Patent Publication Number: US-8983980-B2

Title: Domain constraint based data record extraction

Description:
BACKGROUND 
     Data record extraction pertains to extracting data records containing user-generated content (UGC) from web documents. Data record extraction may be useful in web mining applications such as question answering, blog or review mining, expert searching on web communities, etc. For example, a user who is interested in purchasing a new car may use data record extraction techniques to mine customer reviews pertaining to a new car of interest so that the user can make an informed decision on whether to purchase the new car. 
     In prior implementations, data record extraction techniques generally assume that the data records contain a limited amount of UGC and thus have similar structures. However, due to the free-format nature of UGC, data records containing UGC are generally of unstructured formats. 
     SUMMARY 
     Described herein are techniques and systems for extracting data records containing user-generated content (UGC) from web documents. Unlike previous methods which assume that data records containing UGC have similar structures, the data record extraction techniques described herein adopt a domain constraint approach referred to as Mining Data Records based on Anchor Trees (MiBAT). In general, MiBAT distinguishes a structured portion of the data records (e.g., author, publication date, etc.) from the free-formatted unstructured UGC part. The MiBAT process then uses the structured portion of the data records to locate and extract the data records. 
     In order to analyze the parts of the web document, the web document is represented as a Document Object Model (DOM) tree in which the nodes of the DOM tree include the UGC. Each data record of a data record list is deemed as consisting of the same number of sibling sub-trees on the DOM tree of the web document, where each of the sibling sub-trees is deemed the component sub-tree of the record. The nodes of the DOM tree then are analyzed based on a domain constraint. Domain constraints may be any object type that may be used as a basis for expression matching such as dates, times, numerical strings, etc. The nodes of the DOM tree which contain domain constraints are deemed the pivots. In some instances, the post publication date (i.e., post-date) is selected as the domain constraint since post-dates are commonly found in the structured part of a data record. The nodes of the DOM tree which are identified as containing text having a format of the domain constraint are deemed the candidate pivots of the DOM tree. However, not all the nodes containing text having a format of the domain constraint are real pivots (e.g., in forum posts, UGC may also contain strings in date format). Accordingly, similarity measures are applied to determine the anchor trees as being the component sub-trees of data records which contain the real pivot nodes. After determining the anchor trees, a record boundary (i.e., start offset and length) of the data records is determined. Finally, the data records are extracted based on the record boundary. The data records may then be stored or may be outputted. Other embodiments will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings. 
     This Summary is provided to introduce a selection of concepts in a simplified form that is further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the Figure in which the reference number first appears. The use of the same reference number in different figures indicates similar or identical items. 
         FIG. 1  shows a block diagram that illustrates a computing environment for extracting data records containing user-generated content from web documents, in accordance with various embodiments. 
         FIG. 2  shows an illustrative web document that includes multiple data records. 
         FIG. 3  is a flow diagram that illustrates an illustrative process to extract data records containing user-generated content in accordance with various embodiments. 
         FIG. 4  is a flow diagram that illustrates an illustrative a Mining Data Records based on Anchor Trees (MiBAT) process in accordance with various embodiments. 
         FIG. 5  is a flow diagram that illustrates an illustrative process of identifying anchor trees within a Document Object Model (DOM) tree in accordance with various embodiments. 
         FIG. 6  is a pictorial flow diagram illustrating a process of identifying anchor trees within a Document Object Model (DOM) tree in accordance with various embodiments. 
         FIG. 7  is a flow diagram that illustrates an illustrative process of determining record boundaries in accordance with various embodiments. 
         FIG. 8  is a block diagram that illustrates a representative system that may implement the data extraction engine. 
     
    
    
