Novel Software Assistant for Automating Knowledge Capture

知识库系统（KBS）对于知识交流和学习至关重要，已被证明可以降低成本，缓解劳动力短缺和刺激全球创新。KBS的关键限制是知识获取（KA）——以可计算的格式提取专家知识。KA的准确性高度依赖于面试官的技能和专家表达知识的能力，项目很可能在这个阶段失败，导致“知识获取瓶颈”。experiicom开发了一种新颖的KA方法，SOLARAcquire (SA)，获得专利并商业化：不依赖采访者，它通过使用布尔（封闭）问题的结构化面试过程来帮助表达，从而产生可计算的输出，详尽地记录个人的决策并编码他们在任何主题上的全部专业知识。SA的好处得到了全球组织的认可，然而，有一些障碍阻碍了更广泛的使用：缺乏可扩展性，劳动力密集，需要与专家面对面面谈约20小时，成本高昂（每天1000英镑）。基于现有的客户需求和广泛的市场研究，experiicom已经在开发自然语言处理KA软件助手（NLP KAA）中确定了明确的商业机会，以使experiicom的方法实现自动化，从而消除阻碍B2B应用程序更广泛开发的障碍，其中存在重大的市场机会（2014年全球市场价值67.8亿美元）。然而，B2B应用程序的KA的关键功能需要进一步探索，以调查NLP KAA的可行性：- SA方法的完全自动化，这样，通过与机器的交互，复杂主题领域的专家可以成功地从他们那里获得“n维”知识-自然语言交流包括代词的正确翻译和语法正确的句子重新表述。该项目将产生用于NLP KAA的PoC算法。进一步的原型设计将在2017年推出市场

Knowledge Base Systems (KBS) are vital for knowledge exchange & learning, proven toreduce costs, alleviate workforce shortfalls & stimulate innovation globally. The keylimitation of KBS is Knowledge Acquisition (KA) - extracting expert knowledge in acomputable format. Accuracy of KA is highly dependent on interviewer skill & expert abilityto articulate knowledge, & projects are likely to fail at this stage causing a “KnowledgeAcquisition Bottleneck”.Empiricom has developed, patented & commercialised a novel KA methodology, SOLARAcquire (SA): free from interviewer dependency, it assists articulation via a structuredinterview process using Boolean (closed) questions to produce a computable output thatexhaustively documents an individual’s decision-making & encodes their entire expertise onany subject. Benefits of SA are recognised by global orgs, however barriers prevent wider mktuptake: lacks scalability, labour intensive requiring ~20 hrs of face-to-face interviews withexperts at a high cost (£1k/day).Based on existing customer demand & extensive mkt research, Empiricom have identified aclear business opportunity in the development of a Natural Language Processing KA softwareassistant (NLP KAA), to enable Empiricom’s methodology to be automated thus removingbarriers preventing wider exploitation in B2B apps where a significant mkt opportunity exists(global mkt worth $6.78bn in 2014).However, critical functionality of the KA for B2B apps requires further exploration, toinvestigate the feasibility of a NLP KAA with:- Full automation of the SA method such that, via an interaction with a machine, an expert in acomplex subject area could successfully have “n-dimensional” knowledge captured from them- Natural language communication incl. correct translation of pronouns & grammaticallycorrectsentential reformulations.The project will result in PoC algorithms for the NLP KAA. Further prototyping will followwith expected mkt intro 2017