Semantic Web technologies can be used to solve almost any information problem, so a critical question to consider is when they should be used. RDF: The graph nature of this data model means that it is by nature open-ended, so new data and new relationships can always be added. OWL: This language is descriptive, as opposed to prescriptive, so ontologies are independent of the data that they describe—unlike in a traditional database schema, where the data described are determined directly.
By looking at these technologies in this light, a couple of important observations can be made. The first is that all three of these technologies are open-ended. RDF can accept new data. Even the data model itself can be modified after the fact! This open-endedness makes Semantic Web technologies a natural fit for agile development. More significantly, it makes Semantic Web technologies a natural fit for solving open-ended problems. A specific problem can be open-ended at many different points and in many different ways e.
Thus, we need to be more explicit regarding for which specific kinds ofopenness such Semantic Web technologies are particularly well-suited:. Complete Data Model Unknown: You are not absolutely certain that you will not need more data at some point in the future than you think you need today.
For example, you may know now that you need to track a couple of specific elements e. Complete Usage Model Unknown: You are not completely sure that you are aware of every possible view or report that all your users might need. Today, you know that you need to group by regions and sum total sales, but are you sure that you will never need to group by product family? Adding new usages means new kinds of data and new usages of data.
Semantic Web applications thrive in these circumstances.
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A detailed, technical comparison of Semantic Web technologies versus relational database technologies will make this distinction clear in a future lesson. One entire class of problems that, by its very nature, is open-ended is any problem that includes unstructured information—that is, documents Word, PDF, Excel, etc. If your application could benefit by including data from sources such as these, then you will almost certainly not be able to anticipate from the beginning everything that you might possibly want to extract from your sources.
Just because you have a great hammer does not mean that every problem in the world is a nail. The flexibility inherent in Semantic Web applications comes with some drawbacks, and sometimes a problem can be more efficiently solved using other tools. Data Scale: Although Franz broke the Trillion Triple mark in , a single Semantic Web solution cannot yet store as much data as a relational data warehouse can. Some workarounds can possibly be employed to boost the scale of effectiveness e.
Semantic Web servers are not optimized for high volume writes. The traditional technology stack is optimized for high volumes of transactions, which, in theory, could be applied to Semantic Web tools, but no simple, out-of-the-box integrations have yet been developed for these high-volume solutions. Computational Scale: Semantic Web servers are not optimized for high-scale numeric computations on a huge amount of numeric data.
That said, it is easy to pull data from Semantic Web systems into traditional BI tools for calculation and visualization. These caveats aside, one of the great benefits of Semantic Web solutions is that they arestorage agnostic.
Similarly, if you have an existing data warehouse containing petabytes of data, keep it there! Just define OWL ontologies for the subsets of warehoused data that you would like to consume in your Semantic Web application.
Is the transaction volume modest? Finally, remember that this range between relational databases to Semantic Web technologies is a continuum. A Semantic Web application can—and almost always does—incorporate data from relational databases, content management systems CMS , and other complementary technologies. In addition, semantic technology seems predestined to support in rendering explainable those systems that are not themselves based on semantic technologies. However, how can such systems make use of these ontologies to generate explanations of actions they performed and decisions they took?
Which criteria must an ontology fulfill so that it supports the generation of explanations? Do we have adequate ontologies that enable to express explanations and enable to model and reason about what is understandable or comprehensible for a certain user? What kind of lexicographic information is necessary to generate linguistic utterances?
How to design ontologies for system understandability? What are models of human-machine interaction where the system enables to interact with the system until the user understood a certain action or decision? How can explanatory components be reused with other systems that they have not been designed for? Some efforts in this field have been referred to as neural-symbolic integration. However, this field has so far only been only rarely explored.
The constant growth of Linked Data on the Web raises new challenges for querying and integrating massive amounts of data across multiple datasets. In addition, various sources produce streaming data. Efficiently querying these sources is of central importance for the scalability of Linked Data and Semantic Web technologies.
To exploit the massive amount of data to its full potential, users should be able to query and combine this data easily and effectively. This workshop at the International Semantic Web Conference ISWC seeks original articles describing theoretical and practical methods and techniques for fostering, querying, consuming, and benchmarking the Web of Data.
This workshop at the International Semantic Web Conference ISWC seeks original articles describing theoretical and practical methods and techniques for fostering, querying, and consuming the Data Web. Topics relevant to this workshop include — but are not limited to — the following: — SPARQL query processing — Centralized, decentralized, federated, and distributed — Demos and applications — Optimization and source selection — Benchmarks, ranking, measures, and performance evaluation — Lightweight Linked Data interfaces — Streams — Big Data techniques — Entailment regimes — Caching and replication — Integrating public and private Linked Data — Querying personal Linked Data stores — Domain-specific query languages e.
The Agenda for Sustainable Development is the plan for transforming our world. These goals try to realize the human rights of all, to achieve gender equality and quality education, and to combat climate change, among other issues. In order to successfully implement and monitor such an Agenda, it is crucial to have available, accessible, high-quality and reliable data generated by governments, organizations and citizens.
Nowadays, datasets coming from different agents are emerging. This data, needed to fully understand how the SDGs are being achieved and could be reached in the future, are inherently complex, often inconsistent, and dynamic. Thus, knowledge acquisition and modelling, ontologies, vocabularies, reasoning, and linking, among other topics related to the Semantic Web are key for supporting the implementation and monitoring of the Agenda.fiegocarnore.ga/831.php
Biomedical ontologies and controlled terminologies provide structured domain knowledge to a variety of health information systems. The rich thesaurus with concepts linked by semantic relationships has been widely used in natural language processing, data mining, machine learning, semantic annotation, and automated reasoning.
