Method for entity-driven alerts based on disambiguated features

A method for entity-driven alerts based on disambiguated features, is disclosed. According to an embodiment, disclosed method may refer to entity-driven alerts based on trending or new knowledge of a disambiguated feature. The alerts may be sent to a user when new knowledge is discovered about the disambiguated feature, a new association (such as new features, facts, quotations, or topic IDs related, among others) with the feature of interest, and/or new trending changes are emerging about the feature of interest. According to various embodiments, method for entity-driven alerts based on disambiguated features may reduce the number of false positives resulting in a normal search query. Which in turn, may increase the efficiency of monitoring, allowing for broadened universe of alerts.

FIELD OF THE DISCLOSURE

The present disclosure relates in general to databases; and, more particularly, to data management systems and alerting systems.

BACKGROUND

A well-designed meta-analysis can provide valuable information for researchers, policy-makers, or data analysts in general. These users face an overwhelming amount of information, even in narrow areas of interest. In response, search engines designed to send alerts are frequently employed on large volumes of information. However, there are many critical caveats in performing and interpreting such large amount of information, and thus many ways in which meta-analyses can yield misleading information. To further reduce information overload, users may only want to be alerted when new trends emerge about an entity.

Searching information about entities (i.e. people, locations, organizations) in a large amount of documents, including sources such as a network, may often be ambiguous, which may lead to imprecise text processing functions, imprecise association of features during a knowledge extraction, and, thus, imprecise data analysis. Therefore, alerts based on keywords may be problematic because references to named entities are ambiguous and many alerts that are not on topic may be provided in the search results. In addition, people may not want to get alerted on everything related to an entity, but only when new knowledge (new information) about an entity is available.

Keyword search may not solve these problems as it is not easy to do that kind of filtration.

Therefore, there is still a need for tailored alerts following certain criteria to reduce results with misleading information or false positives, to increase the efficiency of monitoring, allowing for broadened universe of alerts.

SUMMARY

An aspect of the present disclosure is a method for entity-driven alerts based on disambiguated features. The method may include a news feed, an entity disambiguation module, and an alert database including one or more software modules.

A system for disambiguating features may include one or more modules, such as one or more feature extraction modules, one or more disambiguation modules, one or more scoring modules, and one or more linking modules. Embodiments of a method for disambiguating features may improve the accuracy of entity disambiguation beyond what may be achieved by considering no document linking. Taking account of document linkage may allow better disambiguation by considering document and entity relationships implied by links. Additionally, method for disambiguating features may be based on topics. Disambiguated features based on topics may allow to disambiguate one or more features/entities of interest occurring in a document by extracting meaningful context from a document (topics, entities, events, sentiment, and other features); and by disambiguating the extracted features by linking the co-occurrence of extracted features (topics, entities, etc.) using the knowledge base of co-occurring features.

The components within alert database (AD) may vary according to the type of alert the user wants to receive. The AD may have at least the components discussed below.

According to various embodiments, the AD may have a user identifier to which the alerts may be going to be sent; a collection of disambiguated features from which the user may select which feature the user wants to monitor; an alert specification describing the type of alert the user wants to receive; and a known-knowledge base in which known knowledge about the feature of interest may be stored. Any suitable methods may be employed for the user to communicate to the system which feature is of interest. According to other embodiments, AD may include other components such as a module that keeps record of the number or volume, and average of documents related to the feature of interest, in the case that the type of alert that the user chooses is based on trends emerging of the feature of interest.

Another aspect of the present disclosure may be an alerting system based on new knowledge discovered about a feature of interest, where an alert may be sent to a user when new information or new knowledge (for instance, new topics or frequently co-occurring entities) about the feature of interest is discovered.

Another aspect of the present disclosure may be an alerting system based on new associations between a feature and the feature of interest, where an alert may be sent to a user when new types of association are found between features and the feature of interest.

Another aspect of the present disclosure may be an alerting system based on new trends emerging about a feature of interest, where an alert may be sent to a user when detecting new trending changes on number of occurrences for the feature of interest. Trending changes may include changes in the number of documents (considered as the number of documents mentioned per day/week, depending on the user specifications), changes in the average of the number of documents per day, and changes in the number of occurrences, among others.

