Large scale item representation matching

A two-phase process quickly and accurately identifies representations of the same items within a collection of item representations. In the first phase, referred to as a “blocking phase,” frequency information indicating the frequency with which terms appear within the collection of item representations is used to quickly identify “candidate pairs” (i.e., pairs of item representations that have a relatively high probability of matching). The blocking phase results in a reduced subset of the data for further analysis during the second phase. In the second phase, referred to as a “matching phase,” the candidate pairs are analyzed using fuzzy matching functions to accurately identify “matching pairs” (i.e., representations of the same items).

BACKGROUND

Many data driven applications, including web-based applications, typically rely heavily on and use textual data that originates from different and diverse data sources. This often results in multiple and different representations of the same items (or entities) in the data. For instance, a data set may include a collection of citations that represent academic publications, and there may be multiple citations within the collection that represent the same academic publications. However, because these citations may originate from a variety of different sources, the various citations that represent the same academic publications may differ. In particular, the citations may include numerous variations, such as listing all authors or only partial authors, using abbreviations, including or excluding different elements (e.g., author, title, venue, volume information, page information, publication date, etc.), including misspellings, and reordering elements to name a few.

Recognizing these different (and possibly erroneous) representations of the same items facilitates consolidating and cleaning the data and creating cohesion in the data. In some cases, only by matching representations of items in the data may particular applications be applied. However, it is difficult to obtain high accuracy in matching between different representations of the same item. The difficulty is further exacerbated when matching is to be performed over a large collection of data.

BRIEF SUMMARY

Embodiments relate to a two-phase process for quickly and accurately identifying representations of the same items within a collection of item representations. In the first phase, or “blocking phase,” information indicative of the frequency with which terms appear within the collection of item representations is used to quickly identify “candidate pairs” (i.e., pairs of item representations that have a relatively high probability of matching). The blocking phase results in a reduced subset of the data for further analysis during the second phase. In the second phase, or “matching phase,” the candidate pairs are analyzed using fuzzy matching functions to accurately identify “matching pairs” (i.e., representations of the same items).

DETAILED DESCRIPTION

Embodiments of the present invention facilitate matching item representations using a two-phase process that includes a “blocking phase” and a “matching phase.” The process provides a fast and accurate approach to identify representations of the same items or entities within a data set. For instance, the process may be used to identify citations corresponding to the same publications or to identify representations of the same events within a collection of event information (e.g., concerts, plays, movies, etc.). While embodiments of the invention will be further illustrated herein primarily in the context of academic citations, one skilled in the art will recognize that the process may also be applied to representations of other types of items.

The first phase, or “blocking phase,” applies a fast but crude matching algorithm over the complete data. The blocking phase results in a highly reduced subset of the data that contains candidate pairs of item representations with high probability of being actual matches. The blocking phase determines candidate pairs using frequency information indicating the frequency of terms appearing in the collection of item representations. In some embodiments, an inverted index is generated that maps terms to item representations in which the terms appear. The inverted index also includes an inverse document frequency (IDF) score for each term indicating the frequency of the term within the collection of item representations. The inverted index is then employed to identify the candidate pairs.

Although the blocking phase quickly identifies pairs of item representations that have a relatively high probability of matching, the results are not highly accurate. Accordingly, the second phase, or “matching phase,” operates on the set of candidate pairs determined during the blocking phase to identify matching pairs with high accuracy. In the matching phase, the candidate pairs are analyzed using fuzzy matching functions to determine if each candidate pair should be considered a matching pair, indicating that the pair of item representations represent the same item. In some embodiments, the matching phase combines a library of reusable fuzzy matching functions and a decision tree based classifier. In such embodiments, different fuzzy matching functions may be applied to different segments of the item representations based on the suitability of the fuzzy matching functions for the various segments. The classifier then combines the results of the fuzzy matching functions that are applied to the different segments of the candidate pair to determine if the candidate pair is a matching pair.

Matching representations of the same items facilitates removing redundancy and cleaning the data, as well as allowing different applications to be applied. For instance, in the case of academic citations, identifying matching citations (i.e., citations that represent the same publication) enables a variety of applications, such as, for instance, performing static ranking for academic web search, grouping together different sources of the same article, and introducing a “cited by” feature.

