Skip to main content
SpringerLink
Log in

Information Retrieval and Artificial Intelligence

  • Chapter
  • First Online:
A Guided Tour of Artificial Intelligence Research

Abstract

Information Retrieval (IR) is a process involving activities related to human cognition and to knowledge management; as such, the definition of Information Retrieval Systems can benefit of the application of artificial intelligence techniques to account for the intrinsic uncertainty and imprecision that characterize the subjectivity of this task. This chapter presents a synthetic analysis of the IR task from an AI perspective and explores how AI techniques are employed within IR.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://wordnet.princeton.edu/.

  2. 2.

    http://mesh.inserm.fr/FrenchMesh/.

  3. 3.

    \(\models \) is a meta-language symbol, where \(s_1 \models s_2\) means that in any interpretation if \(s_1\) is true then \(s_2\) is also true.

References

  • Abdulahhad K (2014) Information retrieval (IR) modeling by logic and lattice. Application to conceptual IR. Theses, Université de Grenoble

    Google Scholar 

  • Abdulahhad K, Chevallet JP, Berrut C (2017) Logics, lattices and probability: the missing links to information retrieval. Comput J 60(7):995–1018

    MathSciNet  Google Scholar 

  • Abdullah HS, Hadi MJ (2014) Artificial bee colony based approach for web information retrieval. Eng Technol J 32(5):899–909

    Google Scholar 

  • Ai Q, Yang L, Guo J, Croft WB (2016) Analysis of the paragraph vector model for information retrieval. In: Proceedings of the 2016 ACM international conference on the theory of information retrieval, ACM, pp 133–142

    Google Scholar 

  • Al-Khateeb B, Al-Kubaisi AJ, Al-Janabi ST, (2017) Query reformulation using wordnet and genetic algorithm. In: Annual conference on new trends in information and communications technology applications (NTICT) (2017), IEEE, pp 91–96

    Google Scholar 

  • De Almeida HM, Gonçalves MA, Cristo M, Calado P (2007) A combined component approach for finding collection-adapted ranking functions based on genetic programming. In: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval, ACM, pp 399–406

    Google Scholar 

  • Amati G, Ounis I (2000) Conceptual graphs and first order logic. Comput J 43(1):1–12

    Article  MATH  Google Scholar 

  • Amati G, Van Rijsbergen CJ (2002) Probabilistic models of information retrieval based on measuring the divergence from randomness. ACM Trans Inf Syst (TOIS) 20(4):357–389

    Article  Google Scholar 

  • Araujo L, Pérez-Iglesias J (2010) Training a classifier for the selection of good query expansion terms with a genetic algorithm. In: IEEE congress on evolutionary computation (CEC), IEEE, pp 1–8

    Google Scholar 

  • Baziz M, Boughanem M, Aussenac-Gilles N (2005) Conceptual indexing based on document content representation, vol CoLIS’05. Springer, Berlin, Heidelberg, pp 171–186

    Google Scholar 

  • Baziz M, Boughanem M, Prade H, Pasi G (2006) A fuzzy logic approach to information retrieval using an ontology-based representation of documents. In: Sanchez E (ed) Fuzzy logic and the semantic web, capturing intelligence, vol 1. Elsevier, Amsterdam, pp 363–377

    Chapter  Google Scholar 

  • Baziz M, Boughanem M, Pasi G, Prade H (2007) An information retrieval driven by ontology from query to document expansion. In: Large Scale Semantic Access to Content (Text, Image, Video, and Sound), Le Centre des Hautes Etudes Internationales D’Informatique Documentaire, pp 301–313

    Google Scholar 

  • Bebis G, Georgiopoulos M (1994) Feed-forward neural networks. IEEE Potentials 13(4):27–31

    Article  Google Scholar 

  • Belew RK (1987) A connectionist approach to conceptual information retrieval. In: Proceedings of the 1st international conference on artificial intelligence and law, ACM, New York, NY, USA, ICAIL ’87, pp 116–126

    Google Scholar 

  • Belkin NJ, Marchetti PG (1989) Determining the functionality features of an intelligent interface to an information retrieval system. In: Proceedings of the 13th annual international ACM SIGIR conference on research and development in information retrieval, ACM, pp 151–177

    Google Scholar 

  • Belkin NJ, Brooks HM, Daniels PJ (1987) Knowledge elicitation using discourse analysis. Int J Man-Mach Stud 27(2):127–144

    Article  Google Scholar 

  • Bengio Y, Ducharme R, Vincent P, Jauvin C (2003) A neural probabilistic language model. J Mach Learn Res 3(Feb):1137–1155

    MATH  Google Scholar 

  • Bindal AK, Sanyal S (2012) Query optimization in context of pseudo relevant documents. In: 3rd Italian Information Retrieval (IIR) workshop, EPFL-CONF-174006

    Google Scholar 

  • Bookstein A (1980) Fuzzy requests: an approach to weighted boolean searches. J Assoc Inf Sci Technol 31(4):240–247

    MathSciNet  Google Scholar 

  • Bordogna G, Pasi G (1993) A fuzzy linguistic approach generalizing boolean information retrieval: a model and its evaluation. J Am Soc Inf Sci 44(2):70

    Article  Google Scholar 

  • Bordogna G, Pasi G (1995) Linguistic aggregation operators of selection criteria in fuzzy information retrieval. Int J Intell Syst 10(2):233–248

    Article  Google Scholar 

  • Bordogna G, Pasi G (2001) An ordinal information retrieval model. Int J Uncertain Fuzziness Knowl-Based Syst 09(supp01):63–75

    Article  MathSciNet  MATH  Google Scholar 

  • Bordogna G, Pasi G (2005) Personalised indexing and retrieval of heterogeneous structured documents. Inf Retr 8(2):301–318

