Alchemy Open Source AI – Meta-Guide.com

Notes:

Alchemy is a hybrid statistical-logical learning and inference tool that was developed to support machine learning and artificial intelligence applications. It is based on a probabilistic logic programming language called ProbLog, which combines elements of statistical machine learning and logical programming.

Alchemy is designed to be a flexible and powerful tool for machine learning and artificial intelligence, and it has been used in a variety of applications, including natural language processing, data mining, and knowledge representation. It provides a number of features that are useful for these applications, including support for probabilistic graphical models, optimization algorithms, and probabilistic reasoning.

Alchemy is implemented as a software library, and it can be used in a variety of programming languages, including Python, C++, and Java. It is available as open-source software, and it has a large and active community of users and developers.

Alchemy Open Source AI is a tool that could potentially be used in the development of dialog systems, as it provides a number of features that are useful for natural language processing and machine learning tasks.

One way that Alchemy could be used with dialog systems is by leveraging its support for probabilistic graphical models and probabilistic reasoning to model and understand the relationships between different concepts and ideas in the dialog. For example, Alchemy could be used to build a model of the knowledge and concepts that the dialog system is designed to handle, and to use this model to generate responses or make decisions based on the content of the dialog.

Another way that Alchemy could be used with dialog systems is by leveraging its optimization algorithms and machine learning capabilities to train the system to recognize and understand spoken language. For example, Alchemy could be used to build machine learning models that are trained on large datasets of spoken language, and to use these models to recognize and understand spoken input in real-time.

Resources:

alchemy.cs.washington.edu
dblp: DEXA (Database and EXpert systems Applications)
unbbayes.sourceforge.net

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Modeling Cognitive Frames for Situations with Markov Logic Networks WR Murray, D Jain – Proceedings of the 8th International NLPCS …, 2011 – books.google.com … computer interaction (HCI). Adaptive systems, such as intelligent tutoring systems, and natural language systems, such as discourse management systems, are examples of the application areas that would bene?t. More opaque … Cited by 1 Related articles All 2 versions

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Markov Logic: A Language and Algorithms for Link Mining P Domingos, D Lowd, S Kok, A Nath, H Poon… – Link Mining: Models, …, 2010 – Springer … including all of the relevant formulas. Many algorithms, as well as sample data sets and applications, are available in the open-source Alchemy system [ 17 ] (alchemy.cs.washington.edu). In this chapter, we describe Markov … Cited by 1 Related articles All 8 versions

ArThUR: A Tool for Markov Logic Network A Bodart, K Evrard, J Ortiz, PY Schobbens – On the Move to Meaningful …, 2014 – Springer … ArThUR has been written in Java and more specifically in Java SE 1.6 edition, an object-oriented 15 http://alchemy.cs.washington.edu/ 16 https://jena.apache.org/ Page 6. … An another processing step of this component is to translate FOL to natural language rules (English rules). …

Markov logic networks for situated incremental natural language understanding C Kennington, D Schlangen – Proceedings of the 13th Annual Meeting …, 2012 – dl.acm.org … exe- cution. 3 Experiments We will now describe our experiments with using Markov Logic Networks for situated incremental natural language understanding. 3.1 Data and Task For … posi- 2http://alchemy.cs.washington.edu/ tive; ie … Cited by 7 Related articles All 9 versions

Coreference based event-argument relation extraction on biomedical text. K Yoshikawa, S Riedel, T Hirao… – J. Biomedical …, 2011 – biomedcentral.com … resulting from high throughput experi- ments demands the automatic extraction of useful information by Natural Language Processing techniques. … Then they use software packages such as Alchemy (http://alchemy.cs.washington.edu/) and Markov thebeast (http://code.google … Cited by 19 Related articles All 18 versions

Maritime Threat Detection Using Probabilistic Graphical Models. B Auslander, KM Gupta, DW Aha – FLAIRS Conference, 2012 – aaai.org … CRFs have been applied to natural language processing, bio-sequencing, and computer vision tasks. Unlike HMMs, a CRF can model local and temporal features. … References [http://alchemy.cs.washington.edu] Auslander, B., Gupta, KM, & Aha, DW (2011). … Cited by 4 Related articles All 8 versions