     DETAILED DESCRIPTION 
     The embodiments described herein pertain to a Mining Data Records based on Anchor Trees (MiBAT) process that utilizes domain constraints to extract data records containing User-generated Content (UGC). The MiBAT process distinguishes a structured portion of the data records which is structurally similar across data records (e.g., author, publication date, etc.) from the free-formatted unstructured UGC part (which may be structurally diverse across records) rather than assuming that data records containing UGC have similar structures overall. Accordingly, based on the detection of the repetition of similar structures of the structured portion, the MiBAT process automatically extracts consecutive or non-consecutive data records containing UGC from web documents even though the UGC is unstructured. 
     By focusing on the repetitive similar structures of the data records, the MiBAT process is able to automatically extract data records without too much prior knowledge or assumptions on the overall structure of the data records on the web documents. As a result, the MiBAT process pertains to extracting data records from a single web document that contains at least two or more data records. The MiBAT process is not designed to extract data records from a web document that contains only one single data record. 
     The techniques and systems described herein may be implemented in a number of ways. Example implementations are provided below with reference to the following figures. 
     Illustrative Scheme 
       FIG. 1  illustrates a computing environment  100  for extracting web data records, in accordance with various embodiments. The computing environment  100  may include a computing device  102  to extract the web data records. The computing devices  102  may include any sort of such as personal computers, laptop computers, mobile phones, set-top boxes, game consoles, personal digital assistants (PDAs), portable media players (PMPs) (e.g., portable video players (PVPs) and digital audio players (DAPS)), and other types of computing devices. 
     The computing device  102  device may access one or more web documents  104  via one or more networks  106 . The one or more networks  106  may include at least one of wide-area networks (WANs), local area networks (LANs), and/or other network architectures. The web documents  104  may be located at one or more locations such as a World Wide Web  108  (hereinafter “web”), one or more content provider(s)  110  or servers, a local memory  112 , or any combination thereof. 
     Each of the web documents  104  may contain one or more data records  114 ( 1 )-( n ). In some instances, one or more of the data records  114 ( 1 )-( n ) may contain user-generated content (UGC). As used herein, UGC includes any media content which is produced by end-users and is available to a plurality of users (e.g., publically available, available on a local intranet, etc.). For instance, UGC may include forum posts, reviews, blogs, comments, or any other end-user produced content that is publically available. 
     The computing device  102  may include a data extraction engine  116  to extract the data records  114 ( 1 )-( n ) containing the UGC from the web documents  104 . For example, a user who is interested in purchasing a new car may use the data extraction engine  116  to mine all customer reviews pertaining to a new car of interest so that the user can make an informed decision on whether to purchase the new car. In other examples, the data extraction engine  116  may be used for question answering, blog or review mining, expert searching on web communities, etc. 
     In some instances, the techniques of the data extraction engine  116  may be implemented for visualizing, reorganizing or reformatting of the information or the layout of the web documents  104  and thus providing a better browsing experience for users. For example, the data extraction engine techniques may be used to highlight, selectively display only or reformat, certain important blocks (or areas) of the web documents  104  as users browse the web documents in web browsers or mobile devices with limited size of screens. In some instances, this kind of use may be achieved by implementing the techniques of the data extraction engine  116  on the client side such as add-ons of web browsers or applications on mobile devices. In some instances, this kind of use may also be achieved by incorporating the techniques of the data extraction engine  116  into remote intermediate web services between the original web documents  104  and the local browsing devices such as web browsers or mobile devices, which perform the visualizing, reorganizing or reformatting of the web documents  104  and return the processed documents to the local devices; such web services may be located at one or more locations such as a World Wide Web  108 , one or more content provider(s)  110  or servers, or any combination thereof. 
     In other instances, the techniques of the data extraction engine  116  may be used to reduce a size of an index of a search engine, or to improve the relevance of an index of a search engine. For example, for forum pages, if the data records  114  recognized by the data extraction engine  116  are assumed to be the most important parts of the web documents  104 , then a search engine may ignore other blocks of the web documents and only index the blocks identified by the data extraction engine  116 ; by this means the relevance of the index is also improved because other irrelevant and noisy blocks are not indexed. 
     The data extraction engine  116  may include one or more software application components such as a document retrieval module  118 , a HyperText Markup Language (HTML) parser module  120 , and a record extraction module  122 , wherein the components perform data record extraction. In some instances, the various software application components of the data extraction engine  116  may be implemented as a web browser, or any other application to perform the web data record extraction. 
     The document retrieval module  118  may retrieve the web documents  104 . The web documents  104  may be retrieved from the web  108 , the content providers  110 , the local memory  112 , or any combination thereof. For example, the document retrieval module  118  may retrieve the web documents  104  from either the web  108  or the content providers  110  via a local network transmission line or the Internet. In another example, the document retrieval module  118  may retrieve the web documents  104  from the local memory  112  via a local file I/O path such as a local bus. 
     After retrieving the web documents  104 , the HTML parser module  120  may process the web documents  104  into a Document Object Model (DOM) tree, in which the nodes of DOM tree include the UGC of the web documents. The record extraction module  122  may then use the DOM tree to extract the data records  114  including the UGC from the web documents. Data record of a data record list are deemed as consisting of the same number of sibling sub-trees on the DOM tree of the web document, where each of the sibling sub-trees is deemed the component sub-tree of the record. In various embodiments, the record extraction module  122  utilizes a Mining Data Records Based on Anchor Trees (MiBAT) process to extract the data records  114  from the web documents  104 . The extracted data records  114  may be stored, such as to the web  108 , content providers  110 , and/or the local memory or the extracted data records may be processed for output to the computing device  102 . 
       FIG. 2  illustrates an example web document  200 . The example web document  200  is depicted as a web forum and includes a first data record  202  and a second data record  204 , both of which contain UGC. The first data record  202  is a question posted by an online user Adam regarding how to calculate percentages. The second data record  204  is a response to Adam&#39;s question posted by an online user MathWiz and includes an equation that can be used to calculate percentages. In general, data records containing UGC usually consist of two parts. First, the data records include a structured part such as author, publication date, etc. Second, the data records include free-format UGC, referred to as the unstructured part. For example, the first data record  202  includes structured part  206  and unstructured part  208 . Similarly, the second data record  204  includes structured part  210  and unstructured part  212 . 
