Patent Application
20070288442
The present invention can be appreciated by the description which follows in conjunction with the following figures, wherein:
The information terminal 10 includes a CPU 101, a memory 102, a keyboard and a mouse 103, a display unit 104 and a data communication part 109. The information terminal 10 stores programs which constitute a document searching part 105, a document classification part 106, a document expansion part 107, and a document displaying part 108.
The CPU 101 performs various processes by executing the various programs for the document searching part 105, document classification part 106, document expansion part 107, and document displaying part 108. The memory 102 temporarily stores a program to be executed by the CPU 101 and required data to execute the program.
The keyboard and mouse 103 are devices with which a user inputs information. The display unit 104 shows search results, etc.
The data communication part 109 is an interface for data communication via the network 113 and may be a LAN card which enables communication according to the TCP/IP protocol via local area network. The information terminal 10 communicates with the databases connected with the network 113 through the data communication part 109.
The document DB 110 stores various data related to documents.
The document index DB 111 stores relations between documents and keywords. The document index DB 111 allows the user to retrieve a list of keywords included in a document or a list of documents including a keyword.
The citation index DB 112 stores citation relations between documents. The citation index DB 112 allows the user to retrieve a list of documents cited by a certain document or a list of documents citing a certain document.
First, the user inputs a keyword 201 with the keyboard and/or mouse 103. The document searching part 105 searches the document index DB 111 for documents which include the keyword 201 and gets search results 203 (202).
Then, the document classification part 106 refers to the citation index DB 112 to classify the search results 203 into several groups (204). In the case of
The document expansion part 107 performs document expansion on each group in reference to the citation index DB 112 (207). For example, the document expansion part 107 gets expansion results 1 (209) by searching the citation index DB 112 to extract documents other than those in group 1 which have citation relations with a document in group 1. Likewise, it performs document expansion (207) on the other groups search results. The process will be detailed later referring to
Lastly, the document displaying part 108 displays the groups and the expansion results of the groups on a display image 213 (212). A concrete display image will be described later referring to
Next, the search result display image will be described and the databases (document DB, document index DB and citation index DB) and the various processes shown in
The keyword entry field 304 receives keywords which the user inputs. The link selection field 306 allows the user to select the kind of link which is shown in the graph window 303. The kind of link is the kind of citation relation between documents: if documents to be searched are patent specifications, two kinds of citations may be made: citations made by applicants in their patent specifications and those by examiners for reasons of rejection. Clicking a link select button 307 allows the user to select whether to display one kind of citation or both kinds of citations in the graph window 303. For display of plural citation relations in the graph window, links may be distinguished by color or line type.
After inputting a search condition and clicking the search button 305, the searching process as shown in
The list window 302 shows lists of classified search results group by group. The list window 302 includes a group number field 308, a search score field 309, and a document title field 310.
In the group number field 308, group identification numbers appear: e.g. Group 1 (315), Group 2 (316) and so on as shown in
In the graph window 303, a graph which shows citation relations among a set of documents as search results and a set of documents collected by expansion of search results. In this embodiment, the graph window 303 shows search results group by group and switching from one group to another is made by the use of tabs.
Nodes in the graph (e.g. 313, 314) represent documents. A link which connects nodes (e.g. 317) expresses that the connected documents mutually have a citation relation and the direction of arrow denotes the direction of citation. A black node (e.g. 313) indicates that the document concerned is a searched document and a white node (e.g. 314) indicates that the document concerned is a non-searched document (document as an expansion result). When the document type is identified by node color like this, it is easy to distinguish between searched documents and non-searched documents related to the searched documents.
If documents to be searched are documents whose publication years are known, such as papers or patent specifications, the horizontal axis of the graph may represent year. In this embodiment, the horizontal axis 311 represents publication year. When the horizontal axis represents publication year, the arrows which represent the direction of citation (link) may be omitted because the direction of citation is automatically determined (chronological order).
Next, the databases used in various processes will be explained.
The document ID 401 is a number which uniquely identifies a stored document. The author 402 denotes the author of the document. The publication year 403 denotes the year when the document was published. The category 404 is the category (e.g. the IPC) to which the document belongs. The table shown here is just one example. What columns (factors) should be defined depends on the type of document. The full text 405 is a column in which the full text of the document is stored.
