Semantic table of contents for search results

A table of contents is generated for search results in response to a search query by identifying semantic concepts as topics for inclusion within the table of contents. When a search query is received, a search is performed to identify search results. The search results are compared to an ontology of topics to identify relevant topics. Additionally, search results are compared to an ontology of partial topics to identify relevant partial topics, which are named. Independent key-phrases are further generated from search results and identified as key-phrase topics. The identified topics, named partial topics, and key-phrase topics are ranked, and topics are selected for inclusion within a table of contents. A search results page is returned in response to the search query that includes search results and the generated table of contents.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related by subject matter to the invention disclosed in the following U.S. patent application filed on even date herewith: U.S. application Ser. No. 12/701,330, entitled “Semantic Advertising Selection from Lateral Concepts and Topics;” which is assigned or under obligation of assignment to the same entity as this application, and incorporated in this application by reference.

BACKGROUND

Although computer systems can store a wealth of information, it can often be difficult for users to find specific information or effectively explore a particular subject area of interest. A variety of search engines currently exist that allow users to search for information by entering a search query comprising one or more keywords that may be of interest to the user. After receiving a search request from a user, a search engine identifies documents and/or web pages that are relevant based on the keywords. Often, the search engine returns a large number of documents or web page addresses, and the user is then left to sift through the list of documents, links, and associated information to find desired information. This process can be cumbersome, frustrating, and time consuming for the user.

A variety of techniques have been employed by search engines in an attempt to assist users in navigating search results and finding relevant documents. One approach is to provide a table of contents (TOC) that includes a list of topics relevant to the search query. A user may select a topic from the TOC and view search results relevant to the selected topic. In some implementations, the TOC remains static as the user selects different topics from the TOC, allowing the user to navigate to different sets of search results within the context of the original search query.

Often, TOCs are manually generated by search engine personnel. In particular, search engine personnel identify top-end queries (i.e., the search queries having the greatest search volumes for the search engine) and manually identify the topics relevant to each search query. However, this approach is very labor intensive, and it would be impractical to manually generate TOCs for torso and tail-end queries (i.e., the search queries having lower search volumes for the search engine). In some instances, a TOC may be algorithmically determined for a search query, for instance, by identifying a domain to which the search query pertains (e.g., auto, finance, etc.) and providing a TOC based on the domain. However, this approach may be ineffective for some search queries. As a result, the TOC provided for some search queries (e.g., torso and tail-end queries) is either non-existent or of very poor quality. This leads to an inconsistent experience for search users.

SUMMARY

Embodiments of the present invention relate to identifying semantic concepts as topics for inclusion in a table of contents in response to search query. When a search query is received, search results are identified and a table of contents is generated that includes a list of topics for navigating search results. In some embodiments, topics for the table of concepts are identified by analyzing an ontology of topics to identify topics relevant to the search results. In further embodiments, an ontology of partial topics is analyzed to identify relevant partial topics, which are named. In still further embodiments, key-phrases are generated from search results and analyzed to identify key-phrase topics. Identified topics are ranked and selected for inclusion in the table of contents.

DETAILED DESCRIPTION

Embodiments of the present invention are generally directed to generating a table of contents (TOC) for search results in response to a search query. When a search query is received, search results are retrieved. Additionally, topics relevant to the search query and search results are identified, and a TOC is generated from identified topics. A search results page is returned in response to the search query that includes search results and the generated TOC. A user may select topics from the TOC to view different search results relevant to each topic. In some embodiments, the TOC is static as the user selects different topics from the TOC to view different sets of search results, thereby allowing the user to navigate search results within the context of the initial search query.

Topics may be identified for inclusion within a TOC for a search query in a number of different manners within various embodiments of the present invention. In some embodiments, when a search query is received, it is determined whether an ontology mapping already exists for the search query. For instance, a number of topics may have been manually generated for a search query matching the received search query. As another example, a search query matching the received search query may have been previously received and topics previously identified and cached. In such embodiments, topics from the existing ontology mapping are retrieved for the TOC. In further embodiments, search results are retrieved for the search query and compared to an ontology of topics and/or an ontology of partial topics to identify relevant topics. In still further embodiments, search results are analyzed to identify independent key-phrases, and key-phrase topics are selected. When a large number of topics are identified, the topics are ranked, and the highest ranking topics are selected for generating the TOC for the search query.