     The techniques discussed herein for extracting the data records  202 ,  204  from the example web document  200  assume that the structured parts  206 ,  210  have a same structure. For instance, both structured parts  206 ,  210  include a publication date of the post  214 ,  216  (i.e., post-date), a username  218 ,  220  of the entity that made the post, a join date  222 ,  224  indicating a date the entity joined the forum, and a post number  226 ,  228  indicating the number of posts made under the username, etc. Due to the free-format nature of UGC, the unstructured part  208 ,  212  of the data records  202 ,  204  may not have a same structure. For instance, the unstructured part  208  of the first data record  202  includes two sections. It includes a post title  230  and post content  232  (i.e., original post). However, the unstructured part  212  of the second data record  204  includes three sections. It includes a quote  234  of the original post, a response  236  to the original post, and a quote of the day  238 . In addition to the data records  202 ,  204 , the example web document  200  also includes an advertisement section  240  which includes two advertisements. As discussed further below, the data record extraction techniques discussed herein pertain to automatically extracting data records containing the UGC such as data records  202 ,  204  from such as the example web document  200  even though the data records are not consecutive (i.e., there is an advertisement section between the first data record  202  and the second data record  204 ) and even though the unstructured parts  208 ,  212  of the data records vary from data record to data record within the web document. Although the example web document  200  is illustrated as having two data records, record extraction techniques discussed herein may pertain to web documents having more than two data records. 
     The data record extraction techniques discussed herein are generally discussed in terms of extracting data records from web forums such as the example web document  200 . However, the data record extraction techniques may be applied to other types of applications such as blogs, web documents containing user comments (e.g., Twitter®, Flickr®, YouTube®, Digg®), web documents containing user reviews, etc. Accordingly, the data record extraction techniques are not restricted to web forums. 
     Illustrative Processes 
       FIGS. 3-7  describe various illustrative processes for implementing data records extraction techniques. The order in which the operations are described in each illustrative process is not intended to be construed as a limitation, and any number of the described blocks can be combined in any order and/or in parallel to implement each process. Moreover, the blocks in  FIGS. 3-7  may be operations that can be implemented in hardware, software, and a combination thereof. In the context of software, the blocks represent computer-executable instructions stored on one or more computer-readable storage media that, when executed by one or more processors, cause one or more processors to perform the recited operations. Generally, computer-executable instructions include routines, programs, objects, components, data structures, and the like that cause the particular functions to be performed or particular abstract data types to be implemented. 
       FIG. 3  describes an illustrative process  300  to extract data records containing user-generated content (UGC) from web documents in accordance with various embodiments. The process  300  may be implemented by the data extraction engine  116  of the computing device  102 . 
     At  302 , the document retrieval module  118  retrieves the one or more web documents  104 . The document retrieval module  118  may retrieve the web documents  104  from the web  108 , the content providers  110 , the local memory  112 , or any combination thereof. 
     At  304 , the HyperText Markup Language (HTML) parser module  120  may process the web documents  104  into a Document Object Model (DOM) tree. For example, an example DOM tree  306  illustrated in  FIG. 3  corresponds to the example web document  200  of  FIG. 2 . Specifically, the example DOM tree  306  includes a question data record node  308  corresponding to the first data record  202 , an advertisement section node  310  corresponding to the advertisement section  240 , and a response data record node  312  corresponding to the second data record  204 . Note that in the example DOM tree  306 , each of the two data records consists of only one component sub-tree, being sub-tree  308  and  312 , respectively. 
     At  314 , the record extraction module  122  may use the DOM tree to extract the data records containing UGC from the web document. In some instances, the record extraction module  122  utilizes a Mining Data Records Based on Anchor Trees (MiBAT) process to extract the data records from the web documents  104 . In general, the MiBAT process may be formulated as follows. Along a traversal on the DOM tree, for each parent node, (1) locate the anchor trees from the child sub-tree list, (2) determine the record boundary, (i.e. start offset and length), and (3) extract the data record around each anchor tree given the record boundary. 
     Anchor trees are the component sub-trees of data records on the DOM tree which contain a domain constraint. Although any domain constraint may be used to locate the anchor trees, in some instances the domain constraint is part of the structured data which occurs in every data record once and exactly once and can be easily identified. The lowest nodes of the DOM tree containing the domain constraint are deemed the pivots. For instance, the post-date (i.e., publication date of a data record) may be selected as the domain constraint since almost all data records containing UGC include a publication date and its format can be easily identified. Accordingly the lowest nodes containing the post-date may be the pivots. The record extraction module  122  may use the domain constraint to locate the anchor trees as being the component sub-trees of data records containing the domain constraint at  314 . For instance, in the example DOM tree  306 , the record extraction module  122  may locate pivot nodes  316 ,  318  since they are the lowest nodes of the illustrative DOM tree that contain the publication date, and thus locate anchor trees  308  and  312 , since they are sibling sub-trees that contain the domain constraints (i.e., the pivots), therefore being component sub-trees of the two data records corresponding to the data records  202  and  204 , respectively, in  FIG. 2 . Note that the advertisement section node  310  does not contain a pivot and thus cannot be an anchor tree since the advertisement section  240  of the example web document  200  does not contain any publication date data. 
     After locating the anchor trees  308 ,  312 , the record extraction module  122  may determine the record boundary, (i.e. start offset and length) at  314 . In general, the record boundary is defined as a set of adjacent sibling component sub-trees around every anchor tree that comprises the data records including both the anchor trees as well as the UGC. For instance, in the example DOM tree  306 , the UGC for the question data record node  308  is the collection of nodes represented by a first triangle  320  and the UGC for the response data record node  312  is the collection of nodes represented by a second triangle  322 . The start offset of the record boundary is deemed the offset of the left most component sub-tree relative to the anchor tree of each record, and the length is the number of component sub-trees of each record. For the example DOM tree  306 , since both records consist of exactly one component sub-tree (i.e., the anchor trees  308  and  312 , respectively), the start offset of the record boundary is 0 and the length of the record boundary is 1. Note that this is a fairly easy case for record boundary determination, but there exist more complicated cases as will be discussed in later sections. 
     After locating the anchor trees  308 ,  312 , and determining the record boundary, the record extraction module  122  may extract the data records containing the UGC from the web document at  314 . Unlike prior processes which assume that the data records containing the UGC have similar DOM tree structures, the MiBAT process of the data extraction engine  116  utilizes the domain constraint (i.e., post-date) as part of the MiBAT process to locate and extract the data records. Accordingly, the data extraction engine  116  is able to extract data records containing UGC even if the data records are non-consecutive (i.e., there are advertisements stuck between the data records) and even if a structure of the UGC varies from data record to data record within the web document. 
     At  328 , the data extraction engine  116  may store the extracted data records. For instance, the data extraction engine  116  may store the extracted data records to the web  108 , content providers  110 , and/or the local memory or the extracted data records may be processed for output to the computing device  102 . 
       FIG. 4  describes an illustrative process  400  to extract data records containing UGC using the Mining Data Records Based on Anchor Trees (MiBAT) process. The illustrative process  400  may further illustrate operations performed during the determining the extract data records block  314  of the illustrative process  300 . 
     In general, the MiBAT process uses domain constraints to locate and extract data records that contain UGC. For reference, pseudo-code illustrating MiBAT process is provided below in Table 1. 
     
