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2018年1月16日 星期二

使用Microsoft Cognitive QnA Maker Service,快速建立線上客服問答服務


https://dotblogs.com.tw/maduka/2017/09/03/155724
AI在這兩年來一直都是一個很熱門的話題,自從去年微軟推出了LUIS(Language Understanding Intelligent Service, 語意辨識服務)以及Bot Framework後,越來越多的公司與第一線服務開始透過這兩個雲端功能打造自己的智慧無人客服或是問答系統。但是透過LUIS的訓練,必須花費大量的人力與時間並輸入大量的語句進行訓練,才能得到有效並準確的識別。2017年3月,微軟在Cognitive識別服務中,加入了QnA Maker的辨識服務,可以更快速的打造問答服務的訓練,並找出詢問內容中所需要的解答。
簡單來說,QnA Maker完整的機制就是可以將已經整理好的FAQ內容,透過文字檔、PDF檔或是Word檔的方式匯入至QnA Maker並進行訓練,完成後再透過Microsoft Bot Framework的介接,將這樣的問答內容串接至不同的Channel上,讓多種IM或是訊息溝通平台都可以完成Q&A的服務整合,看似很像以前透過LUIS的串接與整合方式,但是QnA Maker的訓練內容與辨識卻不用像LUIS那樣的複雜。使用者只要將常用的FAQ匯入並訓練,QnA Maker就會完成後續學習的機制與任務
QnA Maker這個辨識服務,透過問答的方式比對問題的內容並回傳有可能的答案,這樣快速方便的機制,除了省去需要大量訓練語意辨識的資料外,對於希望能快速建立無人客服或是線上機器人回覆的機制,大大加快了開發的效益並縮短開發時間。
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2017年4月23日 星期日

Agile Knowledge Engineering and Semantic Web (AKSW)

一個有關semantic web和knowledge engineering的研究群,http://aksw.org/About.html

hosted by the Chair of Business Information Systems (BIS) of the Institute of Computer Science (IfI) / University of Leipzig as well as the Institute for Applied Informatics (InfAI).

Goals
  • Development of methods, tools and applications for adaptive Knowledge Engineering in the context of the Semantic Web
  • Research of underlying Semantic Web technologies and development of fundamental Semantic Web tools and applications
  • Maturation of strategies for fruitfully combining the Social Web paradigms with semantic knowledge representation techniques

AKSW is committed to the free software, open source, open access and open knowledge movements.

Groups


The following subgroups belong to AKSW

  • Adaptive Information and Knowledge Engineering
    • Agile collaborative requirements engineering
    • Creation and evolution of knowledge bases from legacy databases
    • Software product-line engineering
    • Vocabulary alignment
  • Emergent Semantics
    • Agile Knowledge Engineering
    • Distributed / Federated Social Networks
    • Linked Data
    • Semantic Software Engineering
    • Semantic Web Infrastructure
  • Knowledge Integration and Linked Data Technologies
    • Data Engineering
    • Data Integration
    • Data-driven Artificial Intelligence
    • DBpedia
    • Knowledge Engineering
    • Language Technology
  • Machine Learning and Ontology Engineering
    • Creating knowledge bases from weakly structured data
    • Quality assurance and enhancement in ontologies
    • Semi-automatic instance matching
    • Supervised Machine Learning in OWL/RDF knowledge bases
  • Semantic Abstraction
    • Knowledge Access, e.g., keyword-based search, question answering, and interfaces
    • Knowledge Extraction, e.g., extraction of RDF and OWL from unstructured data
    • Knowledge Integration, e.g., link discovery and linked data fusion
    • Knowledge Storage, e.g., federated queries, triple stores
    • Knowledge-Driven applications, e.g., industry 4.0, big data, benchmark

2017年2月14日 星期二

OAQA Biomedical Question Answering (BioASQ) System

This system uses the ECD/CSE framework (an extension to the Apache UIMA framework which support formal, declarative YAML-based descriptors for the space of system and component configurations to be explored during system optimization), BaseQA type system as well as various natural language processing and information retrieval algorithms and tools.
https://github.com/oaqa/bioasq

Document, concept, and snippet retrieval

Exact answer generation for factoid and list questions

Answer prediction for yes/no questions

Types of Bots: An Overview

Learn more about all the different varieties of bots, and what they can do for you http://botnerds.com/types-of-bots/ In this articl...