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Just Add Weights: Markov Logic for the Semantic Web
[chapter]
2008
Lecture Notes in Computer Science
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Markov logic brings the power of probabilistic modeling to first-order logic by attaching weights to logical formulas and viewing them as templates for features of Markov networks. ...
In recent years, it has become increasingly clear that the vision of the Semantic Web requires uncertain reasoning over rich, firstorder representations. ...
, ONR, or the United States Government. ...
doi:10.1007/978-3-540-89765-1_1
fatcat:msbve5kalvgdtk44mvcsxhjale
Learning action models from plan examples using weighted MAX-SAT
2007
Artificial Intelligence
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These example plans are obtained by an observation agent who does not know the logical encoding of the actions and the full state information between the actions. ...
It then builds a weighted propositional satisfiability (weighted MAX-SAT) problem and solves it using a MAX-SAT solver. ...
Acknowledgement We wish to thank the Hong Kong RGC grant 621606 for supporting this research. ...
doi:10.1016/j.artint.2006.11.005
fatcat:nitcegmzw5d6lffvjhr2r76lqe
Modelling architectures of parametric weighted component-based systems
[article]
2020
arXiv
pre-print
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For this, we introduce a weighted first-order extended interaction logic over a commutative semiring in order to serve as a modelling language for parametric quantitative architectures. ...
Moreover, we show that our weighted logic can efficiently describe well-known parametric architectures with quantitative characteristics. ...
In [21] the authors studied population protocols, a specific class of parametric systems, modelled by labelled transition systems with Markov chains semantics. ...
arXiv:1905.05607v4
fatcat:5tkeb26xdjgenienp3fy4k3rrm
Symmetric Weighted First-Order Model Counting
[article]
2015
arXiv
pre-print
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Our motivation comes from an important application, inference in Knowledge Bases with soft constraints, like Markov Logic Networks, but the problem is also of independent theoretical interest. ...
by associating a weight to each tuple and defining the weight of a model to be the product of weights of its tuples. ...
We thank Ronald Fagin, Phokion Kolaitis and Lidia Tendera for discussions on topics related to this paper. ...
arXiv:1412.1505v3
fatcat:kl4qpimzxjdafkrh3suwytufoq
Association Rule Mining Based Extraction of Semantic Relations Using Markov Logic Network
2014
International journal of Web & Semantic Technology
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Here, also find the inference and learning weights using Markov Logic Network. ...
It does not produce the semantic relation between the concepts. Here, we have to do the process of constructing the predicates and also first order logic formula. ...
Markov Logic, linguistics internet languages can be created probabilistic just by adding weights to statements and linguistics web illation engines may be extended to perform probabilistic reasoning merely ...
doi:10.5121/ijwest.2014.5403
fatcat:ntxcpkrrarfdbkkeainy6mk4g4
Symmetric Weighted First-Order Model Counting
2015
Proceedings of the 34th ACM Symposium on Principles of Database Systems - PODS '15
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Our motivation comes from an important application, inference in Knowledge Bases with soft constraints, like Markov Logic Networks, but the problem is also of independent theoretical interest. ...
by associating a weight to each tuple and defining the weight of a model to be the product of weights of its tuples. ...
We thank Ronald Fagin, Phokion Kolaitis and Lidia Tendera for discussions on topics related to this paper. ...
doi:10.1145/2745754.2745760
dblp:conf/pods/BeameBGS15
fatcat:yt752ppyrzcxfdwgdwxshukztq
Neural Network-Based Dynamic Segmentation and Weighted Integrated Matching of Cross-Media Piano Performance Audio Recognition and Retrieval Algorithm
2022
Computational Intelligence and Neuroscience
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This paper presents a dynamic segmentation and weighted comprehensive matching algorithm based on neural networks for cross-media piano performance audio recognition and retrieval. ...
This paper implements the data collection and processing, audio recognition, and retrieval algorithm for cross-media piano performance big data through three main modules: the collection, processing, and ...
For example, the advanced semantic association is modeled by a Markov random field-based topic model; the optimal latent space for multimedia data sharing is obtained by joint co-learning latent space ...
doi:10.1155/2022/9323646
pmid:35602641
pmcid:PMC9122679
fatcat:lmgefjsmdzagdekurjwqs6xwju
Ontology Based Concept Hierarchy Extraction of Web Data
2015
Indian Journal of Science and Technology
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Markov Logic Network is a technique in which identify the concept in the domain and order the candidate terms in hierarchical way. ...