       
         
           
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 Pseudo-Code for MiBAT Process 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
            
               
                 MiBAT(T) 
               
            
           
           
               
               
            
               
                 1: 
                 Ω ← { } 
               
               
                 2: 
                 For parent tree node p in T 
               
            
           
           
               
               
            
               
                 3: 
                 t 1  ... t n  ← the child sub-tree list of p 
               
               
                 4: 
                 Δ ← FINDANCHORTREES (t 1  ... t n ) 
               
               
                 5: 
                 for anchor tree list (a 1  ... a m ) in Δ 
               
            
           
           
               
               
            
               
                 6: 
                 R ← DETERMINEBOUNDRY (t 1  ... t n , a 1  ... a m ) 
               
               
                 7: 
                 Ω ← Ω ∪{R)    a list of data records found 
               
            
           
           
               
               
               
            
               
                 8: 
                 return Ω 
                     return all record lists 
               
               
                   
               
            
           
         
       
     
     At  402 , the record extraction module  122  identifies the anchor trees, (a 1  . . . a n ) as being a set of one or more sibling sub-trees which include the domain constraint (i.e., line 4 of the pseudo-code of Table 1), from the child sub-tree list (t 1  . . . t n ) of a parent tree node p. For example, with respect to the example DOM tree  306  of  FIG. 3 , under the parent node  330 , the record extraction module  122  may identify the anchor tree represented by the tree  308  and the anchor tree represented by the tree  312  at  402 . The process of identifying the anchor trees is discussed further below with respect to  FIG. 5 . At  404 , the record extraction module  122  determines the record boundary (i.e., line 6 of the pseudo-code of Table 1) which is discussed further below with respect to  FIG. 6 . At  406 , the record extraction module  122  returns the data records (i.e., line 8 of the pseudo-code of Table 1). 
       FIG. 5  describes an illustrative process  500  to locate anchor trees. The illustrative process  500  may further illustrate operations performed during the determining the extract data records block  402  of the illustrative process  400  (i.e., line 4 of the pseudo-code of Table 1). 
     In general, the record extraction module  122  utilizes domain constraint similarity measures to locate the anchor trees. For reference, pseudo-code illustrating the process  500  of identifying anchor trees is provided below in Table 2. 
     
       
         
           
               
             
               
                 TABLE 2 
               
               
                   
               
               
                 Pseudo-Code for identifying anchor trees 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
            
               
                 FINDANCHORTREES (t 1 ...t n ) 
               
            
           
           
               
               
            
               
                  1: 
                 Δ ← { } 
               
               
                  2: 
                 covered[i] ← 0 for i = 1 . . . n 
               
               
                  3: 
                 for i ← 1 to n 
               
            
           
           
               
               
            
               
                  4: 
                 if covered[i] = 1 then continue 
               
               
                  5: 
                 a i  ← i, m ← 1    anchor tree list with counter of m 
               
               
                  6: 
                 CPSet ← candidate pivots in t 1     by classifier 
               
               
                  7: 
                 for j ← i + 1 to n 
               
            
           
           
               
               
            
               
                  8: 
                 if covered[j] = 1 then continue 
               
               
                  9: 
                 matchedCP ← DOMAINCOMPARE(t i , t j , CPSet) 
               
               
                 10: 
                 If matchedCP ≠ Ø    similarity test succeeds 
               
            
           
           
               
               
            
               
                 11: 
                 m ← m + 1, a m  ← j    expand the list 
               
               
                 12: 
                 CPSet ← CPSet ∩ matchedCP    update 
               
               
                 13: 
                 covered[j] ← 1 
               
            
           
           
               