Embodiments of the invention also provide an approach that is highly scalable as the design of the blocking phase allows blocking to be performed over subsets of data by multiple machines. Accordingly, to determine candidate matches (i.e., blocks) for a set of item representations A from a set of target item representations B, multiple machines may be used with each machine examining a subset of both set A and set B. In other words, not only can blocking be performed over a subset of source information but can also be performed on a subset of target information. Results from the various machines may then be aggregated together. This property enables massive scaling, parallel execution, and distribution of both blocking and matching (since matching is performed over the results of blocking).

Accordingly, in one aspect, an embodiment of the invention is directed to a computerized method for matching item representations within a collection of item representations. The method includes determining candidate pairs of item representations based on frequency information indicative of the frequency at which terms appear in the collection of item representations. The method also includes matching item representations by analyzing the candidate pairs using one or more fuzzy matching functions.

In another embodiment of the invention, an aspect is directed to one or more computer-readable media embodying computer-useable instructions for performing a method of matching item representations from a collection of item representations. The method includes extracting terms from the collection of item representation and determining frequency information indicative of the frequency with which the terms appear within the collection of item representations. The method also includes generating an inverted index mapping the terms to the item representations in which the terms appear, wherein the inverted index further includes the frequency information for the terms. The method further includes determining one or more candidate pairs of item representations using the inverted index based on terms shared between item representations and frequency information associated with the terms. The method still further includes identifying one or more matching pairs of item representations by analyzing the candidate pairs using the fuzzy matching algorithms.

A further aspect of the invention is directed to a computerized system including one or more computer-readable media embodying software components for matching item representations from a collection of item representations. The software components include a blocking component that identifies candidate pairs of item representations based on frequency information associated with terms shared between the candidate pairs. The software components also include a matching component that identifies matching pairs of item representations by analyzing the candidate pairs using one or more fuzzy matching algorithms.

The system200facilitates matching representations of the same items within a collection of item representations202. The collection of item representations202may generally include representations of items from data that originates from different and diverse data sources. As a result, the collection202may include representations of the same items that differ in content and form. Accordingly, the system200facilitates identifying representations of the same items.

The collection of item representations202may be maintained by one or more computing devices that are accessible by an extraction component204. The extraction component204scans the item representations within the collection202and extracts data regarding terms appearing in the item representations and frequency information indicative of the frequency with which the terms appear within the collection202. In various embodiments of the invention, terms extracted from the item representations may include individual words and/or phrases.

As will be described in further detail below, the frequency information may be used by the blocking component206as a measure of a term's importance for matching. In particular, terms that are common and appear frequently within the collection of item representations202are not likely to provide a good indication that item representations sharing those common terms are matching. Conversely, terms that are rare and appear less frequently within the collection of item representations202are likely to provide a good indication that item representations sharing those rare terms are matching. In some embodiments, the frequency information comprises an IDF score calculated for each term based on the frequency with which each term appears in the collection202. In further embodiments, an inverted index is generated that maps terms to the item representations containing the terms and includes the IDF score for each term.

The data extracted by the extraction component204is used by the blocking component206to identify candidate pairs208. As such, the blocking component206quickly reduces the large collection of items202into a subset of candidate pairs208that have a relatively higher probability of being a matching pair (i.e., a pair of representations of the same item). To identify candidate pairs, the blocking component206examines terms shared between pairs of item representations taking into account frequency information associated with each of the terms. If a pair of item representations share terms having a sufficient level of importance (based on frequency information), the pair is considered a candidate pair.

The matching component210analyzes each of the candidate pairs identified by the blocking component to determine if a candidate pair is a matching pair with high accuracy. The matching component210applies fuzzy matching functions to each candidate pair to determine if the candidate pair represent the same item. In some embodiments, a single fuzzy matching function may be applied to a candidate pair to determine if the candidate pair is a matching pair. In other embodiments, corresponding segments with each item representation may be identified, and a suitable fuzzy matching function may be applied to each segment. A decision tree classifier then combines the results of the fuzzy matching functions for each of the different segments to determine if the candidate pair is a matching pair.

