Answering Who/When, What, How, Why through Constructing Data Graph, Information Graph, Knowledge Graph and Wisdom Graph

Knowledge graphs have been widely adopted, in large part owing to their schema-less nature. It enables knowledge graphs to grow seamlessly and allows for new relationships and entities as needed. Natural language questions are the most intuitive way of formulating an information need. People can formulate questions to express their information needs. Natural language questions as a query language present an ideal compromise between keyword and structured querying. Questions can be used to express complex information needs that cannot be expressed as keywords without a significant loss in structure and semantics. Knowledge graph has abundant natural semantics and can contain various and more complete information. Its expression mechanism is closer to natural language. We propose to clarify the expression of knowledge graph as a whole. We use knowledge graph to solve the Five Ws problems respectively which are guided by interrogative words such as who/when, what, how and why. We also propose to specify knowledge graph in a progressive manner as four basic forms including data graph, information graph, knowledge graph and wisdom graph. 1

[1]  Anthony Mills,et al.  Data, Information, Knowledge, and Wisdom , 2011 .

[2]  Mohamed Yahya,et al.  Question answering and query processing for extended knowledge graphs , 2016 .

[3]  Ming-Wei Chang,et al.  Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base , 2015, ACL.

[4]  Nicola Fanizzi,et al.  Scalable Learning of Entity and Predicate Embeddings for Knowledge Graph Completion , 2015, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA).

[5]  Kuldar Taveter,et al.  eContractual choreography-language properties towards cross-organizational business collaboration , 2015, Journal of Internet Services and Applications.

[6]  Dongyan Zhao,et al.  Question Answering on Freebase via Relation Extraction and Textual Evidence , 2016, ACL.

[7]  Chaim Zins,et al.  Conceptual approaches for defining data, information, and knowledge , 2007, J. Assoc. Inf. Sci. Technol..

[8]  Gerhard Weikum,et al.  YAGO-QA: Answering Questions by Structured Knowledge Queries , 2011, 2011 IEEE Fifth International Conference on Semantic Computing.

[9]  Terje Aven,et al.  A conceptual framework for linking risk and the elements of the data-information-knowledge-wisdom (DIKW) hierarchy , 2013, Reliab. Eng. Syst. Saf..

[10]  Jason Weston,et al.  Question Answering with Subgraph Embeddings , 2014, EMNLP.

[11]  Ni Lao,et al.  Relational retrieval using a combination of path-constrained random walks , 2010, Machine Learning.

[12]  Mandar Joshi,et al.  Knowledge Graph and Corpus Driven Segmentation and Answer Inference for Telegraphic Entity-seeking Queries , 2014, EMNLP.

[13]  J. Carroll,et al.  Jena: implementing the semantic web recommendations , 2004, WWW Alt. '04.

[14]  Huanbo Luan,et al.  Modeling Relation Paths for Representation Learning of Knowledge Bases , 2015, EMNLP.

[15]  Olivier Serrat,et al.  The Five Whys Technique , 2017 .

[16]  John Miller,et al.  Traversing Knowledge Graphs in Vector Space , 2015, EMNLP.