Next, the processes of document searching 202, document classification 204, document expansion 207, and document displaying 212 in this embodiment will be detailed.
The document searching part 105 performs the process of document searching 202 using a known document searching method. For example, it uses the index 503 to search documents which include a specified keyword. When more than one keyword are specified, logical computation such as logic operation “AND” or logic operation “OR” between sets of documents searched by the keywords is done.
As the process of document classification 204 starts, the document classification part 106 first makes initialization (S701). D(={d_1, d_2, . . . , d_n}) represents a set of documents to be classified and C(={C_1, C_2, . . . , C_n}) represents a set of clusters. The set of clusters C in its initial state is a set of singleton clusters, each of which, say C_i, includes the document d_i as a element, and is expressed by C_i={d_i}. Function map represents a function which returns ID of the cluster to which a document belongs. In the initial state, the function for document d_i is map(i)=i.
Upon completion of initialization, the document classification part 106 performs Loop 1 on all document pairs that satisfy j<k(d_j, d_k). Here Loop 1 is steps from S702 to S706. At the step of S702B, whether the condition to end Loop 1 is met is decided.
The document classification part 106 decides whether d_j and d_k can be merged (S703). In this embodiment, if there is a citation relation between documents, the paired documents are decided to be mergeable.
Citations 801 and 802 represent direct citation relations where either d_j or d_k cites the other. Citation 803 represents a co-citation relation where d_j and d_k cite a common document x. Citation 804 represents bibliographic coupling where d_j and d_k are cited by a common document x. Whether a citation relation is a direct citation, bibliographic coupling or co-citation is easily investigated by referring to the indices 605 and 610 of the citation index DB 112. In this embodiment, when d_j and d_k have a direct relation, bibliographic coupling or co-citation relation, they are decided to be mergeable. However, other criteria for mergeability (for example, combination of the three types of citation relation) may also be used.
Looking back at the flowchart in
If paired documents (d_j, d_k) are mergeable (the answer at S703 is “Yes”), the document classification part 106 updates the set of clusters C so that the documents d_j, d_k belong to the same cluster. If they are not mergeable (the answer at S703 is “No”), the document classification part 106 determines the mergeability of another document pair.
If paired documents (d_j, d_k) are mergeable, the document classification part 106 first obtains cluster ID jc of the cluster to which document d_j belongs, using the map function (S704). Similarly it obtains cluster ID kc of the cluster to which document d_k belongs (S704). Specifically this leads to jc=map(d_j), kc=map(d_k).
Then, the document classification part 106 merges the clusters which include the documents d_j and d_k and updates the map function (S705). In this embodiment, a cluster with a larger ID number is merged into a cluster with a smaller ID number. Hence, cluster C_kc is merged into cluster C_jc and cluster C_jc is the union of cluster C_jc and cluster C_kc (C_jc=C_jc U C_kc). Furthermore, it removes C_kc from the whole set of clusters C. Also it updates the map function so that the relation map(m)=jc holds for all the documents d_m included in C_kc and changes the cluster to which they belong from C_kc to C_jc.
Upon completion of the step S705, the document classification part 106 finishes the merging process for the document pair (d_j, d_k) and returns to 702A to determine the mergeability of another document pair.
After the mergeability of all document pairs has been determined and the condition to end Loop 1 is satisfied (the answer at S702A is “Yes”), the document classification part 106 ends Loop 1 to finish the process of document classification 204. This creates a set of clusters C where documents which can be merged belong to a cluster. The clusters included in the set C correspond to Group 1 (205) to Group n (206) as shown in
As the process of document expansion 207 starts, the document expansion part 107 first makes initialization (S901). C(={C_1, C_2, . . . , C_n}) represents a set of documents to be expanded which is a set of clusters created by document classification 204. E(={E_1, E_2, . . . , E_n}) represents a set of expanded documents. The elements of the set of expanded documents E are a set of documents E_i corresponding to cluster C_i in C, which is an empty set in its initial state. Variable i is a loop variable which controls Loop 2, which is zero in its initial state. Function exp(X) is a function which, upon input of a set of documents X, returns a set of documents which cite any document in X or which are cited by any document in X.
