Unsupervised extraction of facts

A system and method for extracting facts from documents. A fact is extracted from a first document. The attribute and value of the fact extracted from the first document are used as a seed attribute-value pair. A second document containing the seed attribute-value pair is analyzed to determine a contextual pattern used in the second document. The contextual pattern is used to extract other attribute-value pairs from the second document. The extracted attributes and values are stored as facts.

TECHNICAL FIELD

The disclosed embodiments relate generally to fact databases. More particularly, the disclosed embodiments relate to extracting facts from documents.

BACKGROUND

The internet provides access to a wealth of information. Documents created by authors all over the world are freely available for reading, indexing, and extraction of information. This incredible diversity of fact and opinion that make the internet the ultimate information source.

However, this same diversity of information creates a considerable challenge when extracting information. Information may be presented in a variety of formats, languages, and layouts. A human user may (or may not) be able to decipher individual documents to gather the information contained therein, but these differences may confuse or mislead an automated extraction system, resulting in information of little or no value. Extracting information from documents of various formats poses a formidable challenge to efforts to create an automated extraction system.

SUMMARY

A system and method for extracting facts from documents. A fact is extracted from a first document. The attribute and value of the fact extracted from the first document is used as a seed attribute-value pair. A second document containing the seed attribute-value pair is analyzed to determine a contextual pattern used in the second document. The contextual pattern is used to extract other attribute-value pairs from the second document. The extracted attributes and values are stored as facts.

DESCRIPTION OF EMBODIMENTS

Embodiments of the present invention are now described with reference to the figures where like reference numbers indicate identical or functionally similar elements.

FIG. 1shows a system architecture100adapted to support one embodiment of the invention.FIG. 1shows components used to add facts into, and retrieve facts from a repository115. The system architecture100includes a network104, through which any number of document hosts102communicate with a data processing system106, along with any number of object requesters152,154.

Document hosts102store documents and provide access to documents. A document is comprised of any machine-readable data including any combination of text, graphics, multimedia content, etc. A document may be encoded in a markup language, such as Hypertext Markup Language (HTML), i.e., a web page, in a interpreted language (e.g., JavaScript) or in any other computer readable or executable format. A document can include one or more hyperlinks to other documents. A typical document will include one or more facts within its content. A document stored in a document host102may be located and/or identified by a Uniform Resource Locator (URL), or Web address, or any other appropriate form of identification and/or location. A document host102is implemented by a computer system, and typically includes a server adapted to communicate over the network104via networking protocols (e.g., TCP/IP), as well as application and presentation protocols (e.g., HTTP, HTML, SOAP, D-HTML, Java). The documents stored by a host102are typically held in a file directory, a database, or other data repository. A host102can be implemented in any computing device (e.g., from a PDA or personal computer, a workstation, mini-computer, or mainframe, to a cluster or grid of computers), as well as in any processor architecture or operating system.

FIG. 1shows components used to manage facts in a fact repository115. Data processing system106includes one or more importers108, one or more janitors110, a build engine112, a service engine114, and a fact repository115(also called simply a “repository”). Each of the foregoing are implemented, in one embodiment, as software modules (or programs) executed by processor116. Importers108operate to process documents received from the document hosts, read the data content of documents, and extract facts (as operationally and programmatically defined within the data processing system106) from such documents. The importers108also determine the subject or subjects with which the facts are associated, and extract such facts into individual items of data, for storage in the fact repository115. In one embodiment, there are different types of importers108for different types of documents, for example, dependent on the format or document type.

Janitors110operate to process facts extracted by importer108. This processing can include but is not limited to, data cleansing, object merging, and fact induction. In one embodiment, there are a number of different janitors110that perform different types of data management operations on the facts. For example, one janitor110may traverse some set of facts in the repository115to find duplicate facts (that is, facts that convey the same factual information) and merge them. Another janitor110may also normalize facts into standard formats. Another janitor110may also remove unwanted facts from repository115, such as facts related to pornographic content. Other types of janitors110may be implemented, depending on the types of data management functions desired, such as translation, compression, spelling or grammar correction, and the like.

