[1] HUANG J Z, DING S Q, WANG H F, et al. Learning to recommend related entities with serendipity for Web search users[J]. ACM transactions on Asian and low-resource language information processing, 2018, 17(3):1-22.
[2] POUND J, MIKA P, ZARAGOZA H. Ad-Hoc object retrieval in the Web of data[C]//Proceedings of the 19th international conference on World Wide Web. New York:ACM Press, 2010:771-780.
[3] SUCHANEK F M, KASNECI G, WEIKUM G. YAGO:a core of semantic knowledge[C]//Proceedings of the 16th international conference on World Wide Web. New York:ACM Press, 2007:697-706.
[4] HOFFART J, SUCHANEK F M, BERBERICH K, et al. YAGO2:a spatially and temporally enhanced knowledge base from Wikipedia[J]. Artificial intelligence, 2013,194(1):28-61.
[5] MAHDISOLTANI F, BIEGA J, SUCHANEK F. YAGO3:a knowledge base from multilingual Wikipedias[EB/OL].[2020-05-11]. https://hal-imt.archives-ouvertes.fr/hal-01699874/document.
[6] AUER S, BIZER C, KOBILAROV G, et al. DBpedia:a nucleus for a web of open data[C]//Proceedings of the 6th international semantic Web conference. Berlin:Springer-Verlag, 2007:722-735.
[7] BOLLACKER K, EVANS C, PARITOSH P, et al. Freebase:a collaboratively created graph database for structuring human knowledge[C]//Proceedings of the 2008 ACM SIGMOD international conference on management of data. New York:ACM Press, 2008:1247-1250.
[8] CARLSON A, BETTERIDGE J, KISIEL B, et al. Toward an architecture for never-ending language learning[C]//Proceedings of the 24th AAAI conference on artificial intelligence. Menlo Park:AAAI, 2010:1306-1313.
[9] WU W, LI H, WANG H, et al. Probase:a probabilistic taxonomy for text understanding[C]//Proceedings of the 2012 ACM SIGMOD international conference on management of data. New York:ACM Press, 2012:481-492.
[10] 徐增林,盛泳潘,贺丽荣,等.知识图谱技术综述[J].电子科技大学学报,2016,45(4):589-606.
[11] 刘峤,李杨,段宏,等.知识图谱构建技术综述[J].计算机研究与发展,2016,53(3):582-600.
[12] 林海伦,王元卓,贾岩涛,等.面向网络大数据的知识融合方法综述[J].计算机学报,2017,40(1):1-27.
[13] QUILLIAN M R. Word concepts:a theory and simulation of some basic semantic capabilities[J].Behavioral science, 1967,12(5):410-430.
[14] SIMMONS R F.Natural language question answering systems:1969[J].Communications of the ACM, 1970,13(1):15-30.
[15] BIZER C, HEATH T, BERNERS-LEE T. Linked data-the story so far[J].International journal on semantic Web and information systems, 2009,5(3):1-12.
[16] DONG X L, GABRILOVICH E, HEITZ G, et al. Knowledge vault:a Web-scale approach to probabilistic knowledge fusion[C]//Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining. New York:ACM Press, 2014:601-610.
[17] LAMURIAS A, FERREIRA J D, CLARKE L A, et al. Generating a tolerogenic cell therapy knowledge graph from literature[J]. Frontiers in immunology, 2017,8(11):1-12.
[18] MUNGALL C J, MCMURRY J A, KÖHLER S, et al. The monarch initiative:an integrative data and analytic platform connecting phenotypes to genotypes across species[J]. Nucleic acids research, 2017,45(1):712-722.
[19] YU T, LI J, YU Q, et al. Knowledge graph for TCM health preservation:design, construction, and applications[J]. Artificial intelligence in medicine, 2017,77(3):48-52.
[20] SHI L, LI S, YANG X, et al. Semantic health knowledge graph:semantic integration of heterogeneous medical knowledge and services[J]. BioMed research international, 2017, Article ID 2858423.
[21] FATHALLA S. Detecting human diseases relatedness[J]. International journal on semantic web and information systems, 2018,14(3):120-133.
[22] BAKAL G, TALARI P, KAKANI E V, et al. Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations[J]. Journal of biomedical informatics, 2018,82(6):189-199.
