WO2020178626A1 - Systems and methods for adaptive question answering - Google Patents
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- WO2020178626A1 WO2020178626A1 PCT/IB2019/053080 IB2019053080W WO2020178626A1 WO 2020178626 A1 WO2020178626 A1 WO 2020178626A1 IB 2019053080 W IB2019053080 W IB 2019053080W WO 2020178626 A1 WO2020178626 A1 WO 2020178626A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/02—Knowledge representation; Symbolic representation
- G06N5/022—Knowledge engineering; Knowledge acquisition
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
- G06F3/0484—Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/205—Parsing
- G06F40/211—Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
- G06F40/284—Lexical analysis, e.g. tokenisation or collocates
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/30—Semantic analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
Definitions
- the invention relates to systems and methods in the field of computer science, including hardware and software, and artificial intelligence.
- the present disclosure describes an Adaptive Question Answering Engine (AQUAE) system which is adaptive to user’s characteristics, goals and needs by continuously learning from user interactions and adapting both the context and data visualization, thereby improving quality and experience of the user. Furthermore, the natural language interface allows a more natural flow of business queries for non-technical business users who don’t need to face discomfort and difficulty while using technical terminology.
- AQUAE Adaptive Question Answering Engine
- One exemplary system embodiment herein provides an adaptive question answering engine system comprising software modules embodied on a computer network, and the software modules comprise an Interpretation Engine, an Answering Engine and a Learning Engine.
- the Interpretation Engine receives questions in natural language from a user and processes the question for holistic understanding of the user’s question by incorporating semantic and usage knowledge from a Learning Engine.
- the question understanding is not restricted to question text, but also identifies user’s intent, makes intelligent assumptions in case of insufficiently elucidated questions, performs disambiguation in case of ambiguities.
- Interpretation Engine generates an Interpretation which is passed to an Answering Engine for generation of relevant answer(s).
- An Answering Engine formulates various intermediate queries based on the Interpretation and retrieve appropriate answers and metadata associated with the answers for individual intermediate query.
- the Answering Engine determines visualization preference by incorporating semantic and usage knowledge from a Learning Engine and aggregate and rank answers as appropriate.
- the Answering Engine also recommends follow-up actions that user can perform to aid user on his information needs and further analysis.
- Learning Engine augments, adapts and improves knowledge based on user interactions which are fed back to the Learning Engine.
- User interactions comprise data enquiry, correction of ambiguous entities, actions on interpretation, actions on answer, tracking of answer, drill-down part of data, visualization changes, up-vote/down- vote on answers, actions on suggested analysis, and follow-up on suggested questions.
- One exemplary method embodiment herein provides a method for adaptive question answering, comprising steps of
- the Learning Engine augments, adapts and improves knowledge based on user interactions which comprise data enquiry, correction of ambiguous entities, actions on interpretation, actions on answer, tracking of answer, drill-down part of data, visualization changes, up-vote/down-vote on answers, actions on suggested analysis, or follow-up on suggested questions.
- An additional embodiment herein comprises a computer network for adaptive question answering comprises a first subnetwork for data processing and a second subnetwork for data storage.
- An embodiment for the first subnetwork for data processing comprises at least one virtual or physical server node for implementing an Interpretation Engine, an Answering Engine, an Learning Engine, data synchronization or other modules.
- Another embodiment for the first subnetwork for data processing comprises a multi-server-node cluster which gets deployed with Interpretation Engine, Answering Engine, Learning Engine and all other required modules, and a second server node for data synchronization.
- a further embodiment for the first subnetwork for data processing provides serverless architectures.
- An embodiment for the second subnetwork for data storage comprises a big data framework and a database system for data inquiry and retrieval.
- FIG.l shows the high-level process flow of AQUAE.
- FIG. 2 depicts the hardware details of AQUAE.
- FIG. 3 shows high-level representation of what constitutes a question.
- FIG. 4 details high-level building blocks of Analytics specific Meta Ontology (AMO).
- FIG. 5 details high level user knowledge captured from various user/system interaction.
- FIG. 6 depicts the details of the Interpretation Engine.