Accordingly, in one aspect, an embodiment of the present invention is directed to one or more computer-readable media storing computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to perform a method. The method includes receiving a search query, performing a search using the search query, and receiving a plurality of documents snippets from the search. The method also includes identifying a first set of one or more candidate topics by comparing one or more document snippets to an ontology of topics. The method further includes identifying a second set of one or more candidate topics by comparing one or more document snippets to an ontology of partial topics. The method also includes identifying a third set of one or more candidate topics by generating key-phrase topics from one or more document snippets. The method further includes ranking candidate topics from the first, second, and third set of candidate topics and selecting one or more topics based on ranking of the candidate topics. The method still further includes providing a search results page in response to the search query, the search results page having a table of contents containing the one or more topics and a search results area for presenting one or more search results.

In another embodiment, an aspect of the invention is directed to one or more computer-readable media storing computer-useable instructions that, when used by one or more computing devices, causes the one or more computing devices to perform a method. The method includes receiving a search query and determining if an ontology mapping exists for the search query. If an ontology mapping exists for the search query, the method includes retrieving a first set of topics based on the ontology mapping and adding the first set of topics to a list of topics. The method also includes performing a search using the search query to obtain a plurality of search results, each search result corresponding with a document snippet, and receiving at least a portion of the document snippets as a document set for further analysis. The method further includes comparing each document snippet in the document set to an ontology of topics. For each document snippet in which positive topic identification is determined, the method includes assigning the document snippet to a corresponding topic and removing the document snippet from the document set. The method also includes adding at least one topic identified from the ontology of topics to the list of topics. The method further includes comparing each document snippet remaining in the document set to an ontology of partial topics. For each document snippet in which positive partial topic identification is determined, the method includes assigning the document snippet to a corresponding partial topic and removing the document snippet from the document set. The method also includes naming at least one partial topic having one or more assigned document snippets and adding at least one named partial topic to the list of topics. The method further includes computing independent key-phrases from document snippets remaining in the document set, assigning documents to independent key-phrases, identifying at least one key-phrase topic, and adding the at least one key-phrase topic to the list of topics. The method further includes ranking topics within the list of topics, selecting topics based on ranking, and generating a table of contents using the selected topics. The method still further includes providing a search results page in response to the search query, the search results page including the table of contents and a search results area for presenting search results.

A further embodiment of the present invention is directed to a method for identifying topics from a set of search results for generating a table of contents for the search results. The method includes receiving a search query, performing a search using the search query, and receiving a plurality of document snippets from the search. The method also includes generating candidate key-phrases from at least a portion of the document snippets. The method further includes evaluating candidate key-phrases for independence, merging mutually dependent candidate key-phrases, and identifying a most frequent candidate key-phrase for each group of mutually dependent key-phrases to generate a plurality of independent key-phrases. The method further includes assigning one or more document snippets to each independent key-phrases. The method still further includes selecting key-phrase topics based on assignment of document snippets to independent key-phrases.

Computing device100typically includes a variety of computer-readable media. Computer-readable media can be any available media that can be accessed by computing device100and includes both volatile and nonvolatile media, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Computer-readable media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computing device100. Combinations of any of the above should also be included within the scope of computer-readable media.

Among other components not shown, the system200includes a user device202, a search engine204, and a semantic topic engine206. Each of the components shown inFIG. 2may be any type of computing device, such as computing device100described with reference toFIG. 1, for example. The components may communicate with each other via a network208, which may include, without limitation, one or more local area networks (LANs) and/or wide area networks (WANs). Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets, and the Internet. It should be understood that any number of user devices, search engines, and semantic topic engines may be employed within the system200within the scope of the present invention. Each may comprise a single device or multiple devices cooperating in a distributed environment. For instance, the search engine204and semantic topic engine206may be part of a search system that comprises multiple devices arranged in a distributed environment that collectively provide the functionality of the search engine and semantic topic engine described herein. Additionally, other components not shown may also be included within the system200.