Upon completion of initialization, the document expansion part 107 performs document expansion 207 on the set of expansion source documents C. At the step of S902, 1 is added to loop variable i.
The document expansion part 107 collects a set of documents citing any document in the set of documents C_i or documents being cited by any document in C_i, using the function exp (X) (S903).
As the process for the function exp (X) is started, first initialization is made. A(={a_1, a_2, . . . , a_n}) represents a set of expansion source sets as a set of documents to be expanded. P(={P_1, P_2, . . . , P_n}) represents a set of processing document sets which include transitional documents which are being expanded in the course of document expansion. R(={R_1, R_2, . . . , R_n}) represents a set of expanded document sets collected by a single expansion loop process which will be described later. E(={E_1, E_2, E_n}) represents a set of expanded documents finally collected by the process of collecting citing or cited documents. The document expansion part 107 sets defaults as follows: P_i={a_i}; R_i={ }; and E_i={ } (S1501). Here the sets of documents P, R, and E are sets of document sets which correspond to element sets P_i, R_i, and E_i respectively. N_max represents the maximum number of documents included in the valid set of expanded document sets E. The maximum number of expanded documents N_max may be either a predetermined value or a user-defined value.
Function get-cited (X,t) is a function which, upon input of a set of documents X(={X_1, X_2, . . . , X_n}) and kind of citation t, collects a set of documents citing the set of documents X_i or being cited by X_i and returns a set of possible expanded documents Y(={Y_1, Y_2, . . . , Y_n}). Function disclim (Y) is a function which, upon input of a set of documents Y(={Y_1, Y_2, . . . , Y_n}), selects only documents that satisfy the given condition for expanded documents (stated later) from the documents included in Y_i to create a set of documents Z_i and outputs a final set of expanded document sets Z(={Z_1, Z_2, . . . , Z_n}). Function count ( ) is a function which returns the total number of documents in the union of E and R.
Upon completion of initialization, the document expansion part 107 starts Loop 3. The document expansion part 107 adds the set-of expanded document sets R to the valid set of expanded document sets E (S1502). Specifically, it calculates the union of sets of documents E_i and R_i included in E and R respectively (E_i U R_i) and regards it as a new valid set of expanded document sets E.
Then, upon input of a set of processing document sets P and kind of citation t, the document expansion part 107 collects a set of possible expanded documents B(={B_1, B_2, . . . , B_n}) using the function get_cited (P, t) (S1503). Typical methods of collecting possible expanded documents are: breadth-first search in which documents to be expanded are searched from documents in a brotherly relation and depth-first search in which they are searched from documents in a parent-child relation. Several other methods are available and detailed information is well known. In this embodiment, possible expanded documents are documents which directly cite processing documents to be expanded, or documents which are directly cited by processing documents. The process of collecting citing or cited documents uses the citation index DB112. The kind of citation t may be user-defined as shown in
Upon input of the set of possible expanded documents collected at step S1503, the document expansion part 107 collects a set of expanded document sets R which satisfy the given condition for expanded documents using the function disclim (B) (S1504). In this embodiment, the condition for expanded documents includes four requirements: document z (1) should not overlap document a_i included in the set of expansion source sets A; (2) should not overlap document e_i included in the valid set of expanded document sets E; (3) should have a depth from the document a_i in the set of expansion source sets which is less than maximum depth Dp_max; and (4) should have a high importance. The function disclim ( ) selects only documents that satisfy all these four requirements. For example, “importance” of a document in the fourth requirement is determined according to the number of times the document has been cited and if its importance exceeds a preset importance level, it is decided to have a high importance.
Looking back at the flowchart in
Upon collection of the set of expanded document sets R, the document expansion part 107 calculates the number of elements of the union of sets (E U R) obtained by adding the set of expanded document sets R to the set of collected document sets E using the function count ( ) and decides whether it is larger than the maximum number of expanded documents N_max (S1505A) or not. If it is smaller than the maximum number of expanded documents N_max (the answer at S1505A is “No”), the document expansion part 107 updates the set of processing document sets P to the set of expanded document sets R (S1506) and returns to S1502 and repeats the steps of Loop 3.