[23] WANG X, WANG Y, GAO C, et al. Automatic diagnosis with efficient medical case searching based on evolving graphs[J]. IEEE access, 2018,6:53307-53318.
[24] CHI Y, YU C, QI X, et al. Knowledge management in healthcare sustainability:a smart healthy diet assistant in traditional Chinese medicine culture[J]. Sustainability, 2018,10(11):4197-4217.
[25] RUAN T, HUANG Y, LIU X, et al. QAnalysis:a question-answer driven analytic tool on knowledge graphs for leveraging electronic medical records for clinical research[J]. BMC medical informatics and decision making, 2019,19(1):82-94.
[26] ADAMS B, JANOWICZ K. Thematic signatures for cleansing and enriching place-related linked data[J]. International journal of geographical information science, 2015,29(4):556-579.
[27] FANG Y X, CHENG R, LI X D, et al. Effective community search over large spatial graphs[J].Proceedings of the VLDB endowment, 2017,10(6):709-720.
[28] QIAO B, FANG K, CHEN Y, et al. Building thesaurus-based knowledge graph based on schema layer[J]. Cluster computing, 2017,20(1):81-91.
[29] GU L, XIA Y, YUAN X, et al. Research on the model for tobacco disease prevention and control based on case-based reasoning and knowledge graph[J]. Filomat, 2018,32(5):1947-1952.
[30] CHI Y, QIN Y, SONG R, et al. Knowledge graph in smart education:a case study of entrepreneurship scientific publication management[J]. Sustainability, 2018,10(4):995-1015.
[31] AYALA-GÓMEZ F, DARÓCZY B, BENCZU'R A, et al. Global citation recommendation using knowledge graphs[J]. Journal of intelligent & fuzzy systems, 2018,34(5):3089-3100.
[32] LIU D, LAI C. Mining group-based knowledge flows for sharing task knowledge[J]. Decision support systems, 2011,50(2):370-386.
[33] TOMEO P, FERNÁNDEZ-TOBÍAS I, CANTADOR I, et al. Addressing the cold start with positive-only feedback through semantic-based recommendations[J]. International journal of uncertainty, fuzziness and knowledge-based systems, 2017,25(S2):57-78.
[34] KARIM F, LYTRA I, MADER C, et al. DESERT:a continuous SPARQL query engine for on-demand query answering[J]. International journal of semantic computing, 2018,12(3):373-397.
[35] 张香玲, 陈跃国, 马登豪,等.实体搜索综述[J].软件学报.2017,28(6):1584-1605.
[36] NEUMAYER R, BALOG K, NORVAG K. On the modeling of entities for ad-hoc entity search in the Web of data[C]//Proceedings of the advances in information retrieval, 33rd European conference on IR research. Berlin:Springer-Verlag, 2012:133-145.
[37] BALOG K, NØRVÅG K. On the use of semantic knowledge bases for temporally-aware entity retrieval[C]//Proceedings of the 5th workshop on exploiting semantic annotations in information retrieval. New York:ACM Press, 2012:1-2.
[38] LASHKARI F, ENSAN F, BAGHERI E, et al. Efficient indexing for semantic search[J]. Expert systems with applications, 2017,73(5):92-114.
[39] KATIB A, SLAVOV V, RAO P. RIQ:fast processing of SPARQL queries on RDF quadruples[J]. Journal of Web semantics, 2016,37-38(S):90-111.
[40] HAO Y, LI G, YUAN P, et al. An association-oriented partitioning approach for streaming graph query[EB/OL].[2020-05-11].http://downloads.hindawi.com/journals/sp/2017/2573592.pdf.
[41] ZHENG W, LIAN X, ZOU L, et al. Online subgraph skyline analysis over knowledge graphs[J]. IEEE transactions on knowledge and data engineering, 2016,28(7):1805-1819.
[42] SONG Q, WU Y, LIN P, et al. Mining summaries for knowledge graph search[J]. IEEE transactions on knowledge and data engineering, 2018,30(10):1887-1900.
[43] LI J, WU F D, YANG X,et al.APPQKW:an efficient algorithm for processing path queries on the knowledge graph under ecosystem[J].Ekoloji, 2019,28(107):2031-2040.
