Aspect-based query improvement method and system based on multiple query interpretations

Abstract

Aspect-based query improvement based on multiple query interpretations is provided by a process including receiving a query for searching for documents that deviate from one or more aspect values ​​in an entire document set. The process generates different query interpretations based on the query text of the query. The process executes the different query interpretations to obtain a result document set. For each different query interpretation, the query interpretation is executed to obtain a result document set corresponding to the query interpretation, and the aspect values ​​represented in the result document set are identified. The process constructs and presents a query improvement interface to a user, presenting different query interpretations and attributes of the result document set, and for each of the one or more aspect values ​​and relative to each other, presenting aspect value representations of the aspect values ​​in each of the result document set.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to aspect-based query refinement based on multiple query interpretations. Background Art

[0002] The present invention generally relates to the field of document querying, and more specifically to facet-based query improvement. Facet-based text mining is a solution for text mining of large amounts of unstructured data. A "facet" is an information entity associated with an information element, sometimes referred to as a "document". Aspects and facet values ​​can be generated as a result of a natural language processing (NLP) pipeline, where annotator software detects conditions and adds aspects. With respect to documents, NLP processes documents, looking for these facet values ​​or features therein. For example, aspects can be categories for classifying products in an e-commerce space. As an example, natural language processing can assign categories of different granularity to documents, such as identifying specific words or phrases included in a document, the creation date or time range of the document or project they represent, or categories of sentiment, tone, or expression for the document or project they represent. Aspects can be aggregated (e.g., when searching) to understand corpus characteristics. Aggregating aspects refers to adding conditions, such as query constraints, when searching a document set. The system or user can specify a document set (perhaps via a query), and then within that document set, the software can classify, rank, and perform other processing on the categories. In some instances, a formula is applied to determine the importance of each category. Summary of the invention

[0003] By providing a computer-implemented method, the shortcomings of the prior art are overcome and additional advantages are provided. The method receives a query for querying documents of an entire document set. The documents of the entire document set deviate from one or more aspect values. The method generates multiple different query interpretations based on the query text of the query. The method executes multiple different query interpretations to obtain a result document set. For each different query interpretation of multiple different executable query interpretations, executing the query interpretation to obtain a result document set corresponding to the query interpretation from the entire document set, and identifying the aspect values ​​represented in the result document set. The method also constructs a query improvement interface and presents it to a user. The query improvement interface presents multiple different query interpretations and attributes of the result document set. In addition, it presents aspect value representations of the aspect values ​​in each result document set in the result document set for each aspect value in one or more aspect values, and relative to each other.

[0004] Further, a computer system is provided, which includes a memory and a processor in communication with the memory, wherein the computer system is configured to perform a method. The method receives a query for querying documents of an entire document set. The documents of the entire document set deviate from one or more aspect values. The method generates multiple different query interpretations based on the query text of the query. The method executes multiple different query interpretations to obtain a result document set. For each different query interpretation of multiple different executable query interpretations, the query interpretation is executed to obtain a result document set corresponding to the query interpretation from the entire document set, and the aspect values ​​represented in the result document set are identified. The method also constructs a query improvement interface and presents it to the user. The query improvement interface presents multiple different query interpretations and attributes of the result document set. In addition, it presents aspect value representations of the aspect values ​​in each result document set in the result document set for each aspect value in one or more aspect values, and relative to each other.

[0005] Further, a computer program product is provided, which includes a computer-readable storage medium that can be read by a processing circuit and stores instructions for execution by the processing circuit, for performing a method. The method receives a query for querying documents of an entire document set. The documents of the entire document set deviate from one or more aspect values. The method generates multiple different query interpretations based on the query text of the query. The method executes multiple different query interpretations to obtain a result document set. For each different query interpretation of multiple different executable query interpretations, the query interpretation is executed to obtain a result document set corresponding to the query interpretation from the entire document set, and the aspect values ​​represented in the result document set are identified. The method also constructs a query improvement interface and presents it to the user. The query improvement interface presents multiple different query interpretations and attributes of the result document set. In addition, it presents aspect value representations of the aspect values ​​in each result document set in the result document set for each aspect value in one or more aspect values, and relative to each other.

[0006] Additional features and advantages are realized through the concepts described herein. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Preferred embodiments of the present invention will now be described by way of example only and with reference to the following drawings:

[0008] Figure 1 depicts an example of aspect-based text mining;

[0009] Figure 2A-2B depicts example rule-based query expansion facet navigation according to embodiments described herein;

[0010] Figure 2C-2D depicts an example of concept-based query modification for aspect navigation according to embodiments described herein;

[0011] Figure 3 depicts an example process for facet navigation over multiple query interpretations according to embodiments described herein;

[0012] Figure 4 depicts an example query refinement interface according to embodiments described herein;

[0013] Figure 5 depicts an example process for aspect-based query refinement based on multiple query interpretations according to embodiments described herein;

[0014] Figure 6 One example of a computer system and associated devices that may incorporate and / or use the embodiments described herein is depicted; Figure 7 A cloud computing environment according to an embodiment of the present invention is described; and

[0015] Figure 8 Abstract model layers according to an embodiment of the present invention are depicted. DETAILED DESCRIPTION

[0016] This paper describes facet-based query refinement based on multiple query interpretations. Different query interpretations are generated from an initial query. The different query interpretations are performed, information about facets / facet values ​​is determined, and refinement is applied to assist facet-based navigation.

[0017] As mentioned above, an aspect is an information entity associated with or included in a document (information element), and it projects or represents the characteristics of the document. Documents are classified across multiple aspects according to a classification system that allows aspect search, navigation and browsing. The term "document" is used to refer to an information entity or mining unit, however, it should be understood that each "document" can be generated from any type of data. The word 'document' should not be strictly interpreted as a digital document of, for example, a word processing software (although such a file may be an example of a "document" as used in this article). A document in the sense of an information element may refer to an image, for example. In the e-commerce and product space, a database record may be a "document" as used in this article. Document databases have an (almost) infinitely flexible structure that provides multiple different functional key areas, for example, they can be schema-less-document databases do not have to predefine the structure of the data that must be stored in the database. Document databases can store any information. A widely used structural format is JSON, an object notation format from the JavaScript language that allows the storage of strings, numbers, arrays, and record (hash) data as well as combinations of those core types.

