Validating ChatGPT Facts through RDF Knowledge Graphs and Sentence Similarity

Since ChatGPT offers detailed responses without justifications, and erroneous facts even for popular persons, events and places, in this paper we present a novel pipeline that retrieves the response of ChatGPT in RDF and tries to validate the ChatGPT facts using one or more RDF Knowledge Graphs (KGs). To this end we leverage DBpedia and LODsyndesis (an aggregated Knowledge Graph that contains 2 billion triples from 400 RDF KGs of many domains) and short sentence embeddings, and introduce an algorithm that returns the more relevant triple(s) accompanied by their provenance and a confidence score. This enables the validation of ChatGPT responses and their enrichment with justifications and provenance. To evaluate this service (such services in general), we create an evaluation benchmark that includes 2,000 ChatGPT facts; specifically 1,000 facts for famous Greek Persons, 500 facts for popular Greek Places, and 500 facts for Events related to Greece. The facts were manually labelled (approximately 73% of ChatGPT facts were correct and 27% of facts were erroneous). The results are promising; indicatively for the whole benchmark, we managed to verify the 85.3% of the correct facts of ChatGPT and to find the correct answer for the 58% of the erroneous ChatGPT facts

LODsyndesis: Global Scale Knowledge Services

In this paper, we present LODsyndesis, a suite of services over the datasets of the entire Linked Open Data Cloud, which offers fast, content-based dataset discovery and object co-reference. Emphasis is given on supporting scalable cross-dataset reasoning for finding all information about any entity and its provenance. Other tasks that can be benefited from these services are those related to the quality and veracity of data since the collection of all information about an entity, and the cross-dataset inference that is feasible, allows spotting the contradictions that exist, and also provides information for data cleaning or for estimating and suggesting which data are probably correct or more accurate. In addition, we will show how these services can assist the enrichment of existing datasets with more features for obtaining better predictions in machine learning tasks. Finally, we report measurements that reveal the sparsity of the current datasets, as regards their connectivity, which in turn justifies the need for advancing the current methods for data integration. Measurements focusing on the cultural domain are also included, specifically measurements over datasets using CIDOC CRM (Conceptual Reference Model), and connectivity measurements of British Museum data. The services of LODsyndesis are based on special indexes and algorithms and allow the indexing of 2 billion triples in around 80 min using a cluster of 96 computers

High Performance Methods for Linked Open Data Connectivity Analytics

The main objective of Linked Data is linking and integration, and a major step for evaluating whether this target has been reached, is to find all the connections among the Linked Open Data (LOD) Cloud datasets. Connectivity among two or more datasets can be achieved through common Entities, Triples, Literals, and Schema Elements, while more connections can occur due to equivalence relationships between URIs, such as owl:sameAs, owl:equivalentProperty and owl:equivalentClass, since many publishers use such equivalence relationships, for declaring that their URIs are equivalent with URIs of other datasets. However, there are not available connectivity measurements (and indexes) involving more than two datasets, that cover the whole content (e.g., entities, schema, triples) or “slices” (e.g., triples for a specific entity) of datasets, although they can be of primary importance for several real world tasks, such as Information Enrichment, Dataset Discovery and others. Generally, it is not an easy task to find the connections among the datasets, since there exists a big number of LOD datasets and the transitive and symmetric closure of equivalence relationships should be computed for not missing connections. For this reason, we introduce scalable methods and algorithms, (a) for performing the computation of transitive and symmetric closure for equivalence relationships (since they can produce more connections between the datasets); (b) for constructing dedicated global semantics-aware indexes that cover the whole content of datasets; and (c) for measuring the connectivity among two or more datasets. Finally, we evaluate the speedup of the proposed approach, while we report comparative results for over two billion triples

Linking Entities from Text to Hundreds of RDF Datasets for Enabling Large Scale Entity Enrichment

There is a high increase in approaches that receive as input a text and perform named entity recognition (or extraction) for linking the recognized entities of the given text to RDF Knowledge Bases (or datasets). In this way, it is feasible to retrieve more information for these entities, which can be of primary importance for several tasks, e.g., for facilitating manual annotation, hyperlink creation, content enrichment, for improving data veracity and others. However, current approaches link the extracted entities to one or few knowledge bases, therefore, it is not feasible to retrieve the URIs and facts of each recognized entity from multiple datasets and to discover the most relevant datasets for one or more extracted entities. For enabling this functionality, we introduce a research prototype, called LODsyndesisIE, which exploits three widely used Named Entity Recognition and Disambiguation tools (i.e., DBpedia Spotlight, WAT and Stanford CoreNLP) for recognizing the entities of a given text. Afterwards, it links these entities to the LODsyndesis knowledge base, which offers data enrichment and discovery services for millions of entities over hundreds of RDF datasets. We introduce all the steps of LODsyndesisIE, and we provide information on how to exploit its services through its online application and its REST API. Concerning the evaluation, we use three evaluation collections of texts: (i) for comparing the effectiveness of combining different Named Entity Recognition tools, (ii) for measuring the gain in terms of enrichment by linking the extracted entities to LODsyndesis instead of using a single or a few RDF datasets and (iii) for evaluating the efficiency of LODsyndesisIE