Alternatively it is also possible to arrange that even if the result of count ( ) is below N_max, Loop 3 is ended when a given number of steps in Loop 3 has been carried out.
If the result of count ( ) is N_max or more (the answer at S1505A is “Yes”), the document expansion part 107 decides whether the result of count ( ) is equal to the maximum number of expanded documents N_max (S1505B).
If the result of count ( ) is larger N_max (the answer at S1505B is “No”), excess documents are removed from the set of expanded document sets R (S1507). Specifically, (count( )−N_max) documents are removed from the set of expanded document sets R in ascending order of importance. The importance of a document may be determined according to the number of times the document has been cited, as mentioned above.
If the answer at S1505B is “Yes”, or when the step S1507 has been finished, the document expansion part 107 takes the union of sets E and R ({E U R}) as the final set of expanded documents E (S1508).
Lastly the document expansion part 107 returns the set of expanded documents E as the return value of the function exp(X) and ends the process of collecting citing or cited documents (S1509).
Looking back at the flowchart in
Upon completion of step S903, the document expansion part 107 decides whether the condition to end Loop 2 is satisfied (S904). If loop variable i is below the number of elements n of the set of expansion source documents (the answer at S904 is “No”), it returns to S902. If loop variable i is equal to the number of elements n in the set of expansion source documents (the answer at S904 is “Yes”), it ends Loop 2 and finishes the process of document expansion 207.
When the document expansion process has been done on all groups, a set of documents as an expansion result is obtained for each group. The sets of documents thus obtained as expansion results correspond to expansion result 1 (209) through expansion result n (210) in
Next, the process of document displaying 212 displays groups as search results, and results of expansion of the groups, on the display image 213.
As the process of document displaying 212 starts, the document displaying part 108 first makes initialization (S1001). C(={C_1, C_2, . . . , C_n}) represents a set of clusters as classified search results and E(={E_1, E_2, . . . , E_n}) represents a set of expanded document sets as collected by document expansion 207. E_i is a set of documents as obtained by expansion of the corresponding C_i.
Upon completion of initialization, the document displaying part 108 displays the list window 302 as shown in
As displaying of the list window 302 starts, the document displaying part 108 makes initialization (S1101). C(={C_1, C_2, . . . , C_n})represents a set of documents as classified search results. When a document number is input, function rankd returns the ranking of the document in search results. When cluster number i is entered, the function rankc returns the highest ranking in search results among documents in cluster C_i. The highest ranking among documents in a cluster is regarded as the ranking of that cluster.
Then the document displaying part 108 sorts the set of clusters C according to cluster ranking (S1103). Further, the documents in cluster C_i are sorted according to the ranking of documents in each cluster C_i (S1104).
Lastly, the document displaying part 108 displays clusters in the list window 302 in descending order of cluster ranking. It displays documents in each cluster in descending order of document ranking (S1105).
As the process of displaying the graph window 303 starts, the document displaying part 108 makes initialization (S1201). C(={C_1, C_2, . . . , C_n}) represents a set of clusters as classified search results and E(={E_1, E_2, . . . , E_n}) represents a set of expanded document sets as collected by document expansion 207. E_i an element of E, is a set of documents as obtained by expansion of the corresponding C_i. Variable i is a loop variable which controls Loop 4 and its initial value is 0.
Upon completion of initialization, the document displaying part 108 starts the process of displaying for each set of documents. At step S1202, number i increases one by one until loop variable i reaches the number of elements in the set of clusters C.
The document displaying part 108 makes an initial display of nodes representing the documents in C_i and E_i (S1203). In this embodiment, the horizontal axis of the graph window 303 expresses document publication year and nodes are arranged according to document publication year. A node may be positioned anywhere on the vertical axis as far as it is within the horizontal axis's region corresponding to the publication year of the document concerned. The publication year of each document can be obtained by reference to the document DB 110.
