000			05123nam a2200769 i 4500
001			6812892
003			IEEE
005			20200413152854.0
006			m eo d
007			cr cn |||m|||a
008			090708s2009 caua foab 000 0 eng d
020			_a9781598296938 (electronic bk.)
020			_z9781598296921 (pbk.)
024	7		_a10.2200/S00206ED1V01Y200907AIM007 _2doi
035			_a(CaBNVSL)gtp00534962
035			_a(OCoLC)428525464
040			_aCaBNVSL _cCaBNVSL _dCaBNVSL
050		4	_aQ336 _b.D655 2009
082	0	4	_a006.3 _222
100	1		_aDomingos, Pedro.
245	1	0	_aMarkov logic _h[electronic resource] : _ban interface layer for artificial intelligence / _cPedro Domingos and Daniel Lowd.
260			_aSan Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : _bMorgan & Claypool Publishers, _cc2009.
300			_a1 electronic text (viii, 145 p. : ill.) : _bdigital file.
490	1		_aSynthesis lectures on artificial intelligence and machine learning, _x1939-4616 ; _v# 7
538			_aMode of access: World Wide Web.
538			_aSystem requirements: Adobe Acrobat reader.
500			_aPart of: Synthesis digital library of engineering and computer science.
500			_aSeries from website.
504			_aIncludes bibliographical references (p. 131-143).
505	0		_aIntroduction -- The interface layer -- What is the interface layer for AI -- Markov logic and alchemy: an emerging solution -- Overview of the book -- Markov logic -- First-order logic -- Markov networks -- Markov logic -- Relation to other approaches -- Inference -- Inferring the most probable explanation -- Computing conditional probabilities -- Lazy inference -- Lifted inference -- Learning -- Weight learning -- Structure learning and theory revision -- Unsupervised learning -- Transfer learning -- Extensions -- Continuous domains -- Infinite domains -- Recursive Markov logic -- Relational decision theory -- Applications -- Collective classification -- Social network analysis and link prediction -- Entity resolution -- Information extraction -- Unsupervised coreference resolution -- Robot mapping -- Link-based clustering -- Semantic network extraction from text -- Conclusion -- The alchemy system -- Input files -- Inference -- Weight learning -- Structure learning -- Bibliography -- Biography.
506	1		_aAbstract freely available; full-text restricted to subscribers or individual document purchasers.
510	0		_aCompendex
510	0		_aINSPEC
510	0		_aGoogle scholar
510	0		_aGoogle book search
520	3		_aMost subfields of computer science have an interface layer via which applications communicate with the infrastructure, and this is key to their success (e.g., the Internet in networking, the relational model in databases, etc.). So far this interface layer has been missing in AI. First-order logic and probabilistic graphical models each have some of the necessary features, but a viable interface layer requires combining both. Markov logic is a powerful new language that accomplishes this by attaching weights to first-order formulas and treating them as templates for features of Markov random fields. Most statistical models in wide use are special cases of Markov logic, and first-order logic is its infinite-weight limit. Inference algorithms for Markov logic combine ideas from satisfiability, Markov chain Monte Carlo, belief propagation, and resolution. Learning algorithms make use of conditional likelihood, convex optimization, and inductive logic programming. Markov logic has been successfully applied to problems in information extraction and integration, natural language processing, robot mapping, social networks, computational biology, and others, and is the basis of the open-source Alchemy system.
530			_aAlso available in print.
588			_aTitle from PDF t.p. (viewed on July 8, 2009).
650		0	_aArtificial intelligence _xData processing.
650		0	_aInterface circuits.
650		0	_aMarkov processes.
690			_aMarkov logic
690			_aStatistical relational learning
690			_aMachine learning
690			_aGraphical models
690			_aFirstorder logic
690			_aProbabilistic logic
690			_aMarkov networks
690			_aMarkov random fields
690			_aInductive logic programming
690			_aSatisfiability
690			_aMarkov chain Monte Carlo
690			_aBelief propagation
690			_aCollective classification
690			_aLink prediction
690			_aLink-based clustering
690			_aEntity resolution
690			_aInformation extraction
690			_aSocial network analysis
690			_aNatural language processing
690			_aRobot mapping
690			_aComputational biology
700	1		_aLowd, Daniel.
730	0		_aSynthesis digital library of engineering and computer science.
830		0	_aSynthesis lectures on artificial intelligence and machine learning, _x1939-4616 ; _v# 7.
856	4	2	_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=6812892
999			_c561691 _d561691