This paper proposes the method of Ontology Based Concept Hierarchy Extraction of Web Data. This helps to extract Concept Hierarchy efficient way for ontology construction. ...
For learning weight decide to use the method of Markov Logic Network (MLN). To produce the inference values we have to take the alchemy process. ...
doi:10.17485/ijst/2015/v8i6/61070
fatcat:be65gb26pjdtbmstln6tsj3ciy
Markov Logic: A Language and Algorithms for Link Mining
[chapter]
2010
Link Mining: Models, Algorithms, and Applications
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Markov logic provides this by attaching weights to formulas in first-order logic and viewing them as templates for features of Markov networks. ...
Markov logic has been used successfully in a wide variety of link mining applications, and is the basis of the open-source Alchemy system. ...
, NSF, ONR, or the United States Government. ...
doi:10.1007/978-1-4419-6515-8_5
fatcat:xxlhk6x6rfbg7kreiwnt437s5q
Ontology Matching with Knowledge Rules
[chapter]
2015
Lecture Notes in Computer Science
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Ontology matching is the process of automatically determining the semantic equivalences between the concepts of two ontologies. ...
Our method achieves substantially better F-score than the previous state-of-the-art on three ontology matching domains. ...
We encode the knowledge-based strategy and other strategies in Markov logic and find the best alignment with its inference tools. ...
doi:10.1007/978-3-319-22849-5_7
fatcat:l6demvblsjehnitermhetutagq
Structured machine learning: the next ten years
2008
Machine Learning
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The goal of the current paper is to consider these emerging trends and chart out the strategic directions and open problems for the broader area of structured machine learning for the next 10 years. ...
The field of inductive logic programming (ILP) has made steady progress, since the first ILP workshop in 1991, based on a balance of developments in theory, implementations and applications. ...
Acknowledgements The authors thank the reviewers for their helpful comments. Thanks are also due to Ronald Bjarnason, Janardhan Rao Doppa, and Sriraam Natarajan for proof-reading the paper. ...
doi:10.1007/s10994-008-5079-1
fatcat:arzjk4d7wrgffnzt4znrsfrb5q
R-KG: A Novel Method for Implementing a Robot Intelligent Service
2020
AI
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To this end, a knowledge reasoning model based on a Markov logic network is proposed to realize the self-developmental ability of the knowledge graph and to further enrich it. ...
Then, the deep logical relationship hidden in the knowledge graph is explored. ...
Learn Weights of Rules After learning the inference rules, the method of maximum likelihood parameter estimation is commonly used to learn Markov logic network model weights [40] . ...
doi:10.3390/ai1010006
fatcat:dhbkgbqwijhs3nlz7uoufikyrm
A Survey on State-of-the-art Techniques for Knowledge Graphs Construction and Challenges ahead
[article]
2021
arXiv
pre-print
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The knowledge graph is an emerging technology that allows logical reasoning and uncovers new insights using content along with the context. ...
Structuring this data into a knowledge graph enables multitudes of intelligent applications such as deep question answering, recommendation systems, semantic search, etc. ...
It applies first-order logic over the proposed a bitemporal model for maintaining and querying
Markov network to detect inaccuracies. ...
arXiv:2110.08012v2
fatcat:q6utzgjahfehpftol3dttgolui
Towards Machine Learning on the Semantic Web
[chapter]
2008
Lecture Notes in Computer Science
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In this paper we explore some of the opportunities and challenges for machine learning on the Semantic Web. ...
The Semantic Web provides standardized formats for the representation of both data and ontological background knowledge. ...
We thank Frank Harmelen, and two anonymous reviewers for their valuable comments. ...
doi:10.1007/978-3-540-89765-1_17
fatcat:ixm5kgibajgmvohdfffo43cijm
Markov Logic: An Interface Layer for Artificial Intelligence
2009
Synthesis Lectures on Artificial Intelligence and Machine Learning
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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. ...
Just as sums of child features act as conjunctions, the summations over g parameters act as universal quantifiers with Markov logic semantics. ...
doi:10.2200/s00206ed1v01y200907aim007
fatcat:em6ggc2ha5f4lgaie53jkdjtbu
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