    Article  Google Scholar 

  • Bordogna G, Carrara P, Pasi G (1991) Query term weights as constraints in fuzzy information retrieval. Inf Process Manage 27(1):15–26

    Article  Google Scholar 

  • Bordogna G, Carrara P, Pasi G (1992) Extending boolean information retrieval: a fuzzy model based on linguistic variables. In: 1992 IEEE international conference on fuzzy systems, IEEE, pp 769–776

    Google Scholar 

  • Boughanem M, Loiseau Y, Prade H (2007) Refining aggregation functions for improving document ranking in information retrieval. In: International conference on scalable uncertainty management, Springer, pp 255–267

    Google Scholar 

  • Boughanem M, Brini A, Dubois D (2009) Possibilistic networks for information retrieval. Int J Approx Reason 50(7):957–968

    Article  MathSciNet  MATH  Google Scholar 

  • Boughanem SDC M (1992) A connexionist model for information retrieval. In: Proceeding of DEXA, pp 260–265

    Google Scholar 

  • Bruza PD, van der Gaag LC (1994) Index expression belief networks for information disclosure. Int J Expert Syst 7(2):107–138

    Google Scholar 

  • Buell DA (1985) A problem in information retrieval with fuzzy sets. J Assoc Inf Sci Technol 36(6):398–401

    Google Scholar 

  • Burges C, Shaked T, Renshaw E, Lazier A, Deeds M, Hamilton N, Hullender G (2005) Learning to rank using gradient descent. In: Proceedings of the 22nd international conference on machine learning, ACM, pp 89–96

    Google Scholar 

  • Cao G, Nie JY, Bai J (2005) Integrating word relationships into language models. In: Proceedings of the 28th annual international ACM SIGIR conference on research and development in information retrieval, ACM, pp 298–305

    Google Scholar 

  • Chawla S (2013) Personalised web search using aco with information scent. Int J Knowl Web Intell 4(2–3):238–259

    Article  Google Scholar 

  • Chawla S (2017) Web page ranking using ant colony optimisation and genetic algorithm for effective information retrieval. Int J Swarm Intell 3(1):58–76

    Article  Google Scholar 

  • Chen H (1995) Machine learning for information retrieval: neural networks, symbolic learning, and genetic algorithms. J Assoc Inf Sci Technol 46(3):194–216

    Google Scholar 

  • Chen H, Dhar V (1989) Online query refinement on information retrieval systems: a process model of searcher/system interactions. In: Proceedings of the 13th annual international ACM SIGIR conference on Research and development in information retrieval, ACM, pp 115–133

    Google Scholar 

  • Chevallet JP, Chiaramella Y (1998) Experiences in information retrieval modelling using structured formalisms and modal logic. Information retrieval: uncertainty and logics. Springer, Berlin, pp 39–72

    Chapter  Google Scholar 

  • Chiaramella Y, Chevallet JP (1992) About retrieval models and logic. Comput J 35(3):233–242

    Article  MATH  Google Scholar 

  • Choquet G (1954) Theory of capacities. Annales de l’institut Fourier 5:131–295

    Article  MathSciNet  MATH  Google Scholar 

  • Chowdhury A, McCabe MC (1998) Improving information retrieval systems using part of speech tagging. Tech rep

    Google Scholar 

  • Cleverdon CW, Keen M (1966) Factors determining the performance of indexing systems; volume 2, test results, aslib cranfield research project. Tech rep

    Google Scholar 

  • Clinchant S, Gaussier E (2010) Information-based models for ad hoc ir. In: Proceedings of the 33rd international ACM SIGIR conference on research and development in information retrieval, ACM, pp 234–241

    Google Scholar 

  • Cole C (1998) Intelligent information retrieval: diagnosing information need. Part i. the theoretical framework for developing an intelligent ir tool. Inf Process Manag 34(6):709–720

    Article  Google Scholar 

  • Colorini A, Dorigo M, Maniezzo V (1991) Distributed optimization by ant colonies. The 1st european conference on artificial life. Pans, France

    Google Scholar 

  • Cooper WS (1971) A definition of relevance for information retrieval. Inf Storage Retr 7(1):19–37

    Article  Google Scholar 

  • Crammer K, Singer Y (2002) Pranking with ranking. In: Advances in neural information processing systems, pp 641–647

    Google Scholar 

  • Crestani F (1998) Logical imaging and probabilistic information retrieval. In: Information Retrieval: uncertainty and logics. Springer, Berlin, pp 247–279

    Google Scholar 

  • Crestani F, van Rijsbergen CJ (1995) Information retrieval by logical imaging. J Doc 51(1):3–17

    Article  Google Scholar 

  • Crestani F, De Campos LM, Fernández-Luna JM, Huete JF (2003) A multi-layered bayesian network model for structured document retrieval. In: European conference on symbolic and quantitative approaches to reasoning and uncertainty. Springer, pp 74–86

    Google Scholar 

  • Croft WB (1987) Approaches to intelligent information retrieval. Inf Process Manage 23(4):249–254

    Article  Google Scholar 

  • Damiani E, Marrara S, Pasi G (2007) Fuzzyxpath: using fuzzy logic an ir features to approximately query xml documents. In: International fuzzy systems association world congress. Springer, pp 199–208

    Google Scholar 

  • De Baets B, Fodor J (1997) On the structure of uninorms and their residual implicators. In: 18th Proceedings Linz seminar on fuzzy set theory, pp 81–87

    Google Scholar 

  • De Campos LM, Fernández-Luna JM, Huete JF (2002) A layered bayesian network model for document retrieval. In: European conference on information retrieval. Springer, pp 169–182

    Google Scholar 

  • De Campos LM, Fernández-Luna JM, Huete JF (2003) Improving the efficiency of the bayesian network retrieval model by reducing relationships between terms. Int J Uncertain Fuzziness Knowl-Based Syst 11(supp01):101–116