[0018] The value of the aspect may be stored in the metadata of the document or in metadata associated with the document. Examples of such metadata may be simple features of the document, such as a location (state, province), noun, adjective, or keyword associated with the document, or more complex features, such as a positive or negative impression conveyed by the document. The process of aspect-based text mining is to repeat aspect aggregation, browse aspects, and improve an initial "document set" that is a collection of documents representing specified aspect values ​​into an improved document set. Aspect aggregation may refer to grouping all result documents that fit a query into a document set, counting aspect values ​​associated with the document set, and calculating frequency, relevance, co-occurrence, and / or timeline analysis (as examples) for the document set. In document set improvement, consideration of aspect aggregation results in extracting an improved document set based on, for example, adding additional keywords or aspect conditions as constraints or selecting query interpretations as described herein. As a result of repeated aggregation and improvement, knowledge is acquired and new discoveries are discovered.

[0019] Figure 1 Depicts an example of aspect-based text mining. Figure 1 In , each dotted circle represents a set of documents (a collection of documents) that includes a particular fact or concept, that is, in which a particular aspect value is represented. The universe of documents is represented as set D (the "complete" document set), including the indicated document set represented by the dotted circle. Although the document group in D is Figure 1 are shown as mutually exclusive, in reality, there may be some overlap between at least some of the document sets in D, for example, a document representing a product of size 'medium' and 'color' green will appear in the document set for products of size 'medium' and in the document set for products of color 'green'.

[0020] These figures show two refinements. The first is to refine document set D into document set D' based on some constraints or conditions that isolate the three document sets from document set D to form document set D'. The second refinement refines D' into a single document set, in this example, knowledge or discovery results.

[0021] By way of specific example, assume that D is the universe of vehicle accident records and that the first query is for those accidents involving green vehicles. "Vehicle color" is an aspect and "green" is the aspect value of the aspect for searching the initial document set. This results in a refinement of the three document sets of D'. Assume that among all events involving green vehicles, a distinction is made between those events based on the aspect of age (represented as three non-overlapping document sets of D') - events involving drivers under 20 years old, events involving drivers between 20 and 70 years old, and events involving drivers over 70 years old. The second refinement - such as a query on D' for those events involving drivers under 20 years old - refines D' into a single document set representing the resulting knowledge - events involving green vehicles and drivers under 20 years old.

[0022] According to an embodiment described herein, a process accepts a query string to query documents of an entire document set, wherein the documents of the entire document set deviate from one or more aspect values. The process interprets the query in different ways, produces query interpretations that are different from the query text of the query, and executes those query interpretations to obtain a result document set. A 'result document set' is a document set returned as a result of executing one of the query interpretations. Thus, for each different query interpretation, the query interpretation is executed to obtain a result document set corresponding to the query interpretation from the entire document set (i.e., a subset as a whole). The process identifies aspect values ​​represented in the result document set. For example, for each interpretation, a result document set is determined, and aspect values ​​represented in the documents of the result document set are identified. The process can display an interface to a user, for example, the interface arranges or sorts the represented aspect values ​​according to a sorting method applied to aspect values ​​extracted from the result document sets of different query interpretations.

[0023] Thus, the sorting can sort the aspect values ​​relative to each other according to at least one aspect value sorting method, and the interface presents a representation of any given aspect value in each of the result document sets in the sorted order of the aspect values ​​relative to each of the other aspect values. When performing query interpretation, for each result document set, the number of documents in the result document set can be counted, and for each aspect value, the number of documents in the result document set and representing the aspect value therein can be determined. The following are some example sorting methods.

[0024] An example ranking method is, for example, prioritization of explanations based on the number of search result documents. As described herein, queries can be interpreted in different ways. Corresponding different result document sets can be sorted relative to each other. A first explanation can deliver 1000 result documents in a result document set, a second explanation can deliver 300 result documents in a result document set, and a third explanation can deliver 12 result documents in a result document set. These explanations can be sorted relative to each other, for example, in ascending order of the number of results.

[0025] Other example ranking methods consider aspect value scores. An example of such a score is document frequency - how often a particular aspect value appears in the result document set. One aspect value (e.g., the word "cognition") may appear more frequently than other aspect values ​​(e.g., the words "quiz" and "game"). This score can be intuitive, but may not be useful for a small set of search result documents. Relevance is another aspect value score used for ranking. Relevance refers to the relative density of aspect values ​​in the search result document set to the entire document set; across aspect values, the frequency with which those aspect values ​​appear in the result document set relative to the entire document set. This is discussed below in Figure 2D Described in further detail.

[0026] One example of aspect value ranking is based on the maximum score of aspect values ​​across different query interpretations. For a given aspect value, each query interpretation will deliver a result document set with the number of times the aspect value is represented by documents in the result document set. The number of times for each result document set can be compared and ranked with each other. This is described below with reference to Figure 2B Further description.

[0027] The representation of aspect rankings may be effected in any desired manner. Figure 2B ), cumulative bar charts are used to display facet values, for example when search result document sets from different interpretations are inclusively related. Additionally or alternatively, a histogram can be used when each interpretation produces an independent set of search result documents.

[0028] In interpreting a query, in some examples, a set of query translation rules is used. Different query interpretations are generated by applying each query translation rule in the set of query translation rules to the query text, the application including expanding, modifying and / or transforming the query. An example implementation for rule-based query expansion is now presented.

[0029] Assume that the natural language query text is "Company Machine Learning Ideas". In this example, four query interpretations are generated by gradually tightening the conditions based on simple OR-concatenated keywords:

[0030] Query Explanation A: Exact phrase: "Company Machine Learning Ideas".

[0031] Query Explanation B: Partial noun phrase: (company AND “machine learning” AND idea).

[0032] Query Explanation C: AND-ed keywords: (company AND idea AND machine AND learning).

[0033] Query explanation D: OR-ed keywords: (company OR idea OR machine OR learning).

