Not enough data to create a plot.
Try a different view from the menu above.
"An ontology defines the terms used to describe and represent an area of knowledge. … Ontologies include computer-usable definitions of basic concepts in the domain and the relationships among them."
– from OWL Web Ontology Language Use Cases and Requirements. W3C Recommendation (10 February 2004). Jeff Heflin, editor.
An RDF statement expresses a relationship between two resources. The subject and the object represent the two resources being related; the predicate represents the nature of their relationship. The relationship is phrased in a directional way (from subject to object) and is called an RDF property. RDF allows us to communicate much more than just words; it allows us to communicate data that can be understood by machines as well as people. In this tutorial, we'll do the RDF Processing in Python with RDFLib.
The showy science projects get all the attention in the constant quest to automate everything. That includes gigantic natural language processing models such as OpenAI's GPT-3, which can complete sentences, answer questions, and even write poetry. For those making commercial software, there is a more mundane but perhaps equally valuable task, which is to figure out what facts a machine should have access to and make that actually have value for humans. "We don't apologize for the fact that some of this requires brute force," says Dan Turchin, chief executive and co-founder of PeopleReign, a San Jose, California software startup that is automating the handling of support calls for things such as IT and benefits. His software has compiled, over a period of five years, a kind of encyclopedia of more than five million "domain concepts," structured information relating to things such as employee benefits, requests for computer support, and all manner of other things customers or employees might request, culled from a billion examples such as IT tickets, wikis, chat transcripts, etc.
Federated Analytics is the latest feature added to Raven Distribution Framework that allows for the safe dynamic aggregation of statistics such as mean, variance, and standard deviation across data that is privately held on several clients. RDF's Ravop library now supports the creation of federated operations which developers can leverage to conduct analysis without directly observing a client's private data. Federated analytics is a new approach to data analysis in which key statistics like mean, variance, and standard deviation can be calculated across various private datasets without compromising privacy. It operates similarly to federated learning in that it runs local calculations over each client device's data and only makes the aggregated findings -- never any data from a specific device -- available to developers. Sensitive data like medical records, financial transactions, employee data, and others can be analyzed without leaving the premise.
The current release of Raven Distribution Framework (RDF v0.3)provides an easy to use library that allows developers to build mathematical algorithms or models and computes these operations by distributing them across multiple clients. This provides an increase in speed and efficiency when dealing with a large number of mathematical operations. Distributed Computing is the linking of various computing resources like PCs and smartphones to share and coordinate their processing power for a common computational requirement, such as the training of a large Machine Learning model. These resources or nodes communicate with a central server and in some cases with each other, such that each node receives some data and completes a subset of a task. These nodes can coordinate their computations to complete a large and complex computational requirement in a fast and efficient manner.
Knowledge graph (KG) reasoning is becoming increasingly popular in both academia and industry. Conventional KG reasoning based on symbolic logic is deterministic, with reasoning results being explainable, while modern embedding-based reasoning can deal with uncertainty and predict plausible knowledge, often with high efficiency via vector computation. A promising direction is to integrate both logic-based and embedding-based methods, with the vision to have advantages of both. It has attracted wide research attention with more and more works published in recent years. In this paper, we comprehensively survey these works, focusing on how logics and embeddings are integrated. We first briefly introduce preliminaries, then systematically categorize and discuss works of logic and embedding-aware KG reasoning from different perspectives, and finally conclude and discuss the challenges and further directions.
We study the problem to decide, given sets T1,T2 of tuple-generating dependencies (TGDs), also called existential rules, whether T2 is a conservative extension of T1. We consider two natural notions of conservative extension, one pertaining to answers to conjunctive queries over databases and one to homomorphisms between chased databases. Our main results are that these problems are undecidable for linear TGDs, undecidable for guarded TGDs even when T1 is empty, and decidable for frontier-one TGDs.
The use of Semantic Technologies - in particular the Semantic Web - has revealed to be a great tool for describing the cultural heritage domain and artistic practices. However, the panorama of ontologies for musicological applications seems to be limited and restricted to specific applications. In this research, we propose HaMSE, an ontology capable of describing musical features that can assist musicological research. More specifically, HaMSE proposes to address issues that have been affecting musicological research for decades: the representation of music and the relationship between quantitative and qualitative data. To do this, HaMSE allows the alignment between different music representation systems and describes a set of musicological features that can allow the music analysis at different granularity levels.
Warded Datalog+- extends the logic-based language Datalog with existential quantifiers in rule heads. Existential rules are needed for advanced reasoning tasks, e.g., ontological reasoning. The theoretical efficiency guarantees of Warded Datalog+- do not cover extensions crucial for data analytics, such as arithmetic. Moreover, despite the significance of arithmetic for common data analytic scenarios, no decidable fragment of any Datalog+- language extended with arithmetic has been identified. We close this gap by defining a new language that extends Warded Datalog+- with arithmetic and prove its P-completeness. Furthermore, we present an efficient reasoning algorithm for our newly defined language and prove descriptive complexity results for a recently introduced Datalog fragment with integer arithmetic, thereby closing an open question. We lay the theoretical foundation for highly expressive Datalog+- languages that combine the power of advanced recursive rules and arithmetic while guaranteeing efficient reasoning algorithms for applications in modern AI systems, such as Knowledge Graphs.
Feel free to contribute! （英文） The current content includes JS, network, browser related, performance optimization, security, framework, Git, data structure, algorithm, etc. Almost complete answers to "Front-end Job Interview Questions" which you can use to interview potential candidates, test yourself or completely ignore （英文） How to pass the Node.js Build fast and easy multiple beautiful resumes and create your best CV ever! A complete computer science study plan to become a software engineer.