    Article  MathSciNet  MATH  Google Scholar 

  • Deerwester S, Dumais ST, Furnas GW, Landauer TK, Harshman R (1990) Indexing by latent semantic analysis. J Am Soc Inf Sci 41(6):391

    Article  Google Scholar 

  • Dehghani M, Zamani H, Severyn A, Kamps J, Croft WB (2017) Neural ranking models with weak supervision. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, ACM, pp 65–74

    Google Scholar 

  • Denoyer L, Gallinari P (2003) A belief networks-based generative model for structured documents. an application to the xml categorization. In: International workshop on machine learning and data mining in pattern recognition. Springer, pp 328–342

    Google Scholar 

  • Diaz F, Mitra B, Craswell N (2016) Query expansion with locally-trained word embeddings. In: Proceedings of the 54th annual meeting of the association for computational linguistics (volume 1: long papers), Association for computational linguistics, pp 367–377

    Google Scholar 

  • Ding Y (2001) Ir and ai: the role of ontology. In: International conference of Asian digital libraries, India

    Google Scholar 

  • Dinh D, Tamine L, Boubekeur F (2013) Factors affecting the effectiveness of biomedical document indexing and retrieval based on terminologies. Artif Intell Med 57(2):155–167

    Article  Google Scholar 

  • Dubois D, Prade H (1985) A review of fuzzy set aggregation connectives. Inf Sci 36(1–2):85–121

    Article  MathSciNet  MATH  Google Scholar 

  • Dubois D, Prade H, Testemale C (1988) Weighted fuzzy pattern matching. Fuzzy Sets Syst 28(3):313–331

    Article  MathSciNet  MATH  Google Scholar 

  • Dubois D, Fargier H, Prade H (1997) Beyond min aggregation in multicriteria decision:(ordered) weighted min, discri-min, leximin. The ordered weighted averaging operators. Springer, Berlin, pp 181–192

    Chapter  Google Scholar 

  • Enembreck F, Barthès JP, Ávila BC (2004) Personalizing information retrieval with multi-agent systems. In: International workshop on cooperative Information Agents. Springer, pp 77–91

    Google Scholar 

  • Evans DA, Zhai C (1996) Noun-phrase analysis in unrestricted text for information retrieval. In: Proceedings of the 34th annual meeting on association for computational linguistics, Association for computational linguistics, pp 17–24

    Google Scholar 

  • Fagan J (1987a) Automatic phrase indexing for document retrieval. In: Proceedings of the 10th annual international ACM SIGIR conference on research and development in information retrieval, ACM, pp 91–101

    Google Scholar 

  • Fagan JL (1987b) Experiments in automatic phrase indexing for document retrieval: a comparison of syntactic and non-syntactic methods. PhD thesis

    Google Scholar 

  • Fang H (2008) A re-examination of query expansion using lexical resources. In: Proceedings of ACL-08: HLT, pp 139–147

    Google Scholar 

  • Firth JR (1957) A synopsis of linguistic theory, 1930-1955. Studies in linguistic analysis

    Google Scholar 

  • Fox EA, Sharan S (1986) A comparison of two methods for soft boolean operator interpretation in information retrieval. Tech. rep. Tech., Department of. Computer Science, Blacksburg, VA: Virginia

    Google Scholar 

  • Freund Y, Iyer R, Schapire RE, Singer Y (2003) An efficient boosting algorithm for combining preferences. J Mach Learn Res 4(Nov):933–969

    MathSciNet  MATH  Google Scholar 

  • Fuhr N (1995) Probabilistic dataloga logic for powerful retrieval methods. In: Proceedings of the 18th annual international ACM SIGIR conference on research and development in information retrieval, ACM, pp 282–290

    Google Scholar 

  • Fung R, Del Favero B (1995) Applying bayesian networks to information retrieval. Commun ACM 38(3):42–50

    Article  Google Scholar 

  • Ganguly D, Roy D, Mitra M, Jones GJ (2015) Word embedding based generalized language model for information retrieval. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, ACM, pp 795–798

    Google Scholar 

  • Goldberg DE, Corruble V (1994) Algorithmes génétiques: exploration, optimisation et apprentissage automatique. Ed. Addison-Wesley, France

    Google Scholar 

  • Gonzalo J, Verdejo F, Chugur I, Cigarrin J (1998) Indexing with wordnet synsets can improve text retrieval. In: Workshop on usage Of WordNet in natural language processing systems, pp 38–44

    Google Scholar 

  • Gonzalo J, Li H, Moschitti A, Xu J (2014) Semantic matching in information retrieval. In: Proceedings of the 37th international ACM SIGIR conference on research and development in information retrieval, ACM, pp 1296–1296

    Google Scholar 

  • Gordon MD (1991) User-based document clustering by redescribing subject descriptions with a genetic algorithm. J Am Soc Inf Sci 42(5):311

    Article  Google Scholar 

  • Guo J, Fan Y, Ai Q, Croft WB (2016) A deep relevance matching model for ad-hoc retrieval. In: Proceedings of the 25th ACM international on conference on information and knowledge management, ACM, pp 55–64

    Google Scholar 

  • Hammache A, Boughanem M, Ahmed-Ouamer R (2014) Combining compound and single terms under language model framework. Knowl Inf Syst 39(2):329–349

    Article  Google Scholar 

  • Harman D (1991) How effective is suffixing? J Am Soc Inf Sci 42(1):7

    Article  MathSciNet  Google Scholar 

  • Hassan A, Hadi M (2016) Sense-based information retrieval using artificial bee colony approach. Int J Appl Eng Res 11(15):8708–8713

    Google Scholar 

  • Hayashi I, Nomura H, Yamasaki H, Wakami N (1992) Construction of fuzzy inference rules by ndf and ndfl. Int J Approx Reason 6(2):241–266