By using entity disambiguation for the alert systems, documents may be accurately determined to be associated with the entity of interest, allowing the systems to alert users when new information about a feature is available, but only when it is about the correct feature of interest; i.e., the disclosed method eliminates alerts on documents that mention a different feature with the same name.

According to various embodiments, method for entity-driven alerts based on disambiguated features may reduce the number of false positives resulting from a state of the art search queries. This in turn, may increase the efficiency of monitoring, allowing for a broadened universe of alerts.

In one embodiment, a computer-implemented method comprises disambiguating, by a disambiguation computer, a document feature from an electronic document by way of extracting, by a feature extraction computer, the document feature from the electronic document, and linking, by a linking computer, the extracted document feature to one or more document features stored in a knowledge database of co-occurring document features of a plurality of electronic documents; assigning, by a scoring computer, to the disambiguated document feature a confidence score indicative of a level of confidence associated with a degree of disambiguation of the document feature; and adding, by an in-memory database computer, the disambiguated document feature to the knowledge database of co-occurring document features when the disambiguated document feature matches a document feature of interest in an alert database based at least in part on the confidence score.

In another embodiment, a system comprises a disambiguation computer configured to disambiguate a document feature from an electronic document by being further configured to extract a document feature from an electronic document, and link the extracted document feature to one or more document features stored in a knowledge database of co-occurring document features of a plurality of electronic documents; a scoring computer configured to assign to the disambiguated document feature a confidence score indicative of a level of confidence associated with a degree of disambiguation of the document feature; and an in-memory database computer configured to add the disambiguated document feature to the knowledge database of co-occurring document features when the disambiguated document feature matches a document feature of interest in an alert database based at least in part on the confidence score.

Numerous other aspects, features and benefits of the present disclosure may be made apparent from the following detailed description taken together with the drawing figures.

DEFINITIONS

As used herein, the following terms have the following definitions:

“Database” refers to any system including any combination of clusters and modules suitable for storing one or more collections and suitable to process one or more queries.

“Document” refers to a discrete electronic representation of information having a start and end.

“Corpus” refers to a collection of one or more documents.

“Feature” refers to any information which is at least partially derived from a document.

“Feature attribute” refers to metadata associated with a feature; for example, location of a feature in a document, confidence score, among others.

“Feature extraction” refers to information processing methods for extracting information such as names, places, and organizations.

“Fact” refers to objective relationships between features.

“Knowledge Base” refers to a base containing features/entities.

“Live corpus”, or “Document Stream”, refers to a corpus that is constantly fed as new documents are uploaded into a network.

“Memory” refers to any hardware component suitable for storing information and retrieving said information at a sufficiently high speed.

“In-Memory Database”, or “MEMDB”, refers to a database in which all records are stored in memory.

“Module” refers to a computer hardware or software components suitable for carrying out at least one or more tasks.

“Link on-the-fly module” refers to any linking module that performs data linkage as data is requested from the system rather than as data is added to the system.

“Topic” refers to a set of thematic information which is at least partially derived from a corpus.

“Topic Model” refers to a hypothetical description of a complex entity or process.

“Query” refers to a request to retrieve information from one or more suitable databases.

DETAILED DESCRIPTION

The present disclosure is here described in detail with reference to embodiments illustrated in the drawings, which form a part here. Other embodiments may be used and/or other changes may be made without departing from the spirit or scope of the present disclosure. The illustrative embodiments described in the detailed description are not meant to be limiting of the subject matter presented here.

The present disclosure describes a method for entity-driven alerts based on disambiguated features. According to various embodiments, the disclosed method for entity-driven alerts may be based on different filters based on criteria specified by a user, who is interested in receiving information about a feature of interest. The criteria may include restrictions, such as new knowledge of a disambiguated feature, new associations with the disambiguated feature, or new trends about the disambiguated feature, among others.