Various janitors110act on facts to normalize attribute names, and values and delete duplicate and near-duplicate facts so an object does not have redundant information. For example, we might find on one page that Britney Spears' birthday is “Dec. 2, 1981” while on another page that her date of birth is “Dec. 2, 1981.” Birthday and Date of Birth might both be rewritten as Birthdate by one janitor and then another janitor might notice that Dec. 2, 1981 and Dec. 2, 1981 are different forms of the same date. It would choose the preferred form, remove the other fact and combine the source lists for the two facts. As a result when you look at the source pages for this fact, on some you'll find an exact match of the fact and on others text that is considered to be synonymous with the fact.

Build engine112builds and manages the repository115. Service engine114is an interface for querying the repository115. Service engine114's main function is to process queries, score matching objects, and return them to the caller but it is also used by janitor110.

Repository115stores factual information extracted from a plurality of documents that are located on document hosts102. A document from which a particular fact may be extracted is a source document (or “source”) of that particular fact. In other words, a source of a fact includes that fact (or a synonymous fact) within its contents.

Repository115contains one or more facts. In one embodiment, each fact is associated with exactly one object. One implementation for this association includes in each fact an object ID that uniquely identifies the object of the association. In this manner, any number of facts may be associated with an individual object, by including the object ID for that object in the facts. In one embodiment, objects themselves are not physically stored in the repository115, but rather are defined by the set or group of facts with the same associated object ID, as described below. Further details about facts in repository115are described below, in relation toFIGS. 2(a)-2(d).

It should be appreciated that in practice at least some of the components of the data processing system106will be distributed over multiple computers, communicating over a network. For example, repository115may be deployed over multiple servers. As another example, the janitors110may be located on any number of different computers. For convenience of explanation, however, the components of the data processing system106are discussed as though they were implemented on a single computer.

In another embodiment, some or all of document hosts102are located on data processing system106instead of being coupled to data processing system106by a network. For example, importer108may import facts from a database that is a part of or associated with data processing system106.

FIG. 1also includes components to access repository115on behalf of one or more object requesters152,154. Object requesters are entities that request objects from repository115. Object requesters152,154may be understood as clients of the system106, and can be implemented in any computer device or architecture. As shown inFIG. 1, a first object requester152is located remotely from system106, while a second object requester154is located in data processing system106. For example, in a computer system hosting a blog, the blog may include a reference to an object whose facts are in repository115. An object requester152, such as a browser displaying the blog will access data processing system106so that the information of the facts associated with the object can be displayed as part of the blog web page. As a second example, janitor110or other entity considered to be part of data processing system106can function as object requester154, requesting the facts of objects from repository115.

FIG. 1shows that data processing system106includes a memory107and one or more processors116. Memory107includes importers108, janitors110, build engine112, service engine114, and requester154, each of which are preferably implemented as instructions stored in memory107and executable by processor116. Memory107also includes repository115. Repository115can be stored in a memory of one or more computer systems or in a type of memory such as a disk.FIG. 1also includes a computer readable medium118containing, for example, at least one of importers108, janitors110, build engine112, service engine114, requester154, and at least some portions of repository115.FIG. 1also includes one or more input/output devices120that allow data to be input and output to and from data processing system106. It will be understood that data processing system106preferably also includes standard software components such as operating systems and the like and further preferably includes standard hardware components not shown in the figure for clarity of example.

FIG. 2(a) shows an example format of a data structure for facts within repository115, according to some embodiments of the invention. As described above, the repository115includes facts204. Each fact204includes a unique identifier for that fact, such as a fact ID210. Each fact204includes at least an attribute212and a value214. For example, a fact associated with an object representing George Washington may include an attribute of “date of birth” and a value of “Feb. 22, 1732.” In one embodiment, all facts are stored as alphanumeric characters since they are extracted from web pages. In another embodiment, facts also can store binary data values. Other embodiments, however, may store fact values as mixed types, or in encoded formats.

As described above, each fact is associated with an object ID209that identifies the object that the fact describes. Thus, each fact that is associated with a same entity (such as George Washington), will have the same object ID209. In one embodiment, objects are not stored as separate data entities in memory. In this embodiment, the facts associated with an object contain the same object ID, but no physical object exists. In another embodiment, objects are stored as data entities in memory, and include references (for example, pointers or IDs) to the facts associated with the object. The logical data structure of a fact can take various forms; in general, a fact is represented by a tuple that includes a fact ID, an attribute, a value, and an object ID. The storage implementation of a fact can be in any underlying physical data structure.