In embodiments of the present invention, the system200includes a search system that comprises, among other components not shown, the search engine204and semantic topic engine206. A user may employ the user device202to enter a search query and submit the search query to the search system. For instance, the user may employ a web browser on the user device202to access a search input web page of the search system and enter a search query. As another example, the user may enter a search query via a search input box provided by a search engine toolbar located, for instance, within a web browser, the desktop of the user device202, or other location. One skilled in the art will recognize that a variety of other approaches may also be employed for providing a search query within the scope of embodiments of the present invention.

When the search system receives a search query from a user device, such as the user device202, the search engine204performs a search on a search system index220, knowledge base222, and/or other data storage containing searchable content maintained by the search system. The search system index220may generally contain unstructured and/or semi-structured data, while the knowledge base222may generally contain structured data. Accordingly, the search engine204identifies a number of search results in response to the received search query. Additionally, the semantic topic engine206operates on the received search query to identify relevant topics for generation of a TOC. In response to the search query, a search results page may be provided to the user device202that includes search results with a TOC that includes topics identified by the semantic topic engine206.

As shown inFIG. 2, the semantic topic engine206generally includes an ontology mapping component210, an ontology topic identification component212, an ontology partial topic identification component214, a key-phrase topic identification component216, and a topic ranking/selection component218. The semantic topic engine206identifies semantic topics employing any of components210,212,214, and216. In some embodiments of the present invention, each of the components210,212,214, and216may be employed to identify topics for a given search query, and the identified topics may be ranked and certain topics selected by the ranking/selection component218for inclusion in the TOC. In other embodiments, topics may be identified by only a portion of the components210,212,214, and216. For instance, in one embodiment, once a threshold number of topics are identified by one or more of components210,212,214,216, further analysis by remaining components is not performed. In further embodiments, the semantic topic engine206may include only a portion of the components210,21,214, and216shown inFIG. 2. Any and all such variations are contemplated to be within the scope of embodiments of the present invention.

When a search query is received from a user device, such as the user device202, the ontology mapping component210operates to identify whether an ontology mapping already exists for the search query. For instance, the search query may be a top-end search query for which search system personnel have manually identified relevant topics for a TOC for the search query. As another example, the received search query may correspond with a search query for which relevant topics have already been identified and cached for the search query. If the ontology mapping component210determines that an ontology mapping already exists for a received search query, topics are retrieved based on the ontology mapping. In some embodiments, a TOC is generated based only on the topics retrieved by the ontology mapping component210. In other embodiments, additional topics are identified by one or more of the other components212,214, and216, as described in further detail below.

The ontology topic identification component212operates on a received search query in conjunction with an ontology of topics stored in the ontology storage component224to identify relevant topics for the search query. The ontology storage component224may store one or more ontologies, which are used by the ontology topic identification component212to identify semantic concepts as topics for received search queries. Each ontology includes a collection of words and phrases defining concepts and relationships between the concepts. In some embodiments, a search is performed on the search system index220and/or knowledge base222to retrieve search results for the search query, and the ontology topic identification component212analyzes the search results in conjunction with the ontology of topics to identify relevant topics for possible inclusion in a TOC for the search query.

The ontology partial topic identification component214functions in a manner similar to the ontology topic identification component212but uses an ontology of partial topics instead of an ontology to topics. As used herein, a partial topic refers to partially-named topics. Each partial topic includes a partial topic identifier word that may be combined with an additional word or phrase to create a topic for use in a TOC. For example, “reviews” may be a partial topic. When analyzed in context, the partial topic identifier word “reviews” may be combined with additional words, such as, “expert” or “user” to generate the topics “expert reviews” or “user reviews.” Accordingly, once partial topics are identified for a search query, the ontology partial topic identification component214or an associated component names the partial topic for possible inclusion in a TOC for the search query.

The key-phrase topic identification component216analyzes search results for a received search query to generate candidate key-phrases. Generally, the key-phrase topic identification component216generates key-phrases from search results and identifies independent key-phrases. The independent key-phrases are evaluated to identify candidate topics for possible inclusion in a TOC for the search query.