[0034] Query interpretation A may return no results or too few results for useful mining (e.g., no aspect values ​​that, if applied, would provide further useful refinement among the results). Query B may return a reasonably sized set of search result documents and include excellent aspect values ​​such as 'cognition', 'quiz', and 'games' (and as a subsequent step, the user can, for example, drill down into the document set by one or more of those aspect values). Query C may return a large number of search result documents, but a flat aspect distribution (meaning relatively equal frequencies for 'cognition', 'quiz', and 'games'). Query interpretation D is the broadest, and this example may deliver too large a set of documents to deliver added value.

[0035] These results may be presented in a query refinement interface for the user to refine the query. For example, a button or other interactive interface aspect element may be presented to the user for selection to select one (or more) of the interpretations by which to refine the initial document set. Each of the interpretations may be individually selected to narrow the current document set representing the results to be communicated to the user to a refined document set that includes the result document set obtained from executing the selected query interpretation.

[0036] Similarly, the interface may additionally or alternatively present corresponding interactive interface aspect elements for individually selecting one or more of the aspect values ​​to narrow the current document set to an improved document set including documents in which the selected aspect values ​​are represented.

[0037] Further details of the query improvement interface are given below. Figure 4 According to the embodiments described herein, combined with Figure 2A-2B ,Some elements of such an improved interface are presented in conjunction with an example rule-based query expansion aspect navigation.

[0038] See first Figure 2AAnd using the above example query ("company machine learning idea"), W represents the full document set of the database, that is, the set of all documents in the search domain. D represents the document result set of executing the above query interpretation D (company OR idea OR machine OR learning). Each of the several document sets that include specific facts / aspects is identified by a dotted circle. C represents the result document set of executing the above query interpretation C (company AND idea AND machine AND learning). Within the result document set C, several different aspect values ​​are represented in the documents of the set, in this example, documents with all / some of the 5 different aspect values ​​as indicated by the 5 dotted circles that are at least partially surrounded by the set C.

[0039] B represents the result document set of executing the above query interpretation B (company AND "machine learning" AND idea). Document set B is a proper subset of document set C because query B is a special case of query C. A represents the result document set of executing the above query interpretation A ("company machine learning idea").

[0040] Figure 2B An example cumulative bar chart is given for displaying the aspect values ​​present in the result document set. The y-axis lists three aspect values ​​for the aspect 'name': "cognition", "quiz" and "game". The document frequency (x-axis) refers to the number of documents in which the aspect value is represented. Each bar 202, 204 and 206 has group segments associated with the four different query interpretations in the above example - clean, dotted, single-line hatched and cross-hatched, and represents the accumulation of documents from each of the different query interpretations. The cross-hatched portion of each bar represents the frequency of the corresponding aspect value being represented in the result document set obtained from executing query D. The single-line (diagonal) hatched portion of each bar represents the frequency of the corresponding aspect value being represented in the result document set obtained from executing query C. The dotted portion of each bar represents the frequency of the corresponding aspect value being represented in the result document set obtained from executing query B. The clean (white) portion of each bar represents the frequency of the corresponding aspect value being represented in the result document set obtained from executing query A.

[0041] Thus, for example, as seen from bar 202, the facet value "cognition" (keyword) appears in a greater number of documents in the document set delivered from running query D than in the document sets delivered from running queries C, B, and A. The number of documents in the document sets delivered from running queries B and C is roughly equal in terms of the frequency of "cognition" in the document sets obtained from running queries B and C. For the facet value "quiz", because the single-line shaded portion of bar 204 is significantly larger than the other segments of bar 204, the frequency of documents from query C (single-line shaded portion) is much greater than the frequency of documents from queries A, B, and D. In this particular example, the facet values ​​are sorted by the overall cumulative document frequency (sorted from top to bottom on the y-axis), meaning that the document frequency of the facet value "cognition" - the total number of times "cognition" appears in running queries A through D - is greater than the total number of times the facet value "quiz" appears in running queries A through D. In this regard, the facet values ​​("cognition", "quiz", "game") are sorted relative to each other according to at least one facet value sorting method (e.g., document frequency in this example). The query refinement interface may present the facet value representation (i.e., a measure of the facet value's representation - in this example, frequency in documents) of each facet value in each result document set (i.e., across interpretations) in sorted order of the facet values ​​relative to every other facet value of the facet value.

[0042] An example implementation of concept-based query modification for aspect navigation is now presented. In this approach, starting from a simple OR-concatenated query, different interpretations are generated by intelligently extracting categories or expanding keywords.

[0043] Assume that the natural language query text is "What are the most prominent company inventions related to natural language processing?". In this example, three query interpretations are generated:

[0044] Query Explanation A: Phrase, Word w / Category: (“Natural Language Processing” OR “NLP”) AND Category: AI AND Company: Company.

[0045] Query Explanation B: Extracted Category:Company:Company AND Category:AI.

[0046] Query Explanation C: OR-ed Basic Keywords: Highlight * OR company OR invention OR language OR process.

[0047] As previously described, the results of performing these interpretations can be delivered within a query refinement interface for the user to refine the query. Figure 2C-2D Presents sample interface elements.

[0048] First reference Figure 2C, W represents the entire document set of the database, that is, the set of all documents in the search domain. C represents the result document set obtained by executing the above query interpretation C (highlighting * OR company OR invention OR language OR processing). B represents the result document set obtained by executing the above query interpretation B (company: company AND category: AI), which means searching for documents in which the aspect value of the company attribute is the name of the company ("company") and the aspect value of the category attribute is artificial intelligence or AI. A represents the result document set obtained by executing the above query interpretation A (company: company AND category: AI). As mentioned above, dotted circles represent document sets in which specific aspect values ​​are represented.

[0049] Figure 2D An example bar chart showing the facet values ​​and relevance present in the result document set is presented. The y-axis lists two facet values ​​for the facet 'noun' - "cognition" and "games". In this example, each facet value has three associated bars corresponding to the three query interpretations. The cross-hatched bar represents query C, the single-hatched bar represents query B, and the dotted bar represents query A. The facet value "cognition" is associated with bars 210, 212, 214, and the facet value "games" is associated with bars 216, 218, and 220.