According to various embodiments, disambiguated features, on which method for entity-driven alerts is based, may be disambiguated by a plurality of suitable methods. According to one embodiment, a system for disambiguating features may include multiple computer modules, such as one or more feature extraction modules, one or more disambiguation modules, one or more scoring modules, and one or more linking modules. The method for disambiguating features may improve the accuracy of entity disambiguation beyond what may be achieved by considering no document linking. Taking account of document linkage may allow better disambiguation by considering document and entity relationships implied by links.

According to various embodiments, the types of features extracted by the method for disambiguating features may include topic IDs, employing multiple modules to combine extracted entities. The topics may be machine generated (not human generated), thus, may be derived directly from a corpus.

According to one embodiment, the disclosed method may identify topic relatedness of new and existing topic IDs employing one or more disambiguating modules including one or more disambiguating algorithms, forming a normalized set of topic IDs.

According to various embodiments, the disclosed method may include a construction of a knowledge base to extract meaningful context from each document in a massive corpus using multiple topic models with differing levels of granularity to classify documents to topics, feature and entity extraction, event extraction, fact extraction, and sentiment extraction, among others.

Disambiguated features based on topics may allow to disambiguate one or more features/entity of interest occurring in a document by extracting meaningful context from a document (topics, entities, events, sentiment, and other features); and by disambiguating the extracted features by linking the co-occurrence of extracted features (topics, entities, etc.) using the knowledge base of co-occurring features.

Thus, the disclosed method may have an improved accuracy of feature disambiguation by establishing more accurate relationships between entities and documents, by considering only the entities which occur closer in text to the link location in the source document. This may increase the possibility of deriving useful relationships from long documents having many entities, which would complicate a typical entity disambiguation algorithm by introducing a large number of irrelevant co-occurrences. Similarly, the method may potentially handle documents that have occurrences of entities with different disambiguation. The disambiguation algorithm may generate different associations for different features. In some embodiments, computers of the in-memory database are configured to add disambiguated document features to a knowledge database, which contains co-occurring document features, based at least in part on a distance in text from a link location in the electronic document.

An alert mechanism's novelty can be based on “disambiguated features” that a user can specify around an already existing and disambiguated feature of interest (can be any feature such as entities, topics, etc.) in the knowledge base. Conventional alert systems may be based on keyword search alert mechanisms, wherein a “disambiguated feature” guides the alert, providing better relevance and precision. The alert mechanism described herein can provide a way to detect and communicate emerging trends related to a “feature of interest,” new associations to the “feature of interest,” and new knowledge discovered to the “feature of interest,” besides just a group of documents that mention the “feature of interest.” The methods can also provide a system-wide knowledge base update process by support of dynamic on-the-fly-linking mechanism in an in-memory database based on an individual user's alert query. Thus, this feature can provide a framework to support collaborative knowledge sharing among different users in a given system establishment.

System for Disambiguating Features

FIG. 1is a block diagram of a system100for disambiguating features, according to an embodiment. In the system100for disambiguating features a new document102is input into the system, such as into a feature extraction module104, which performs feature extraction from the document102. The new document102may be fed from any suitable source, such as a massive corpus or live corpus of documents that may have a continuous input of documents, e.g. from an internet or network connection106(NC).

One or more feature recognition and extraction algorithms may be employed by the feature extraction module104to analyze the document102. A score may be assigned to each extracted feature. The score may indicate the level of certainty of the feature being correctly extracted and linked with the correct attributes. Additionally, during feature extraction by the module104, one or more primary features may be identified from document102. Each primary feature may have been associated with a set of feature attributes and one or more secondary features (like proximity cluster of co-occurring features like entities).

During the process of disambiguation, the system may be constantly getting new knowledge, updated by users108, that are not pre-linked in a static way; thus, the number of documents to be evaluated may be infinitely increasing. This may be achieved because of the use of MEMDB module110. The MEMDB module110may allow to perform a faster disambiguation process, and may allow to do a Link On-the-Fly (OTF) passing through link OTF module112, which enables to get the latest information that is going to contribute to MEMDB110. The disclosed link OTF module112may be capable of constantly evaluating, scoring, linking, and clustering a feed of information.

