Building nomenclature in a set of documents while building associative document trees

A method for topic discovery through structural knowledge in an associative document tree building system includes inserting a set of documents into nodes of a document tree, extracting a tag set of tags from the set of documents and inserting each tag into a different node of a tag tree. The method also includes conducting a search engine query using the tags of the extracted tag set to produce a new set of documents and inserting the new set of documents into nodes of the document tree. The method yet further includes extracting a new tag set of tags from the new set of documents and inserting each tag of the new tag set into a different node of the tag tree. Finally, the method includes displaying at least a portion of each of the document tree and tag tree in a user interface displayed.

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates to topic discovery and more particularly to topic discovery through structural knowledge in an associative document tree building system.

Description of the Related Art

Topic discovery refers to the location of an index entry of content in a content repository or corpus of information. Learning a topic may be more successful when the associative links are established between a selected topic and other topics. These links can include differences or commonalities in the underlying topics. Of note, the curiosity of an end user is better satisfied and their understanding of a topic improved when a wider context of a selected topic is discovered. There are several methods of finding topics, including following hyperlinks between topic documents, making text-based queries to suggest topics, investigating a tree of topics, and invoking context-sensitive searching in which topics are suggested based upon the context of content already accessed by an end user.

Finding a topic of interest for content may require several iterations of a range of the foregoing methodologies with reference to a history of previously-viewed topics. So much can be time-consuming, complex and a frustrating process to perform. In the course of locating a topic of interest, the end user may need to refine previously submitted queries, improve an understanding of the desired topic, or change the terminology used. Some technical knowledge is also required to run effective searches—particularly query-based syntactical knowledge—as well as to recognize and use terms matching those associated with the desired help topics—namely domain-based knowledge.

To address the difficulties in finding a topic of interest, some software application help systems—a species of a topic discovery engine—have been made more effective by automatically exploring associative links between documents to present the wider context of a chosen topic. As described in Toru Takaki, Atsushi Fujii and Tesuya Ishikawa,Associated Document Retrieval by Query Subtopic Analysis and its Application to Invalidity Patent Search, presented at the Conference of Information Knowledge Management of the Association of Computing Machinery in 2004, associated document retrieval is a process in which a document is used as a long query to search for other similar documents. In non-associated document retrieval, by comparison, the end user must select each search term carefully whereas in associative document retrieval, the burden of search term selection is no longer present thereby improving the efficiency of a topic search by an end user.

BRIEF SUMMARY OF THE INVENTION

Embodiments of the present invention address deficiencies of the art in respect to topic discovery and provide a novel and non-obvious method, system and computer program product for topic discovery through structural knowledge in an associative document tree building system. In an embodiment of the invention, a method for topic discovery through structural knowledge in an associative document tree building system is provided. The method includes inserting a set of documents into nodes of a document tree in memory of a host computing system, extracting a tag set of tags from the set of documents and inserting each tag into a different node of a tag tree in the memory of the host computing system. The method also includes conducting a search engine query through the host computing system using the tags of the extracted tag set to produce a new set of documents and inserting the new set of documents into nodes of the document tree. The method yet further includes extracting a new tag set of tags from the new set of documents and inserting each of tag of the new tag set into a different node of the tag tree. Finally, the method includes displaying at least a portion of each of the document tree and tag tree in a user interface displayed by the host computing system.

DETAILED DESCRIPTION OF THE INVENTION

Embodiments of the invention provide for topic discovery through structural knowledge in an associative document tree building system. In accordance with an embodiment of the invention, a set of documents produced by a topic search can be selected, for example a set of documents produced through context sensitive help or a set of documents produced from an initial keyword search. Thereafter, a pair of trees—a document tree and a tag tree—are created and populated respectively with references to the set of documents, and corresponding tags produced for the set of documents. The document subsequently can be expanded to include associated documents referenced by the initial set of documents and the tag tree can be expanded with tags produced for the associated documents referenced by the initial set of documents. Of import, each tag in the tag tree can include a logical link to one or more of the documents in the document tree. Conversely, each document in the document tree can include a logical link to one or more of the tags in the tag tree. In this way, topic discovery can be facilitated without requiring a burdensome manual query process dependent upon the knowledge of relevant nomenclature of associated documents.

In further illustration,FIG. 1pictorially shows a process for topic discovery through structural knowledge in an associative document tree building system. As shown inFIG. 1, two different trees110,120can be created—a document tree of associative documents130A,130B,130N, and a tag tree of tags140A,140B,140N. The tags140A,140B,140N can be linked to corresponding ones of the documents130A,130B,130N which in turn can be linked to one another where one of the documents130A,130B,130N references another of the documents130A,130B,130N. Topic discovery logic160can create both of the trees110,120by creating a hierarchical tier in one of the trees110,120using data in a corresponding hierarchical tier of the other of the trees110,120.

In this regard, either of the trees110,120can be populated with an initial tier of either documents130A or tags140A as the case may be. In the circumstance of documents130A, the results of a keyword search can be provided as the first tier of documents130A, or a selection of documents referenced in an index can be provided as the first tier of documents130A. Correspondingly, in the circumstance of tags140A, a first tier of tags140A can be provided through manual keying or by extracting the tags140A from the results of a keyword search can be provided as the first tier of documents130A, or a selection of documents referenced in an index.