    Article  MATH  Google Scholar 

  • Herbrich R (2000) Large margin rank boundaries for ordinal regression. Advances in large margin classifiers. MIT Press, Oxford, pp 115–132

    Google Scholar 

  • Herrera-Viedma E (2001) Modeling the retrieval process for an information retrieval system using an ordinal fuzzy linguistic approach. J Assoc Inf Sci Technol 52(6):460–475

    Article  Google Scholar 

  • Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press, Oxford

    Book  Google Scholar 

  • Hollink V, Kamps J, Monz C, De Rijke M (2004) Monolingual document retrieval for european languages. Inf Retr 7(1–2):33–52

    Article  Google Scholar 

  • Hosanagar K (2011) Usercentric operational decision making in distributed information retrieval. Inf Syst Res 22(4):739–755

    Article  Google Scholar 

  • Hu B, Lu Z, Li H, Chen Q (2014) Convolutional neural network architectures for matching natural language sentences. In: Advances in neural information processing systems, pp 2042–2050

    Google Scholar 

  • Huang PS, He X, Gao J, Deng L, Acero A, Heck L (2013) Learning deep structured semantic models for web search using clickthrough data. In: Proceedings of the 22nd ACM international conference on Conference on information and knowledge management, ACM, pp 2333–2338

    Google Scholar 

  • Hubert G, Pitarch Y, Pinel-Sauvagnat K, Tournier R, Laporte L (2018) Tournarank: when retrieval becomes document competition. Inf Process Manag 54(2):252–272

    Article  Google Scholar 

  • Huibers TWC (1994) Situations, a general framework for studying information retrieval, vol 1994. Unknown Publisher

    Google Scholar 

  • Hunter A (1997) Using default logic for lexical knowledge. Qualitative and quantitative practical reasoning. Springer, Berlin, pp 322–335

    Chapter  Google Scholar 

  • Indrawan M, Ghazfan D, Srinivasan B (1996) Using bayesian networks as retrieval engines. In: Proceedings of the text retrieval conference TREC’96

    Google Scholar 

  • Jabeur LB, Tamine L, Boughanem M (2012) Active microbloggers: Identifying influencers, leaders and discussers in microblogging networks. In: International symposium on string processing and information retrieval. Springer, pp 111–117

    Google Scholar 

  • Jones KS (1983) Intelligent retrieval. Proceedings of Informatics

    Google Scholar 

  • Jones KS (1991) The role of artificial intelligence in information retrieval. J Am Soc Inf Sci 42(8):558

    Article  Google Scholar 

  • Jones KS (1999) Information retrieval and artificial intelligence. Artif Intell 114(1–2):257–281

    Article  MATH  Google Scholar 

  • Jozefowicz R, Vinyals O, Schuster M, Shazeer N, Wu Y (2016) Exploring the limits of language modeling. arXiv:160202410

  • Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Tech. rep., Technical report-tr06, Erciyes university, engineering faculty, computer engineering department

    Google Scholar 

  • Karaboga D, Basturk B (2008) On the performance of artificial bee colony (abc) algorithm. Appl Soft Comput 8(1):687–697

    Article  Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. In: 1995 Proceedings of IEEE international conference on neural networks, vol 4, pp 1942–1948

    Google Scholar 

  • Kenter T, De Rijke M (2015) Short text similarity with word embeddings. In: Proceedings of the 24th ACM international on conference on information and knowledge management, ACM, pp 1411–1420

    Google Scholar 

  • Khennak I, Drias H (2017) An accelerated pso for query expansion in web information retrieval: application to medical dataset. Appl Intell 1–16

    Google Scholar 

  • Kim S, Zhang BT (2003) Genetic mining of html structures for effective web-document retrieval. Appl Intell 18(3):243–256

    Article  MATH  Google Scholar 

  • Kim Y, Jernite Y, Sontag D, Rush AM (2015) Character-aware neural language models. In: AAAI, pp 2741–2749

    Google Scholar 

  • Kiros R, Zhu Y, Salakhutdinov RR, Zemel R, Urtasun R, Torralba A, Fidler S (2015) Skip-thought vectors. In: Advances in neural information processing systems, pp 3294–3302

    Google Scholar 

  • Kraaij W, Pohlmann R (1996) Viewing stemming as recall enhancement. In: Proceedings of the 19th annual international ACM SIGIR conference on research and development in information retrieval, ACM, pp 40–48

    Google Scholar 

  • Kraft DH, Buell DA (1983) Fuzzy sets and generalized boolean retrieval systems. Int J Man-Mach Stud 19(1):45–56

    Article  Google Scholar 

  • Kraft DH, Colvin E (2017) Fuzzy information retrieval. Synthesis lectures on information concepts, retrieval, and services. Morgan & Claypool Publishers, San Rafael

    Google Scholar 

  • Kraft DH, Bordogna G, Pasi G (1999) Fuzzy set techniques in information retrieval. Fuzzy sets in approximate reasoning and information systems. Springer, Berlin, pp 469–510

    Chapter  MATH  Google Scholar 

  • Kraft DH, Colvin E, Bordogna G, Pasi G (2015) Fuzzy information retrieval systems: a historical perspective. Fifty years of fuzzy logic and its applications. Springer, Berlin, pp 267–296

    Chapter  MATH  Google Scholar 

  • Kripke SA (1963) Semantic analysis of modal logic I: Normal modal and propositional calculi. Zeitschrift für Mathematische Logik und Grundlagen der Mathematik 9:67–96

    Article  MathSciNet  MATH  Google Scholar 

  • Krovetz R (1993) Viewing morphology as an inference process. In: Proceedings of the 16th annual international ACM SIGIR conference on research and development in information retrieval, ACM, pp 191–202