Any suitable method for linking the features may be employed, which may essentially use a weighted model for determining which feature types are most important, which have more weight, and, based on confidence scores, determine the confidence level of feature extraction by feature extraction module104and confidence level of feature disambiguation by feature disambiguation module114with regard to the correct features. Consequently, the correct feature may go into the resulting cluster of features. As more nodes are working in parallel, the process may be more efficient. The result of all process aforementioned may be output as one or more newly disambiguated features116.

By using feature disambiguation modules, documents may be accurately determined to be associated with the entity of interest, which may allow the system to alert users when new information about an feature is available but only when it is about the correct feature of interest.

After feature disambiguation114of new document102has been made, the extracted new features may be included in MEMDB110to pass through link OTF module112; where the features may be compared and linked, and an ID of disambiguated feature116may be returned to a user as a result from a query. In addition to the ID, the resulting feature cluster defining the disambiguated feature116, may optionally be returned.

Once features are disambiguated the number of alerts to be sent to a user may be further reduced by letting the user specify addition restrictions.

Disambiguated features116may then be included in an Alert Database (AD). Components within AD may vary according to the type of alert the user wants to receive. The AD may have at least the following components.

According to various embodiments, the AD may have a user identifier to which the alerts may be going to be sent; a collection of disambiguated features from which the user may select which feature wants to monitor; an alert specification describing the type of alert the user wants to receive; and a known-knowledge base on which known knowledge about the feature of interest may be stored. Any suitable methods may be employed for the user to communicate to the system which is the feature of interest.

According to various embodiments, a feature of interest may include a person, a phone number, a place, a company, among others.

The types of alert the user may select from may include alerts by e-mail, phone number, or other type of feature to which the system may reach the user.

According to one embodiment, known-knowledge base may be stored in another MEMDB. Known-knowledge base may have any suitable structure, which may be processed by any suitable algorithms, such as associated topics, proximity cluster of other feature (like entities, events), derived prominence factor (based on simple frequency counts or a weighted association with global events and importance automatically captured via a large time bound corpus), and temporally linked events, among others. Additionally, known-knowledge base may include restrictions from which the alerts may be going to be based, according to the user specifications. Knowledge within known-knowledge base may have any suitable representation, such as incremental graphs, among others.

According to an embodiment of the present disclosure, restrictions within known-knowledge base may include a selection or criteria that may be here classified as new knowledge about a feature of interest, new association with the feature of interest, and new trends emerging about the feature of interest, among others.

According to other embodiments, AD may include other components such as a module that keeps record of the number or volume, and average of documents related to the feature of interest, in the case that the type of alert that the user chooses is based on trends emerging of the feature of interest.

Alert when New Knowledge is Discovered about the Feature of Interest

After disambiguated feature116is obtained, disambiguated feature116may be sent to AD to be compared with the feature of interest previously selected by the user. If disambiguated features116match with the feature of interest, disambiguated features116are included within known-knowledge base.

FIG. 2is a flowchart of alerting method200based on new knowledge discovered about a feature of interest, according to one embodiment.

According to one embodiment, in step202, when a new document is input into the system, the document is processed by a disambiguation module where features within the document are extracted104and disambiguated114. In step204, disambiguated feature116may be subsequently sent to the Alert Database to be compared, in step206, with the existing knowledge included within the known-knowledge base in order to determine the relationship between disambiguated features116and the feature of interest. In step208, if disambiguated feature116does not match the feature of interest, then the process may end210.

If disambiguated features116match, step208, the feature of interest, disambiguated features116are compared, in step212, with the knowledge within known-knowledge base to determine if there is a match between the new features and the already extracted features that form part of the known-knowledge base, for example, if an alert has already been sent about that knowledge. If the is no new knowledge, step214, the process may end, step216. If, in step214, new knowledge is found, the known-knowledge base is updated, step218, and an alert is sent, step220, to the user by the specified notification method, for example email or mobile device messaging, among others.

Alert when the Feature of Interest has a New Association to New Features

After disambiguated feature116is obtained, disambiguated feature116may be sent to AD to be compared with the feature of interest previously selected by the user. If disambiguated features116match with the feature of interest, disambiguated features116are included within known-knowledge base.