A number of topics may be identified for a received search query by the ontology mapping component210, ontology topic identification component212, ontology partial topic identification component214, and/or key-phrase topic identification component216. In some instances, all identified topics may be included in the TOC provided on a search results page in response to the search query. In other instances, a large number of topics may be identified, and only a subset of the identified topics is included in the TOC. The topic ranking/selection component218operates to rank and select topics for inclusion in the TOC. Topics may be ranked using a number of different factors in accordance with various embodiments of the present invention. By way of example only and not limitation, each topic may be ranked based on the total number of documents assigned to each topic. A larger number of documents assigned to a given topic may provide a higher ranking for the topic. A topic may also be ranked based on the ranking of each search result (or a selection of search results—e.g., the top N search results) assigned to the topic. The ranking of each search result corresponds with each search result's relevance to the search query. Accordingly, more highly relevant search results being assigned to a given topic may provide a higher ranking for the topic. The length (e.g., number of words) of each topic may further be used to rank the topics. Any and all such variations are contemplated to be within the scope of embodiments of the present invention. After ranking the candidate topics, the topic ranking/selection component216selects topics for the TOC.

Turning toFIG. 3, a flow diagram is provided that illustrates a method300for generating a TOC for a search query received at a search system in accordance with an embodiment of the present invention. As shown at block302, a search query is received. As one skilled in the art will recognize, the search query may comprise one or more search terms entered by a user (although search terms may be automatically provided in some embodiments). Additionally, the search query may be provided in a number of different ways. By way of example only and not limitation, a user may employ a web browser to navigate to a search engine web page and enter the search query in an input box. As another example, a user may enter the search query in an input box provided by a search engine toolbar located, for instance, within a web browser, the desktop of the user's computing device, or other location. One skilled in the art will recognize that a variety of other approaches may also be employed for providing a search query within the scope of embodiments of the present invention.

In accordance with the embodiment shown inFIG. 3, a determination is made at block304regarding whether an ontology mapping already exists for the search query. For instance, the search query may be a top-end search query for which search system personnel have manually identified relevant topics for the search query. Alternatively, the received search query may correspond with a search query that has been previously processed by the search system to identify relevant topics, and the search system may have cached the identified topics for the search query. If it is determined that an ontology mapping already exists at block306, topics for the search query are retrieved at block308. In some embodiments, only the topics retrieved at block308are used for generating the TOC in response to the search query and the process ends. In such embodiments, a search page is generated that includes a TOC generated from the topics retrieved at block308. In other embodiments, the process continues at block310, and additional topics are algorithmically identified.

If it is determined at block306that an ontology mapping does not exist for the search query (or if the process continues after retrieving topics at block308), a search is performed using the search query, as shown at block310. Search results for the search query are returned, and the top N document snippets from the search are received at block312as a document set to be analyzed.

As shown at block314, each document snippet in the document set is compared to an ontology of topics (or a collection of ontologies) to identify whether each document snippet maps to a topic in the ontology. Identifying a document snippet as being associated with a topic in the ontology of topics may be performed in a number of different manners within the scope of embodiments of the present invention. By way of example only and not limitation, in one embodiment, a document snippet is converted to a feature vector based on words contained in the document snippet, and the feature vector is compared to topics in the ontology to determine distance of the feature vector to the topics. Positive topic identification is determined for a given document snippet by determining that the feature vector for the document snippet is within a predetermined distance of a given topic. If topic identification is positive for a given document snippet at block316based on analysis of the document snippet and ontology, the document snippet is assigned to the identified topic, as shown at block318. Additionally, the document snippet is removed from the document set at block320.

After identifying a relevant topic for a given document snippet (e.g., via blocks316-320) or determining that no topic from the ontology is sufficiently relevant for the document snippet (e.g., via block316), a determination is made at block322regarding whether the document snippet analyzed was the last document snippet in the document set to be analyzed. If additional document snippets remain for analysis, the process of blocks316-322is repeated until all document snippets in the document set have been compared to the ontology of topics. After all document snippets in the document set have been compared to the ontology of topics, topics identified from the ontology of topics are added to a list of candidate topics for consideration, as shown at block324. In some embodiments, all identified topics are added to the list. In other embodiments, only a portion of the topics are added. For instance, in some embodiments, only topics having a predetermined number of assigned document snippets are added to the list of topics.

As shown at block326, each remaining document snippet in the document set is compared to an ontology of partial topics (or a collection of ontologies). As indicated above, a partial topic is a topic that is only partially-named. Each partial topic includes a partial topic identifier word that may be combined with an additional word or phrase to create a topic for use in a TOC.