The overall process for identifying matching item representations will now be further illustrated using a specific example in the context of academic citations with reference toFIG. 3. As shown inFIG. 3, an incoming citation302is received at the blocking phase304. In the present example, the incoming reference is a citation for an academic publication: “Pratt J. and Johnson R., Joining Decision Trees, Artificial Intelligence Vol. pp. 344-356.” The blocking phase304employs frequency information from a collection of citations to identify citations within the collection that have a probability of matching the incoming citation302. Accordingly, the outcome of the blocking phase304is a number of blocked citations306, which comprise a subset of all citations within the collection. Each of the blocked citations306identified during the blocking phase304in conjunction with the incoming reference302comprise a candidate pair having a probability of representing the same academic publication. The candidate pairs (i.e., each block citation306and the incoming citation302) are analyzed during the matching phase308using one or more fuzzy matching algorithms to identify citations that match the incoming citation302. In the present example, the first citation in the blocked citations306is identified as a matching citation310.

As indicated previously, in some embodiments of the invention, an inverted index is generated to facilitate the blocking phase. With reference toFIG. 4, a flow diagram is provided showing a method for generating an inverted index to facilitate blocking and identifying candidate pairs for matching in accordance with an embodiment of the present invention. Initially, as shown at block402, a collection of item representations is preprocessed. Preprocessing may include a number of different steps to normalize and prepare the data for further processing within various embodiments of the invention. For instance, preprocessing may include removing delimiters, such as commas and quotes, and lowercasing all characters in the data.

As shown at block404, the preprocessed item representations are parsed to identify and extract terms from the item representations. In some embodiment, individual words may extracted from the item representations and identified as terms. In other embodiments, phrasal extraction may also be employed to identify extract phrases, such as “tropical storm” or “human embryo.” Each phrase may then be treated as a discrete term and included in the list of terms for the item representations. In some embodiments of the invention, stop-word filtering may be applied to identify and filter out stop words (i.e., words that are unimportant to determining matching pairs such as “the” and “a”).

After parsing the item representations to identify terms, an IDF score is determined for the extracted terms, as shown at block406. The IDF scores are used as a measure of the general importance of terms for matching item representations. The IDF score for each term is a function of the frequency of term in the collection of item representations. The greater the frequency of a term in the collection (i.e., a common word), the less likely the term will provide a good indication of matching between item representations. Conversely, the lower the frequency of a term in the collection (i.e., a rare word), the more likely the term will provide a good indication of matching between item representations.

An inverted index is generated using the frequency information, as shown at block408. The inverted index maps the extracted terms to the item representations containing the terms. Additionally, the inverted index includes the IDF score calculated for each of the extracted terms. As indicated previously, the inverted index may be used in the blocking phase to quickly and efficiently determine candidate pairs for analysis during the matching phase.

As discussed previously, frequency information is used during the blocking phase to determine the likelihood that pairs of item representations are matching pairs. In particular, if a pair of item representations has rare terms (i.e., terms have a low frequency within the collection of item representations) in common, there is a greater likelihood that the item representations are a matching pair. A variety of algorithms may be employed to determine candidate pairs during the blocking phase using the frequency information. By way of example only and not limitation,FIG. 5illustrates a flow diagram showing one method500for determining candidate pairs based on frequency information in accordance with an embodiment of the present invention. As shown at block502, pairs of item representations having one or more terms in common are identified. An aggregate IDF score is then computed for each identified pair by aggregating the IDF score for all terms in common for each pair, as shown at block504. In some embodiments, an inverted index such as that generated in accordance with the method400ofFIG. 4may be employed to determine the aggregate IDF score for each pair.

The aggregate IDF score for a pair of item representations is then employed as an indicator of the likelihood that the pair qualifies as a matching pair. In particular, the aggregate IDF score for each pair is compared against a predetermined threshold at block506to determine if the pair should be considered a candidate pair for analysis during the matching phase. If the aggregate IDF score for a pair is greater than the threshold, the pair is identified as a candidate pair, as shown at block508. Conversely, if the aggregate IDF score for a pair is less than the threshold, the pair is not identified as a candidate pair, as shown at block510.