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Situated incremental natural language understanding using Markov Logic Networks C Kennington, D Schlangen – Computer Speech & Language, 2014 – Elsevier … Cover image Cover image. Situated incremental natural language understanding using Markov Logic Networks ?. … Abstract. We present work on understanding natural language in a situated domain in an incremental, word-by-word fashion. … Cited by 2 Related articles All 5 versions

Ensemble semantics for large-scale unsupervised relation extraction B Min, S Shi, R Grishman, CY Lin – … Methods in Natural Language …, 2012 – dl.acm.org Page 1. Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 1027–1037, Jeju Island, Korea, 12–14 July 2012. cO2012 Association for Computational Linguistics … Cited by 18 Related articles All 12 versions

User Manual Of Felix 0.2 F Niu, C Ré, J Shavlik, J Slauson, C Zhang – 2011 – hazy.cs.wisc.edu … Markov logic adds weights to first-order logic rules, and has rigorous semantics based on the exponential models. It has been successfully applied to a wide range of appli- cations including information extraction, entity resolution, text mining, and natural language processing. … Related articles All 3 versions

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Markov Logic Networks for Incremental Natural Language Understanding C Kennington, D Schlangen – … of the 13th Meeting of the …, 2012 – pub.uni-bielefeld.de … exe- cution. 3 Experiments We will now describe our experiments with using Markov Logic Networks for situated incremental natural language understanding. 3.1 Data and Task For … posi- 2http://alchemy.cs.washington.edu/ tive; ie … Related articles

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A semantic weighting method for document classification based on Markov logic networks E Lee, J Kim, J Choi, C Choi, B Ko, P Kim – Proceedings of the 2014 …, 2014 – dl.acm.org … Riedel and MezaYRuiz use MLNs for natural language processing, taNing advantage of relational aspects of semantics [9]. This section is the … MLNs was applied to perform probabilistic inference according to inference rules using Alchemy(http://alchemy.cs.Washington.edu)[16 …

AKMiner: Domain-Specific Knowledge Graph Mining from Academic Literatures S Huang, X Wan – Web Information Systems Engineering–WISE 2013, 2013 – Springer … Most keyphrase extraction methods first extract candidate phrases with natural language processing techniques, and then rank the candidate phrases and select the final keyphrases with supervised or unsupervised algorithms [14], [18], [30]. … 4 http://alchemy.cs.washington.edu/ … Related articles

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Using First-Order Logic to Compress Sentences. M Huang, X Shi, F Jin, X Zhu – AAAI, 2012 – aaai.org … MLN is a statistical relational learning framework which has been widely applied in natural language processing such as se- mantic role … 1http://alchemy.cs.washington.edu/ 2http://www9-old.in.tum.de/people/jain/mlns/ 3http://thebeast.googlecode.com/ 4http://nlp.stanford. … Cited by 3 Related articles All 3 versions

Building the Relationship Between Web Entities Incrementally Y Ding, H Wang – Information Engineering and Applications, 2012 – Springer … 7. R. Feldman and B. Rosenfeld, Boosting unsupervised relation extraction by using NER, In: Proceedings of Conference on Empirical Methods in Natural Language Processing, 2006. … 14. http://alchemy.cs.washington.edu/ 15. … Related articles

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Unsupervised ontology induction from text H Poon, P Domingos – Proceedings of the 48th annual meeting of the …, 2010 – dl.acm.org Page 1. Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 296–305, Uppsala, Sweden, 11-16 July 2010. cO2010 Association for Computational Linguistics Unsupervised Ontology Induction from Text … Cited by 77 Related articles All 22 versions