[0050] The size of each bar is a measure of relevance (X-axis), referring to the relative density of facet values ​​in the resulting document set relative to the facet values ​​represented in the entire document set. Figure 2D , query interpretation B (bar 212) indicates the highest relevance for the aspect value "cognition", meaning that of all documents in W that include "cognition", the document set produced by executing query interpretation B captures the highest number of those documents compared to the document sets produced by executing query interpretations A or C. Query interpretation A (dotted bar 210) has the second highest relevance score for the aspect value "cognition". With respect to the aspect value "games", query interpretation A has the highest relevance score, meaning that interpretation A results in a document set that includes a greater percentage of documents having "games" as an aspect value represented by / in the document than the document sets resulting from queries B or C. It can be seen that the interface presents a representation of each such determined relevance score relative to the other determined relevance scores.

[0051] Some interpretations are not inclusively relevant, so association with the entire corpus may be useful. The term 'game' may be useful because its relevance is highest for query A in this example. At the same time, without repeated query execution trial and error, the user may notice that combining query B and the term 'cognition' is also useful for mining by its relevance value.

[0052] exist Figure 2D, the facet values ​​"cognition" and "games" are sorted along the y-axis by maximum relevance. The maximum relevance score seen in the facet value "cognition" (bar 212) is higher than the maximum relevance score seen in the facet value "games" (bar 216).

[0053] Thus, as part of the execution, and for each of the facet values ​​and each result document set, a relevance score can be determined that represents the density of the facet value represented in the result document set (i.e., the occurrences of the facet value in documents in the result document set) relative to the facet value represented in the entire document set (i.e., the occurrences of the facet value in documents in the entire document set).

[0054] Figure 3 An example process for facet navigation on multiple query interpretations according to embodiments described herein is depicted. The process is performed by one or more computer systems (eg, a database system or components thereof) and / or one or more other computer systems in communication with a database.

[0055] The process initially sets the 'current document set' to the entire document set, or the universe of documents for which the initial query will be performed (302). The current document set is content that has been refined according to the embodiments described herein for aspect-based query refinement. The process accepts a user query text (304), which can be text entered by a user or can be text generated based on a user query (e.g., a spoken natural language query transcribed into text). The process generates an interpretation of the initial query (306) via query expansion, modification, and / or transformation. As an example, this refers to the removal, addition, or change of the terms of the query, conceptual transformation, for example, by way of grammatical changes, category constraints, or the introduction or change of concepts represented in the initial query. In the above example of describing query modification via concept information, the category 'artificial intelligence' (AI) is injected into interpretations A and B. Another example is the change of the word "computer" to "system". Query interpretations can be generated from query translation rules applied to the query text.

[0056] continue Figure 3 , the process computes result document sets and facet values ​​(represented in those sets) for each query interpretation, for example by executing the query and evaluating the results (308). The process computes scores for the facet values ​​of the result document sets for each different interpretation (310). These scores may be useful when presenting the results to the user in a query improvement interface. For example, when constructing the interface, the process sorts the facet values ​​by a sorting method (312) and returns the interpretations and sorted facet values ​​to the user in the form of a query improvement interface (314). The user may use the interface to select interpretations and / or facet values ​​for improvement, as described in detail in the examples in the accompanying drawings. Figure 5The process determines whether the user selects interpretations and / or aspect values ​​for refinement. If not, (316, No), the process ends. Otherwise (316, Yes), the user chooses to refine the initial query, such as by refining the query to one of the generated and executed interpretations and / or by refining to one or more selected aspect values. In this case, the process updates the current document set with the specified conditions (interpretations and / or aspect values) (318) and returns to 304. Returning to 304, in one example, the user can type or provide a new or modified query, and this is run against the (now refined) current document set, repeating for the new / modified query. Figure 3 The explanation of generation, etc.

[0057] Thus, the process can be performed 304-316 for an initial query, and for a new query, iteratively generates (306), calculates / ranks (308-312), and presents (314) based on a user selection of (i) a query interpretation and / or (ii) an aspect value that improves the entire document set into an improved document set. When iterating, the improved document set is used as the entire document set in the iteration, the query used in the iteration is a new query, and the different query interpretations generated are different query interpretations of the query text of the new query. The new query can include (i) another query entered by the user and / or (ii) a modified version of the initial query, the modified version being generated based on the selected query interpretation or aspect value.

[0058] Figure 4 An example query refinement interface according to embodiments described herein is depicted. Interface 400 presents a raw NLQ text box 402 and a corresponding search button 404 for a user to enter a natural language query. In an alternative embodiment, the user indicates a query that is transposed into a text-based input query. User input of a query may trigger actions described herein, such as Figure 3 and / or Figure 6 In this example, the NLQ is “Company Machine Learning Ideas” (the example described above).

[0059] The generated interpretations 406a (Explanation A), 406b (Explanation B), 406c (Explanation C), 406d (Explanation D) are presented in interface 400. Next to interpretations 406a, 406b, and 406c is an indication of the number of documents in each result document set - one document set for each interpretation. The user can use "Add to Search" interface elements 408a, 408b, 408c (buttons in this example) to refine the query to interpretation A, B, or C, respectively, in other words, tell the system to use that interpretation when communicating the query results to the user. Clicking one of these buttons allows the user to refine / narrow the current document set ( Figure 3 316, is) to achieve an example selection of the mining experience. This is similar to see Figure 1 The improvement from D to D' described.

[0060] Interface 400 also presents a cumulative bar graph, which in this example is Figure 2B Cumulative bar chart of the facet values ​​"cognition", "quiz", and "game" are sorted from top to bottom in order of decreasing document frequency, and each bar indicates the contribution of each document set to the total number of occurrences of the facet value across all result document sets explained by the four queries.

[0061] Interface 400 presents a respective interactive interface element (buttons 410a, 410b, 410c) for each of these facet values ​​for individually selecting each of the facet values ​​to add to the search. Selecting the Add to Search button for one of the facet values ​​narrows the current document set to an improved document set that includes documents in which the selected facet value is represented.

[0062] Interface 400 may be constructed and presented to a user. The interface may present different query interpretations and properties of the result document set for each interpretation (e.g., the number of hits / documents in each result document set). In addition, for each facet value, and relative to each other, it presents facet value representations of the facet values ​​in each result document set.

