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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.


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.


Semantic integration of disease-specific knowledge

arXiv.org Artificial Intelligence

Motivation: Biomedical researchers working on a specific disease need up-to-date and unified access to knowledge relevant to the disease of their interest. Knowledge is continuously accumulated in scientific literature and other resources such as biomedical ontologies. Identifying the specific information needed is a challenging task and computational tools can be valuable. In this study, we propose a pipeline to automatically retrieve and integrate relevant knowledge based on a semantic graph representation, the iASiS Open Data Graph . Results: The disease-specific semantic graph can provide easy access to resources relevant to specific concepts and individual aspects of these concepts, in the form of concept relations and attributes. The proposed approach is applied to three different case studies: T wo prevalent diseases, Lung Cancer and Dementia, for which a lot of knowledge is available, and one rare disease, Duchenne Muscular Dystrophy, for which knowledge is less abundant and difficult to locate. Results from exemplary queries are presented, investigating the potential of this approach in integrating and accessing knowledge as an automatically generated semantic graph.


Learning Word Representations from Relational Graphs

AAAI Conferences

If we already know a particular concept representations by considering the semantic relations between such as pets, we can describe a new concept such as dogs words. Specifically, given as input a relational graph, by stating the semantic relations that the new concept shares a directed labelled weighted graph where vertices represent with the existing concepts such as dogs belongs-to pets. Alternatively, words and edges represent numerous semantic relations we could describe a novel concept by listing all that exist between the corresponding words, we consider the the attributes it shares with existing concepts. In our example, problem of learning a vector representation for each vertex we can describe the concept dog by listing attributes (word) in the graph and a matrix representation for each label such as mammal, carnivorous, and domestic animal that it type (pattern). The learnt word representations are evaluated shares with another concept such as the cat. Therefore, both for their accuracy by using them to solve semantic word attributes and relations can be considered as alternative descriptors analogy questions on a benchmark dataset. of the same knowledge. This close connection between Our task of learning word attributes using relations between attributes and relations can be seen in knowledge representation words is challenging because of several reasons. First, schemes such as predicate logic, where attributes there can be multiple semantic relations between two words.