    Google Scholar 

  • Krovetz R (1997) Homonymy and polysemy in information retrieval. In: Proceedings of the 35th annual meeting of the association for computational linguistics and eighth conference of the European chapter of the association for computational linguistics, Association for computational linguistics, Stroudsburg, PA, USA, ACL ’98, pp 72–79

    Google Scholar 

  • Krovetz R, Croft WB (1992) Lexical ambiguity and information retrieval. ACM Trans Inf Syst 10(2):115–141

    Article  Google Scholar 

  • Kwok KL (1989) A neural network for probabilistic information retrieval. In: Proceedings of the 12th annual international ACM SIGIR conference on research and development in information retrieval, ACM, New York, NY, USA, SIGIR ’89, pp 21–30

    Google Scholar 

  • Lalmas M (1998) Logical models in information retrieval: introduction and overview. Inf Process Manag 34(1):19–33

    Article  Google Scholar 

  • Lalmas M, van Rijsbergen K (1993) A logical model of information retrieval based on situation theory. In: 14th information retrieval colloquium. Springer, pp 1–13

    Google Scholar 

  • Lavrenko V, Croft WB (2017) Relevance-based language models. In: ACM SIGIR Forum, ACM, vol 51, pp 260–267

    Google Scholar 

  • Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: International conference on machine learning, pp 1188–1196

    Google Scholar 

  • Lee H, Grosse R, Ranganath R, Ng AY (2009) Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proceedings of the 26th annual international conference on machine learning, ACM, pp 609–616

    Google Scholar 

  • Lewis DD (1992) Representation and learning in information retrieval. PhD thesis, University of Massachusetts at Amherst

    Google Scholar 

  • Lewis DD, Jones KS (1996) Natural language processing for information retrieval. Commun ACM 39(1):92–101

    Article  Google Scholar 

  • Li H (2011) Learning to rank for information retrieval and natural language processing. Synth Lect Hum Lang Technol 4(1):1–113

    Article  Google Scholar 

  • Li H, Xu J et al (2014) Semantic matching in search. Found Trends\({\textregistered }\) Inf Retrieva 7(5):343–469

    Google Scholar 

  • Li P, Wu Q, Burges CJ (2008) Mcrank: learning to rank using multiple classification and gradient boosting. In: Advances in neural information processing systems, pp 897–904

    Google Scholar 

  • Lioma C, Blanco R (2009) Part of speech based term weighting for information retrieval. In: European conference on information retrieval. Springer, pp 412–423

    Google Scholar 

  • Liu S, Liu F, Yu C, Meng W (2004) An effective approach to document retrieval via utilizing wordnet and recognizing phrases. In: Proceedings of the 27th annual international ACM SIGIR conference on research and development in information retrieval, ACM, pp 266–272

    Google Scholar 

  • Liu S, Yu C, Meng W (2005) Word sense disambiguation in queries. In: Proceedings of the 14th ACM international conference on information and knowledge management, ACM, pp 525–532

    Google Scholar 

  • Liu TY et al (2009) Learning to rank for information retrieval. Found Trends\({\textregistered }\) Inf Retr 3(3):225–331

    Google Scholar 

  • Loiseau Y, Prade H, Boughanem M (2004) Qualitative pattern matching with linguistic terms. AI Commun 17(1):25–34

    MathSciNet  MATH  Google Scholar 

  • Losada DE, Barreiro A (2001) A logical model for information retrieval based on propositional logic and belief revision. Comput J 44(5):410–424

    Article  MATH  Google Scholar 

  • Losada DE, Barreiro A (2003) Propositional logic representations for documents and queries: a large-scale evaluation. In: European conference on information retrieval, Springer, pp 219–234

    Google Scholar 

  • Lu Z, Li H (2013) A deep architecture for matching short texts. In: Advances in neural information processing systems, pp 1367–1375

    Google Scholar 

  • Luhn HP (1957) A statistical approach to mechanized encoding and searching of literary information. IBM J Res Dev 1(4):309–317

    Article  MathSciNet  Google Scholar 

  • Lv Y, Zhai C (2009) Positional language models for information retrieval. In: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, ACM, pp 299–306

    Google Scholar 

  • MacAvaney S, Hui K, Yates A (2017) An approach for weakly-supervised deep information retrieval. In: Workshop on neural information retrieval (Neu-IR ’17) at SIGIR 2017

    Google Scholar 

  • Maes P et al (1994) Agents that reduce work and information overload. Commun ACM 37(7):30–40

    Article  Google Scholar 

  • Mandl T (2009) Artificial intelligence for information retrieval. In: Encyclopedia of artificial intelligence, IGI Global, pp 151–156

    Google Scholar 

  • Manning CD, Raghavan P, Schtze H (2008) Introduction to information retrieval. Cambridge University Press, Cambridge, UK

    Book  MATH  Google Scholar 

  • Marrara S, Pasi G, Viviani M (2017) Aggregation operators in information retrieval. Fuzzy Sets Syst 324:3–19

    Article  MathSciNet  MATH  Google Scholar 

  • Mauldin ML (1991) Retrieval performance in ferret a conceptual information retrieval system. In: Proceedings of the 14th annual international ACM SIGIR conference on research and development in information retrieval, ACM, pp 347–355

    Google Scholar 

  • Meghini C, Straccia, (1996) A relevance terminological logic for information retrieval. In: Proceedings of the 19th annual international ACM SIGIR conference on research and development in information retrieval, ACM, pp 197–205

    Google Scholar 

  • Meghini C, Sebastiani F, Straccia U, Thanos C (1993) A model of information retrieval based on a terminological logic. In: Proceedings of the 16th annual international ACM SIGIR conference on research and development in information retrieval, ACM, pp 298–307