Why did the person cross the road (there)? scene understanding using probabilistic logic models and common sense reasoning A Kembhavi, T Yeh, LS Davis – Computer Vision–ECCV 2010, 2010 – Springer … The video data 6 Code available: http://alchemy.cs.washington.edu/ Page 12. 704 A. Kembhavi, T. Yeh, and LS Davis … In: CVPR (2009) 25. Wang, J., Markert, K., Everingham, M.: Learning models for object recognition from natural language descriptions (2009) 26. … Cited by 20 Related articles All 4 versions

Joint modeling of trigger identification and event type determination in chinese event extraction LIP Feng, ZHUQ Ming, DH Jun, ZG Dong – 2012 – kde.cs.tut.ac.jp Page 1. Proceedings of COLING 2012: Technical Papers, pages 1635–1652, COLING 2012, Mumbai, December 2012. Joint Modeling of Trigger Identification and Event Type Determination in Chinese Event Extraction LI Pei … Cited by 1 Related articles All 5 versions

Machine Reading: A” Killer App” for Statistical Relational AI. H Poon, P Domingos – Statistical Relational Artificial Intelligence, 2010 – aaai.org … 2http://alchemy.cs.washington.edu/papers/- poon09. Table 1: Comparison of question answering results on the GENIA dataset. … In Proc. of ICML. Petrov, S. 2009. Coarse-to-Fine Natural Language Processing. Ph.D. Dissertation, Dept. of Comp. Sci., Univ. of Berkeley. … Cited by 6 Related articles All 12 versions

From information to knowledge: harvesting entities and relationships from web sources G Weikum, M Theobald – Proceedings of the twenty-ninth ACM SIGMOD …, 2010 – dl.acm.org … 4. Alchemy- Open-Source AI:riptsize alchemy.cs.washington.edu. 5. B. Aleman-Meza, C. Halaschek, A. Sheth, IB Arpinar, G. Sannapareddy. … Extracting relations from text: From word sequences to dependency paths. Text Mining & Natural Language Processing, 2007. … Cited by 72 Related articles All 6 versions

Using Background Knowledge to Support Coreference Resolution. V Bryl, C Giuliano, L Serafini, K Tymoshenko – ECAI, 2010 – ebooks.iospress.nl … REFERENCES [1] Alchemy – http://alchemy.cs.washington.edu/. … In Proceedings of the 2007 Joint Conference on Em- pirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pages 708–716, 2007. … Cited by 17 Related articles All 9 versions

Bayesian Abductive Logic Programs. S Raghavan, RJ Mooney – Statistical Relational Artificial Intelligence, 2010 – aaai.org … This extra knowledge engineering was required to 2http://alchemy.cs.washington.edu make MLN inference tractable. … MIT Press. Stickel, ME 1988. A Prolog-like inference system for comput- ing minimum-cost abductive explanations in natural-language in- terpretation. … Cited by 23 Related articles All 11 versions

Constraint-Driven Training of Complex Models Using MCMC S SINGH, G DRUCK, A MCCALLUM – 2012 – cs.umass.edu … max ? L(?,Dl) = ? Dl log p(yi|xi;?) ? ?||?||2. (2) Log-linear models have been applied to many problems in natural language processing and information extraction. … 2Available at http://alchemy.cs.washington.edu/ 9 Page 10. gradients computed using MCMC samples. … Related articles

User Manual Of Tuffy 0.3 A Doan, F Niu, C Ré, J Shavlik, C Zhang – 2011 – hazy.cs.wisc.edu … Markov logic networks (MLNs) [7, 1] have emerged as a powerful framework that combines statistical and logical reasoning; they have been applied to many data intensive problems including information extraction, entity resolution, text mining, and natural language processing. … Cited by 1 Related articles All 3 versions

Contextual word spotting in historical manuscripts using Markov logic networks D Fernández, S Marinai, J Lladós… – Proceedings of the 2nd …, 2013 – dl.acm.org … SCFG have been used in different domains, such as Natural Language Pro- cessing. … 33, no. 7, pp. 853–862, 2012. [2] R. Grishman, “Information extraction,” The Handbook of Computational Linguistics and Natural Language Processing, pp. 515–530, 2003. … Cited by 4 Related articles All 4 versions