FIG. 2(b) shows an example of facts having respective fact IDs of10,20, and30in repository115. Facts10and20are associated with an object identified by object ID “1.” Fact10has an attribute of “Name” and a value of “China.” Fact20has an attribute of “Category” and a value of “Country.” Thus, the object identified by object ID “1” has a name fact205with a value of “China” and a category fact206with a value of “Country.” Fact30208has an attribute of “Property” and a value of ““Bill Clinton was the 42nd President of the United States from 1993 to 2001.” Thus, the object identified by object ID “2” has a property fact with a fact ID of30and a value of “Bill Clinton was the 42nd President of the United States from 1993 to 2001.” In the illustrated embodiment, each fact has one attribute and one value. The number of facts associated with an object is not limited; thus while only two facts are shown for the “China” object, in practice there may be dozens, even hundreds of facts associated with a given object. Also, the value fields of a fact need not be limited in size or content. For example, a fact about the economy of “China” with an attribute of “Economy” would have a value including several paragraphs of text, numbers, perhaps even tables of figures. This content can be formatted, for example, in a markup language. For example, a fact having an attribute “original html” might have a value of the original html text taken from the source web page.

Also, while the illustration ofFIG. 2(b) shows the explicit coding of object ID, fact ID, attribute, and value, in practice the content of the fact can be implicitly coded as well (e.g., the first field being the object ID, the second field being the fact ID, the third field being the attribute, and the fourth field being the value). Other fields include but are not limited to: the language used to state the fact (English, etc.), how important the fact is, the source of the fact, a confidence value for the fact, and so on.

FIG. 2(c) shows an example object reference table210that is used in some embodiments. Not all embodiments include an object reference table. The object reference table210functions to efficiently maintain the associations between object IDs and fact IDs. In the absence of an object reference table210, it is also possible to find all facts for a given object ID by querying the repository to find all facts with a particular object ID. WhileFIGS. 2(b) and2(c) illustrate the object reference table210with explicit coding of object and fact IDs, the table also may contain just the ID values themselves in column or pair-wise arrangements.

FIG. 2(d) shows an example of a data structure for facts within repository115, according to some embodiments of the invention showing an extended format of facts. In this example, the fields include an object reference link216to another object. The object reference link216can be an object ID of another object in the repository115, or a reference to the location (e.g., table row) for the object in the object reference table210. The object reference link216allows facts to have as values other objects. For example, for an object “United States,” there may be a fact with the attribute of “president” and the value of “George W. Bush,” with “George W. Bush” being an object having its own facts in repository115. In some embodiments, the value field214stores the name of the linked object and the link216stores the object identifier of the linked object. Thus, this “president” fact would include the value214of “George W. Bush”, and object reference link216that contains the object ID for the for “George W. Bush” object. In some other embodiments, facts204do not include a link field216because the value214of a fact204may store a link to another object.

Each fact204also may include one or more metrics218. A metric provides an indication of the some quality of the fact. In some embodiments, the metrics include a confidence level and an importance level. The confidence level indicates the likelihood that the fact is correct. The importance level indicates the relevance of the fact to the object, compared to other facts for the same object. The importance level may optionally be viewed as a measure of how vital a fact is to an understanding of the entity or concept represented by the object.

Each fact204includes a list of one or more sources220that include the fact and from which the fact was extracted. Each source may be identified by a Uniform Resource Locator (URL), or Web address, or any other appropriate form of identification and/or location, such as a unique document identifier.

The facts illustrated inFIG. 2(d) include an agent field222that identifies the importer108that extracted the fact. For example, the importer108may be a specialized importer that extracts facts from a specific source (e.g., the pages of a particular web site, or family of web sites) or type of source (e.g., web pages that present factual information in tabular form), or an importer108that extracts facts from free text in documents throughout the Web, and so forth.

Some embodiments include one or more specialized facts, such as a name fact207and a property fact208. A name fact207is a fact that conveys a name for the entity or concept represented by the object ID. A name fact207includes an attribute224of “name” and a value, which is the name of the object. For example, for an object representing the country Spain, a name fact would have the value “Spain.” A name fact207, being a special instance of a general fact204, includes the same fields as any other fact204; it has an attribute, a value, a fact ID, metrics, sources, etc. The attribute224of a name fact207indicates that the fact is a name fact, and the value is the actual name. The name may be a string of characters. An object ID may have one or more associated name facts, as many entities or concepts can have more than one name. For example, an object ID representing Spain may have associated name facts conveying the country's common name “Spain” and the official name “Kingdom of Spain.” As another example, an object ID representing the U.S. Patent and Trademark Office may have associated name facts conveying the agency's acronyms “PTO” and “USPTO” as well as the official name “United States Patent and Trademark Office.” If an object does have more than one associated name fact, one of the name facts may be designated as a primary name and other name facts may be designated as secondary names, either implicitly or explicitly.