Then, the document displaying part 108 updates the positions of documents on the vertical axis so that documents citing a common document or cited by a common document are gathered and adjacent to each other (S1204). The subsequent steps are explained referring to
Since documents 1706, 1707, and 1708 are cited by a common document 1705, they are adjacent to each other. However, since document 1708 is also cited by another document 1709, there is a possibility that document 1708 cannot be adjacent to documents 1706 and 1707. At step S1204 it is unnecessary to ensure that arrows indicating citations do not cross and at step S1205 the positions of nodes on the vertical axis are finally determined.
The document displaying part 108 determines the final value (node position) on the vertical axis (S1205). This embodiment employs a known method which takes into consideration the positional center of gravity of a set of cited/citing documents. Various methods of determining positional data on documents mutually having citation relations are available, as discussed in “How to Draw a Directed Graph”, Eades, P. et al (Journal of Information Processing, 13, pp. 424-437, 1990).
The document displaying part 108 arranges documents in sets of documents C_i and E_i according to positional data as determined at steps S1204 and S1205 and adds arrows which indicate citations to make a display (S1206). The document displaying part 108 uses different colors so that it is easy to visually discriminate between documents in the set of clusters C and those in the set of expanded document sets E. Also, different colors may be used for documents according to author or category in reference to the data stored in the document DB 111. Moreover, the nodes for the documents in the set of clusters C may be different in shape from those for the documents in the set of expanded document sets E to facilitate discrimination between them.
Lastly, the document displaying part 108 decides whether the condition to end Loop 4 is satisfied (S1207). Specifically, if loop variable i is below the number of elements n in the set of clusters (the answer at S1207 is “No”), it returns to S1202. If loop variable i is equal to the number of elements n in the set of clusters (the answer at S1207 is “Yes”), it ends Loop 4 and finishes the process of displaying the graph window 303 for documents.
With the procedure explained above, the document displaying part 108 displays the list window 302 and the graph window 303. Although the above embodiment uses a double-window structure as shown in
While classification and expansion of documents are done on the basis of citations in the above embodiment, an embodiment of the invention in which classification and expansion are done on the basis of similarity between documents is also possible. Similarity between documents can be determined using the method called the vector space model (refer to “Modem Information Retrieval”, Ricardo Baeza-Yates et al., Addison Weisley, 1999) in which the degree of overlap of keywords in documents is used as a measure for calculation.
Specifically, in order to calculate similarity between two documents d_i and d_j, the index 506 which includes document IDs, and keyword ID-frequency relations as shown in
Some methods of clustering documents on the basis of similarity between documents are well known. In the method called bottom-up clustering, first minimum clusters, each of which includes only one document are generated and the nearest cluster pairs are merged sequentially. Here the vector of a cluster is the average of vectors of documents in the cluster.
One approach to expanding documents on the basis of document similarity is to re-search documents which are similar to documents in clusters as expansion sources. This is done, for example, by extracting a set of keywords which all documents in an expansion source cluster include and searching documents which include these keywords. In searching documents by keywords, the index 503 which includes keyword IDs and document ID-frequency relations is used. This kind of searching technique is well known and its detailed description is omitted here. If too many keywords are involved, weighting should be done to use only higher-ranking keywords. The abovementioned TF-IDF method may be used for weighting.
In an embodiment in which classification and expansion are done on the basis of similarity, it is impossible to generate only one link between documents and; therefore, for display in the graph window, a process to generate a link only between documents the similarity of which exceeds a given threshold is necessary. Search results and expansion results may be displayed simultaneously in the list window as shown in
According to the preferred embodiments of this invention, since a citation relation between documents has a definite meaning, clustering on the basis of citation has a definite meaning that documents in a cluster mutually have direct or indirect citation relations. Clustering on the basis of citation may be easier for the user to understand than the conventional clustering method based on the degree of word overlap, enabling search results to be narrowed or expanded effectively.
According to the preferred embodiments of this invention, citation relations among documents in a cluster are graphically displayed so that the user can visually grasp the relations among the documents and retrieve a desired document from the documents in the cluster more easily.
While the present invention has been described in detail and pictorially in the accompanying drawings, the present invention is not limited to such detail but covers various obvious modifications and equivalent arrangements, which fall within the purview of the appended claims.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2006-161206 | Jun 2006 | JP | national |