Evaluating markov logic networks for collective classification R Crane, LK McDowell – Proceedings of the 9th MLG Workshop at …, 2011 – cs.purdue.edu … ing, and object identification in an MLN framework. Riedel and Meza-Ruiz use MLNs for natural language processing, taking advantage of relational aspects of semantics [18]. Some of these applications of MLNs involve reasoning … Cited by 6 Related articles All 8 versions

Using compositional semantics and discourse consistency to improve Chinese trigger identification PF Li, QM Zhu, GD Zhou – Information Processing & Management, 2014 – Elsevier … 1. Introduction. As a compromise to natural language understanding, Information Extraction (IE) aims to extract structured information (eg, entities, relations and events) from a text. As a classic IE task, event extraction is to identify … Cited by 2 Related articles All 3 versions

Evidence-Based Clustering for Scalable Inference in Markov Logic D Venugopal, V Gogate – Machine Learning and Knowledge Discovery in …, 2014 – Springer … representation for statistical relational learning. They have been used in a wide variety of application domains including natural language un- derstanding [17], computer vision [22] and planning [21]. Just as in conventional prob …

Lifted MAP inference for Markov Logic Networks S Sarkhel, D Venugopal, P Singla, V Gogate – Proceedings of the 17th …, 2014 – jmlr.org … probabilistic graphical models. They are rou- tinely used to solve hard problems in a wide variety of real-world application domains including computer vision, natural language processing, robotics and the Web. Re- cently, there … Cited by 3 Related articles All 5 versions

Protein Fold Recognition Using Markov Logic Networks M Biba, S Ferilli, F Esposito – … to Polymer Sequence Analysis and Related …, 2011 – Springer … In Proc. of the 2002 Conference on Empirical Methods in Natural Language Processing, pp. … Department of Computer Science and Engineering, University of Washington, Seattle, WA, 2005. http://?alchemy.?cs.?washington.?edu/?. 19. … Related articles All 6 versions

Probabilistic databases with MarkoViews A Jha, D Suciu – Proceedings of the VLDB Endowment, 2012 – dl.acm.org … MLNs have been demonstrated to be effective at a variety of tasks, such as Information Ex- traction [26], Record Linkage [31], Natural Language Pro- cessing [27]. A benefit of MLNs is that the same framework can be used both for learning the weights, and for inferring … Cited by 17 Related articles All 7 versions

Discovery of logic relations for text mining adaptation using unlabeled data K Chan, W Lam, TL Wong – Adaptivity, Personalization and Fusion of …, 2010 – dl.acm.org … Categories and Subject Descriptors I.2.7 [Artifical Intelligence]: Natural Language Process- ing – Text analysis General Terms Design, Experimentation … The alchemy system for statistical relational ai. In http://alchemy.cs.washington.edu/, 2006. … Related articles

Generative structure learning for markov logic networks QT Dinh, M Exbrayat, C Vrain – Proceedings of the 2010 conference on …, 2010 – dl.acm.org … 12. S. Kok, M. Sumner, M. Richardson, P. Singla, H. Poon, D. Lowd, J. Wang, P. Domingos: The Alchemy system for statistical relational AI, Technical report, Univ. of Washington, (2009), http://alchemy.cs.washington.edu. 13. … Cited by 2 Related articles All 6 versions

Entity Correspondence with Second-Order Markov Logic Y Xu, Z Gao, C Wilson, Z Zhang, M Zhu, Q Ji – Web Information Systems …, 2013 – Springer … 4.3 Weight Learning Up to this point, we have introduced the model for entity correspondence and property relation discovery. In practice, weights can be specified manually or 4 http://alchemy.cs.washington.edu/ 5 http://hazy.cs.wisc.edu/hazy/tuffy/ Page 8. 8 Y. Xu et al. … Related articles All 2 versions

UnBBayes: a java framework for probabilistic models in AI S Matsumoto, RN Carvalho, M Ladeira… – Java in Academia …, 2011 – iconceptpress.com … Fuzzy logic models also have been increasingly popular for representing and processing degrees of truth about imprecise or vague pieces of linguistic information, and became a strong candidate for incorporating impreciseness derived from applications using natural language … Cited by 15 Related articles All 5 versions

