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Probabilistic Soft Logic for Social Good
"... As governments, non-profit organizations, researchers, and corporations collect data on social phenomena, opportuni-ties have emerged for data science applications that can benefit society. However, modeling these types of complex, real-world phenomena requires new tools to address inher-ent computa ..."
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computational challenges. Social data is intrinsically relational, noisy, partially observed, and large scale, and it is composed of both continuous and discrete informa-tion. Probabilistic soft logic (PSL) [3, 5] is a general-purpose framework we are developing to solve these challenges. Since the value
A short introduction to Probabilistic Soft Logic.
- In Proceedings of NIPS Workshop on Probabilistic Programming: Foundations and Applications (NIPS Workshop-12).
, 2012
"... Abstract Probabilistic soft logic (PSL) is a framework for collective, probabilistic reasoning in relational domains. PSL uses first order logic rules as a template language for graphical models over random variables with soft truth values from the interval [0, 1]. Inference in this setting is a co ..."
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Cited by 29 (14 self)
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Abstract Probabilistic soft logic (PSL) is a framework for collective, probabilistic reasoning in relational domains. PSL uses first order logic rules as a template language for graphical models over random variables with soft truth values from the interval [0, 1]. Inference in this setting is a
Probabilistic soft logic for semantic textual similarity.
- for Computational Linguistics.
, 2014
"... Abstract Probabilistic Soft Logic (PSL) is a recently developed framework for probabilistic logic. We use PSL to combine logical and distributional representations of natural-language meaning, where distributional information is represented in the form of weighted inference rules. We apply this fra ..."
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Cited by 8 (0 self)
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Abstract Probabilistic Soft Logic (PSL) is a recently developed framework for probabilistic logic. We use PSL to combine logical and distributional representations of natural-language meaning, where distributional information is represented in the form of weighted inference rules. We apply
Social group modeling with probabilistic soft logic
- In NIPS Workshop on Social Network and Social Media Analysis: Methods, Models, and Applications
, 2012
"... In this work, we show how to model the group affiliations of social media users using probabilistic soft logic. We consider groups of a broad variety, motivated by ideas from the social sciences on groups and their roles in social identity. By modeling group affiliations, we allow the possibility of ..."
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Cited by 6 (4 self)
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In this work, we show how to model the group affiliations of social media users using probabilistic soft logic. We consider groups of a broad variety, motivated by ideas from the social sciences on groups and their roles in social identity. By modeling group affiliations, we allow the possibility
Decision-Driven Models with Probabilistic Soft Logic
"... We introduce the concept of a decision-driven model, a probabilistic model that reasons directly over the uncertain information of interest to a decision maker. We motivate the use of these models from the perspective of personalized medicine. Decision-driven models have a number of benefits that ar ..."
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Cited by 2 (2 self)
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that are of particular value in this domain, such as being easily interpretable and naturally quantifying confidences in both evidence and predictions. We show how decision-driven models can easily be constructed using probabilistic soft logic, a recently introduced framework for statistical relational learning
Probabilistic soft logic for trust analysis in social networks
- In: International Workshop on Stat. Relational Artif. Intelligence. (2012
"... Trust plays a key role in social interactions. Explicitly modeling trust is therefore an important aspect of social network analysis in settings such as reputation management systems, recommendation systems, and viral marketing. Within the social sciences, trust is known to depend on network structu ..."
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Cited by 6 (5 self)
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(SRL). Additionally, we show that probabilistic soft logic (PSL) is particularly well-suited for this problem. PSL, like many SRL languages, provides an intuitive framework for capturing the relational aspects of trust modeling, while its soft truth values easily accommodate varying strengths of trust
Graph Summarization in Annotated Data Using Probabilistic Soft Logic
"... Abstract. Annotation graphs, made available through the Linked Data initiative and Semantic Web, have significant scientific value. However, their increasing complexity makes it difficult to fully exploit this value. Graph summaries, which group similar entities and relations for a more abstract vie ..."
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Cited by 4 (2 self)
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view on the data, can help alleviate this problem, but new methods for graph summarization are needed that handle uncertainty present within and across these sources. Here, we propose the use of probabilistic soft logic (PSL) [1] as a general framework for reasoning about annotation graphs
Modeling Learner Engagement in MOOCs using Probabilistic Soft Logic
"... Massive open online courses (MOOCs) attract a large number of student registra-tions, but recent studies have shown that only a small fraction of these students complete their courses. Student dropouts are thus a major deterrent for the growth and success of MOOCs. We believe that understanding stud ..."
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Cited by 8 (2 self)
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content and structural features from forum interaction to identify two different forms of student engagement (passive and ac-tive) in MOOCs. We use probabilistic soft logic (PSL) to model student engage-ment by capturing domain knowledge about student interactions and performance. We test our models
Network-Based Drug-Target Interaction Prediction with Probabilistic Soft Logic
- IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
, 2014
"... Drug-target interaction studies are important because they can predict drugsâ unexpected therapeutic or adverse side effects. In silico predictions of potential interactions are valuable and can focus effort on in vitro experiments. We propose a prediction framework that represents the problem us ..."
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Cited by 6 (3 self)
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using a bipartite graph of drug-target interactions augmented with drug-drug and target-target similarity measures and makes predictions using probabilistic soft logic (PSL). Using probabilistic rules in PSL, we predict interactions with models based on triad and tetrad structures. We apply (blocking
Hinge-loss Markov random fields and probabilistic soft logic
, 2015
"... A fundamental challenge in developing high-impact machine learning technologies is balancing the ability to model rich, structured domains with the ability to scale to big data. Many important problem areas are both richly structured and large scale, from social and biological networks, to knowledge ..."
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Cited by 6 (4 self)
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-MRFs by generalizing this unified objective. The second new formalism, probabilistic soft logic (PSL), is a probabilistic programming language that makes HL-MRFs easy to define using a syntax based on first-order logic. We next introduce an algorithm for inferring most-probable variable assignments (MAP inference
Results 1 - 10
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4,365
