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A question is received to be answered by a question answering (QA) system. The question may be a business intelligence question that is expressed in a natural language.

The question is parsed. The parsed question is matched to a pattern from a number of patterns. A technical query associated with the matched pattern is processed to retrieve data relevant to the question from a number of data sources. The QA system generates an answer to the question based on retrieved data.

In one aspect, the QA system generates answers based contextual information. ” is an entered request for information to be answered by a QA system.

Parse tree 620 is an exemplary parse tree generated as a result of parsing query 610. Parse tree 620 may represent syntactic structure of query 610. Query tree 630 represents parse tree 620 enriched with sematic annotations in accordance with method and techniques described herein. In one embodiment, semantic annotation may include attaching names, attributes, or other descriptions to a question.

Query tree 630 may provide additional semantic, contextual information and metadata about identified entities and relationships between entities in query 610. For example, nodes of the query tree 630 may be labeled to represent the type of entities that are represented by the nodes.

Relations between entities are represented as labeled edges of query tree 630 to show the type of relationships between the entities. In one embodiment, query tree 630 may include semantic annotations derived from or based on contextual information. For example, predicate 650 “near” represents a semantic relationship between two nodes of geographical type, where the semantic relationship is relative location. Entities represented by nodes in query tree 630 may be identified by URIs. In one embodiment, query tree 630 may be generated by one or more of computer modules 132- 144 in FIG.

Query tree 630 may be implemented in Resource Description Framework (RDF). Pattern 640 represents an exemplary pattern that may be associated to query tree 630. Pattern 640 may include a combination of one or more constraints such as projections, filters, sorting criteria clauses, truncation clauses, and the like. In one embodiment, pattern 640 may be implemented in SPARQL.

Some embodiments of the invention may include the above-described methods being written as one or more software components. These components, and the functionality associated with each, may be used by client, server, distributed, or peer computer systems. These components may be written in a computer language corresponding to one or more programming languages such as, functional, declarative, procedural, object-oriented, lower level languages and the like.

They may be linked to other components via various application programming interfaces and then compiled into one complete application for a server or a client. Alternatively, the components maybe implemented in server and client applications.

Further, these components may be linked together via various distributed programming protocols. Some example embodiments of the invention may include remote procedure calls being used to implement one or more of these components across a distributed programming environment.

For example, a logic level may reside on a first computer system that is remotely located from a second computer system containing an interface level (e.g., a graphical user interface). These first and second computer systems can be configured in a server-client, peer-to-peer, or some other configuration. The clients can vary in complexity from mobile and handheld devices, to thin clients and on to thick clients or even other servers.

The above-illustrated software components are tangibly stored on a computer readable storage medium as instructions. The term “computer readable storage medium” should be taken to include a single medium or multiple media that stores one or more sets of instructions. The term “computer readable storage medium” should be taken to include any physical article that is capable of undergoing a set of physical changes to physically store, encode, or otherwise carry a set of instructions for execution by a computer system which causes the computer system to perform any of the methods or process steps described, represented, or illustrated herein. Examples of computer readable storage media include, but are not limited to: magnetic media, such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROMs, DVDs and holographic devices; magneto-optical media; and hardware devices that are specially configured to store and execute, such as application-specific integrated circuits (“ASICs”), programmable logic devices (“PLDs”) and ROM and RAM devices.

Examples of computer readable instructions include machine code, such as produced by a compiler, and files containing higher-level code that are executed by a computer using an interpreter. For example, an embodiment of the invention may be implemented using Java, C++, or other object-oriented programming language and development tools. Another embodiment of the invention may be implemented in hard-wired circuitry in place of, or in combination with machine readable software instructions. 7 is a block diagram of an exemplary computer system 700. The computer system 700 includes a processor 705 that executes software instructions or code stored on a computer readable storage medium 755 to perform the above-illustrated methods of the invention.

The computer system 700 includes a media reader 740 to read the instructions from the computer readable storage medium 755 and store the instructions in storage 710 or in random access memory (RAM) 715. The storage 710 provides a large space for keeping static data where at least some instructions could be stored for later execution. The stored instructions may be further compiled to generate other representations of the instructions and dynamically stored in the RAM 715. The processor 705 reads instructions from the RAM 715 and performs actions as instructed.