A property fact208is a fact that conveys a statement about the entity or concept represented by the object ID. Property facts are generally used for summary information about an object. A property fact208, being a special instance of a general fact204, also includes the same parameters (such as attribute, value, fact ID, etc.) as other facts204. The attribute field226of a property fact208indicates that the fact is a property fact (e.g., attribute is “property”) and the value is a string of text that conveys the statement of interest. For example, for the object ID representing Bill Clinton, the value of a property fact may be the text string “Bill Clinton was the 42nd President of the United States from 1993 to 2001.” “Some object IDs may have one or more associated property facts while other objects may have no associated property facts. It should be appreciated that the data structures shown inFIGS. 2(a)-2(d) and described above are merely exemplary. The data structure of the repository115may take on other forms. Other fields may be included in facts and some of the fields described above may be omitted. Additionally, each object ID may have additional special facts aside from name facts and property facts, such as facts conveying a type or category (for example, person, place, movie, actor, organization, etc.) for categorizing the entity or concept represented by the object ID. In some embodiments, an object's name(s) and/or properties may be represented by special records that have a different format than the general facts records204.

As described previously, a collection of facts is associated with an object ID of an object. An object may become a null or empty object when facts are disassociated from the object. A null object can arise in a number of different ways. One type of null object is an object that has had all of its facts (including name facts) removed, leaving no facts associated with its object ID. Another type of null object is an object that has all of its associated facts other than name facts removed, leaving only its name fact(s). Alternatively, the object may be a null object only if all of its associated name facts are removed. A null object represents an entity or concept for which the data processing system106has no factual information and, as far as the data processing system106is concerned, does not exist. In some embodiments, facts of a null object may be left in the repository115, but have their object ID values cleared (or have their importance to a negative value). However, the facts of the null object are treated as if they were removed from the repository115. In some other embodiments, facts of null objects are physically removed from repository115.

FIG. 2(e) is a block diagram illustrating an alternate data structure290for facts and objects in accordance with preferred embodiments of the invention. In this data structure, an object290contains an object ID292and references or points to facts294. Each fact includes a fact ID295, an attribute297, and a value299. In this embodiment, an object290actually exists in memory107.

FIG. 3(a) is a block diagram illustrating the extraction of facts from a plurality of documents, according to one embodiment of the present invention. Document302and document308are analogous to the documents described herein with reference toFIG. 1. According to one embodiment of the present invention, the document302and the document308are stored in a document repository (not shown).

The importer304processes the document302and extracts facts306. The importer304may employ any of a variety of methods for extracting the facts306from the document302, such as one of those described in “Supplementing Search Results with Information of Interest” or in the other incorporated applications. For the purposes of illustration, a single document302is shown in the figure. In practice, importer304can process a plurality of documents302to extract the facts306.

According to one embodiment of the present invention, the importer304identifies a predefined pattern in the document302and applies the predefined pattern to extract attribute-value pairs. The extracted attribute-value pairs are then stored as facts306. As described in “Supplementing Search Results with Information of Interest”, a predefined pattern defines specific, predetermined sections of the document which are expected to contain attributes and values. For example, in an HTML document, the presence of a text block such as “<BR>*:*<BR>” (where ‘*’ can be any string) may indicate that the document contains an attribute-value pair organized according to the pattern “<BR>(attribute text):(value text)<BR>”. Such a pattern is predefined in the sense that it is one of a known list of patterns to be identified and applied for extraction in documents. Of course, not every predefined pattern will necessarily be found in every document; identifying the patterns contained in a document determines which (if any) of the predefined patterns may be used for extraction on that document with a reasonable expectation of producing valid attribute-value pairs. The extracted attribute-value pairs are stored in the facts306.