You should read this! let me explain you why: explaining news recommendations to users R Blanco, D Ceccarelli, C Lucchese, R Perego… – Proceedings of the 21st …, 2012 – dl.acm.org … Markov Logic Netwoks have been used elsewhere for a plethora of natural language and data mining applications. … The dataset contains quadruples in the form (is, it, e, r), this 2http://alchemy. cs.washington.edu/ Explanations Evaluation Dataset Number of Evaluators 5 … Cited by 2 Related articles All 4 versions

Programming with personalized pagerank: a locally groundable first-order probabilistic logic WY Wang, K Mazaitis, WW Cohen – Proceedings of the 22nd ACM …, 2013 – dl.acm.org Page 1. Programming with Personalized PageRank: A Locally Groundable First-Order Probabilistic Logic William Yang Wang Language Technology Institute Carnegie Mellon University Pittsburgh, PA 15213 yww@cs.cmu.edu … Cited by 7 Related articles All 11 versions

Tuffy: Scaling up statistical inference in markov logic networks using an rdbms F Niu, C Ré, AH Doan, J Shavlik – Proceedings of the VLDB Endowment, 2011 – dl.acm.org … Related Work. MLNs are an integral part of state-of-the- art approaches in a variety of applications: natural language processing [22], ontology matching [29], information extrac- tion [18], entity resolution [25], etc. And so, there is an application push to support MLNs. … Cited by 73 Related articles All 26 versions

Investigating Markov Logic Networks for Collective Classification. R Crane, L McDowell – ICAART (1), 2012 – usna.edu … Cora, CiteSeer, spacer WebKB, synthetic an MLN framework. Riedel and Meza-Ruiz (2008) use MLNs for natural language processing, taking ad- vantage of relational aspects of semantics. Some of these applications of MLNs … Cited by 5 Related articles All 3 versions

Logic Relation Refinement Using Unlabeled Data K Chan, TL Wong, W Lam – Proceedings of the World Congress on …, 2010 – iaeng.org … 1 Introduction Domain adaptation is an actively investigated task in the natural language processing community. … However, in many natural language processing tasks, limited annotated data is produced with expensive cost. … Related articles All 2 versions

Felix: Scaling up Global Statistical Information Extraction Using an Operator-based Approach F Niu, C Zhang, C Ré, J Shavlik – 2011 – www-cs.stanford.edu … We choose MLNs because of our work on the machine reading project, and because they have been successfully applied to a broad range of IE-related applications: natural language processing [28], ontology matching [39], and information extraction [41]. … Related articles All 2 versions

The Alchemy Tutorial MSP Domingos – 2010 – alchemy.cs.washington.edu … We start with the basics of Alchemy in the next section before moving on to more interesting tasks which can be accomplished. All of the datasets used in this tutorial are available at http://alchemy.cs.washington.edu in the “Datasets” section. 1 … 9 Natural Language Processing … Cited by 1 Related articles All 2 versions

Probabilistic Event Calculus for Event Recognition A Skarlatidis, G Paliouras, A Artikis… – arXiv preprint arXiv: …, 2012 – arxiv.org … In MLN, each 2. Systems implementing MLN reasoning and learning algorithms can be found in the following links: http://alchemy.cs.washington.edu http://research.cs.wisc.edu/hazy/tuffy http://code.google.com/p/thebeast http://ias.cs.tum.edu/probcog-wiki 6 Page 7. … Cited by 4 Related articles All 3 versions

Gradient-based boosting for statistical relational learning: The relational dependency network case S Natarajan, T Khot, K Kersting, B Gutmann, J Shavlik – Machine Learning, 2012 – Springer … tuning. These real-world prob- lems range over entity resolution, recommendation, information extraction, bio-medical problems, natural language processing, and structure learning across seven different rela- tional data sets. … Cited by 43 Related articles All 28 versions