[0063] Figure 5 Depicted is an example process for aspect-based query refinement based on multiple query interpretations according to embodiments described herein. In some examples, the process is performed by one or more computer systems (such as those described herein), which may include one or more database systems, databases, query processors, and / or one or more other computer systems.

[0064] The process begins by receiving a query for documents of an entire document set (502). The documents of the entire document set deviate from one or more aspect values, meaning that at least some of the documents differ with respect to the aspect values ​​they contain, present, indicate, etc. As an example, the query is a natural language query submitted by a user. The process generates a plurality of different query interpretations from the query text of the query (504). In some examples, the process obtains a set of query transformation rules and generates a plurality of different query interpretations by applying each query transformation rule in the set of query transformation rules to the query text. In this context, applying a query transformation rule in the query transformation rule set includes expanding, modifying, and / or transforming the query.

[0065] The process continues by executing a plurality of different query interpretations to obtain a result document set (506). For each different query interpretation of the plurality of different executable query interpretations, executing the query interpretation to obtain a result document set corresponding to the query interpretation from the entire document set, and identifying the facet values ​​represented in the result document set. For each result document set in the plurality of result document sets, executing may calculate the number of documents in the result document set, and for each facet value in one or more facet values, calculating the number of documents that satisfy the following conditions: (i) the document appears in the document set, and (ii) the facet value is represented in the document. Additionally or alternatively, the executing may also include: for each facet value in the one or more facet values ​​and each result document set in the plurality of result document sets, determining a relevance score representing the density of the facet values ​​represented in the result document set relative to the facet values ​​represented in the entire document set. Representations of the determined relevance scores relative to each other may be presented in a query improvement interface (e.g., in step 510). Using the results of the executing, the process ranks the one or more facet values ​​relative to each other according to at least one facet value ranking method (508).

[0066] The process constructs and presents a query refinement interface (510) to a user. The query refinement interface presents a plurality of different query interpretations and attributes of the result document set, and additionally presents, for each of the one or more facet values ​​and relative to each other, facet value representations in each facet value in the result document set. The query refinement interface can present the facet value representations of each facet value in the result document set in a sorted order of the one or more facet values, relative to each other of the one or more facet values. An example attribute of the result document set is the number of documents in each of the plurality of result document sets.

[0067] The query improvement interface may also present corresponding interactive interface elements, such as buttons, for the user to individually select any of the different query interpretations to narrow down the current document set (which represents the results to be communicated to the user) to an improved document set, which includes the result document set corresponding to the selected query interpretation. The query improvement interface may also present corresponding interactive interface elements for individually selecting each of the one or more aspect values ​​to narrow down the current document set to an improved document set including documents in which the selected aspect values ​​are represented.

[0068] If desired, the user may select refinement(s). Figure 5The process determines whether any improvement is selected (512). If not, (512, no), the process ends. Otherwise (512, yes), the user has selected (one or more) refinements. In these examples, the query is an initial query, and the process returns to 502 to repeat the generation, execution, sorting, construction and presentation, which can be based on the user selecting (i) a query interpretation of multiple different query interpretations, and / or (ii) an aspect value in one or more aspect values. Such a selection improves the entire document set into an improved document set, wherein the improved document set is used as the entire document set in the repetition, the query used in the repetition is a new query, and the multiple different query interpretations generated are different query interpretations of the query text of the new query. The new query may include, for example, (i) another query entered by the user or (ii) a modified version of the initial query, the modified version being generated based on the selected query interpretation or aspect value.

[0069] While various examples are provided, variations are possible without departing from the spirit of the claimed aspects.

[0070] The processes described herein may be performed individually or collectively by one or more computer systems, such as one or more database computer systems and / or computer systems in communication with one or more databases. Figure 6 An example of a computer system and related devices that incorporate and / or use aspects described herein is depicted. A computer system may also be referred to herein as a data processing device / system, a computing device / system / node, or simply a computer. As an example, a computer system may be based on one or more of different system architectures and / or instruction set architectures, such as those provided by International Business Machines Corporation (Armonk, New York, USA), Intel Corporation (Santa Clara, California, USA), or ARM Holdings plc (Cambridge, England, UK).

[0071] Figure 6A computer system 600 is shown communicating with one or more external devices 612. The computer system 600 includes one or more processors 602, such as a central processing unit (CPU). The processor may include functional components used in the execution of instructions, such as obtaining program instructions from locations such as cache or main memory, decoding program instructions, and executing program instructions, accessing memory for instruction execution, and writing the results of executed instructions. The processor 602 may also include registers to be used by one or more of the functional components. The computer system 600 also includes a memory 604, an input / output (I / O) device 608, and an I / O interface 610, which may be coupled to the processor 602 and to each other via one or more buses and / or other connections. The bus connection represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example and not limitation, such architectures include Industry Standard Architecture (ISA), Micro Channel Architecture (MCA), Enhanced ISA (EISA), Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI).

[0072] For example, the memory 604 may be or include a main memory or system memory (e.g., random access memory), a storage device (e.g., a hard drive, a flash memory medium, or an optical medium) for executing program instructions, and / or a cache memory. The memory 604 may include (e.g.,) a cache memory, such as a shared cache memory, which may be coupled to a local cache memory of the processor 602 (examples include an L1 cache memory, an L2 cache memory, etc.). In addition, the memory 604 may be or include at least one computer program product having a set (e.g., at least one) of program modules, instructions, codes, etc., which is configured to perform the functions of the embodiments described herein when executed by one or more processors.

[0073] The memory 604 may store an operating system 605 and other computer programs 606, such as one or more computer programs / applications executed to perform the aspects described herein. Specifically, the programs / applications may include computer-readable program instructions that may be configured to perform the functions of embodiments of the aspects described herein.

[0074] Examples of I / O devices 608 include, but are not limited to, microphones, speakers, global positioning system (GPS) devices, cameras, lights, accelerometers, gyroscopes, magnetometers, sensor devices configured to sense light, proximity, heart rate, body and / or ambient temperature, blood pressure, and / or skin resistance, and activity monitors. I / O devices can be incorporated into the computer system as shown, but in some embodiments, I / O devices can be viewed as external devices (612) coupled to the computer system via one or more I / O interfaces 610.