    Google Scholar 

  • Metzler D, Croft WB (2005) A markov random field model for term dependencies. In: Proceedings of the 28th annual international ACM SIGIR conference on research and development in information retrieval, ACM, pp 472–479

    Google Scholar 

  • Mihalcea R, Moldovan D (2000) Semantic indexing using wordnet senses. In: Proceedings of the ACL-2000 workshop on recent advances in natural language processing and information retrieval: held in conjunction with the 38th annual meeting of the association for computational linguistics - volume 11, Association for computational linguistics, Stroudsburg, PA, USA, RANLPIR ’00, pp 35–45

    Google Scholar 

  • Mikolov T, Chen K, Corrado G (2013) Efficient estimation of word representations in vector space. Dean J CoRR abs/1301.3781

  • Mitra B, Craswell N (2017) An introduction to neural information retrieval. Found Trends\({\textregistered }\) Inf Retr :1–120

    Google Scholar 

  • Mitra B, Nalisnick ET, Craswell N, Caruana R (2016) A dual embedding space model for document ranking. CoRR abs/1602.01137

  • Mitra B, Diaz F, Craswell N (2017) Learning to match using local and distributed representations of text for web search. In: Proceedings of the 26th international conference on world wide web, international world wide web conferences steering committee, pp 1291–1299

    Google Scholar 

  • Miyamoto S (1990) Information retrieval based on fuzzy associations. Fuzzy Sets Syst 38(2):191–205

    Article  MATH  Google Scholar 

  • Molinari A, Pasi G (1996) A fuzzy representation of html documents for information retrieval systems. In: 1996 Proceedings of the fifth ieee international conference on fuzzy systems, IEEE, vol 1, pp 107–112

    Google Scholar 

  • Moon J, Shon T, Seo J, Kim J, Seo J (2004) An approach for spam e-mail detection with support vector machine and n-gram indexing. In: International symposium on computer and information sciences, Springer, pp 351–362

    Google Scholar 

  • Moulahi B, Tamine L, Yahia SB (2014) iaggregator: multidimensional relevance aggregation based on a fuzzy operator. J Assoc Inf Sci Technol 65(10):2062–2083

    Article  Google Scholar 

  • Nalisnick E, Mitra B, Craswell N, Caruana R (2016) Improving document ranking with dual word embeddings. In: Proceedings of the 25th international conference companion on world wide web, international world wide web conferences steering committee, pp 83–84

    Google Scholar 

  • Nallapati R (2004) Discriminative models for information retrieval. In: Proceedings of the 27th annual international ACM SIGIR conference on research and development in information retrieval, ACM, pp 64–71

    Google Scholar 

  • Nie J (1988) An outline of a general model for information retrieval systems. In: Proceedings of the 11th annual international ACM SIGIR conference on research and development in information retrieval, ACM, pp 495–506

    Google Scholar 

  • Nie J (1989) An information retrieval model based on modal logic. Inf Process Manag 25(5):477–491

    Article  Google Scholar 

  • Nie JY, Brisebois M, Lepage F (1995) Information retrieval as counterfactual. Comput J 38(8):643–657

    Article  Google Scholar 

  • Onal KD, Zhang Y, Altingovde IS, Rahman MM, Karagoz P, Braylan A, Dang B, Chang HL, Kim H, McNamara Q et al (2017) Neural information retrieval: at the end of the early years. Inf Retr J 1–72

    Google Scholar 

  • Pang L, Lan Y, Guo J, Xu J, Cheng X (2016) A study of matchpyramid models on ad-hoc retrieval. In: ACM SIGIR workshop on neural information retrieval (Neu-IR)

    Google Scholar 

  • Pasi G (2009) Fuzzy models. Encyclopedia of database systems. Springer, Berlin, pp 1205–1209

    Google Scholar 

  • Pennington J, Socher R, Manning C (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543

    Google Scholar 

  • Phillips-Wren GE, Forgionne GA (2004) Intelligent decision making in information retrieval. In: International conference on intelligent information and engineering systems. Springer, pp 103–109

    Google Scholar 

  • Picard J (1999) Logic as a tool in a term matching information retrieval system. In: Proceedings of the workshop on logical and uncertainty models for information systems, pp 77–90

    Google Scholar 

  • Picard J, Savoy J (2000) A logical information retrieval model based on a combination of propositional logic and probability theory. In: Soft computing in information retrieval, Springer, pp 225–258

    Google Scholar 

  • Ponte JM, Croft WB (1998) A language modeling approach to information retrieval. In: Proceedings of the 21st annual international ACM SIGIR conference on research and development in information retrieval, ACM, pp 275–281

    Google Scholar 

  • Porter MF (1980) An algorithm for suffix stripping. Program 14(3):130–137

    Article  Google Scholar 

  • Qi G, Pan JZ (2008) A tableau algorithm for possibilistic description logic . In: Asian semantic web conference, Springer, pp 61–75

    Google Scholar 

  • Raifer N, Raiber F, Tennenholtz M, Kurland, (2017) Information retrieval meets game theory: The ranking competition between documents’ authors. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, ACM, pp 465–474

    Google Scholar 

  • Rekabsaz N (2016) Enhancing information retrieval with adapted word embedding. In: Proceedings of the 39th international ACM SIGIR conference on research and development in information retrieval, ACM, pp 1169–1169

    Google Scholar 

  • Ribeiro BA, Muntz R (1996) A belief network model for ir. In: Proceedings of the 19th annual international ACM SIGIR conference on research and development in information retrieval, ACM, pp 253–260

    Google Scholar 

  • van Rijsbergen CJ (1989) Towards an information logic. In: ACM SIGIR forum, ACM, vol 23, pp 77–86

    Google Scholar 

  • Robertson SE, Walker S (1994) Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval. In: Proceedings of the 17th annual international ACM SIGIR conference on research and development in information retrieval, Springer-Verlag New York, Inc., pp 232–241

