A more natural way for non-expert users to express their tasks in an open-ended set is to use natural language. In this case,a human-centered intelligent agent/robot is required to be able to understand and generate p...A more natural way for non-expert users to express their tasks in an open-ended set is to use natural language. In this case,a human-centered intelligent agent/robot is required to be able to understand and generate plans for these naturally expressed tasks. For this purpose, it is a good way to enhance intelligent robot's abilities by utilizing open knowledge extracted from the web, instead of hand-coded knowledge. A key challenge of utilizing open knowledge lies in the semantic interpretation of the open knowledge organized in multiple modes, which can be unstructured or semi-structured, before one can use it.Previous approaches used a limited lexicon to employ combinatory categorial grammar(CCG) as the underlying formalism for semantic parsing over sentences. Here, we propose a more effective learning method to interpret semi-structured user instructions. Moreover, we present a new heuristic method to recover missing semantic information from the context of an instruction. Experiments showed that the proposed approach renders significant performance improvement compared to the baseline methods and the recovering method is promising.展开更多
The early concept of knowledge graph originates from the idea of the semantic Web,which aims at using structured graphs to model the knowledge of the world and record the relationships that exist between things.Curren...The early concept of knowledge graph originates from the idea of the semantic Web,which aims at using structured graphs to model the knowledge of the world and record the relationships that exist between things.Currently publishing knowledge bases as open data on the Web has gained significant attention.In China,Chinese Information Processing Society of China(CIPS)launched the OpenKG in 2015 to foster the development of Chinese Open Knowledge Graphs.Unlike existing open knowledge-based programs,OpenKG chain is envisioned as a blockchain-based open knowledge infrastructure.This article introduces the first attempt at the implementation of sharing knowledge graphs on OpenKG chain,a blockchain-based trust network.We have completed the test of the underlying blockchain platform,and the on-chain test of OpenKG’s data set and tool set sharing as well as fine-grained knowledge crowdsourcing at the triple level.We have also proposed novel definitions:K-Point and OpenKG Token,which can be considered to be a measurement of knowledge value and user value.1,033 knowledge contributors have been involved in two months of testing on the blockchain,and the cumulative number of on-chain recordings triggered by real knowledge consumers has reached 550,000 with an average daily peak value of more than 10,000.For the first time,we have tested and realized on-chain sharing of knowledge at entity/triple granularity level.At present,all operations on the data sets and tool sets at OpenKG.CN,as well as the triplets at OpenBase,are recorded on the chain,and corresponding value will also be generated and assigned in a trusted mode.Via this effort,OpenKG chain looks forward to providing a more credible and traceable knowledge-sharing platform for the knowledge graph community.展开更多
This article invites us to a concise walk through the past,offering insights defined by the major challenges science encountered during the centuries.Some lessons for today and tomorrow are enumerated in the three sec...This article invites us to a concise walk through the past,offering insights defined by the major challenges science encountered during the centuries.Some lessons for today and tomorrow are enumerated in the three sections of the article,and they go beyond the relatively few perspectives offered by today’s Data Science:Open Science(OS)is what has always happened and is nothing new,because science has always sought to be open.Esthetical values played a relevant role in the past.Former scientists recognized the intrinsic relation between the way they opened science and the way they followed the principles of beauty and the sense of esthetic.Their groundbreaking heritage still inspires us in being ready to open new ways in science.Whereas Latin was the original lingua franca of European science,and English is the recent lingua franca,the new lingua franca is software.Pieces of software are the filter,which connect researchers to the world,through layers of data.They assist in observing,in choosing,and in selecting.Open scientists should be aware of the fact that their autonomy in science depends on the quality of these pieces.Another lesson is that ethics-regarded as a source of innovative activities-must be a core component of innovative processes in OS,because society needs a responsible use of data and algorithms in corresponding practices that serve OS.展开更多
基金supported by the National Natural Science Foundation of China(61175057)the USTC Key-Direction Research Fund(WK0110000028)
文摘A more natural way for non-expert users to express their tasks in an open-ended set is to use natural language. In this case,a human-centered intelligent agent/robot is required to be able to understand and generate plans for these naturally expressed tasks. For this purpose, it is a good way to enhance intelligent robot's abilities by utilizing open knowledge extracted from the web, instead of hand-coded knowledge. A key challenge of utilizing open knowledge lies in the semantic interpretation of the open knowledge organized in multiple modes, which can be unstructured or semi-structured, before one can use it.Previous approaches used a limited lexicon to employ combinatory categorial grammar(CCG) as the underlying formalism for semantic parsing over sentences. Here, we propose a more effective learning method to interpret semi-structured user instructions. Moreover, we present a new heuristic method to recover missing semantic information from the context of an instruction. Experiments showed that the proposed approach renders significant performance improvement compared to the baseline methods and the recovering method is promising.
文摘The early concept of knowledge graph originates from the idea of the semantic Web,which aims at using structured graphs to model the knowledge of the world and record the relationships that exist between things.Currently publishing knowledge bases as open data on the Web has gained significant attention.In China,Chinese Information Processing Society of China(CIPS)launched the OpenKG in 2015 to foster the development of Chinese Open Knowledge Graphs.Unlike existing open knowledge-based programs,OpenKG chain is envisioned as a blockchain-based open knowledge infrastructure.This article introduces the first attempt at the implementation of sharing knowledge graphs on OpenKG chain,a blockchain-based trust network.We have completed the test of the underlying blockchain platform,and the on-chain test of OpenKG’s data set and tool set sharing as well as fine-grained knowledge crowdsourcing at the triple level.We have also proposed novel definitions:K-Point and OpenKG Token,which can be considered to be a measurement of knowledge value and user value.1,033 knowledge contributors have been involved in two months of testing on the blockchain,and the cumulative number of on-chain recordings triggered by real knowledge consumers has reached 550,000 with an average daily peak value of more than 10,000.For the first time,we have tested and realized on-chain sharing of knowledge at entity/triple granularity level.At present,all operations on the data sets and tool sets at OpenKG.CN,as well as the triplets at OpenBase,are recorded on the chain,and corresponding value will also be generated and assigned in a trusted mode.Via this effort,OpenKG chain looks forward to providing a more credible and traceable knowledge-sharing platform for the knowledge graph community.
文摘This article invites us to a concise walk through the past,offering insights defined by the major challenges science encountered during the centuries.Some lessons for today and tomorrow are enumerated in the three sections of the article,and they go beyond the relatively few perspectives offered by today’s Data Science:Open Science(OS)is what has always happened and is nothing new,because science has always sought to be open.Esthetical values played a relevant role in the past.Former scientists recognized the intrinsic relation between the way they opened science and the way they followed the principles of beauty and the sense of esthetic.Their groundbreaking heritage still inspires us in being ready to open new ways in science.Whereas Latin was the original lingua franca of European science,and English is the recent lingua franca,the new lingua franca is software.Pieces of software are the filter,which connect researchers to the world,through layers of data.They assist in observing,in choosing,and in selecting.Open scientists should be aware of the fact that their autonomy in science depends on the quality of these pieces.Another lesson is that ethics-regarded as a source of innovative activities-must be a core component of innovative processes in OS,because society needs a responsible use of data and algorithms in corresponding practices that serve OS.