               
            
               
                 14: 
                 if m ≧ 2    m = 1 means t i  is not an anchor tree 
               
            
           
           
               
               
            
               
                 15: 
                 Δ ← Δ ∪ {(a 1 ...a m )} 
               
            
           
           
               
               
            
               
                 16: 
                 return Δ    return all anchor tree lists 
               
            
           
           
               
            
               
                 DOMAINCOMPARE(t i , t j , CPSet) 
               
            
           
           
               
               
            
               
                  1: 
                 M ← TREEMATCHING(t i , t j ) 
               
               
                  2: 
                 matchedCP ←{ } 
               
            
           
           
               
               
               
            
               
                  3: 
                 for u in CPSet 
                     check each in CPSet 
               
            
           
           
               
               
            
               
                  4: 
                  if exists for candidate pivot v in t j  that DOMAINSIMILARITY(M, t i , u, t j , v) &gt; τ 
               
            
           
           
               
               
            
               
                   
                     PM or PS, using u as t i &#39;s pivot, v as t j &#39;s pivot 
               
            
           
           
               
               
            
               
                  5: 
                 matchedCP ← matchedCP ∪ {u} 
               
            
           
           
               
               
            
               
                  6: 
                 return matchedCP 
               
               
                   
               
            
           
         
       
     
     As discussed above with respect to  FIG. 3 , the record extraction module  122  utilizes a domain constraint (e.g., post-date), deemed a pivot, to locate the anchor trees. However, not all the nodes containing text having a format of the domain constraint are real pivots. For example, in forum posts, UGC may also contain strings in date format. Accordingly, the process  500  first identifies all candidate pivots at  502  (i.e., line 6 of the pseudo-code of Table 2). The candidate pivots, CPSet, are nodes containing text which is in the format of the domain constraint. For example, if the domain constraint is post-date, then all nodes having text in the format of a date may be considered candidate pivots. 
     Once the record extraction module  122  identifies the candidate pivots, CPSet, at  502 , the record extraction module uses the domain compare function  504  to identify new anchor trees (i.e., lines 9 of the pseudo-code of Table 2). If a new anchor tree is found (i.e., the “yes” path at block  506 , line 10 of the pseudo-code of Table 2), the record extraction module  122  then updates the candidate pivot set using the new anchor trees at  508  (i.e., line 12 of the pseudo-code of Table 2). The record extraction module  122  will then iterate the blocks  502 - 508  until no new anchor trees can be found (i.e., the “no” path at block  506 ), and will then exit and return the anchor trees at  510 . Line 2 of the pseudo-code of Table 2, covered[i]←0 for i=1 . . . n, ensures that a node belongs to at most one anchor tree set. It also helps avoid returning redundant sub-sets of the anchor trees. 
     The record extraction module  122  uses the domain compare function  504  (i.e., line 9 of the pseudo-code of Table 2) to compare two trees for identifying new anchor trees as well as to determining the matched candidate pivots from among the candidate pivots. Unmatched candidate pivots will not be the real pivots and be discarded from CPSet (i.e., line 5 of the pseudo-code of DomainCompare and line 12 of the pseudo-code of FindAnchorTrees of Table 2). At blocks  502 - 508 , the record extraction module  122  will iteratively filter out the unmatched candidate pivots from CPSet which cannot be the real pivots, resulting in the real pivots as well as the anchor trees. In some instances, applying the Domain Compare function at  504  includes applying a domain constraint guided tree similarity measure such as a pivot match, PM,  504   a  or a pivot and sibling match, PS,  504   b  to determine the real pivot node from among the candidate pivots. 
     In some instances, the record extraction module  122  calculates a tree similarity score, deemed similarity measure, at  504  to determine the real pivot node from among the candidate pivots. The similarity score equation is formulated in Equation (1). 
     
       
         
           
             
               
                 
                   
                     
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                   ) 
                 
               
             
           
         
       
     