An attribute-value pair is composed of an attribute and its associated value. An attribute-value pair may be stored as a fact, for example, by storing the attribute in the attribute field of the fact and the value in the value field of the fact. Extracting a fact is synonymous with extracting at least an attribute-value pair and storing the attribute and value as a fact.

In the example illustrated, document302contains at least some attribute-value pairs organized according to one of the predefined patterns recognizable by the importer304. An example of a document containing attribute-value pairs organized according to one of the predefined patterns recognizable by the importer304is described herein with reference toFIG. 4. Applying predefined patterns to documents containing attribute-value pairs organized according to those patterns beneficially extracts valuable information without the need for human supervision.

However, the document302may contain other attribute-value pairs organized differently, such that applying one of the predefined patterns recognizable by the importer304produces incomplete, inconsistent, or erroneous results. Similarly, a document such as the document308may contain attribute-value pairs organized in a manner different from those prescribed by the various predefined patterns. It is possible that, the importer304were applied to the document308, none of the predefined patterns recognizable by the importer304would be identified in the document308.

Advantageously, one embodiment of the present invention facilitates the extraction of attribute-value pairs organized according to a pattern not itself recognizable by the importer304. According to one embodiment of the present invention, a janitor310receives the facts306and the document308. If the document308contains the same (or similar) attribute-value pairs as at least some of the facts306, the facts306may be used to identify a contextual pattern in the document308. A contextual pattern is a pattern that is inferred on the basis of the context in which known attribute-value pairs appear in a document. An example of a contextual pattern in a document is described herein with reference toFIG. 5. The janitor310applies the contextual pattern to the document308to extract additional attribute-value pairs. These attribute-value pairs are then stored as the facts312. Several exemplary methods for identifying a contextual pattern and using it to extract attribute-value pairs are described in “Learning Facts from Semi-Structured Text.”

According to one embodiment of the present invention the janitor310additionally corroborates the facts306using a corroborating document (not shown). For example, as a result of improperly applied predefined patterns (or the document302itself), some of the facts306may contain errors, inconsistent information, or other factual anomalies. If the attribute-value pair of the fact306A cannot be found in any corroborating document, the janitor310may reduce the confidence score of the fact306A. Alternatively, if the attribute-value pair of the fact306A is identified in a corroborating document, the confidence score of the fact306A can be increased, and a reference to the corroborating document can be added to the list of sources for that fact. Several exemplary methods for corroborating facts can be found in “Corroborating Facts Extracted from Multiple Sources.”

According to one embodiment of the present invention, a plurality of documents are used to import and corroborate a group of facts. From this group of imported facts, those associated with a common name may be aggregated to form the facts306. The facts306may be normalized, merged and/or corroborated, and their confidence score may be adjusted accordingly (for example, by the janitor310, or by another janitor). According to one embodiment of the present invention, only facts306having a confidence score above a threshold are used for identification of contextual patterns by the janitor310. Corroborating facts beneficially improves the consistency of extracted facts, and can reduce the influence of improperly applied predefined patterns on the quality of the fact database.

The facts306and facts312may be associated with a common object. For example, the facts306may be extracted from the document302and stored as an object in an object repository. According to one embodiment of the present invention, the facts306may be associated with an object name. An exemplary method for associating an object name with an object is described in “Identifying a Unifying Subject of a Set of Facts”. According to one embodiment of the present invention, the object name (or another property associated with the facts302) are used to retrieve the document308. Using the object name to retrieve the document308is one example of a method for finding a document potentially containing attribute-value pairs common with the document302. As another example, the corroboration janitor306could query a search engine for documents containing one of the attribute-value pairs of the facts306. Other methods will be apparent to one of skill in the art without departing from the scope of the present invention.

According to one embodiment of the present invention, the facts312are further processed by a janitor (either the janitor310or another janitor). For example, the facts312can be merged with another set of facts (for example, the facts306), normalized, corroborated, and/or given a confidence score. According to one embodiment of the present invention, facts312having a confidence score above a threshold are added to a fact repository.

FIG. 3(b) is a block diagram illustrating the extraction of facts from a plurality of documents to produce an object, according to one embodiment of the present invention. The documents313contain at least one attribute-value pair in common, although this attribute-value pair may be organized according to different patterns in the various documents. Document313A and document313B may or may not describe a common subject.