[44] YUAN Y, WANG G, CHEN L, et al. Efficient pattern matching on big uncertain graphs[J]. Information sciences, 2016,339(4):369-394.
[45] LIN X, PENG Y, CHOI B, et al. Human-powered data cleaning for probabilistic reachability queries on uncertain graphs[J]. IEEE transactions on knowledge and data engineering, 2017,29(7):1452-1465.
[46] HE L, LIU B, LI G, et al. Knowledge base completion by variational bayesian neural tensor decomposition[J]. Cognitive computation, 2018,10(6):1075-1084.
[47] KLIEGR T, ZAMAZAL O. LHD 2.0:a text mining approach to typing entities in knowledge graphs[J]. Journal of Web semantics, 2016,39(8):47-61.
[48] CAO T H, HUYNH D T. Subsumption degrees between entity types and names for approximate knowledge retrieval[J].International journal of uncertainty fuzziness and knowledge-based systems, 2007,15(1):21-42.
[49] JIN J, LUO J, KHEMMARAT S, et al. Querying Web-scale knowledge graphs through effective pruning of search space[J]. IEEE transactions on parallel and distributed systems, 2017,28(8):2342-2356.
[50] SUN Y Z, HAN J W, YAN X F, et al. PathSim:meta path-based top-K similarity search in heterogeneous information networks[J]. PVLDB, 2011,4(11):992-1003.
[51] MOTTIN D, LISSANDRINI M, VELEGRAKIS Y, et al. Exemplar queries:a new way of searching[J]. The VLDB journal, 2016,25(6):741-765.
[52] JAYARAM N, KHAN A, LI C, et al. Querying knowledge graphs by example entity tuples[J]. IEEE transactions on knowledge and data engineering, 2015,27(10):2797-2811.
[53] GUPTA M, BENDERSKY M. Information retrieval with verbose queries[J].Foundations and trends in information retrieval, 2015,9(3/4):209-354.
[54] ZHENG W, YU J X, ZOU L, et al. Question answering over knowledge graphs[J]. Proceedings of the VLDB endowment, 2018,11(11):1373-1386.
[55] SHIN S, JIN X, JUNG J, et al. Predicate constraints based question answering over knowledge graph[J]. Information processing & management, 2019,56(3):445-462.
[56] YAHYA M, BERBERICH K, RAMANATH M, et al. Exploratory querying of extended knowledge graphs[J]. Proceedings of the VLDB endowment, 2016,9(13):1521-1524.
[57] CHEN J, CHEN Y, ZHANG X, et al. Entity set expansion with semantic features of knowledge graphs[J]. Journal of Web semantics, 2018,52/53(10):33-44.
[58] METZGER S, SCHENKEL R, SYDOW M. QBEES:query-by-example entity search in semantic knowledge graphs based on maximal aspects, diversity-awareness and relaxation[J]. Journal of intelligent information systems, 2017,49(3):333-366.
[59] SHAN Y, LI M, CHEN Y. Constructing target-aware results for keyword search on knowledge graphs[J]. Data & knowledge engineering, 2017,110(7):1-23.
[60] ZHENG W G, ZOU L, et al. Semantic SPARQL similarity search over RDF knowledge graphs[J].Proceedings of the VLDB endowment, 2016,9(11):840-851.
[61] ZHENG W, CHENG H, YU J X, et al. Interactive natural language question answering over knowledge graphs[J]. Information sciences, 2019,481(5):141-159.
[62] ARENAS M, CUENCA GRAU B, KHARLAMOV E, et al. Faceted search over RDF-based knowledge graphs[J]. Journal of Web semantics, 2016,37-38(3):55-74.
[63] ZHANG G, LI C. Maverick[J]. Proceedings of the VLDB endowment, 2018,11(12):1934-1937.
[64] AEBELOE C, MONTOYA G, SETTY V, et al. Discovering diversified paths in knowledge bases[J]. Proceedings of the VLDB endowment, 2018,11(12):2002-2005.
[65] LISSANDRINI M, MOTTIN D, VELEGRAKIS Y, et al. X(2)Q:Your personal example-based graph explorer[J]. Proceedings of the VLDB endowment, 2018,11(12):2026-2029.