Whether a given document snippet is associated with a partial topic in the ontology of partial topics is determined at block328. Identifying a document snippet as being associated with a partial topic may be performed in a number of different manners within the scope of embodiments of the present invention. By way of example only and not limitation, in one embodiment, a document snippet is converted to a feature vector based on words contained in the document snippet, and the feature vector is compared to partial topics in the ontology of partial topics to determine distance of the feature vector to the partial topics. Positive partial topic identification is determined for a given document snippet by determining that the feature vector for the document snippet is within a predetermined distance of a given partial topic. If partial topic identification is positive for a given document snippet at block328based on analysis of the document snippet and the ontology of partial topics, the document snippet is assigned to the identified partial topic, as shown at block330. Additionally, the document snippet is removed from the document set at block332.

After identifying a relevant partial topic for a given document snippet (e.g., via blocks328-332) or determining that no partial topic from the ontology is sufficiently relevant for a given document snippet (e.g., via block328), a determination is made at block334regarding whether the document snippet analyzed was the last document snippet in the document set to be analyzed. If additional document snippets remain for analysis, the process of blocks328-334is repeated until all document snippets in the document set have been compared to the ontology of partial topics.

After each document snippet remaining in the document set has been compared against the ontology of partial topics, partial topics are named at block336. In some embodiments, all identified partial topics are named. In other embodiments, only a portion of the topics are named and others are not considered for further analysis. For instance, in some embodiments, only partial topics having a predetermined number of assigned document snippets are named and considered for further analysis. A flow diagram is provided inFIG. 4that illustrates a method400for naming a partial topic in accordance with an embodiment of the present invention. As shown at block402, occurrences of the partial topic identifier word within the document snippets assigned to the partial topic are identified. For instance, the partial topic identifier word may be “reviews,” and each occurrence of that term in the document snippets is identified. At block404, one or more words and/or phrases around the partial topic identifier word are extracted. The frequency of each extracted word and/or phrase is counted, as shown at block406. In some embodiments, the location of each extracted word and/or phrase with respect to the partial topic identifier word is tracked and counted. In particular, a word or phrase may appear before or after the partial topic identifier word. The search system may separately track how many times each word and/or phrase appears before the partial topic identifier word and how many times each word and/or phrase appears after the partial topic identifier word.

After each document snippet has been analyzed, the most frequently used word or phrase is selected, as shown at block408. Additionally, the partial topic is named using the partial topic identifier word and the most frequently used word or phrase, as shown at block410. The sequencing of the partial topic identifier word and the most frequently used word or phrase may be determined based on the majority ordering in the analyzed text of the document snippets. For instance, if the selected word or phrase occurred before the partial topic identifier word more often than after the partial topic identifier word, the sequence for the partial topic name will include the selected word or phrase first followed by the partial topic identifier word. Returning toFIG. 3, named partial topics are added to the list of topics, as shown at block338.

Independent key-phrases are generated at block340from the document snippets remaining in the document set after comparison of the documents snippets to the ontology of topics and the ontology of partial topics. Referring toFIG. 5, a flow diagram is provided that illustrates a method500for computing independent key-phrases from the remaining document snippets in accordance with an embodiment of the present invention. As shown at block502, candidate key-phrases are generated from the document snippets remaining in the document set. In accordance with some embodiments of the present invention, a Markov chain based method is used to generate the candidate key-phrases.

Candidate key-phrases are evaluated for independence, as shown at block504. Independence of candidate key-phrases may be evaluated using a number of metrics in accordance with embodiments of the present invention. For instance, independence may be determined based on any combination of the following metrics: the number of words shared between the candidate key-phrases, analysis of acronyms of words in the key-phrases, and the number of documents shared by candidate key-phrases.

For each group of mutually dependent key-phrases, the mutually dependent key-phrases are merged at block506. As such, the most frequent key-phrase from a group of mutually dependent key-phrases is selected as a key-phrase for further analysis, as shown at block508. The process of merging mutually dependent key-phrases to identify key-phrases for further analysis is repeated until no more mutually dependent key-phrases remain. The result of the method500is a collection of one or more independent key-phrases that may be further evaluated as possible topics.