Once a first tier130A,140A has been provided, the first tier130A,140A can be used to locate a candidate set for a second tier130B,140B. For instance, tags140A of the first tier in the tag tree120can be used to conduct a document search to locate documents130B of the second tier of the document tree110. Correspondingly, documents130A of the first tier of the document tree110can be used to extract tags140B for the second tier of the tag tree120. The number of documents130B,140B permitted to be added to the second tier can be limited by tree constraints for the trees110,120and the number of tiers of the trees110,120can be limited according to the tree constraints. For instance, a number of documents130N added to a tier of the document tree110can be limited to only a specified number of most relevant documents130N produced in consequence of a search using tags140B of the tag tree120.

Once the topic discovery logic160has created both the document tree110and the tag tree120, both can be displayed in a user interface of a computer150. Within the user interface, the end user view in the tags140A,140B,140N of the tag tree120an appropriate nomenclature for the documents130A,130B,130N of the document tree110. Further, by selecting any one of the tags140A,140B,140N in a display of the tag tree120, corresponding implicated documents130A,130B,130N of a display of the document tree110can be shown in the user interface. Conversely, a selection of any one of the documents130A,130B,130N in a display of the document tree110can result in a corresponding display of implicated tags140A,140B,140N in a display of the tag tree120. As such, the end user can gain a fuller understanding of relevant topics resulting only from an initial seed set of documents130A or tags140A.

The process described in connection withFIG. 1can be implemented within an associative document tree building data processing system. In yet further illustration,FIG. 2schematically shows an associative document tree building data processing system configured for topic discovery through structural knowledge. The system can include a host computing system210that can include one or more computers each with memory and at least one processor. The host computing system210can be configured for communicative coupling to different content sources270over computer communications network220, for example the global Internet. A search engine230can execute in the memory of the host computing system210can act to retrieve documents from the content sources270utilizing keyword queries.

Of note, a topic discovery module300can be coupled to the search engine230. The topic discovery module300can include program code that when executed in the memory of the host computer system210can be enabled to build and expand a document tree250and a tag tree260according to depth and breadth constraints240. The program code of the topic discovery module300further can be enabled to display all or a portion of each of the document tree250and the tag tree260in a user interface280. The user interface280can provide an interactive display of both trees250,260such that a selection of a node in the document tree250can result in a display of tags in the tag tree260linked to a document in the selected node of the document tree250. Likewise, the selection of a tag in the tag tree260can result in a display in the user interface280of associated documents of the document tree250. Further, meta-data can be displayed in the user interface280for a selected node, such as the frequency of occurrence of a corresponding tag in connection with all documents in the document tree250.

In even yet further illustration of the operation of the topic discovery module300,FIGS. 3A and 3B, taken together, are a flow chart illustrating a process for topic discovery through structural knowledge in an associative document tree building system.FIG. 3Aillustrates a document-centric population of the trees, whereasFIG. 3Billustrates a tag-centric population of the trees, though any combination can be effective for any tier of the tiers in application. Considering first the document-centric approach, beginning in block305A ofFIG. 3A, a set of depth and breadth constraints can be loaded for each of the document and tag trees and in block310A a first set of documents can be retrieved in accordance with the breadth constraints for insertion into the document tree in block315A. In block320A, a set of tags can be extracted from the first set of documents in accordance with the breadth constraints and linked to the documents in the first set in block325A. Thereafter, in block330A, the extracted tag set can be inserted into the tag tree completing the initial population of both the document and tag trees.

In decision block335A, it can be determined if the depth constraints of the trees permits the population of a new tier of tags and documents. If so, in block340A, a new document search can be initiated using the tags extracted from the first tier of documents. In block345A, the set of documents produced by the search can be limited by the breadth constraints and in block350A, individual documents in the retrieved and limited set of documents can be linked to other documents already existent in the tree where referenced by the individual documents. Thereafter, in block315A the limited set of documents can be added into the document tree and a set of tags therefore extracted therefrom in block320A. Finally, in block325A, the extracted tags can be linked to the documents in the document tree and inserted into the tag tree in block330A. In decision block335A, it again can be determined if the depth constraints of the trees permits the population of yet a new tier of tags and documents. If so, the process can return yet again to block340A. Otherwise, in block355A at least a portion of each tree can be displayed in a user interface for use by the end user in discovering the nomenclature of the documents in an associative document tree.

Turning now toFIG. 3B, a tag-centric approach to populating the document tree and tag tree is illustrated therein. Beginning in block305B, a set of depth and breadth constraints can be loaded for each of the document and tag trees and in block310B a first set of tags can be retrieved in accordance with the breadth constraints for insertion into the tag tree in block315B. In block320B, a set of documents can be located by way of a search engine query based upon the tags the results thereof being limited in accordance with the breadth constraints, and in block325B, the resultant documents can be linked to the tags in the tag tree. Thereafter, in block330B, the retrieved documents can be inserted into the document tree completing the initial population of both the document and tag trees.

In decision block335B, it can be determined if the depth constraints of the trees permits the population of a new tier of tags and documents. If so, in block340B, a new set of tags can be extracted from the documents in the first tier of documents. In block345B, the set of tags extracted from the documents can be limited by the breadth constraints and in block350B, individual documents in the document tree can be linked to the tags in the new tag set. Thereafter, in block315B the new set of tags can be added into the tag tree and a new set of documents retrieved in accordance thereof in block320B. Finally, in block325B, the retrieved documents can be linked to the tags in the tag tree and inserted into the document tree in block330B. In decision block335B, it again can be determined if the depth constraints of the trees permits the population of yet a new tier of tags and documents. If so, the process can return yet again to block340B. Otherwise, in block355B at least a portion of each tree can be displayed in a user interface for use by the end user in discovering the nomenclature of the documents in an associative document tree.