Learning Markov logic networks with limited number of labeled training examples TL Wong – International Journal of Knowledge-Based and …, 2014 – IOS Press Page 1. International Journal of Knowledge-based and Intelligent Engineering Systems 18 (2014) 91–98 91 DOI 10.3233/KES-140289 IOS Press Learning Markov logic networks with limited number of labeled training examples … Related articles

Reporting on existing USP-techniques KIT Achim Rettinger, L Zhang, CD Date – 2012 – xlike.org … 8 1.2 Natural Language Processing ….. … First, we will review some basic terms from first-order logic, followed by definitions and tools from the field of Natural Language Processing. … Related articles

Statistical relational data integration for information extraction M Niepert – Reasoning Web. Semantic Technologies for Intelligent …, 2013 – Springer … While this could be explained with the specific applications the creators have in mind (improved keyword search and natural language question answering, for instance) there are some reasonable arguments in favor of not completely ignoring the existing body of work and ex … Cited by 1 Related articles All 3 versions

Scaling Inference for Markov Logic with a Task-Decomposition Approach F Niu, C Zhang, C Ré, J Shavlik – arXiv preprint arXiv:1108.0294, 2011 – arxiv.org … and IBM’s Watson [17]. In their quest to extract knowledge from free-form text, a major problem that all these systems face is coping with inconsistency due to both conflicting information in the underlying sources and the difficulty for machines to understand natural language text. … Related articles All 2 versions

Felix: Scaling inference for markov logic with an operator-based approach F Niu, C Zhang, C Ré, J Shavlik – ArXiv e-prints, 2011 – hazy.cs.wisc.edu … form text. A compelling reason to use frameworks like Markov Logic Networks (MLNs) is that they have demonstrated high quality on semantically challenging tasks, eg, Natural Language Processing [25,39]. However, these … Cited by 8 Related articles All 6 versions

Efficient Inference and Learning in a Large Knowledge Base: Reasoning with Extracted Information using a Locally Groundable First-Order Probabilistic Logic WY Wang, K Mazaitis, N Lao, T Mitchell… – arXiv preprint arXiv: …, 2014 – arxiv.org Page 1. Noname manuscript No. (will be inserted by the editor) Efficient Inference and Learning in a Large Knowledge Base Reasoning with Extracted Information using a Locally Groundable First-Order Probabilistic Logic William … Cited by 2 Related articles All 2 versions

An introduction to conditional random fields C Sutton, A McCallum – Found. Trends Mach. Learn, 2011 – statistics.nowpublishers.com … Much work in learning with graphical models, especially in statisti- cal natural-language processing, has focused on generative models that … Also, software for Markov Logic networks (such as Alchemy: http://alchemy.cs.washington.edu/) can be used to build CRF models. … Cited by 18

Improving Markov network structure learning using decision trees D Lowd, J Davis – The Journal of Machine Learning Research, 2014 – dl.acm.org … Markov networks have been widely used in a number of domains, including computer vision, computational biology, and natural language processing. … 3. These data sets are publicly available at http://alchemy.cs.washington.edu/papers/davis10a. 514 Page 15. … Cited by 2 Related articles All 4 versions

Toward Finding Semantic Relations not Written in a Single Sentence: An Inference Method using Auto-Discovered Rules. M Tsuchida, K Torisawa, S De Saeger, JH Oh… – IJCNLP, 2011 – researchgate.net Proceedings of the 5th International Joint Conference on Natural Language Processing, pages 902–910, Chiang Mai, Thailand, November 8 … 5 The freely available inference system for markov logic Alchemy (http://alchemy.cs.washington.edu/) allows re- cursive rules, but turned … Cited by 5 Related articles All 6 versions

Semantic Annotation for the Digital Humanities-Using Markov Logic for Annotation Consistency Control A Frank, O Hellwig, N Reiter – Linguistic Issues in Language …, 2012 – elanguage.net … 3.2 NLP architecture All systems are integrated in a full-fledged natural language processing architecture based on UIMA4, with analysis results stored as stand-off annotations. … That is, 12http://alchemy.cs.washington.edu Page 17. … Cited by 1 Related articles All 2 versions