The unsupervised fact extractor314identifies in document313A a predefined pattern and applies that pattern to extract a “seed” attribute-value pair. The unsupervised fact extractor314uses the seed attribute-value pair to identify a contextual pattern, in either or both of the documents313, and applies the contextual pattern to extract additional attribute-value pairs. A method used by the unsupervised fact extractor314, according to one embodiment of the present invention, is described herein with reference toFIG. 6. The unsupervised fact extractor314may be composed of any number of sub-components, for example, the importer304and janitor310described herein with reference toFIG. 3(a).

The unsupervised fact extractor314organizes the extracted attribute-value pairs into an object316. The unsupervised fact extractor314may also employ techniques for normalization, corroboration, confidence rating, and others such as those described in the applications incorporated by reference above. Other methods for processing the extracted facts to produce an object will be apparent to one of skill in the art without departing from the scope of the present invention. Furthermore, the unsupervised fact extractor314has been shown as receiving two documents and producing one object for the purposes of illustration only. In practice, the unsupervised fact extractor314may operate on any number of documents, to extract a plurality of facts to be organized into any number of objects.

By identifying both predefined and contextual patterns in the documents313, the unsupervised fact extractor314is able to build objects containing more information than extractors relying on predefined patterns alone, and without the need for document-specific human tailoring or intervention.

FIG. 4is an example of a document containing attribute-value pairs organized according to a predefined pattern. According to one embodiment of the present invention, document402may be analogous to the document302described herein with reference toFIG. 3. Document402includes information about Britney Spears organized according to a two column table404. According to one embodiment of the present invention, the two column table is a predefined pattern recognizable by the unsupervised fact extractor314. The pattern specifies that attributes will be in the left column and that corresponding values will be in the right column. Thus the unsupervised fact extractor314may extract from the document402the following attribute-value pairs using the predefined pattern: (name; Britney Spears), (profession; actress, singer), (date of birth; Dec. 2, 1981), (place of birth; Kentwood, La.), (sign; Sagittarius), (eye color; brown), and (hair color; brown). These attribute-value pairs can then be stored as facts, associated with an object, used as seed attribute-value pairs, and so on.

FIG. 5is an example of a document502from which a contextual pattern can be identified using a seed fact, according to one embodiment of the present invention. Document502may be analogous to the document308described herein with reference toFIG. 3. Document502includes information about Britney Spears. Document502illustrates a list504organized according to a pattern that for the purposes of illustration could be considered whimsical. Attributes are in bold, and values associated with those attributes are listed immediately below in italics. Such a pattern might be intuitive to a human user, but if that particular pattern is not recognizable to an extractor as a predefined pattern, using predefined patterns exclusively could result in the incorrect or failed extraction of the attribute-value pairs.

However, the document502has several attribute-value pairs in common with the document402. Specifically, the (name; Britney Spears) and (date of birth; Dec. 2, 1981) pairs are contained in both documents. The unsupervised fact extractor314can use one (or both) of these pairs as a seed attribute-value pair to identify a contextual pattern of other attribute-value pairs. For example, the (name; Britney Spears) pair might be contained in a context such as the following:<BR><B>Name</B><BR><I>Britney Spears</I>

Thus, using the information extracted from the document402, the unsupervised fact extractor314might identify in document502a contextual pattern for attribute-value pairs organized as:<BR><B>(attribute)</B><BR><I>(value)</I>

The common pair comprised of (date of birth; Dec. 2, 1981) may be used to confirm this contextual pattern, since this pair might also be contained in a context such as:<BR><B>Date of Birth</B><BR><I>Dec. 2, 1981</I>

Once the unsupervised fact extractor314has identified a contextual pattern, the unsupervised fact extractor314uses the contextual pattern to extract additional facts from the document502. Thus the unsupervised fact extractor314may extract from the document502the following attribute-value pairs using the predefined pattern: (Favorite Food; Chicken Parmesan), (Favorite Movie; Back to the Future), and (Profession; Singer-Songwriter).

For the purposes of illustration, the document502shows attribute-value pairs organized according to a single contextual pattern. Documents may contain multiple and various contextual patterns, or a mix of predefined patterns and contextual patterns. Furthermore, the examples of predefined patterns and contextual patterns illustrated herein as been selected for the purposes of illustration only. In some cases the attribute-value pattern used by document502may be recognizable as a predefined pattern, and conversely, in some cases the attribute-value pattern used by document402may not be recognizable as a predefined pattern. Given the scope and diversity of the internet, however, there will always be some documents containing attribute-value pairs not organized by a recognizable predefined pattern, and the ability to identify contextual patterns beneficially facilitates the extraction of at least some of these pairs.

