Towards Automatic Mathematical Exercise Solving

Baldoni R, Coppa E, D’Elia DC, Demetrescu C, Finocchi I (2018) A survey of symbolic execution techniques. ACM Comput Surv 51(3):50

Bollacker KD, Evans C, Paritosh P, Sturge T, Taylor J (2008) Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the ACM SIGMOD international conference on management of data, SIGMOD 2008, Vancouver, BC, Canada, June 10–12, 2008, pp 1247–1250

Carlson A, Betteridge J, Kisiel B, Settles B, Hruschka Jr E. R, Mitchell T. M (2010) Toward an architecture for never-ending language learning. In: Proceedings of the twenty-fourth conference on artificial intelligence (AAAI 2010), vol 5, Atlanta, p 3

Chai C, Fan J, Li G (2018) Incentive-based entity collection using crowdsourcing. In: 34th IEEE international conference on data engineering, ICDE 2018, Paris, France, April 16–19, 2018, pp 341–352

Chai C, Fan J, Li G, Wang J, Zheng Y (2018) Crowd-powered data mining. CoRR, abs/1806.04968

Chai C, Fan J, Li G, Wang J, Zheng Y (2019) Crowdsourcing database systems: overview and challenges. In: 35th IEEE international conference on data engineering, ICDE 2019, Macao, China, April 8–11, 2019, pp 2052–2055

Chai C, Li G, Li J, Deng D, Feng J (2016) Cost-effective crowdsourced entity resolution: a partial-order approach. In: Proceedings of the 2016 international conference on management of data, SIGMOD conference 2016, San Francisco, CA, USA, June 26–July 01, 2016, pp 969–984

Chai C, Li G, Li J, Deng D, Feng J (2018) A partial-order-based framework for cost-effective crowdsourced entity resolution. VLDB J 27(6):745–770

Cousot P, Cousot R (1977) Abstract interpretation: a unified lattice model for static analysis of programs by construction or approximation of fixpoints. In: Proceedings of the 4th ACM SIGACT-SIGPLAN symposium on Principles of programming languages, ACM, pp 238–252

Dongo I, Cardinale Y, Chbeir R (2018) Rdf-f: Rdf datatype inferring framework. Data Sci. Eng. 3(2):115–135

Dumitrache A, Aroyo L, Welty C (2018) Crowdsourcing ground truth for medical relation extraction. TiiS 8(2):11:1–11:20

Fletcher R, Leyffer S (2003) Filter-type algorithms for solving systems of algebraic equations and inequalities. In: High performance algorithms and software for nonlinear optimization, Springer, pp 265–284

Ganesalingam M, Gowers WT (2017) A fully automatic theorem prover with human-style output. J Autom Reason 58(2):253–291

Gao Y, Miao X (2018) Query processing over incomplete databases. Synthesis lectures on data management. Morgan & Claypool Publishers, San Rafael

Guu K, Miller J, Liang P (2015) Traversing knowledge graphs in vector space. In: Proceedings of the 2015 conference on empirical methods in natural language processing, EMNLP 2015, Lisbon, Portugal, September 17–21, 2015, pp 318–327

Inc WR (2018) Mathematica, Version 11.3. Champaign, IL

King JC (1976) Symbolic execution and program testing. Commun ACM 19(7):385–394

Kojiri T, Hosono S, Watanabe T (2005) Automatic generation of answers using solution network for mathematical exercises. In: International conference on knowledge-based and intelligent information and engineering systems, Springer, pp 1303–1309

Li G, Chai C, Fan J, Weng X, Li J, Zheng Y, Li Y, Yu X, Zhang X, Yuan H (2017) CDB: optimizing queries with crowd-based selections and joins. In: Proceedings of the 2017 ACM international conference on management of data, SIGMOD conference 2017, Chicago, IL, USA, May 14–19, 2017, pp 1463–1478

Li G, Chai C, Fan J, Weng X, Li J, Zheng Y, Li Y, Yu X, Zhang X, Yuan H (2018) CDB: a crowd-powered database system. PVLDB 11(12):1926–1929

