TechKG: A Large-Scale Chinese Technology-Oriented Knowledge Graph

1 INTRODUCTION . Generally , a knowledge graph ( KG ) stores factual knowledge in the form of structural triplet like < h , r , t > , in which h and t are entities ( called head entity and tail entity respectively ) and r denotes the relation type from h to t. KG is a kind of valuable knowledge base and is important for many different kinds of AI-related applications . Nowadays , great achievements have been made in building large scale KGs . And there are many available KGs like FreeBase Bollacker et al . ( 2008 ) , WordNet Miller ( 1995 ) , YAGO Suchanek et al . ( 2007 ) , etc . Usually there are millions of entities and billions of relational facts in these KGs . With them , great progress have been made in lots of AI-related applications , such as knowledge graph embedding ( KGE ) , distantly supervised relation extraction ( RE ) , knowledge based question and answering ( KBQA ) , etc . However , there are still following two challenges inherited in these KG-dependent applications . First , almost all of these KGs can only be used in English-related applications . But the fact is that each language has its own characteristics . It is uncertain whether a model that performs well in an English-related KG-dependent task could still perform well in the corresponding task of other language . Thus there is a pressing need to build KGs of different languages other than English . Second , most of existing KGs are designed for general purpose . It is also uncertain whether a model that performs well in an application of general domain could still perform well in applications of specific domains , such as in the technical domains liking medicine , biology , electronics , etc . It is necessary to evaluate whether there are some new kinds of characteristics hidden in KGs that haven ’ t been noticed by researchers up to now . To address these issues , this work introduces TechKG , a large scale Chinese KG built from massive technical papers that are published in Chinese academic journals of different research domains . Compared with existing KGs , TechKG has following important characteristics . First , TechKG is a large scale Chinese KG that is technology-oriented . Totally , it comprises of over 280 million triplets that are built upon more than 60 million entities which come from more than 30 research domains . To the best of our knowledge , this is the first Chinese KG that is built by using multi-domain technical papers . Second , TechKG is a high quality KG . Both of its entities and relations are acquired in two ways . One is directly taking some components of a paper as entities and building relations between entities by simply re-organizing a paper ’ s components . The other is taking carefully selected domain terminologies as entities and building relations between them by some carefully designed heuristic rules . These two ways guarantee TechKG have high accuracies at both entity and relation levels . Third , TechKG can provide more kinds of information associated with the original papers besides common triplets . This makes it can be easily taken as a benchmark dataset for many diverse AIrelated applications , such as KGE , distantly supervised RE , NER , KBQA , text classification ( TC ) , machine translation ( MT ) , among others . Fourth , TechKG can be continually updated as long as there are new available published papers . As the needed data source is readily available in almost all languages , researchers can easily build similar KGs for other languages . In the rest of this paper , we first introduce the building process of TechKG . Then we analyze the characteristics of TechKG in detail . We also conduct some primary experiments using TechKG as a dataset to three diverse tasks ( see the Appendix part for detail ) . Both our analyses and experimental results show that lots of characteractics distinguish TechKG from existing KGs and it raises many new challenges for lots of existing applications . 2 RELATED WORK . At present , there are many available KGs , such as SUMONiles and Pease ( 2001 ) , Cyc Matuszek et al . ( 2006 ) , FreeBase Bollacker et al . ( 2008 ) , WordNet Miller ( 1995 ) , YAGO Suchanek et al . ( 2007 ) , YAGO2 Hoffart et al . ( 2013 ) , YAGO3 Mahdisoltani et al . ( 2015 ) , etc . Among them , FreeBase , WordNet , YAGO and some of their subsets are the most widely used KGs in many diverse applications . We will introduce them in the following . FreeBase Bollacker et al . ( 2008 ) is a database system designed to be a public repository of the word ’ s knowledge . Its design is inspired by Semantic Web and Wikipedia . It tries to merge the scalability of structured databases with the diversity of collaborative wikis into a practical and scalable database of structured general human knowledge . Totally , it contains more than 100 million asserts about topics spread over 4000 types , including people , media , locations , and many others . FB15k Bordes et al . ( 2013 ) is a subset of Freebase . It is one of the most widely used datasets for many AI-related tasks . Totally , it contains 14,951 entities , 592,213 triplets , and 1,345 different relations . A large fraction of the triplets in this KG describe facts about movies , actors , awards , etc . WordNetMiller ( 1995 ) is an electronic lexical database that is considered to be the most important resource available to researchers in computational linguistics , text analysis , and many related areas . Its design is inspired by psycholinguistic and computational theories of human lexical memory . In WordNet , English nouns , verbs , adjectives , and adverbs are organized into synonym sets , each represents one underlying lexicalized concept , and semantic relations link the synonym sets . Specifically , WordNet includes some kinds of semantic relations like synonymy , antonymy , hyponymy , memonymy , troponymy , entailment , etc . WN18 Bordes et al . ( 2013 ) is a subset of WordNet . Like FB15k , it is widely used in many AI-related tasks . Totally , it consists of 18 kinds of relations , 40,943 entities , and 151,442 triplets . Most of its triplets consist of hyponym and hypernym relations . YAGOSuchanek et al . ( 2007 ) is a light-weight and extensible ontology with high coverage and quality . It contains more than 1 million entities and 5 million facts . These facts are expressed by relations and are automatically extracted from Wikipedia and unified with WordNet by a carefully designed combination of rule-based and heuristic methods . In YAGO , the relations include the Is-A hierarchy as well as non-taxonomic relations like HasWonPrize , BornInYear , DiedInYear , etc . YAGO2 Hoffart et al . ( 2013 ) is an extension of YAGO knowledge base , in which entities , facts , and events are anchored in both time and space . YAGO2 is built automatically from Wikipedia , GeoNames , and WordNet . It contains 447 million facts and about 9.8 million entities . YAGO3 Mahdisoltani et al . ( 2015 ) is another extension of the YAGO knowledge base . It combines the information from the Wikipedia in 10 languages including English , German , French , Dutch , Italian , Spanish , Romanian , Polish , Arabic , and Farsi . The choice of languages was determined by the proficiency of the authors , so as to facilitate manual evaluation . Totally , there are 8,936,324 triplet facts built upon 4,595,906 entities . YAGO3-10 Mahdisoltani et al . ( 2015 ) is a subset of YAGO3 and is also widely used in many applications . It consists of entities which have a minimum of 10 relations each . Totally , it has 123,182 entities and 37 kinds of relations . Most of the triplets deal with descriptive attributes of people , such as citizenship , gender , profession , etc . NOTE that here we copy these KGs ’ statistics directly from their corresponding reference papers . There may be some differences for these statistics because almost all of these KGs are continually updating . 3 TECHKG BUILDING . Every day , there are massive Chinese technical papers published in journals of different research domains . These papers often have following characteristics . First , there is a clear structure for each of these papers . For example , each paper often has such components like : a title , an author list , an authors ’ affiliation list , keyword list , abstract , etc . Second , each technical paper can be accurately classified into a proper research domain according to the journal where this paper is published . This is because that each academic journal has its own scope for submissions . Taking “ Chinese Journal of Computers ” for example , it only focuses on the latest research in computer science domain . And it would never accept a paper in chemistry or artist domain . In other word , as long as we know the focused research domain of a journal , we can confidently classify all of its accepted papers into this research domain . There may be some exceptions , but the proportion would be very small . These characteristics inspire us that we can use technical papers as data source to build a large scale Chinese KG that is technology-oriented . 3.1 PAPER COLLECTION . We take technical papers as data source to build TechKG . These papers were mainly collected from journals ’ websites . According to the submission scopes of the journals1 , we classify all of them into 38 types : each of which corresponds to a specific research domain . For simplicity2 , we omit the body part of a paper and only collect its : title , author list , authors ’ affiliation list , keyword list , and abstract . If a paper ’ s these components also have English translations , the corresponding English parts will also be collected . Totally , over 70 million technical papers are collected . 3.2 ENTITY SELECTION . In TechKG , each component of a paper except abstract is taken as an entity . That is to say , title , author , keyword , and affiliation , either in Chinese or English , are all taken as entities . And the name of each research domain is also taken as an entity . Among the obtained entities , keywords account for a very large proportion . We had expected that all of them were high quality domain terminologies , but there are lots of exceptions . For example , in some papers of the computer science domain , there are keywords like “ three phases ” , “ two steps ” , “ generation ” , etc . Obviously , these keywords are not good domain terminologies for they have less to do with computer science . So we use atf * idf method to filter such kind of keywords . Specifically , we concatenate all the keyword lists in each research domain to form a long domain-related document . Then the tf value of a keyword in each domain can be computed . Different domain-related documents are viewed as the counterpart of each other , thus the idf value of a keyword can be computed accordingly . The higher a keyword ’ s tf * idf value , the more possible this keyword is a good domain terminology . 1Here we also use CNKI ’ s ( http : //www.cnki.net/ ) categories for reference , but we merge some similar research domains in CNKI and reduce the amount of research domains . 2It is difficult to effectively analyse the body part of a paper .

Summary: This paper introduces a large-scale knowledge graph database called TechKG, which is constructed from a massive repository of academic papers in Chinese. The authors have described in details the process and heuristics in use for constructing such knowledge base, and also reported important statistics and characteristics of the database, including duplicate name, imbalance issue etc. The main contribution is to provide a KG in Chinese, which wasn’t widely available compared to popular languages such as English.