Dyna: Extending Datalog for modern AI (full version) J Eisner, NW Filardo – Available under http://cs. jhu. edu/~ jason/papers/ …, 2011 – cs.jhu.edu … 1 Our own AI research is mainly on natural language processing, but as we show here, our observations and approach apply to other AI domains as well. Page 2. 1.1 AI and Databases Today Is a new language necessary? … Cited by 3 Related articles All 3 versions

Relation Extraction with Weak Supervision and Distributional Semantics B Min – 2013 – DTIC Document … Introduction People have dreamed of building a computer system that can understand human lanauge since the early days of modern computing. An area of Natural Language Processing (NLP) research that evolves towards this ultimate goal is Information Extraction (IE). … Cited by 1 Related articles All 11 versions

An introduction to conditional random fields C Sutton, A McCallum – arXiv preprint arXiv:1011.4088, 2010 – arxiv.org … Much work in learning with graphical models, especially in statisti- cal natural-language processing, has focused on generative models that … Also, software for Markov Logic networks (such as Alchemy: http: //alchemy.cs.washington.edu/) can be used to build CRF models. … Cited by 42 Related articles All 11 versions

Semantic Annotation for the Digital Humanities A Frank, T Bögel, O Hellwig, N Reiter – Linguistic Issues in Language …, 2012 – elanguage.net … 3.2 NLP architecture All systems are integrated in a full-fledged natural language processing architecture based on UIMA4, with analysis results stored as stand-off annotations. … That is, 12http://alchemy.cs.washington.edu Page 17. … Cited by 2 Related articles All 3 versions

[BOOK] Database and Expert Systems Applications: 22nd International Conference, DEXA 2011, Toulouse, France, August 29-September 2, 2011, Proceedings A Hameurlain, SW Liddle, KD Schewe, X Zhou – 2011 – books.google.com Page 1. LNCS 6860 Abdelkader Hameurlain Stephen W. Liddle Klaus-Dieter Schewe Xiaofang Zhou (Eds.) Database and Expert Systems Applications 22nd International Conference, DEXA 2011 Toulouse, France, August/September 2011 Proceedings, Part I Page 2. … Related articles All 6 versions

CASAM: collaborative human-machine annotation of multimedia RJ Hendley, R Beale, CP Bowers… – Multimedia Tools and …, 2014 – Springer … RMI generates formulae for the Alchemy Markov logic reasoning system (http://alchemy.cs. washington.edu/), which is used for reasoning. Using sampling techniques and in particular Alchemy’s MC-SAT algorithm, Fig. 8 Architecture of the RMI module Multimed Tools Appl … Cited by 1 Related articles

Modeling Success, Failure, and Intent of Multi-Agent Activities Under Severe Noise A Sadilek, H Kautz – Mobile Context Awareness, 2012 – Springer Logo Springer. Search Options: … Related articles All 18 versions

HeteroMF: recommendation in heterogeneous information networks using context dependent factor models M Jamali, L Lakshmanan – … of the 22nd international conference on …, 2013 – dl.acm.org Page 1. HeteroMF: Recommendation in Heterogeneous Information Networks using Context Dependent Factor Models Mohsen Jamali Department of Computing Science University of British Columbia Vancouver, Canada jamalim@cs.ubc.ca … Cited by 5 Related articles All 5 versions

Bayesian Logic Programs for Plan Recognition and Machine Reading SV Raghavan – 2012 – DTIC Document … As a result, they are widely used in domains like social network analysis, biological data analysis, and natural language processing. … Here, we first develop an approach using BLPs to infer implicitly stated facts from natural language text. … Cited by 1 Related articles All 3 versions

Statistical Learning for Relational and Structured Data M Lippi – 2010 – dsi.unifi.it … The great devel- opment of this research area has been mainly due to the large amount of available data, organized in complex relational structures, coming from a vari- ety of fields, like molecular biology, social networks analysis, natural language parsing, and many others. … Related articles All 10 versions