- FIG. 7 depicts the details of the Answering Engine.
- FIG. 8 shows high level representation of what constitutes Answer in AQUAE.
- FIG.l details the high-level process flow of AQUAE.
- a user can interact with the AQUAE in natural language using any system of interaction, such as mobile applications, desktop applications, web applications, voice-based hardware, etc.
- the input from the user is captured as a question.
- the question is further processed by the Interpretation Engine.
- the Interpretation Engine incorporates organization and usage knowledge for holistic understanding of the user’s question.
- the question understanding is not restricted to question text, but also identifies user’s intent, makes intelligent assumptions in case of insufficiently elucidated questions, performs disambiguation in case of ambiguities and so on.
- the semantic understanding of the question context is termed as Interpretation.
- Answering Engine for generation of relevant answer(s).
- Answering Engine is also responsible for determining which data source(s), which slice of data user might be interested in. It also retrieves data from underlying data cluster, determine visualization preference based on past interactions, builds on additional contexts as deemed fit. To aid user on his information needs and further analysis, the Answering Engine also recommends follow-up actions that user can perform.
- FIG. 2 depicts the exemplary hardware details of a computer network of AQUAE.
- the computer network for adaptive question answering comprises a first subnetwork for data processing and a second subnetwork for data storage.
- the first subnetwork for data processing comprises a multi-server-node cluster which gets deployed with Interpretation Engine,
- the second subnetwork for data storage comprises a big data frame work and a database system for data inquiry and retrieval.
- Table 1 provides the exemplary hardware of AQUAE.
- Another embodiment for the first subnetwork for data processing comprises at least one virtual or physical server node for implementing an Interpretation Engine, an
- a further embodiment for the first subnetwork for data processing provides serverless architectures.
- Serverless architectures are application designs that incorporate third-party“Backend as a Service” (BaaS) services, and/or that include custom code run in managed, ephemeral containers on a“Functions as a Service” (FaaS) platform.
- Serverless architectures remove much of the need for a traditional always-on server component and may benefit from significantly reduced operational cost, complexity, and engineering lead time.
- FIG. 3 shows high-level representation of what constitutes a question.
- BI Analytics is widely used by organizations for providing actionable insights from disparate & complex data landscape. This data would be scattered within and outside of the organization. Moreover, each organization has its own nomenclature and the data is very unique on its own.
- An organization agnostic Adaptive Question Answering Engine need to understand this organization specific knowledge. Building an organization specific ontology may not suffice to create a domain or organization agnostic AQUAE.
- FIG. 4 details high-level building blocks of AMO. Each concept would have name, label, glossary, synonym and other relevant properties. This invention captures this understanding of domain and organization specific knowledge in semantic knowledge.
- semantic knowledge as organization specific ontology derived from AMO. For example,‘Brand’ is an Attribute,‘Colgate’ is an entity of type Brand,‘Total Number of Unit’ is a measure with glossary as“total numbers of items being sold”.
- FIG. 5 details high-level user knowledge captured from various user/system interaction.
- User model captures all the user-specific interactions, such as feedback being provided by the user on answers, corrections of assumed entities etc. While organization model captures meta information across organization based on similar feedback.
- Visualization model captures user’s visualization preferences for a given insight.
- Insight knowledge is a repository of all the insights which were served to users and links to AMO for all valid contexts.
- User session keeps track of what users are performing by monitoring interactive information interchanged between user and system. System builds up user specific interest from every user interaction.
- FIG. 6 depicts the details of the Interpretation Engine.
- the Interpretation essentially captures the key entities from the question after analysing and understanding the context of the question.
- Interpretation Engine uses a semantic-parser algorithm to parse the question and identify the key constituent phrases and tokens from the question.
- Named Entity Identification plays a pivotal role in the interpretation Engine.
- the examples of named entities can be, person or organization names, locations, dates and times. Named entities can then be organized under predefined categories, such as“period” - relative, specific & periodic,“business objects” - column values,“measure” - numerical columns,“filters & conditions”, and other important features from question and user context.
- WSD Word Sense Disambiguation
- words can be polysemous (word having more than one sense) in nature.