RDFsim: Similarity-Based Browsing over DBpedia Using Embeddings

Browsing has been the core access method for the Web from its beginning. Analogously, one good practice for publishing data on the Web is to support dereferenceable URIs, to also enable plain web browsing by users. The information about one URI is usually presented through HTML tables (such as DBpedia and Wikidata pages) and graph representations (by using tools such as LODLive and LODMilla). In most cases, for an entity, the user gets all triples that have that entity as subject or as object. However, sometimes the number of triples is numerous. To tackle this issue, and to reveal similarity (and thus facilitate browsing), in this article we introduce an interactive similarity-based browsing system, called RDFsim, that offers “Parallel Browsing”, that is, it enables the user to see and browse not only the original data of the entity in focus, but also the K most similar entities of the focal entity. The similarity of entities is founded on knowledge graph embeddings; however, the indexes that we introduce for enabling real-time interaction do not depend on the particular method for computing similarity. We detail an implementation of the approach over specific subsets of DBpedia (movies, philosophers and others) and we showcase the benefits of the approach. Finally, we report detailed performance results and we describe several use cases of RDFsim

A Brief Survey of Methods for Analytics over RDF Knowledge Graphs

There are several Knowledge Graphs expressed in RDF (Resource Description Framework) that aggregate/integrate data from various sources for providing unified access services and enabling insightful analytics. We observe this trend in almost every domain of our life. However, the provision of effective, efficient, and user-friendly analytic services and systems is quite challenging. In this paper we survey the approaches, systems and tools that enable the formulation of analytic queries over KGs expressed in RDF. We identify the main challenges, we distinguish two main categories of analytic queries (domain specific and quality-related), and five kinds of approaches for analytics over RDF. Then, we describe in brief the works of each category and related aspects, like efficiency and visualization. We hope this collection to be useful for researchers and engineers for advancing the capabilities and user-friendliness of methods for analytics over knowledge graphs

Services for Large Scale Semantic Integration of Data

<p>In recent years, there has been an international trend towards publishing open data and an attempt to comply with standards and good practices that make it easier to find, reuse and exploit open data. Linked Data is one such way of publishing structured data and thousands of such datasets have already been published from various domains.</p> <p>However, the semantic integration of data from these datasets at a large (global) scale has not yet been achieved, and this is perhaps one of the biggest challenges of computing today.  As an example, suppose we would like to find and examine all digitally available data about Aristotle in the world of Linked Data. Even if one starts from DBpedia (the database derived by analysing Wikipedia), specifically from the URI “<a href="http://dbpedia.org/resource/Aristotle">http://dbpedia.org/resource/Aristotle</a>” it is not possible to retrieve all the available data because we should first find ALL equivalent URIs that are used to refer to Aristotle. In the world of Linked Data, equivalence is expressed with “owl:sameAs” relationships. However, since this relation is transitive, one should be aware of the contents of all LOD datasets (of which there are currently thousands) in order to compute the transitive closure of “owl:sameAs”, otherwise we would fail to find all equivalent URIs. Consequently, in order to find all URIs about Aristotle, which in turn would be the lever for retrieving all data about Aristotle, we have to index and enrich numerous datasets, through cross-dataset inference. </p

CIDOC-CRM and Machine Learning: A Survey and Future Research

The CIDOC Conceptual Reference Model (CIDOC-CRM) is an ISO Standard ontology for the cultural domain that is used for enabling semantic interoperability between museums, libraries, archives and other cultural institutions. For leveraging CIDOC-CRM, several processes and tasks have to be carried out. It is therefore important to investigate to what extent we can automate these processes in order to facilitate interoperability. For this reason, in this paper, we describe the related tasks, and we survey recent works that apply machine learning (ML) techniques for reducing the costs related to CIDOC-CRM-based compliance and interoperability. In particular, we (a) analyze the main processes and tasks, (b) identify tasks where the recent advances of ML (including Deep Learning) would be beneficial, (c) identify cases where ML has been applied (and the results are successful/promising) and (d) suggest tasks that can benefit from applying ML. Finally, since the approaches that leverage both CIDOC-CRM data and ML are few in number, (e) we introduce our vision for the given topic, and (f) we provide a list of open CIDOC-CRM datasets that can be potentially used for ML tasks

CIDOC-CRM and Machine Learning: A Survey and Future Research

The CIDOC Conceptual Reference Model (CIDOC-CRM) is an ISO Standard ontology for the cultural domain that is used for enabling semantic interoperability between museums, libraries, archives and other cultural institutions. For leveraging CIDOC-CRM, several processes and tasks have to be carried out. It is therefore important to investigate to what extent we can automate these processes in order to facilitate interoperability. For this reason, in this paper, we describe the related tasks, and we survey recent works that apply machine learning (ML) techniques for reducing the costs related to CIDOC-CRM-based compliance and interoperability. In particular, we (a) analyze the main processes and tasks, (b) identify tasks where the recent advances of ML (including Deep Learning) would be beneficial, (c) identify cases where ML has been applied (and the results are successful/promising) and (d) suggest tasks that can benefit from applying ML. Finally, since the approaches that leverage both CIDOC-CRM data and ML are few in number, (e) we introduce our vision for the given topic, and (f) we provide a list of open CIDOC-CRM datasets that can be potentially used for ML tasks

Quantifying the Connectivity of a Semantic Warehouse

In many applications one has to fetch and assemble pieces of information coming from more than one SPARQL endpoints. In this paper we describe the corresponding requirements and challenges, and then we present a process for constructing such a semantic warehouse. We focus on the aspects of quality and value of the warehouse, and for this reason we introduce various metrics for quantifying its connectivity, and consequently its ability to answer complex queries. We demonstrate the behavior of these metrics in the context of a real and operational semantic warehouse. The results are very promising: the proposed metrics-based matrixes allow someone to get an overview of the contribution (to the warehouse) of each source and to quantify the benefit of the entire warehouse. The later is useful also for monitoring the quality of the warehouse after each reconstruction. 1