In another embodiment of the invention, an algorithm is employed during the blocking phase that is designed on an IDF-based inverted index (such as that generated in accordance with the method400ofFIG. 4) and introduces “shortcuts” that accelerate the overall running time of the blocking phase. Instead of aggregating IDF scores for all terms in common between a pair of item representations as that performed in the method500ofFIG. 5, an iterative process is employed in which terms for a given item representation are prioritized based on importance (i.e., IDF score) and considered in turn based on this priority. With reference toFIG. 6, a flow diagram is provided showing a method600for blocking using an iterative algorithm providing “shortcutting” in accordance with an embodiment of the invention. The method600may be performed for each item representation in a collection to identify blocked item representations for each given item representation. Accordingly, as shown at block602, a target item representation is identified. The terms for the item representation are sorted in order of their respective IDF scores such that the more important terms (i.e., terms having higher IDF scores) appear first in the sorted order, as shown at block604. Additionally, an item representation having at least one term in common with the target representation is identified, as shown at block606.

The process continues at block608, at which the next term in the sort order is selected as the current term. If this is the first iteration, the first term, which has the highest importance based on IDF score, is selected as the current term. As shown at block610, whether the current term exists in both of the item representations is determined. If the term is not common between the two item representations, the process returns to block608, at which the next term in the sort order is selected. Alternatively, if the current term exists in both item representations, the term's IDF score is added to an aggregate IDF score for the pair, as shown at block612. Again, if this is the first iteration, the aggregate IDF score will be the IDF score for the first term shared by the item representations. The aggregate IDF score is then compared against a threshold at block614to determine whether the pair should be considered a candidate pair for further matching analysis. If the aggregate score is above the threshold, the pair is identified as a candidate pair, as shown at block616. Conversely, if the aggregate score is below the threshold, a determination is made whether the current term is the last term from the target item representation, as shown at block618. If the current term is the last term, the pair is not identified as a candidate pair at block620.

Alternatively, if the current term is not the last term, a prediction is made regarding whether a threshold will ever be reached for the current pair of item representations given the shared terms already considered and the remaining terms from the target representation. This consideration allows shortcutting if it is predicted the threshold will not be reached for the pair. To perform this predication, the remaining terms from the target representation are assumed to be shared between the item representations, as shown at block622. Additionally, a maximum possible aggregate score is computed by adding the remaining terms' IDF scores to the current aggregate IDF score, as shown at block624. This maximum possible aggregate IDF score is then compared against the threshold, as shown at block626. If the maximum possible aggregate IDF score is less than the threshold, the pair is not identified as a candidate pair, as shown at block620. Alternatively, if the maximum possible aggregate score is greater then the threshold, the process iterates to the next term in the sort order at block608and the process is repeated using the next term as the current term.

As discussed previously, after candidate pairs have been identified during the blocking phase, the candidate pairs are analyzed using fuzzy matching functions to accurately identify those candidate pairs that represent matching pairs. Any of a variety of fuzzy matching functions may be employed with the scope of embodiments of the present invention. By way of example only and not limitation, the fuzzy matching functions may include: string edit distances (e.g., Levenshtein, Needleman-Wunsh, Smith-Waterman distance), Jaccard distance, TF-IDF cosine similarity, Soft TF-IDF, SoundEX distance. These functions may be applied based on characters, tokens, character n-grams, or token n-grams.

In some embodiments, a single fuzzy matching function may be applied to the item representations as a whole or to a portion of the item representations to determine if the item representations are matching. In further embodiments, however, different segments of the item representations may be identified and fuzzy matching algorithms suitable for matching the different segments may be applied. For instance, a citation for a publication may include segments such as author, title, and venue. Each of these segments have different characteristics. For example, some segments may be more likely to include abbreviations, changes in word order, or other variations. Accordingly, fuzzy matching functions may be selected for each of the segments based on each fuzzy matching functions suitability for handling such characteristics and variations. If different fuzzy matching functions are applied to various segments of item representations, a decision tree classifier may combine the results of the various fuzzy matching functions to determine if a candidate pair is a matching pair.

By way of example,FIG. 7illustrates a matching phase using a library of fuzzy matching functions704applied to different segments of a candidate pair702. In the example ofFIG. 7, the candidate pair702being analyzed is a pair of citations that include the segments: title, author, and venue. Accordingly, fuzzy matching functions from the library704are applied to each of the segments based on the suitability of each fuzzy matching function for the different segments. Additionally, a fuzzy matching function is applied to the item representations as a whole. The results of the fuzzy matching functions are combined using a decision tree classifier706to determine whether the candidate pair is a matching pair.

As can be understood, embodiments of the present invention provide a two-phase process for quickly and accurately identifying representations of the same items within a collection of item representations. 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.