    Google Scholar 

  • Rölleke T, Fuhr N (1996) Retrieval of complex objects using a four-valued logic. In: Proceedings of the 19th annual international ACM SIGIR conference on research and development in information retrieval, ACM, pp 206–214

    Google Scholar 

  • Roy D, Paul D, Mitra M, Garain U (2016) Using word embeddings for automatic query expansion. In: ACM SIGIR workshop on neural information retrieval

    Google Scholar 

  • Salton G (1991) Developments in automatic text retrieval. Science 253(5023):974–980

    Article  MathSciNet  Google Scholar 

  • Salton G, McGill MJ (1986) Introduction to modern information retrieval. McGraw-Hill Inc, New York, NY, USA

    MATH  Google Scholar 

  • Salton G, Wong A, Yang CS (1975) A vector space model for automatic indexing. Commun ACM 18(11):613–620

    Article  MATH  Google Scholar 

  • Sanchez E (1989) Importance in knowledge systems. Inf Syst 14(6):455–464

    Article  Google Scholar 

  • Sanderson M (1994) Word sense disambiguation and information retrieval. In: Proceedings of the 17th annual international ACM SIGIR conference on research and development in information retrieval, Springer-Verlag New York, Inc., pp 142–151

    Google Scholar 

  • Sanderson M (2000) Retrieving with good sense. Inf Retr 2(1):49–69

    Article  Google Scholar 

  • Sathya SS, Simon P (2010) A document retrieval system with combination terms using genetic algorithm. Int J Comput Electr Eng 2(1):1

    Article  Google Scholar 

  • Schütze H, Pedersen JO (1995) Information retrieval based on word senses, pp 161–175

    Google Scholar 

  • Sebastiani F (1994) A probabilistic terminological logic for modelling information retrieval. In: SIGIR94, Springer, pp 122–130

    Google Scholar 

  • Sebastiani F (1998) On the role of logic in information retrieval. Inf Process Manag 34(1):1–18

    Article  MathSciNet  Google Scholar 

  • Shashua A, Levin A (2003) Ranking with large margin principle: two approaches. In: Advances in neural information processing systems, pp 961–968

    Google Scholar 

  • Shen Y, He X, Gao J, Deng L, Mesnil G (2014) Learning semantic representations using convolutional neural networks for web search. In: Proceedings of the 23rd international conference on world wide web, ACM, pp 373–374

    Google Scholar 

  • Sheridan P, Smeaton AF (1992) The application of morpho-syntactic language processing to effective phrase matching. Inf Process Manag 28(3):349–369

    Article  Google Scholar 

  • Shi L, Nie JY (2009) Integrating phrase inseparability in phrase-based model. In: Proceedings of the 32nd international ACM SIGIR conference on research and development in information retrieval, ACM, New York, NY, USA, SIGIR ’09, pp 708–709

    Google Scholar 

  • Silva I, Ribeiro-Neto B, Calado P, Moura E, Ziviani N (2000) Link-based and content-based evidential information in a belief network model. In: Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, ACM, pp pp 96–103

    Google Scholar 

  • Sowa JF (1983) Conceptual structures: information processing in mind and machine

    Google Scholar 

  • Sparck Jones K (1972) A statistical interpretation of term specificity and its application in retrieval. J Doc 28(1):11–21

    Article  Google Scholar 

  • Stokoe C, Oakes MP, Tait J (2003) Word sense disambiguation in information retrieval revisited. In: Proceedings of the 26th annual international ACM SIGIR conference on research and development in informaion retrieval, ACM, pp 159–166

    Google Scholar 

  • Strzalkowski T (1995) Natural language information retrieval. Inf Process Manag 31(3):397–417

    Article  Google Scholar 

  • Tamine L, Chrisment C, Boughanem M (2003) Multiple query evaluation based on an enchanced genetic algorithm. Inf Process Manag 39(2):215–231

    Article  MATH  Google Scholar 

  • Tamir DE, Rishe ND, Kandel A (2015) Fifty years of fuzzy logic and its applications, vol 326. Springer, Berlin

    Book  MATH  Google Scholar 

  • Tao T, Zhai C (2007) An exploration of proximity measures in information retrieval. In: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval, ACM, pp 295–302

    Google Scholar 

  • Thiel U, Müller A (1996) Why was this item retrieved? new ways to explore retrieval results. Information retrieval and hypertext. Springer, Berlin, pp 181–201

    Chapter  Google Scholar 

  • Tong X, Zhai C, Milic-Frayling N, Evans DA (1997) Evaluation of syntactic phrase indexing. In: The fifth text retrieval conference (TREC-5)

    Google Scholar 

  • Trifa A, Sbaï AH, Chaari WL (2017) Evaluate a personalized multi agent system through social networks: web scraping. In: 2017 IEEE 26th international conference on enabling technologies: infrastructure for collaborative enterprises (WETICE), IEEE, pp 18–20

    Google Scholar 

  • Tsai MF, Liu TY, Qin T, Chen HH, Ma WY (2007) Frank: a ranking method with fidelity loss. In: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval, ACM, pp 383–390

    Google Scholar 

  • Turtle H, Croft WB (2017) Inference networks for document retrieval. ACM SIGIR forum, ACM, vol 51, pp 124–147

    Google Scholar 

  • Van Rijsbergen CJ (1986) A non-classical logic for information retrieval. Comput J 29(6):481–485

    Article  MATH  Google Scholar 

  • Vickery A, Brooks HM (1987) Plexus-the expert system for referral. Inf Process Manag 23(2):99–117

    Article  Google Scholar 

  • Voorhees EM (1993) Using wordnet to disambiguate word senses for text retrieval. In: Proceedings of the 16th annual international ACM SIGIR conference on research and development in information retrieval, ACM, pp 171–180