[0075] The computer system 600 can communicate with one or more external devices 612 via one or more I / O interfaces 610. Example external devices include keyboards, pointing devices, displays, and / or any other device that enables a user to interact with the computer system 600. Other example external devices include any device that enables the computer system 600 to communicate with one or more other computing systems or peripherals (such as a printer). A network interface / adapter is an example I / O interface that enables the computer system 600 to communicate with one or more networks (such as a local area network (LAN), a general wide area network (WAN), and / or a public network (e.g., the Internet), providing communication with other computing devices or systems, storage devices, etc. Ethernet-based (such as Wi-Fi) interfaces and The Bluetooth adapter is only an example of a currently available type of network adapter for use in computer systems (BLUETOOTH is a registered trademark of Bluetooth SIG, Inc., Kirkland, Washington, USA).

[0076] Communication between the I / O interface 610 and the external device 612 may occur across a wired and / or wireless communication link 611, such as an Ethernet-based wired or wireless connection. Example wireless connections include cellular, Wi-Fi, Proximity-based, near-field or other types of wireless connections. More generally, communication link(s) 611 may be any suitable wireless and / or wired communication link(s) for communicating data.

[0077] The specific external device 612 may include one or more data storage devices, which may store one or more programs, one or more computer-readable program instructions, and / or data, etc. The computer system 600 may include and / or be coupled to a removable / non-removable, volatile / non-volatile computer system storage medium, and communicate with a removable / non-removable, volatile / non-volatile computer system storage medium (e.g., as an external device of the computer system). For example, it may include and / or be coupled to a non-removable non-volatile magnetic medium (commonly referred to as a "hard drive"), a disk drive for reading from and writing to a removable non-volatile disk (e.g., a "floppy disk"), and / or an optical drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media).

[0078] The computer system 600 can operate with many other general or special computing system environments or configurations. The computer system 600 can take any of different forms, well-known examples of which include, but are not limited to, personal computer (PC) systems, server computer systems (such as messaging servers), thin clients, fat clients, workstations, laptops, handheld devices, mobile devices / computers (such as smart phones), tablet computers, and wearable devices, multiprocessor systems, microprocessor-based systems, telephone devices, network devices (such as edge devices), virtualization devices, storage controllers, set-top boxes, programmable consumer electronics, network PCs, small computer systems, large computer systems, and distributed cloud computing environments including any of the above systems or devices, etc.

[0079] The aspects described herein may be incorporated into and / or use a cloud computing environment. It should be understood that although the present disclosure includes a detailed description of cloud computing, the implementation of the technical solutions described therein is not limited to a cloud computing environment. Instead, the embodiments of the present invention can be implemented in conjunction with any other type of computing environment now known or later developed.

[0080] Cloud computing is a service delivery model for convenient, on-demand network access to a shared pool of configurable computing resources. Configurable computing resources are resources that can be quickly deployed and released with minimal management cost or interaction with the service provider, such as networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services. This cloud model can include at least five characteristics, at least three service models, and at least four deployment models.

[0081] Features include:

[0082] On-demand self-service: Cloud consumers can unilaterally and automatically deploy computing capabilities such as server time and network storage on demand without human interaction with the service provider.

[0083] Broad network access: Computing power can be accessed over the network through standard mechanisms that facilitate the use of the cloud through different types of thin-client or thick-client platforms (e.g., mobile phones, laptops, personal digital assistants (PDAs)).

[0084] Resource pool: The computing resources of the provider are grouped into resource pools and serve multiple consumers through a multi-tenant model, where different physical and virtual resources are dynamically allocated and reallocated on demand. Generally, consumers cannot control or even know the exact location of the provided resources, but can specify the location (such as country, state or data center) at a higher level of abstraction, so it is location-independent.

[0085] Rapid elasticity: The ability to quickly and elastically (sometimes automatically) deploy computing power to scale up quickly, and quickly release it to scale down quickly. To consumers, the computing power available for deployment often appears to be unlimited, and any amount of computing power can be accessed at any time.

[0086] Measurable services: Cloud systems automatically control and optimize resource usage by leveraging metering capabilities at a level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency to both service providers and consumers.

[0087] The service model is as follows:

[0088] Software as a Service (SaaS): The capability provided to consumers is to use the provider's applications running on a cloud infrastructure. Applications can be accessed from a variety of client devices through a thin client interface such as a web browser (e.g., web-based email). Aside from limited user-specific application configuration settings, consumers neither manage nor control the underlying cloud infrastructure including networks, servers, operating systems, storage, or even individual application capabilities.

[0089] Platform as a Service (PaaS): The capability provided to consumers is to deploy applications created or acquired by consumers on the cloud infrastructure, which are created using programming languages ​​and tools supported by the provider. Consumers neither manage nor control the underlying cloud infrastructure including networks, servers, operating systems or storage, but have control over the applications they deploy and may also have control over the configuration of the application hosting environment.

[0090] Infrastructure as a Service (IaaS): The capabilities provided to consumers are processing, storage, network, and other basic computing resources on which consumers can deploy and run arbitrary software including operating systems and applications. Consumers neither manage nor control the underlying cloud infrastructure, but have control over the operating system, storage, and applications they deploy, and may have limited control over selected network components (such as host firewalls).

[0091] The deployment model is as follows:

[0092] Private Cloud: Cloud infrastructure is run solely for an organization. The cloud infrastructure can be managed by the organization or a third party and can exist inside or outside the organization.

[0093] Community Cloud: A cloud infrastructure is shared by several organizations and supports a specific community with common interests (e.g., mission, security requirements, policies, and compliance considerations). A community cloud can be managed by multiple organizations within the community or by a third party and can exist inside or outside the community.

[0094] Public cloud: Cloud infrastructure is provided to the public or to large industry groups and is owned by an organization that sells cloud services.

[0095] Hybrid cloud: A cloud infrastructure consisting of two or more clouds (private, community or public) in a deployment model that remain distinct entities but are bound together by standardized or proprietary technologies (such as cloud burst sharing for load balancing between clouds) that enable data and application portability.

[0096] Cloud computing environments are service-oriented, with characteristics centered on statelessness, loose coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure consisting of a network of interconnected nodes. One such node is Figure 7 Node 10 described in.

