ResearchKnowledge Bases

Overview

In recent years, the Semantic Web has been established as one of the promising advances in the area of Knowledge Representation, receiving substantial research and development effort under the leadership of the World Wide Web Consortium. The underlying motivation is to allow computer programs to read and reason about the vast amount of data in the interconnected Web, towards advances in artificial intelligence and machine learning.

In our lab, we focus both on the creation of high-quality semantic Linked Data, an on the effective consumption of the data by potential end-users. In particular, we represent books from the Jewish Bookshelf as Linked Data and develop web apps for accessing the analyzing the data.

People

Prof. Benny Kimelfeld

Dr. Oren Mishali

Dean Light, Research Consultant

Oren Kalinsky

Asaf Yeshurun

Projects

eLinda: Explorer for Linked Data

eLinda is an explorer for Linked Data that helps in understanding the rich content of a given RDF graph. The core functionality is an exploration path, where each step produces a bar chart (histogram) that visualizes the distribution of classes in a set of nodes (URIs). Three types of explorations are supported: subclass distribution, property distribution, and object distribution for a property of choice. eLinda is implemented as a web app and is available online.

presentation

Jewish Bookshelf Search Engine

Raw textual data from the Sefaria project is analyzed, turned into Linked Data, and then made accessible via a search engine that is implemented on top Elasticsearch. The search engine allows to focus on specific books and/or book collections. Try the new search engine for the ארון הספרים היהודי.

Selected Publications

Oren Mishali, Benny Kimelfeld, "Towards Linked Data of Bible Quotations in Jewish Texts", DH 2018: 455-456 abstractpaper

The Hebrew Bible (the Tanakh) is the most ancient and sacred collection of Jewish texts. Throughout the history, additional religious Jewish texts have been written such as the Mishna, the Babylonian Talmud, and many more. These additional texts are often related to (or inspired by) the Bible. As such, many of them quote verses from the Bible. Depending mostly on their frequency and location within the text, the quotations may indicate a weak or strong semantic relation between a given text and a specific portion of the Bible. Knowing these semantic relations may be beneficial for those interested in studying or investigating the Bible.

We report an ongoing project that aims to establish the machinery for the automatic detection and rigorous representation of quotations of Bible verses within Jewish texts. The project consists of three interleaving components. In the first component, an algorithm for identifying Bible quotations in text is developed. In the second, the results of executing the algorithm on a large and open text corpus are represented as a Linked Data graph (RDF dataset). In the third component, we develop a web frontend for making the dataset accessible to end users.

Tal Yahav, Oren Kalinsky, Oren Mishali, Benny Kimelfeld, "eLinda: Explorer for Linked Data", EDBT 2018: 658-661 abstractpaper

To realize the premise of the Semantic Web towards knowledgeable machines, one might often integrate an application with emerging RDF graphs. Nevertheless, capturing the content of a rich and open RDF graph by existing tools requires both time and expertise. We demonstrate eLinda—an explorer for Linked Data. The challenge addressed by eLinda is that of understanding the rich content of a given RDF graph. The core functionality is an exploration path, where each step produces a bar chart (histogram) that visualizes the distribution of classes in a set of nodes (URIs). In turn, each bar represents a set of nodes that can be further expanded through the bar chart in the path. We allow three types of explorations: subclass distribution, property distribution, and object distribution for a property of choice. To efficiently compute the exploration queries, we offer a query engine powered by a worst-case-optimal join algorithm.

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