[66] ARNAOUT H, ELBASSUONI S. Effective searching of RDF knowledge graphs[J]. Journal of Web semantics, 2018,48(1):66-84.
[67] TONON A, CATASTA M, PROKOFYEV R, et al. Contextualized ranking of entity types based on knowledge graphs[J]. Journal of Web semantics, 2016,37/38(3):170-183.
[68] BIN B I, HAO M A, HSU BO-JUNE (PAUL), et al. Learning to recommend related entities to search users[C]//Proceedings of the 8th ACM international conference on Web search and data mining. New York:Association for Computing Machinery, 2015:139-148.
[69] BLEI M D, NG Y A, JORDAN I M. Latent dirichlet allocation[J]. Journal of machine learning research, 2003, 3(4/5):993-1022.
[70] LIN Y K, LIU Z Y, SUN M S, et al. Learning entity and relation embeddings for knowledge graph completion[EB/OL].[2020-05-11]. https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9571/9523.
[71] JI G L, LIU K, HE S Z, et al. Knowledge graph completion with adaptive sparse transfer matrix[EB/OL].[2020-05-11]. https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/11982/11693.
[72] ALTHOFF T, DONG X L, MURPHY K, et al. TimeMachine:timeline generation for knowledge-base entities[C]//Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. New York:Association for Computing Machinery, 2015:19-28.
[73] VOSKARIDES N, MEIJ E, DE RIJKE M. Generating descriptions of entity relationships[C]//Proceedings of 39th European Conference on IR Research. Switzerland:Springer Nature, 2017:317-330.
[74] WEN J, LI J, MAO Y, et al. On the representation and embedding of knowledge bases beyond binary relations[EB/OL].[2020-05-11].https://arxiv.org/pdf/1604.08642.pdf.
[75] LEE J Y, MIN J K, OH A, et al. Effective ranking and search techniques for Web resources considering semantic relationships[J]. Information processing and management, 2014,50(1):132-155.
[76] SUCHANEK F M, GROSS-AMBLARD D, ABITEBOUL S. Watermarking for ontologies[C]//The Semantic Web - ISWC 2011. Berlin:Springer-Verlag, 2011:697-713.
[77] MIN B, GRISHMAN R, WAN L, et al. Distant supervision for relation extraction with an incomplete knowledge base[C]//Proceedings of the 2013 conference of the North American chapter of the Association for Computational Linguistics:human language technologies. Stroudsburg:ACL, 2013:777-782.
[78] CHEN Y, GOLDBERG S L, WANG D Z, et al. Ontological pathfinding[C]//Proceedings of the 2016 ACM SIGMOD international conference on management of data. New York:ACM Press, 2016:835-846.
[79] LIN Y K, LIU Z Y, SUN M S, et al. Learning entity and relation embeddings for knowledge graph completion[C]//Proceedings of the 29th conference on artificial intelligence. Palo Alto:AAAI, 2015:2181-2187.
[80] GARDNER M, MITCHELL T M. Efficient and expressive knowledge base completion using subgraph feature extraction[C]//Proceedings of the conference on empirical methods in natural language processing. Stroudsburg:Association for Computational Linguistics, 2015:1488-1498.
[81] WIENAND D, PAULHEIM H. Detecting incorrect numerical data in DBpedia[C]//Proceedings of the semantic Web:trends and challenges, 11th internationl conference. Berlin:Springer-Verlag, 2014:504-518.
[82] UYAR A, ALIYU F M. Evaluating search features of Google knowledge graph and Bing satori[J]. Online information review, 2015,39(2):197-213.
[83] FIONDA V, PIRRÒ G. Explaining and querying knowledge graphs by relatedness[J]. Proceedings of the VLDB endowment, 2017,10(12):1913-1916.
[84] YAN J, XU C, LI N, et al. Optimizing model parameter for entity summarization across knowledge graphs[J]. Journal of combinatorial optimization, 2019,37(1):293-318.
[85] PLA KARIDI D, STAVRAKAS Y, VASSILIOU Y. Tweet and followee personalized recommendations based on knowledge graphs[J]. Journal of ambient intelligence and humanized computing, 2018,9(6):2035-2049.