Li K, Li G (2018) Approximate query processing: What is new and where to go? Data Sci Eng 3(4):379–397

Lin P, Song Q, Wu Y (2018) Fact checking in knowledge graphs with ontological subgraph patterns. Data Sci Eng 3(4):341–358

McCoy AB, Wright A, Laxmisan A, Ottosen MJ, McCoy JA, Butten D, Sittig DF (2012) Development and evaluation of a crowdsourcing methodology for knowledge base construction: identifying relationships between clinical problems and medications. JAMIA 19(5):713–718

Meng R, Chen L, Tong Y, Zhang CJ (2017) Knowledge base semantic integration using crowdsourcing. IEEE Trans Knowl Data Eng 29(5):1087–1100

Meurer A, Smith CP, Paprocki M, Čertík O, Kirpichev SB, Rocklin M, Kumar A, Ivanov S, Moore JK, Singh S et al (2017) Sympy: symbolic computing in python. PeerJ Comput Sci 3:e103

Meurer A, Smith CP, Paprocki M, Čertík O, Kirpichev SB, Rocklin M, Kumar A, Ivanov S, Moore JK, Singh S, Rathnayake T, Vig S, Granger BE, Muller RP, Bonazzi F, Gupta H, Vats S, Johansson F, Pedregosa F, Curry MJ, Terrel AR, Roučka v, Saboo A, Fernando I, Kulal S, Cimrman R, Scopatz A (2017) Sympy: symbolic computing in python. PeerJ Comput Sci 3:e103

Miao X, Gao Y, Guo S, Liu W (2018) Incomplete data management: a survey. Front Comput Sci 12(1):4–25

Neo4j I. Neo4j, Version 1.1.12. https://neo4j.com/

Polyak BT (1964) Gradient methods for solving equations and inequalities. USSR Comput Math Math Phys 4(6):17–32

Seifert C, Granitzer M, Höfler P, Mutlu B, Sabol V, Schlegel K, Bayerl S, Stegmaier F, Zwicklbauer S, Kern R (2013) Crowdsourcing fact extraction from scientific literature. In: Human–computer interaction and knowledge discovery in complex, unstructured, big data: third international workshop, HCI-KDD 2013, Held at SouthCHI 2013, Maribor, Slovenia, July 1–3, 2013. Proceedings, pp 160–172

Tomás AP, Leal JP (2003) A clp-based tool for computer aided generation and solving of maths exercises. In: International symposium on practical aspects of declarative languages, Springer, pp 223–240

Toutanova K, Lin V, Yih W.-t, Poon H, Quirk C (2016) Compositional learning of embeddings for relation paths in knowledge base and text. In: Proceedings of the 54th annual meeting of the association for computational linguistics, vol 1, pp 1434–1444

Xin H, Meng R, Chen L (2018) Subjective knowledge base construction powered by crowdsourcing and knowledge base. In: Proceedings of the 2018 international conference on management of data, SIGMOD conference 2018, Houston, TX, USA, June 10–15, 2018, pp 1349–1361

Yan Q, Huang H, Gao Y, Ying C, Hu Q, Qian T, He Q (2016) Modeling for noisy labels of crowd workers. In: 18th Asia-Pacific web conference on APWeb 2016Web technologies and applications, Suzhou, China, September 23–25, 2016. Proceedings, part II, pp 227–238

Zhang Y, Dai H, Kozareva Z, Smola AJ, Song L (2018) Variational reasoning for question answering with knowledge graph. In: Proceedings of the thirty-second AAAI conference on artificial intelligence (AAAI-18), pp 6069–6076

Zhao T, Huang Y, Yang S, Luo Y, Feng J, Wang Y, Yuan H, Pan K, Li K, Li H, et al (2019) Mathgraph: a knowledge graph for automatically solving mathematical exercises. In: International conference on database systems for advanced applications, Springer, pp 760–776

Zheng W, Yu JX, Zou L, Cheng H (2018) Question answering over knowledge graphs: question understanding via template decomposition. Proc VLDB Endow 11(11):1373–1386