least one or more of user demographic characteristics, location, language, social network, social group, and personalization characteristics.   32. The system of claim 31, wherein the one or more factors include at least one of page size, graphics, text elements, and text.   32. The system of claim 31, wherein the one or more factors include at least one or more of a source rating, a reliability factor, and a latency factor.   32. The system of claim 31, wherein the one or more factors include at least one or more of business rules, business relationships, demographic selection, and marketing purposes.   32. The system of claim 31, wherein the one or more factors include at least one of a local ranking score, an information source ordering value, an information source specific general score, and an information source factor.   The one or more factors include at least one of result-based ranking, relevance, accuracy, usage factors, the usage factors being demographics, traffic patterns, user personalization, and community values. 32. The system of claim 31, comprising one or more of:   32. The system of claim 31, wherein the query is classified into one or more taxonomies or categories in a controlled vocabulary. 32. The system of claim 31, wherein the results are merged based at least in part on one or more of text relevance, user selection, ordering, relevance or similarity score, and / or result item factors.   The result item factor includes one or more of information source rating, metadata relevance factor, similarity factor, ranking factor, seller selection, usage pattern, location, device specification, query frequency, and / or category frequency. 49. The system of claim 46, comprising:   At each content information source, one or more local ranking statistics for each result item associated with one or more terms associated with the query and associated with metadata in the query context responsive to the query 32. The system of claim 31, further comprising the step of dynamically calculating. 32. The system of claim 31, further comprising a module that calculates at least one global statistic associated with one or more content items in the result set and calculates a normalization factor .   50. The system of claim 49, wherein one or more relevance scores are determined for the result item from the one or more content information sources according to the at least one global and / or local statistics. 50. The system of claim 49, wherein the one or more relevance scores are normalized according to the normalization factor . 50. The system of claim 49, combining results into a single result set based on an ordering determined by the normalization factor .   32. The system of claim 31, further comprising one or more caches that store results from each content information source. Accessing the one or more caches to obtain existing results;
54. The system of claim 53, wherein the existing result obtained is formatted based on one or more query context parameters.   54. The system of claim 53, wherein accessing the one or more caches mitigate one or more information source operations.   32. The system of claim 31, wherein categorizing the query occurs dynamically when the query is received.   32. The system of claim 31, wherein one or more duplicate results are identified.   58. The system of claim 57, wherein the one or more duplicate results are removed according to one or more of user selection, device selection, and merchant selection.   58. The system of claim 57, wherein the one or more duplicate results are maintained according to one or more of user selection, device selection, and merchant selection.