     In which M is a top down tree mapping result, T 1  and T 2  are trees, f is a tree fragment selection function which maps the node set V of a tree T to a sub-set of nodes f(V) (i.e., f(V) V), and f(V 1 )×f(V 2 )={(u,v)|uεf(V 1 ), vεf(V 2 )}. 
     The pivot match (PM)  504   a  is the tree similarity measure (Equation (1)) defined by a tree fragment selection function of f PM (V)={p} in which p is the pivot and V is the node set of the DOM tree, constructs a sub-tree template which includes only the pivot itself. In other words, a candidate pivot p in tree V is a real pivot node if there is a matching candidate pivot p in all record trees of the same web document. Since the pivot nodes usually belong to the structured part of the data record, it may be helpful to include the sibling nodes of the pivot in the sub-tree template which are also likely to belong to the structured part. Accordingly, the pivot and sibling match (PS)  504   b  is the similarity measure defined by a tree fragment selection function of f PS (V)={v|vεV, parent(v)=parent(p)} in which p is the pivot, v are nodes of the DOM tree, and parent(v) is the parent of v. Thus, under the PS match function, a candidate pivot p in v is a real pivot if all record trees of the same web document are judged to be similar (against a threshold) when taking only p and all of p&#39;s siblings into consideration. Since a pivot is obtained from domain constraints, the pivot match (PM)  504   a  and the pivot and sibling match (PS)  504   b  are deemed domain constraint guided similarity measures. 
       FIG. 6  is a pictorial flow diagram  600  of locating the anchor trees. The pictorial flow diagram  600  begins with three illustrative trees  602 ,  604 ,  606 . The three illustrative trees  602 ,  604 ,  606  may correspond to the example web document  200  of  FIG. 2 . For example, tree  1   602  may correspond to the first data record  202 , tree  2   604  may correspond to the advertisement section  240 , and tree  3   606  may correspond to the second data record  204 . Using the post-date as an illustrative domain constraint, the highlighted nodes of the illustrative trees  602 ,  604 ,  606  represent candidate pivots. For example, node  608  of tree  1   602  is a candidate pivot since it contains the post-date of “Sep. 26, 2010.” Node  610  is also a candidate pivot since it contains the post-date of “Today.” Node  612  is also a candidate pivot (not a real pivot though) since the second data record  204  has the statement “Math Fact of the Day: March 14th (Pi Day) is a great day to celebrate math and science. Spread the word . . . ” which includes the date of March 14 th . 
     Given that candidate pivots are only real pivots if they match among all data records, the record extraction module  122  may apply a top-down tree matching algorithm  614  to compare two trees. In some instances, the record extraction module  122  uses block  504  of  FIG. 5  to apply the top-down tree matching algorithm at  614 . A mapping M from tree T 1  to T 2  is a set of ordered pairs of nodes (u,v), u ε T 1 , v ε T 2  satisfying conditions that for all (u 1 , v 1 ), (u 2 , v 2 ) ε M: 1) u 1 =u 2  iff v 1 =v 2 ; 2) u 1  is on the left of u 2  iff v 1  is on the left of v 2 ; and 3) u 1  is an ancestor of u 2  iff v 1  is an ancestor of v 2 . Under the top-down tree matching algorithm  614 , a mapping M from tree T 1  to T 2  is top-down if it satisfies the condition that for the nodes u ε V 1 , v ε V 2 , if (u, v) ε M, then (parent(u),parent(v)) ε M, where V 1  is a sub-tree template of tree T 1 , V 2  is a sub-tree template of tree T 2 , and parent(v) is the parent of v. To illustrate the top-down tree matching algorithm, for example, the HTML labels in the illustrative trees  602 ,  604 ,  606  may be replaced with letters, wherein like letters indicate a positive top-down mapping from tree  1   602  to tree  2   604  to tree  3   606 . For instance, node B  616  is consistent among the illustrative trees  602 ,  604 ,  606  since node has the same parent node, A, in all three illustrative trees. On the other hand, node D  618  of illustrative tree  602  and  606  does not have a matching node in tree  2   604  since the parent node of D (i.e., A) does not have three children nodes in illustrative tree  2 . 
     In light of the top-down tree matching algorithm  614 , the illustrative trees  602 ,  604 ,  606  may be visually simplified to a collection of trees  620  comprising the candidate pivots. For instance, the collection of trees  620  may include a first tree  622  which comprises node B since node B is the only candidate pivot in the illustrative tree  1   602 . A second tree  624  does not contain any candidate pivots since the illustrative tree  2  (i.e., the advertisement section  240 ) does not contain any date data. A third tree  626  contains the two candidate pivots B and K since the illustrative tree  606  includes dates. 
     At  628 , the record extraction module  122  may compare the illustrative trees  602 ,  604 ,  606  using one of the similarity measures (i.e., pivot match, PM,  504   a  or pivot and sibling match, PS,  504   b ). In some instances, the record extraction module  122  uses the domain compare function  504  to compare the two trees at  628  in order to find the anchor trees  630  and identify the real pivot node  632 . 
     For example, using the illustrative trees  602 ,  604 ,  606 , the record extraction module  122  may start with illustrative tree  1  and define the candidate pivot set (denoted as CPSet in the pseudo-code of Table 2) as being {B}. Illustrative tree  2   604  is skipped since it does not contain any candidate pivots. Lastly, illustrative tree  3   604  is added to the anchor tree list (i.e., line 11 of the pseudo-code of Table 2) and CPSet is updated to be {B} since B is the only candidate pivot that is common to illustrative trees  602  and  606  (K, although also a candidate pivot for tree  606 , will not be added to CPSet because it does not exist in tree  602 ). Accordingly, the domain compare function  508  successfully identifies anchor trees as being illustrative trees  602  and  606  as well as finds the real pivot node B. Note that in most instances, a web document contains more than three records (i.e., there are more than three trees). In such instances, the record extraction module  122  will loop through each of the trees in the document to update the candidate pivot set (CPSet) and determine both the set of anchor trees as well as identify the true pivot node from among the candidate pivots. 
     As illustrated by the pictorial flow diagram  600 , the data extraction engine  116  is able to extract the data records containing the UGC even though the data records containing the UGC are non-consecutive and even though the UGC is unstructured. 
       FIG. 7  describes an illustrative process  700  to determine record boundaries. The illustrative process  700  may be performed by the record extraction module  122  and may further illustrate operations performed during the determine record boundary block  404  of the illustrative process  400  (i.e., line 6 of the pseudo-code of Table 1). 
     For reference, pseudo-code illustrating the process  700  of determining record boundaries is provided below in Table 3. 
     