[0097] The computing node 10 is only an example of a suitable cloud computing node and should not bring any limitation to the functions and scope of use of the embodiments of the present invention. In short, the cloud computing node 10 can be used to implement and / or execute any of the functions described above.

[0098] Reference now Figure 7, in which an exemplary cloud computing environment 50 is shown. As shown, the cloud computing environment 50 includes one or more computing nodes 10 with which a local computing device used by a cloud computing consumer can communicate, and the local computing device can be, for example, a smart phone or other mobile device 54A, a desktop computer 54B, a laptop computer 54C and / or an automobile computer system 54N. The nodes 10 can communicate with each other. The computing nodes 10 can be physically or virtually grouped in one or more networks including but not limited to a private cloud, a community cloud, a public cloud or a hybrid cloud, or a combination thereof as described above (not shown in the figure). In this way, cloud consumers can request infrastructure as a service (IaaS), platform as a service (PaaS) and / or software as a service (SaaS) provided by the cloud computing environment 50 without having to maintain resources on local computing devices. It should be understood that Figure 7 The various types of computing devices 54A-N shown are merely illustrative, and computing node 10 and cloud computing environment 50 may communicate with any type of computing device on any type of network and / or network-addressable connection (eg, using a web browser).

[0099] Reference now Figure 8 , which shows a cloud computing environment 50 ( Figure 6 ) provides a set of functional abstraction layers. First of all, it should be understood that Figure 8 The components, layers, and functions shown are only exemplary and the embodiments of the present invention are not limited thereto. Figure 8 As shown, the following layers and corresponding functions are provided:

[0100] The hardware and software layer 60 includes hardware and software components. Examples of hardware components include host 61; server 62 based on RISC (Reduced Instruction Set Computer) architecture; server 63; blade server 64; storage device 65; and network and network components 66. In some embodiments, software components include network application server software 67 and database software 68.

[0101] The virtualization layer 70 provides an abstraction layer that can provide examples of the following virtual entities: virtual servers 71 , virtual storage 72 , virtual networks 73 (including virtual private networks), virtual applications and operating systems 74 , and virtual clients 75 .

[0102] In one example, the management layer 80 may provide the following functions: Resource provisioning 81: providing dynamic acquisition of computing resources and other resources for performing tasks in a cloud computing environment; Metering and pricing 82: tracking the cost of resource usage within the cloud computing environment and providing bills and invoices for this purpose. In one example, the resource may include an application software license. Security functions: providing identity authentication for cloud consumers and tasks, and providing protection for data and other resources. User portal 83: providing access to the cloud computing environment for consumers and system administrators. Service level management 84: providing allocation and management of cloud computing resources to meet the required service levels. Service level agreement (SLA) planning and fulfillment 85: providing pre-arrangement and provision for future demand for cloud computing resources predicted according to the SLA.

[0103] The workload layer 90 provides examples of functions that may be implemented in a cloud computing environment. In this layer, examples of workloads or functions that may be provided include: mapping and navigation 91; software development and lifecycle management 92; virtual classroom teaching provision 93; data analysis processing 94; transaction processing 95; and query improvement 96.

[0104] The present invention may be a system, method and / or computer program product at any possible level of technical detail integration. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present invention.

[0105] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0106] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0107] The computer program instructions for performing the operation of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, configuration data of an integrated circuit, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present invention.

[0108] Various aspects of the present invention are described herein with reference to the flow charts and / or block diagrams of the methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each box of the flow chart and / or block diagram and the combination of each box in the flow chart and / or block diagram can be implemented by computer-readable program instructions.

[0109] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0110] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0111] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present invention. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special hardware and computer instructions.

[0112] In addition to the above, one or more aspects may be provided, delivered, deployed, managed, serviced, etc. by a service provider that provides management of a customer environment. For example, a service provider may create, maintain, support, etc. computer code and / or computer infrastructure that performs one or more aspects for one or more customers. In return, as an example, the service provider may receive payment from the consumer under a subscription and / or fee agreement. Additionally or alternatively, the service provider may receive payment from the sale of advertising content to one or more third parties.

[0113] In one aspect, an application may be deployed to perform one or more embodiments. As an example, deployment of an application includes providing a computer infrastructure operable to perform one or more embodiments.

[0114] As yet another aspect, a computing infrastructure may be deployed including integrating computer readable code into a computing system, wherein the code in combination with the computing system is capable of performing one or more embodiments.

[0115] As another aspect, a process for integrating a computing infrastructure may be provided, comprising integrating a computer readable code into a computer system. The computer system comprises a computer readable medium, wherein the computer medium comprises one or more embodiments. The code combined with the computer system is capable of executing one or more embodiments.

[0116] Although various embodiments are described above, these are merely examples. For example, one or more embodiments may be incorporated and used using computing environments of other architectures.

[0117] The terms used herein are used only for the purpose of describing specific embodiments and are not intended to be limiting. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that when the terms "comprise" and / or "include" are used in this specification, they specify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0118] The corresponding structures, materials, actions, and equivalents of all means or step plus function elements (if any) in the claims are intended to include any structure, material, or action for performing a function in conjunction with other claimed elements as specifically claimed. The description of one or more embodiments has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to best explain the various aspects and practical applications, and to enable those of ordinary skill in the art to understand different embodiments with different modifications suitable for the intended specific use.

Claims

1. A computer-implemented method comprising: receiving a query for searching for documents of an entire document set, wherein the documents of the entire document set deviate from one or more aspect values; generating a plurality of different query interpretations from a query text of the query; executing the plurality of different query interpretations to obtain a result document set, wherein, for each different query interpretation of the plurality of different executable query interpretations, the executing executes the query interpretation to obtain a result document set corresponding to the query interpretation from the entire document set, and identifies facet values ​​represented in the result document set; and constructing and presenting to a user a query refinement interface, the query refinement interface presenting the plurality of different query interpretations and attributes of the result document set and additionally presenting, for each of the one or more facet values ​​and relative to each other, facet value representations of the facet values ​​in each of the result document sets; Wherein, the execution also includes: determining a relevance score for each of the one or more aspect values ​​and each result document set in the multiple result document sets, wherein the relevance score represents the density of the aspect values ​​represented in the result document set relative to the aspect values ​​represented in the entire document set, and wherein the query improvement interface presents a representation of each such determined relevance score relative to other determined relevance scores, and the relevance score is used to rank the aspect values.