Referring again toFIG. 3, after identifying candidate topics from independent key-phrases, document snippets remaining in the document set are assigned to the key-phrase topics, as shown at block342. Identifying a document snippet as being associated with a key-phrase may be performed in a number of different manners within the scope of embodiments of the present invention. By way of example only and not limitation, in one embodiment, a document snippet is converted to a feature vector based on words contained in the document snippet, and the feature vector is compared to key-phrases to determine distance of the feature vector to the key-phrases. Positive key-phrase identification is determined for a given document snippet by determining that the feature vector for the document snippet is within a predetermined distance of a given key-phrase. Key-phrase topics are identified as shown at block344and added to the list of topics at block346. In some embodiments, all independent key-phrases are identified as key-phrase topics and added to the list of topics. In other embodiments, only a portion of the key-phrases are recognized as topics are added to the list of topics. For instance, in some embodiments, only key-phrases having a predetermined number of assigned document snippets are identified as key-phrase topics and added to the list of topics.

A list of candidate topics is provided as a result of the above-described process and may include topics identified from an existing ontology mapping, analysis of an ontology of topics, analysis of an ontology of partial topics, and/or key-phrase generation. In some instances, a larger number of topics may have been identified than is desired for the TOC. As such, the process continues by ranking and selecting topics for inclusion in the TOC. As shown at block348, the candidate topics are ranked. Candidate topics may be ranked using a number of different factors in accordance with various embodiments of the present invention. By way of example only and not limitation, each candidate topic may be ranked based on the total number of documents assigned to each candidate topic. A larger number of documents assigned to a given candidate topic may provide a higher ranking for the candidate topic. A candidate topic may also be ranked based on the ranking of each document (or a selection of documents—e.g., the top N documents) assigned to the candidate topic. The ranking of each document corresponds with each document's relevance to the search query. Accordingly, more highly relevant documents being assigned to a given candidate topic may provide a higher ranking for the candidate topic. The length (e.g., number of words) of each candidate topic may further be used to rank the candidate topics. Any and all such variations are contemplated to be within the scope of embodiments of the present invention.

As shown at block350, topics are selected from the list of candidate topics based on ranking for inclusion in the TOC to be provided in conjunction with search results in response to the search query. In some embodiments, a predetermined number of topics is selected. For instance, the search system may select the five topics with the highest ranking. In other embodiments, all topics having a ranking satisfying a predetermined or dynamic threshold may be selected. In further embodiments, topics having a significantly higher ranking than other topics are selected. Any combination of the above and/or additional approaches to selecting topics based on ranking may be employed within embodiments of the present invention.

A TOC is generated based on the selected topics, as shown at block352. Additionally, a search results page is generated at block354and returned to the user who submitted the search query. In accordance with an embodiment of the present invention, the search results page includes a listing of search results for the search query. Additionally, the search results page includes the TOC that includes the topics selected at block350. The TOC may be presented in a side panel adjacent to the search results or in another portion of the search results page.

By way of illustration,FIG. 6includes an exemplary screen display showing a search results page600including a TOC generated in accordance with an embodiment of the present invention. It will be understood and appreciated by those of ordinary skill in the art that the screen display ofFIG. 6is provided by way of example only and is not intended to limit the scope of the present invention in any way.

As shown inFIG. 6, the search results page600has been provided in response to the search query602, “sammamish fun.” In response to the search query602, the search results page600includes a TOC604in left-side pane. The TOC604includes the following topics: Local Listings, Hiking, Boating, Sammamish Fireworks, Online Date, and Lacrosse. The topics included in the TOC604are semantic concepts identified for the search query based on analysis of ontological entities and concepts and key-phrase extraction as discussed herein. The search results page600also includes a search results area606for displaying search results relevant to the search query602. In the screen display ofFIG. 6, “All Results” are currently being displayed in the search results area606. If a user selects a topic from the TOC604, search results relevant to the selected topic would be displayed in the search results area606. As shown inFIG. 6, the search results page may include further features, such as, for instance, related search queries608, search history610, and sponsored sites612. Details of these sections have been omitted from the search results page600for clarify purposes.

As can be understood, embodiments of the present invention identify semantic concepts as topics for generation of a TOC for search results. The present invention has been described in relation to particular embodiments, which are intended in all respects to be illustrative rather than restrictive. Alternative embodiments will become apparent to those of ordinary skill in the art to which the present invention pertains without departing from its scope.