The dramatically increasing amount of health-related data poses unprecedented opportunities for mining previously unknown knowledge with semantics-powered data mining and analytics methods. However, due to the heterogeneity of different data sources, it is a challenging problem to exploit multiple sources to solve real-world problems such as designing cost-effective treatment plan for patients, designing generalizable clinical trials, drug repurposing, and clinical phenotyping.
The goal of this workshop is to bring people in the field of ontologies, data mining, knowledge representation, knowledge management, and data analytics to discuss innovative semantic methods, applications, and data analytics to address problems in healthcare, biomedicine, public health, and clinical research with biomedical, clinical, behavioral, and social web data. In the past three years, SEPDA has been established as a key venue for disseminating research on health data analytics using semantic web technologies such as ontologies.
In the past few years, we have seen an increasing interest in using semantic web technologies for health data analysis with more and more submissions that present novel methods and applications for linked open data, information extraction, semantic-web-based knowledge bases, and deep learning.
11th International Conference on Web services & Semantic Technology (WeST )
Semantic web technologies play a crucial role to address the FAIR principles. Meanwhile, we received submissions that use semantic-based methods to tackle critical problems in biomedical informatics such as extracting drug-drug interaction, drug repurposing, adverse drug reaction, detecting early signals for cognitive impairment, and visualizing dietary supplement knowledge.
It is thus critical for SEPDA to continue our momentum and allow researchers to present and discuss novel methods and applications in this fast growing field. Blockchain and artificial intelligence AI are two emerging technologies with the potential to contribute to semantic web efforts. Blockchains have been used for applications such decentralized finance in such a way that establishes trust without a central authority. Artificial Intelligence has been used for decision making, requiring vast amounts of data affected by privacy and trust requirements. To address these overlapping themes, the BlockSW workshop is open to submissions at the intersection of blockchain, semantic web and AI.
International Journal on Semantic Web and Information Systems
CKG is concerned with knowledge graphs with contexts, i. Research topics include contextualized and distributed Description Logics, annotation of statements in the Semantic Web, and Distributed Knowledge Repositories. Real-world use cases include the creation of collaborative knowledge bases, such as Wikidata, where qualifiers and references can be attached to every statement. This workshop aims to serve as a gathering point for researchers and industry interested in CKGs to discuss current challenges and future solutions, and raise awareness about this emerging topic to a more broader Semantic Web community.
This workshop addresses fundamental as well as practical topics including i logical models to encode the contextual annotations in the graph, ii reasoning and querying over CKGs, iii using CKGs in applications such as query answering, data mining, or machine learning, iv techniques to benchmark or improve the performance of CKG storage and querying systems. This workshop is complemented by a W3C community on this topic.
At the same time, we have seen a raise in interest in adding contextual annotations to statements in Knowledge Graphs, with different research communities proposing solutions for representing, reasoning, and querying this knowledge, to actual initiatives to create Knowledge Graphs with contextual annotations, such as Yago, Wikidata, or The Open Knowledge Network.
Topics of interest include but are not limited to, the following
OKN is meant to be an inclusive, open, and community-driven, resulting in a knowledge infrastructure that could facilitate and empower a host of applications and open new research avenues including how to create trustworthy knowledge networks in the form of CKGs. CKGs for answering more complex questions requires the contextual information to be incorporated to the data model.
The complex questions are ranging from the macro have there been unusual clusters of earthquakes in the US in the past six months? While three OKN workshops have been held largely focused on understanding the requirements and building a community, the proposed workshop will be a technical and technological counterpart for OKN workshops. The workshop series covers issues related to quality in ontology design and ontology design patterns ODPs for data and knowledge engineering in Semantic Web.
The increased attention to ODPs in recent years through their interaction with emerging trends of Semantic Web such as knowledge graphs can be attributed to their benefit for knowledge engineers and Semantic Web developers. Such benefits come in the form of direct link to requirements, reuse, guidance, and better communication. This workshop invites papers for life sciences and biomedical data processing, as well as the amalgamation with Linked Data and Semantic Web technologies for better data analytics, knowledge discovery and user-targeted applications.
This research contribution should provide useful information for the Knowledge Acquisition research community as well as the working Data Scientist. This workshop at the International Semantic Web Conference ISWC seeks original contributions describing theoretical and practical methods and techniques that present the anatomy of large scale linked data infrastructure, which covers: the distributed infrastructure to consume, store and query large volumes of heterogeneous linked data; using indexes and graph aggregation to better understand large linked data graphs, query federation to mix internal and external data-sources, and linked data visualisation tools for health care and life sciences.
SeWeBMeDA aims to provide researchers in biomedical and life science, an insight and awareness about large scale data technologies for linked data, which are becoming increasingly important for knowledge discovery in the life sciences domain. Therefore, it is important for a government to offer services that make legislation easily accessible to the citizens aiming at informing them, enabling them to defend their rights, or to use legislation as part of their job. It is equally important to have law professionals lawyers, judges, etc. Finally, in the age of the Web, it is important to enable software developers to develop applications for citizens and law professionals easily, by connecting the available laws with other kinds of government or private sector information.
There also private companies e. As can be seen from the above topics, both research areas have a lot to contribute. Like the Web, the Semantic Web is often reduced to a centralized story: we rely on large-scale server-side infrastructures to perform intense reasoning, data mining, query execution, etc. The DeSemWeb workshop focuses on decentralized and client-side applications to counterbalance the centralized discourse of other tracks.