2. The method according to claim 1, wherein: The attributes of the result document set include the number of documents in each of the multiple result document sets.

3. The method according to claim 1, wherein: The query improvement interface also presents corresponding interactive interface elements for individually selecting each of the different query interpretations to narrow a current document set representing results to be communicated to the user to an improved document set including a set of result documents corresponding to the selected query interpretation.

4. The method according to claim 3, wherein: The query refinement interface also presents corresponding interactive interface elements for individually selecting each of the one or more facet values ​​to narrow the current document set to a refined document set including documents in which the selected facet value is represented.

5. A method according to claim 1, wherein the query is an initial query, and wherein the method further includes repeating the generating, executing, constructing and presenting based on the user selecting at least one selected from the group consisting of: (i) query interpretations of the multiple different query interpretations, and (ii) aspect values ​​from the one or more aspect values ​​to improve the entire document collection into an improved document set, wherein in the repetition the improved document set is used as the entire document set, the query used in the repetition is a new query, and the multiple different query interpretations generated are different query interpretations of the query text of the new query.

6. The method of claim 5, wherein the new query comprises at least one item selected from the group consisting of: (i) another query input by the user, and (ii) a modified version of the initial query, the modified version generated based on the selected query interpretation or aspect value.

7. The method according to claim 1, further comprising: The one or more aspect values ​​are sorted relative to each other according to at least one aspect value sorting method, and wherein the query improvement interface presents the aspect value representations of the aspect values ​​in each of the result document sets in the sorted order of the one or more aspect values, relative to each other of the one or more aspect values.

8. The method according to claim 1, wherein: The query is a natural language query.

9. The method according to claim 1, further comprising: Obtain a set of query transformation rules; as well as The plurality of different query interpretations are generated by applying each query transformation rule in the set of query transformation rules to the query text, wherein applying a query transformation rule in the set of query transformation rules comprises at least one selected from the group consisting of: expanding, modifying, and transforming the query.

10. The method according to claim 1, wherein: The execution also includes calculating, for each of the plurality of result document sets, a number of documents in the result document set, and, for each of the one or more aspect values, calculating a number of documents in the result document set in which the aspect value is represented.

11. A computer system comprising: Memory; as well as a processor in communication with the memory, wherein the computer system is configured to perform a method comprising: receiving a query for searching for documents of an entire document set, wherein the documents of the entire document set deviate from one or more aspect values; generating a plurality of different query interpretations from a query text of the query; executing the plurality of different query interpretations to obtain a result document set, wherein, for each different query interpretation of the plurality of different executable query interpretations, executing the query interpretation to obtain a result document set corresponding to the query interpretation from the entire document set, and identifying facet values ​​represented in the result document set; and constructing and presenting to a user a query refinement interface, the query refinement interface presenting the plurality of different query interpretations and attributes of the result document set and additionally presenting, for each of the one or more facet values ​​and relative to each other, facet value representations of the facet values ​​in each of the result document sets; Wherein, the execution further comprises: for each aspect value among the one or more aspect values ​​and each result document set in the multiple result document sets, determining a relevance score, wherein the relevance score represents the density of the aspect value represented in the result document set relative to the aspect value represented in the entire document set, and wherein the query improvement interface presents a representation of each such determined relevance score relative to other determined relevance scores, and the relevance scores are used to rank the aspect values.

12. The computer system according to claim 11, wherein: The query improvement interface also presents corresponding interactive interface elements for individually selecting each of the different query interpretations to narrow down a current document set representing results to be communicated to the user to an improved document set including a result document set corresponding to the selected query interpretation.

13. The computer system according to claim 12, wherein: The query refinement interface also presents corresponding interactive interface elements for individually selecting each of the one or more facet values ​​to narrow the current document set to a refined document set including documents in which the selected facet value is represented.

14. The computer system according to claim 11, wherein: The method further includes: sorting the one or more aspect values ​​relative to each other according to at least one aspect value sorting method, and wherein the query improvement interface presents the aspect value representations of the aspect values ​​in each of the result document sets in the sorted order of the one or more aspect values, relative to each other of the one or more aspect values.

15. A computer program product comprising instructions which, when executed by a processing circuit, implement a method comprising: receiving a query for searching for documents of an entire document set, wherein the documents of the entire document set deviate from one or more aspect values; generating a plurality of different query interpretations from a query text of the query; executing the plurality of different query interpretations to obtain a result document set, wherein, for each different query interpretation of the plurality of different executable query interpretations, executing the query interpretation to obtain a result document set corresponding to the query interpretation from the entire document set, and identifying facet values ​​represented in the result document set; and constructing and presenting to a user a query refinement interface, the query refinement interface presenting the plurality of different query interpretations and attributes of the result document set and, for each of the one or more facet values ​​and relative to each other, additionally presenting facet value representations of the facet values ​​in each of the result document sets; The execution further comprises: for each of the one or more aspect values ​​and each result document set in the plurality of result document sets, determining a relevance score representing the density of the aspect values ​​represented in the result document set relative to the aspect values ​​represented in the entire document set, and wherein the query improvement interface presents a representation of each such determined relevance score relative to other determined relevance scores, and the relevance scores are used to rank the aspect values.

16. A computer program product according to claim 15, wherein the query improvement interface also presents (i) corresponding interactive interface elements for individually selecting each of the different query interpretations to narrow down the current document set representing the results to be communicated to the user to an improved document set including the result document set corresponding to the selected query interpretation, and (ii) corresponding interactive interface elements for individually selecting each of the one or more aspect values ​​to narrow down the current document set to an improved document set including documents in which the selected aspect value is represented.

17. The computer program product of claim 15, wherein: The method further includes: sorting the one or more aspect values ​​relative to each other according to at least one aspect value sorting method, and wherein the query improvement interface presents the aspect value representations of the aspect values ​​in each of the result document sets in the sorted order of the one or more aspect values, relative to each other of the one or more aspect values.

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