According to one embodiment of the invention, the computer system 700 further includes an output device 725 (e.g., a display) to provide at least some of the results of the execution as output including, but not limited to, visual information to users and an input device 730 to provide a user or another device with means for entering data and/or otherwise interact with the computer system 700. Each of these output devices 725 and input devices 730 could be joined by one or more additional peripherals to further expand the capabilities of the computer system 700. A network communicator 735 may be provided to connect the computer system 700 to a network 750 and in turn to other devices connected to the network 750 including other clients, servers, data stores, and interfaces, for instance. The modules of the computer system 700 are interconnected via a bus 745.

Computer system 700 includes a data source interface 720 to access data source 760. Ecs Motherboard Drivers For Windows 7 Download. The data source 760 can be accessed via one or more abstraction layers implemented in hardware or software. For example, the data source 760 may be accessed by network 750. In some embodiments the data source 760 may be accessed via an abstraction layer, such as, a semantic layer. A data source is an information resource.

Data sources include sources of data that enable data storage and retrieval. Data sources may include databases, such as, relational, transactional, hierarchical, multi-dimensional (e.g., OLAP), object oriented databases, and the like. Further data sources include tabular data (e.g., spreadsheets, delimited text files), data tagged with a markup language (e.g., XML data), transactional data, unstructured data (e.g., text files, screen scrapings), hierarchical data (e.g., data in a file system, XML data), files, a plurality of reports, and any other data source accessible through an established protocol, such as, Open DataBase Connectivity (ODBC), produced by an underlying software system (e.g., ERP system), and the like. Data sources may also include a data source where the data is not tangibly stored or otherwise ephemeral such as data streams, broadcast data, and the like. These data sources can include associated data foundations, semantic layers, management systems, security systems and so on. In the above description, numerous specific details are set forth to provide a thorough understanding of embodiments of the invention.

One skilled in the relevant art will recognize, however that the invention can be practiced without one or more of the specific details or with other methods, components, techniques, etc. In other instances, well-known operations or structures are not shown or described in details to avoid obscuring aspects of the invention.

Although the processes illustrated and described herein include series of steps, it will be appreciated that the different embodiments of the present invention are not limited by the illustrated ordering of steps, as some steps may occur in different orders, some concurrently with other steps apart from that shown and described herein. In addition, not all illustrated steps may be required to implement a methodology in accordance with the present invention. Moreover, it will be appreciated that the processes may be implemented in association with the apparatus and systems illustrated and described herein as well as in association with other systems not illustrated.

The above descriptions and illustrations of embodiments of the invention, including what is described in the Abstract, is not intended to be exhaustive or to limit the invention to the precise forms disclosed. While specific embodiments of, and examples for, the invention are described herein for illustrative purposes, various equivalent modifications are possible within the scope of the invention, as those skilled in the relevant art will recognize. These modifications can be made to the invention in light of the above detailed description. Rather, the scope of the invention is to be determined by the following claims, which are to be interpreted in accordance with established doctrines of claim construction. Patent Citations Cited Patent Filing date Publication date Applicant Title * 28 Jun 1996 12 Oct 1999 Microsoft Corporation Method and system for computing semantic logical forms from syntax trees * 6 Feb 1997 13 Jun 2000 Paik; Woojin Information extraction system and method using concept relation concept (CRC) triples * 8 Jan 2001 30 Mar 2004 Softface, Inc. Creation of structured data from plain text 24 Oct 2000 25 May 2004 Sap Aktiengesellschaft System and method to retrieving information with natural language queries 19 Jan 2005 15 Apr 2008 Sap Ag Database management systems and methods for managing a database * 12 Mar 2010 16 Sep 2010 Invention Machine Corporation Question-answering system and method based on semantic labeling of text documents and user questions.

Referenced by Citing Patent Filing date Publication date Applicant Title * 31 Aug 2012 9 Feb 2016 Infotech Soft, Inc. Query optimization for SPARQL * 29 Jul 2014 11 Apr 2017 International Business Machines Corporation Changed answer notification in a question and answer system * 31 Aug 2012 6 Mar 2014 Infotech Soft, Inc.

Download Lagu Malaysia Kristal Cinta Tiga Segi. Query Optimization for SPARQL * 29 Dec 2014 2 Jul 2015 Kt Corporation Biology-related data mining * 29 Jul 2014 4 Feb 2016 International Business Machines Corporation Changed Answer Notification in a Question and Answer System * 29 Jun 2015 23 Sep 2015 华东师范大学 Unsupervised automatic Q&A method based on semantic web.