       
         
           
               
             
               
                 TABLE 3 
               
               
                   
               
               
                 Pseudo-Code for determining record boundaries 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
            
               
                 DETERMINEBOUNDARY (t 1 ...t n, a 1 ...a m ) 
               
            
           
           
               
               
            
               
                  1: 
                 anchorGap ← min 1&lt;i≦m (a i  − a i−1 ) 
               
            
           
           
               
               
               
            
               
                  2: 
                 left ← 0 
                     left boundary of expansion 
               
            
           
           
               
               
            
               
                  3: 
                 for k ← 1 to min{anchorGap, a 1 } − 1 
               
            
           
           
               
               
            
               
                  4: 
                 if exists 1 ≦ i,j ≦ m that DIFFTAG(t a     i     −k , t a     j     −k ) 
               
            
           
           
               
               
            
               
                  5: 
                 break 
               
            
           
           
               
               
            
               
                  6: 
                 else 
               
            
           
           
               
               
            
               
                  7: 
                 left ← left − 1 
               
            
           
           
               
               
               
            
               
                  8: 
                 right ← 0 
                     right boundary of expansion 
               
            
           
           
               
               
            
               
                  9: 
                 for k ← 1 to min{anchorGap − 1,n − a m } 
               
            
           
           
               
               
            
               
                 10: 
                 if exists 1 ≦ i,j ≦ m that DIFFTAG(t a     i     +k , t a     j     +k ) 
               
            
           
           
               
               
            
               
                 11: 
                 break 
               
            
           
           
               
               
            
               
                 12: 
                 else 
               
            
           
           
               
               
            
               
                 13: 
                 right ← right + 1 
               
            
           
           
               
               
               
            
               
                 14: 
                 expanLen ← right − left + 1 
                     length of expansion 
               
               
                 15: 
                 R* = [ ] 
                     initialize the result 
               
               
                 16: 
                 k ← min{anchorGap, expanLen} 
                     length of record 
               
               
                 17: 
                 for x ← k − expanLen to 0 
                     enumerate start offset 
               
            
           
           
               
               
            
               
                 18: 
                 R i   (x)  ← t a     i     +x ...t a     i     +x+k   −1  for i = 1...m 
               
            
           
           
               
               
               
            
               
                 19: 
                 R (x)  ← R 1   (x) ...R m   (x)   
                     records of the current offset 
               
            
           
           
               
               
            
               
                 20: 
                 R* = argmax{Score(R*), Score(R (x) )}    Equation (2) 
               
            
           
           
               
               
               
            
               
                 21: 
                 return R* 
                     return the best record list 
               
               
                   
               
            
           
         
       
     
     At  702 , the record extraction module  122  obtains the minimal distance, anchorGap, between two anchor trees (i.e., line 1 of the pseudo-code of Table 3). In some instances, the record extraction module  122  calculates the distance between each pair of the anchor trees located in the illustrative process  500  and then sets anchorGap to be the minimum of these distances. Using the collection of tree fragments  620  as an example, the record extraction module  122  may calculate the minimal distance, anchorGap, to be 2 since the second tree  624  is not an anchor tree which separates the first tree  622  (i.e., an anchor tree) from the third tree  626  (i.e., an anchor tree). 
     At  704 , the record extraction module  122  determines the expansion length, expanLen (i.e., lines 2-14 of the pseudo-code of Table 3). In some instances, the record extraction module  122  starts from each anchor tree and expands the data record in both directions from the anchor tree until one of two conditions are met. First, the record extraction module  122  ceases expanding in a direction if the record extraction module  122  encounters either a left or right boundary of the child sub-tree list or another anchor tree (i.e., line 3 and 9 of the pseudo-code of Table 3). Second, the record extraction module  122  ceases expanding in a direction if the newly expanded data record violates a similarity assumption (i.e., line 4 and 10 of the pseudo-code of Table 3). 
     The similarity assumption may be formulated as follows. Data records must be structurally similar with each other to some extent. Specifically, given any two records, the structure of the data records must satisfy two conditions. First, all pairs of corresponding sub-trees in the data records must have the same HTML tag at root (i.e. the two sub-tree lists must have the same tag sequence at the top level). Second, one pair of corresponding sub-trees in the data records such as the anchor trees must be judged as similar with respect to the domain constraint guided similarity measure in use (i.e., either PM or PS). 
     At blocks  706 - 722 , the record extraction module  122  determines the record length, k, and the start offset, x. In some instances, the record extraction module  122  may use logic to determine the record length. For instance, if the minimal distance obtained at block  702  is equal to 1 (i.e., the “yes” path at block  706 ), then the record length, k, is determined to be 1 and the start offset, x, is determined to be 0 at block  708 . One example of the situation discussed at block  708  is illustrated in the first illustrative DOM tree  710  where the triangles denote anchor trees and the dashed boxes denote expansions. In other words, if the minimal distance obtained at block  702  is equal to 1 then two or more of the anchor trees are adjacent and thus every single anchor tree forms its own data record. 
     If the minimal distance obtained at block  702  is 2 or greater (i.e., the “no” path at block  706 ), then the process  700  continues to block  712 . At  712 , the record extraction module  122  determines whether the expanLen determined at  704  is less than or equal to the minimal distance calculated at  702 . If the expanLen calculated at  704  is less than or equal to the minimal distance calculated at  702  (i.e., the “yes” path at block  712 ), then, at block  714 , the record length, k, is determined to be the expanLen (i.e., determined at block  704 ) and the start offset, x, is determined to be 0. One example of the situation discussed at block  714  is illustrated in the second illustrative DOM tree  716  where the triangles denote anchor trees and the dashed boxes denote expansions. In other words, the length of each expansion is less than or equal to the minimal distance between two anchor trees. For instance, in the second illustrative DOM tree  716 , the expansion is circle, circle, triangle (i.e., TR, TR, DIV). In such a case, no two expansion regions around different anchor trees overlap with each other and it is natural that the sub-trees within each expansion form a data record. 
     If the expanLen calculated at  704  is greater than the minimal distance calculated at  702  (i.e., the “no” path at block  712 ), then the process  700  continues to block  718 . At  718 , the record length, k, is the smaller one between the minimal distance obtained at block  702  (anchorGap) and the expanLen determined at block  704  (i.e., line 16 of the pseudo-code of Table 3). At  720 , the record extraction module  122  calculates the similarity score, Score(R (x) ), for each record list, R (x) , using Equation (2).
 