FIG. 6is a flow chart illustrating a method for extracting facts, according to one embodiment of the present invention. According to one embodiment of the present invention, the method is performed by the unsupervised fact extractor314.

The method begins with a document302. The document302contains an attribute-value pair organized according to a predefined pattern. The unsupervised fact extractor314extracts604an attribute-value pair from the document302, producing a seed attribute-value pair606. According to one embodiment of the present invention, the unsupervised fact extractor314can extract604the attribute and value from the document by applying a predefined pattern; other methods for extracting604the attribute and value will be apparent to one of skill in the art without departing from the scope of the present invention. Additionally, the unsupervised fact extractor314may store the seed attribute-value pair606in a fact (not shown). According to one embodiment of the present invention, the fact in which the seed attribute-pair606is stored is associated with an object.

The unsupervised fact extractor314retrieves608a document610that contains the seed attribute-value pair606organized according to a contextual pattern.

According to one embodiment of the present invention, the unsupervised fact extractor314retrieves608the document610by searching (for example, on document hosts or in a document repository) for documents containing the attribute and value of the seed attribute-value pair. According to another embodiment of the present invention, the seed attribute-value pair is stored as a fact associated with an object. This object may have a name, and the unsupervised fact extractor314may retrieve608a document610by searching in a document repository for documents containing the object name. Other methods for retrieving608a document610will be apparent to one of skill in the art without departing from the scope of the present invention.

The unsupervised fact extractor314identifies612a contextual pattern associated with the seed attribute-value pair606and uses the pattern to extract an attribute-value pair614from the document610. The attribute-value pair614may then be stored as a fact and processed by further janitors, importers, and object retrievers as appropriate. According to one embodiment of the present invention, the fact in which the attribute-value pair614is stored is associated with an object. The fact containing attribute-value pair614may be associated with the same object as the fact containing seed attribute-value pair606, or it may be associated with a different object.

By extracting attributes and value using both predefined and contextual patterns, the unsupervised fact extractor314is able to collect a larger amount of information into facts than an extractor relying on either approach alone. Advantageously, information may be extracted into facts efficiently, accurately, and without need for human supervision.

Additionally, the unsupervised fact extractor314may also use the contextual pattern to extract another attribute-value pair from a third document. According to one embodiment of the present invention, the unsupervised fact extractor314determines if the third document is similar to the document610, for example, by comparing the domain hosting the document610to the domain hosting the third document. Using the contextual pattern to extract another attribute-value pair from a third document may be responsive to the determination that the third document is similar to the document610. Using the contextual pattern to extract another attribute-value pair from a third document advantageously facilitates the extracting of attribute-value pairs organized according to patterns not recognizable as predefined patterns, even from documents not containing a seed attribute-value pair.

While a method for extracting facts has been shown for the purposes of illustration as extracting a single seed attribute-value pair606and a single attribute-value pair614, it will be apparent to one of skill in the art that in practice the unsupervised fact extractor314may extract604a plurality of attribute-value pairs and extract612a plurality of attribute-value pairs614. When a plurality of attribute-value pairs are extracted604, any number of that plurality may be used as seed attribute-value pairs606. According to one embodiment of the present invention, extracting612additional attribute-value pairs from the document610is responsive to the number of seed-attribute-value pairs606contained in the document610. According to another embodiment of the present invention, a first seed attribute-value pair606may be used to identify612a contextual pattern and a second seed attribute-value pair606may be used to verify that contextual pattern, for example, by determining if the second seed attribute-value pair606is organized in the document610according to the contextual pattern. By using a plurality of seed attribute-value pairs606, the efficiency and accuracy of the unsupervised fact extractor314may be improved.

Certain aspects of the present invention include process steps and instructions described herein in the form of an algorithm. It should be noted that the process steps and instructions of the present invention can be embodied in software, firmware or hardware, and when embodied in software, can be downloaded to reside on and be operated from different platforms used by a variety of operating systems.

While the invention has been particularly shown and described with reference to a preferred embodiment and several alternate embodiments, it will be understood by persons skilled in the relevant art that various changes in form and details can be made therein without departing from the spirit and scope of the invention.