FIG. 3is a flowchart of alerting method300based on new association discovered about the feature of interest, according to one embodiment.

According to one embodiment, when a new document is input, step302, into the system, the document is processed by a disambiguation module where features within the document are extracted104and disambiguated114. Disambiguated feature116may be subsequently sent, step304, to the Alert Database to be compared, step306, with the existing knowledge included within the known-knowledge base in order to determine if there is an association between disambiguated features116and the feature of interest. If disambiguated feature116does not match, step308, the feature of interest, then the process ends in step310.

If disambiguated features116match, in step308, the feature of interest, disambiguated features116are compared, step312, with the knowledge within known-knowledge base to determine if there is a match between the new features and the already extracted features that form part of the known-knowledge base, for example, if an alert has already been sent about that knowledge. If the is no new association, step314, the process ends, step316. If new association in step314is found, the known-knowledge base is updated in step318and an alert is sent in step320to the user by the specified notification method, for example email or mobile device messaging, among others.

Alert when New Trends about the Feature of Interest Emerge

After disambiguated feature116is obtained, disambiguated feature116may be sent to AD to be compared with the feature of interest previously selected by the user. If disambiguated features116match with the feature of interest, disambiguated features116are included within known-knowledge base.

FIG. 4is a flowchart of alerting method400based on new trends emerging about the feature of interest, according to one embodiment.

According to one embodiment, when a new document is input, in step402, into the system, the document is processed by a disambiguation module where features within the document are extracted104and disambiguated114. Disambiguated feature116may be subsequently sent, in step404, to the Alert Database to be compared, in step406, with the existing knowledge included within the known-knowledge base in order to determine if there is an association between disambiguated features116and the feature of interest. If in step408, the disambiguated feature116does not match the feature of interest, then the process may end, step410.

If disambiguated features116match, step308, the feature of interest, the documents including disambiguated features116of interest are accounted and an indicator of the total amount of such documents are stored in the Alert Database to check, in step412, if the volume of documents about the feature of interest is greater than the daily average. If the volume of documents is not greater, step414, than the average, the process may end, step416. If the volume of documents is greater, step414, than the average, the known-knowledge base is updated, step418, and an alert is sent, in step420, to the user by the specified notification method, for example email or mobile device messaging, among others.

According to one embodiment, the volume may be considered as the number of documents mentioned per day, week, or month, among other, depending on the user specifications.

According to other embodiments, the volume may be considered as the number of occurrences of the feature of interest.

According to various embodiments, method for entity-driven alerts based on disambiguated features may reduce the number of false positives resulting from a state of the art search queries. This in turn, may increase the efficiency of monitoring, allowing for a broadened universe of alerts.

Example #1 is an embodiment of alerting method200, where a user is interested in finding new knowledge about John Doe, the football player. In this embodiment, the known knowledge is that John Doe appears in sports magazines; but, after applying a method for disambiguation features, a new document input, in step202, where the same John Doe appears on an economic magazine is found. As the known-knowledge base has no records of this John Doe appearing on economic magazines, an alert is sent 220 to the user.

Example #2 is an embodiment of alerting method300, where a user is interested in finding new associations with John Doe, the musician. In this embodiment, the known knowledge is that John Doe has been associated to music concerts and to a music company named “Re”; but, after applying a disambiguation method, a new document input, in step302, where the same John Doe is associated with a music company named “Fa” is found. As the known-knowledge base has no records of this John Doe association with “Fa,” an alert is sent, in step320, to the user.

Example #3 is an embodiment of alerting method400, where a user is interested in keeping track of the trend changes in documents about John Doe, an environmental activist. In this embodiment, the known knowledge is that the average number of documents mentioning the same John Doe is 50; but, after applying a disambiguation method and employing the AD to monitor the total mentions of the same John Doe per day, the average of mentions on the third they of monitoring has been calculated to be 80. As the average of mentions in the third day is greater than the average of mentions during the past days, an alert is sent, in step420, to the user.