    Google Scholar 

  • Voorhees EM (1994) Query expansion using lexical-semantic relations. In: Proceedings of the 17th annual international ACM SIGIR conference on research and development in information retrieval, Springer-Verlag New York, Inc., pp 61–69

    Google Scholar 

  • Vrajitoru D (2013) Large population or many generations for genetic algorithms? Soft computing in information retrieval: techniques and applications, vol 50, p 199

    Google Scholar 

  • Vulić I, Moens MF (2015) Monolingual and cross-lingual information retrieval models based on (bilingual) word embeddings. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, ACM, pp 363–372

    Google Scholar 

  • Wan S, Lan Y, Guo J, Xu J, Pang L, Cheng X (2016) A deep architecture for semantic matching with multiple positional sentence representations. In: AAAI, pp 2835–2841

    Google Scholar 

  • Wang C, Akella R (2015) Concept-based relevance models for medical and semantic information retrieval. In: Proceedings of the 24th ACM international on conference on information and knowledge management, ACM, New York, NY, USA, CIKM ’15, pp 173–182

    Google Scholar 

  • Wu Q, Burges CJ, Svore KM, Gao J (2010) Adapting boosting for information retrieval measures. Inf Retr 13(3):254–270

    Article  Google Scholar 

  • Xia F, Liu TY, Wang J, Zhang W, Li H (2008) Listwise approach to learning to rank: theory and algorithm. In: Proceedings of the 25th international conference on machine learning, ACM, pp 1192–1199

    Google Scholar 

  • Yager RR (1988) On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Trans Syst Man Cybern 18(1):183–190

    Article  MathSciNet  MATH  Google Scholar 

  • Yang GH, Sloan M, Wang J (2016) Dynamic information retrieval modeling, vol 8(3). Synthesis lectures on information concepts, retrieval, and services, pp 1–144

    Google Scholar 

  • Yang J, Korfhage RR, Rasmussen E (1993) Query improvement in information retrieval using genetic algorithms: a report on the experiments of the trec project. In: Proceedings of the 1st text retrieval conference, pp 31–58

    Google Scholar 

  • Yin W, Schütze H, Xiang B, Zhou B (2015) Abcnn: attention-based convolutional neural network for modeling sentence pairs. arXiv preprint arXiv:151205193

  • Yue Y, Finley T, Radlinski F,Joachims T (2007) A support vector method for optimizing average precision. In: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval, ACM, pp 271–278

    Google Scholar 

  • Zakos J (2005) A novel concept and context-based approach for web information retrieval. PhD thesis

    Google Scholar 

  • Zamani H, Croft WB (2016a) Embedding-based query language models. In: Proceedings of the 2016 ACM international conference on the theory of information retrieval, ACM, pp 147–156

    Google Scholar 

  • Zamani H, Croft WB (2016b) Estimating embedding vectors for queries. In: Proceedings of the 2016 ACM international conference on the theory of information retrieval, ACM, pp 123–132

    Google Scholar 

  • Zamani H, Mitra B, Song X, Craswell N, Tiwary S (2018) Neural ranking models with multiple document fields. In: Proceedings of the eleventh ACM international conference on web search and data mining, ACM, pp 700–708

    Google Scholar 

  • Zhai C (1997) Fast statistical parsing of noun phrases for document indexing. In: Proceedings of the fifth conference on applied natural language processing, Association for computational linguistics, pp 312–319

    Google Scholar 

  • Zhai C (2008) Statistical language models for information retrieval. Synthesis lectures on human language technologies, vol 1(1), pp 1–141

    Google Scholar 

  • Zhai C (2016) Towards a game-theoretic framework for text data retrieval. IEEE Data Eng Bull 39(3):51–62

    Google Scholar 

  • Zhao B, Feng J, Wu X, Yan S (2017) A survey on deep learning-based fine-grained object classification and semantic segmentation. Int J Autom Comput 14(2):119–135

    Article  Google Scholar 

  • Zhao J, Yun Y (2009) A proximity language model for information retrieval. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, ACM, pp 291–298

    Google Scholar 

  • Zheng G, Callan J (2015) Learning to reweight terms with distributed representations. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, ACM, pp 575–584

    Google Scholar 

  • Zuccon G, Azzopardi L, van Rijsbergen CJ (2009) Revisiting logical imaging for information retrieval. In: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, ACM, New York, NY, USA, SIGIR ’09, pp 766–767

    Google Scholar 

  • Zuccon G, Koopman B, Bruza P, Azzopardi L (2015) Integrating and evaluating neural word embeddings in information retrieval. In: Proceedings of the 20th Australasian document computing symposium, ACM, p 12

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IRIT, Université Toulouse III, Toulouse, France

    Mohand Boughanem & Imen Akermi

  2. Università degli Studi di Milano-Bicocca, Milan, Italy

    Gabriella Pasi

  3. GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany

    Karam Abdulahhad

Authors
  1. Mohand Boughanem
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Imen Akermi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gabriella Pasi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Karam Abdulahhad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohand Boughanem .

Editor information

Editors and Affiliations

  1. CRIL-CNRS, Université d'Artois and Institut Universitaire de France, Lens, France

    Pierre Marquis

  2. Aix Marseille Université, Université de Toulon, CNRS, LIS, Marseille, France

    Odile Papini

  3. IRIT, CNRS and Université Paul Sabatier, Toulouse, France

    Henri Prade

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Boughanem, M., Akermi, I., Pasi, G., Abdulahhad, K. (2020). Information Retrieval and Artificial Intelligence. In: Marquis, P., Papini, O., Prade, H. (eds) A Guided Tour of Artificial Intelligence Research. Springer, Cham. https://doi.org/10.1007/978-3-030-06170-8_5

Download citation

Publish with us

Policies and ethics

Search

Navigation