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Semantic Web and Semantic Technology Trends in 2018 - DATAVERSITY

@machinelearnbot

There have been some exciting developments of late in the Semantic Web and Technology space. Semantic Technology trends in 2018 will continue to advance many of the trends discussed in 2017 and build upon a number of new changes just entering the marketplace.


Introducing a Graph-based Semantic Layer in Enterprises

@machinelearnbot

Things, not Strings Entity-centric views on enterprise information and all kinds of data sources provide means to get a more meaningful picture about all sorts of business objects. This method of information processing is as relevant to customers, citizens, or patients as it is to knowledge workers like lawyers, doctors, or researchers. People actually do not search for documents, but rather for facts and other chunks of information to bundle them up to provide answers to concrete questions. Strings, or names for things are not the same as the things they refer to. Still, those two aspects of an entity get mixed up regularly to nurture the Babylonian language confusion.


Solving Semantic Problems Using Contexts Extracted from Knowledge Graphs

AAAI Conferences

This thesis seeks to address word reasoning problems from a semantic standpoint, proposing a uniform approach for generating solutions while also providing human-understandable explanations. Current state of the art solvers of semantic problems rely on traditional machine learning methods. Therefore their results are not easily reusable by algorithms or interpretable by humans. We propose leveraging web-scale knowledge graphs to determine a semantic frame of interpretation. Semantic knowledge graphs are graphs in which nodes represent concepts and the edges represent the relations between them. Our approach has the following advantages: (1) it reduces the space in which the problem is to be solved; (2) sparse and noisy data can be used without relying only on the relations deducible from the data itself; (3) the output of the inference algorithm is supported by an interpretable justification. We demonstrate our approach in two domains: (1) Topic Modeling: We form topics using connectivity in semantic graphs. We use the same topic models for two very different recommendation systems, one designed for high noise interactive applications and the other for large amounts of web data. (2) Analogy Solving: For humans, analogies are a fundamental reasoning pattern, which relies on abstraction and comparative analysis. In order for an analogy to be understood, precise relations have to be identified and mapped. We introduce graph algorithms to assess the analogy strength in contexts derived from the analogy words. We demonstrate our approach by solving standardized test analogy question.


Introducing a Graph-based Semantic Layer in Enterprises

@machinelearnbot

Things, not Strings Entity-centric views on enterprise information and all kinds of data sources provide means to get a more meaningful picture about all sorts of business objects. This method of information processing is as relevant to customers, citizens, or patients as it is to knowledge workers like lawyers, doctors, or researchers. People actually do not search for documents, but rather for facts and other chunks of information to bundle them up to provide answers to concrete questions. Strings, or names for things are not the same as the things they refer to. Still, those two aspects of an entity get mixed up regularly to nurture the Babylonian language confusion.


Knowledge Graphs Strengthen Your AI Strategy - PoolParty Semantic Suite

#artificialintelligence

Companies that develop and build Knowledge Graphs are taking large amounts of data from various data silos and adding value to it so it can be used in a meaningful and more intelligent way. Download this presentation to learn how our customers are using Knowledge Graphs to drive the business value of their data and strengthen their AI strategy.