Score( R   (x) )=Σ 1&lt;i≦m Σ 0≦j&lt;k TreeSim( t   a     i     +x+j   ,t   a     i−1     +x+j )  (2)
 
In which the record list is R (x) =R 1   (x)  . . . R m   (x)  (i.e., line 19 of the pseudo-code of Table 3), where R i   (x) =t a     i     +x  . . . t a     i     +x+k−1  is the sub-tree list of the ith record (i.e., line 18 of the pseudo-code of Table 3); TreeSim(t 1 , t 2 ) is computed as Equation (1) by taking a tree fragment selection of f(V)=V.
 
     After calculating the similarity score for each record list, the record extraction module  122  determines the start offset to be the offset leading to the record list that has the best similarity score at  722 . One example of the situation discussed at blocks  718 - 722  is illustrated in the third illustrative DOM tree  724  where the triangles denote anchor trees and the dashed boxes denote expansions. In other words, if length of each expansion is greater than the minimal distance between two anchor trees, there must be two expansion regions overlapping on a few sub-trees. For instance, in the third illustrative DOM tree  724 , where the expansion around each anchor tree contains exactly sub-trees of circle, circle, triangle, circle, circle, (i.e., TR, TR, DIV, TR, TR) and two consecutive expansion regions overlap on two sub-trees of circle, circle (i.e., TR, TR). In this case, the largest record length will be determined by the minimal distance of two anchor trees, (i.e. 3 in third illustrative DOM tree  724 ), and there will be ambiguity about the start offset of the data record. For example in third illustrative DOM tree  724  there are three possible start offsets, i.e. −2, −1 and 0 respectively. In this case, the record extraction module  122  finds the start offset leading to the maximum similarity among each of the possible choices using Equation (2). 
     Illustrative Computing Device 
       FIG. 8  illustrates a representative system  800  that may be used to implement the data extraction engine  116 . However, it will readily appreciate that the techniques and mechanisms may be implemented in other systems, computing devices, and environments. The representative system  800  may include the computing device  102  of  FIG. 1 . However, the computing device  102  shown in  FIG. 8  is only one illustrative of a computing device and is not intended to suggest any limitation as to the scope of use or functionality of the computer and network architectures. Neither should the computing device  102  be interpreted as having any dependency nor requirement relating to any one or combination of components illustrated in the representative system  800 . 
     The computing device  102  may be operable to extract data records containing user-generated content (UGC) from web documents. For instance, the computing device  102  may be operable to receive web documents, parse web documents into DOM trees, and extract data records from the web documents. 
     In at least one configuration, the computing device  102  comprises one or more processors  802  and memory  804 . The computing device  102  may also include one or more input devices  806  and one or more output devices  808 . The input devices  806  may be a keyboard, mouse, pen, voice input device, touch input device, etc., and the output devices  808  may be a display, speakers, printer, etc. coupled communicatively to the processors  802  and the memory  804 . The computing device  102  may also contain communications connection(s)  810  that allow the computing device  102  to communicate with other computing devices  812  such as via a network. 
     The memory  804  of the computing device  102  may store an operating system  814 , the data extraction engine  116 , and may include program data  816 . The memory  804 , or portions thereof, may be implemented using any form of computer-readable media that is accessible by the computing device  102 . Computer-readable media includes, at least, two types of computer-readable media, namely computer storage media and communications media. 
     Computer storage media includes volatile and non-volatile, 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 storage 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 storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information for access by a computing device. 
     As described above, the data extraction engine  116  may extract data records containing UGC using the processes illustrated in  FIGS. 3-7 . For instance, the data extraction engine  116  may enable the computing device  102  to retrieve web documents, process the web documents into a DOM tree, extract data records from the web document, and store the extracted data records to the program data  816 . 
     CONCLUSION 
     In closing, although the various embodiments have been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended representations is not necessarily limited to the specific features or acts described. Rather, the specific features and acts are disclosed as example forms of implementing the claimed subject matter.