- measures and business objects can be often polysemous in nature. In such cases, we use the context and usage knowledge to disambiguate the entities.
- the measures are disambiguated and ranked using inferencing algorithm and weighted context similarity approach. For example, in a question -“Sales of Region East for this month”, the word“Sales” have more than two senses (“Total Unit Sales”, “Total Dollar Sales”). In such A scenario, as per usage knowledge and question context,‘Sales’ could be associated with‘Total Dollar Sales”.
- Disambiguation While lexical disambiguation is all about disambiguating entities at word level, semantic disambiguation deals with disambiguation of entities considering the entire context of the question. This involves disambiguating entities considering the data source information of measures and also with respect to the other entities in the question. Once all the measures and business objects are disambiguated appropriate filters and conditions are applied on the measure entities.
- Last step in Interpretation Engine is to identify user’s intent which can be further utilized by Answering Engine.
- the main task of the Answering Engine is to generate appropriate answer(s) using the semantic Interpretation considering the user intent as deduced from the question.
- FIG. 7 depicts the details of the Answering Engine.
- the Engine formulates various intermediate queries required to answer the questions.
- the queries can range from one to many based on the user’s intent.
- the answers are equivalent to the queries formed.
- period and measures are inferred using a Bayesian formulation in case they are not mentioned in the question.
- the Answering Engine consults the enterprise data to obtain the appropriate answer and metadata associated with the answer for individual intermediate query. Using the interpretation, the Answering Engine identifies and recommends the most frequent and relevant information to the user along with the answer(s).
- the next step is to determine the visualization for the answer. It helps to improve the ability to understand the hidden information in a more constructive way. Business leaders need the ability to easily drill down into the data to see where they can improve, take actions and to grow their business. Data visualization brings business intelligence to life. Depending on the answer data and past user interactions, the AQUAE provides the user with the best visualization along with alternate visualizations supported for the answer(s).
- Giving answer would trigger next set of questions that user might ask.
- AQUAE recommends follow-up actions to ease discovery and effective analysis.
- the user is served with assembled and ranked answer(s) for the question asked.
- FIG. 8 shows high level representation of what constitutes Answer in AQUAE. Learning Engine & Interactions
- Learning Engine is responsible for augmentation/adaptation of knowledge based on user interactions. Following user interactions are supported by AQUAE. Learning Engine improves knowledge based on each interaction to make AQUAE smarter.
- the first interaction starts with user asking a question in natural language.
- AQUAE considers period entities as a special case and allows user to change the same when answer is being served. This allows user for data exploration with respect to different time frames. Learning Engine learns relevant time period for a given context from this interaction. The knowledge will be later used by AQUAE to infer time periods in case of incomplete questions (for all the users in an organization)
- Answers can be tracked/untracked based on user’s changing business preferences.
- Learning Engine captures user’s interest from these interactions. This knowledge helps AQUAE to better rank the answers as well as helps in disambiguating entities.
- AQUAE enables the same and learns user’s interest areas. Also, these interactions allow AQUAE to predict and pre-empt follow-up questions that users might have, thereby improving suggested analysis based on answers.
- AQUAE allows user to provide feedback on relevancy and validity of answer using up-vote/down-vote actions. This helps AQUAE to learn and adapt user model, thereby improving experience with subsequent data enquiries (for all the users in an organization)
- AQUAE pre-empts follow-up questions that user might have when presented with an answer by recommending related analysis. For example,“benchmark across all region” might be recommended for the answer,“sales of West region in 2018”. Invocation or non-invocation of these recommended analyses along with context allows Learning Engine to learn about user’s way of interaction with the data, giving opportunity to improve the same.
- AQUAE recommends suggested questions to the user, when data is not available for a given context. This recommendation is based on usage knowledge.
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CN201980000567.XA CN111886601B (en) | 2019-03-01 | 2019-04-15 | System and method for adaptive question-answering |
US16/752,868 US11347803B2 (en) | 2019-03-01 | 2020-01-27 | Systems and methods for adaptive question answering |
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CN111886601B (en) | 2024-03-01 |
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