Location-based reasoning about complex multi-agent behavior A Sadilek, H Kautz – Journal of Artificial Intelligence Research, 2012 – dl.acm.org … cf. Figure 4). For a BN with n nodes, the joint probability distribution is given by Pr(X1,…,Xn) = n ? i=1 Pr(Xi|Pa(Xi)), (3) 1. http://alchemy.cs.washington.edu/ 2. http://code.google.com/p/theBeast/ 94 Page 9. LOCATION-BASED … Cited by 19 Related articles All 11 versions

Finding relational redescriptions E Galbrun, A Kimmig – Machine Learning, 2013 – Springer Page 1. Mach Learn DOI 10.1007/s10994-013-5402-3 Finding relational redescriptions Esther Galbrun · Angelika Kimmig Received: 13 December 2012 / Accepted: 25 July 2013 © The Author(s) 2013 Abstract We introduce … Related articles All 5 versions

Bayesian Logic Programs for plan recognition and machine reading S Vijaya Raghavan – 2012 – repositories.lib.utexas.edu … tured/relational data. As a result, they are widely used in domains like social net- work analysis, biological data analysis, and natural language processing. Bayesian … reading, which involves automatic extraction of knowledge from natural language text. … Related articles All 4 versions

Logic-based event recognition A Artikis, A Skarlatidis, F Portet… – The Knowledge …, 2012 – Cambridge Univ Press Page 1. The Knowledge Engineering Review, Vol. 27:4, 469–506. & Cambridge University Press, 2012 doi:10.1017/S0269888912000264 Logic-based event recognition ALEXANDER ARTIKIS 1 , ANASTASIOS SKARLATIDIS … Cited by 20 Related articles All 15 versions

Tensor factorization for relational learning M Nickel – 2013 – edoc.ub.uni-muenchen.de … Nakashole et al., 2012b) aim to create large knowledge bases via the extraction of relational information from natural language; a very difficult task in which these projects achieve good … or natural language processing and can benefit significantly from relational information. … Cited by 4 Related articles All 2 versions

SnoMedTagger: A semantic tagger for medical narratives S Hina, E Atwell, O Johnson – International Journal of Computational …, 2013 – gelbukh.com … JAPE: a JAVA Annotation Patterns Engine Second Edition ed. Sheffield: University of Sheffield. 5. Gaizauskas, R., Cunningham, H., Wilks, Y., Rodgers, P. & HumphreyS, K. GATE: an environment to support research and development in natural language engineering. … Cited by 1

Structure Learning in Markov Logic Networks S Kok – 2010 – people.sutd.edu.sg Page 1. Structure Learning in Markov Logic Networks Stanley Kok A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Washington 2010 Program Authorized to Offer Degree: Computer Science & Engineering … Cited by 2 Related articles All 10 versions

Modeling human behavior at a large scale A Sadilek – 2012 – DTIC Document Page 1. Modeling Human Behavior at a Large Scale by Adam Sadilek Submitted in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy Supervised by Henry A. Kautz Department of Computer Science … Cited by 1 Related articles All 5 versions

Improving the Accuracy and Scalability of Discriminative Learning Methods for Markov Logic Networks TN Huynh – 2011 – DTIC Document … I admire Ray for his tremendous passion for science and his broad knowledge about Artificial Intelligence, Machine Learning, Natural Language Processing and many more including academic genealogy. I enjoyed meeting with Ray every week. … Cited by 1 Related articles All 2 versions

Exploiting symmetries for scaling loopy belief propagation and relational training B Ahmadi, K Kersting, M Mladenov, S Natarajan – Machine learning, 2013 – Springer Page 1. Mach Learn (2013) 92:91–132 DOI 10.1007/s10994-013-5385-0 Exploiting symmetries for scaling loopy belief propagation and relational training Babak Ahmadi · Kristian Kersting · Martin Mladenov · Sriraam Natarajan … Cited by 13 Related articles All 15 versions