Axel-Cyrille Ngonga Ngomo

MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach

Entity linking has recently been the subject of a significant body of research. Currently, the best performing approaches rely on trained mono-lingual models. Porting these approaches to other languages is consequently a difficult endeavor as it requires corresponding training data and retraining of the models. We address this drawback by presenting a novel multilingual, knowledge-base agnostic and deterministic approach to entity linking, dubbed MAG. MAG is based on a combination of context-based retrieval on structured knowledge bases and graph algorithms. We evaluate MAG on 23 data sets and in 7 languages. Our results show that the best approach trained on English datasets (PBOH) achieves a micro F-measure that is up to 4 times worse on datasets in other languages. MAG on the other hand achieves state-of-the-art performance on English datasets and reaches a micro F-measure that is up to 0.6 higher than that of PBOH on non-English languages.

Ensemble Learning of Named Entity Recognition Algorithms using Multilayer Perceptron for the Multilingual Web of Data

Implementing the multilingual Semantic Web vision requires transforming unstructured data in multiple languages from the Document Web into structured data for the multilingual Web of Data. We present the multilingual version of FOX, a knowledge extraction suite which supports this migration by providing named entity recognition based on ensemble learning for five languages. Our evaluation results show that our approach goes beyond the performance of existing named entity recognition systems on all five languages. In our best run, we outperform the state of the art by a gain of 32.38% F1-Score points on a Dutch dataset. More information and a demo can be found at http://fox.aksw.org as well as an extended version of the paper descriping the evaluation in detail.

Enhancing Community Interactions with Data-Driven Chatbots--The DBpedia Chatbot

In this demo, we introduce the DBpedia chatbot, a knowledge-graph-driven chatbot designed to optimize community interaction. The bot was designed for integration into community software to facilitate the answering of recurrent questions. Four main challenges were addressed when building the chatbot, namely (1) understanding user queries, (2) fetching relevant information based on the queries, (3) tailoring the responses based on the standards of each output platform (i.e. Web, Slack, Facebook) as well as (4) developing subsequent user interactions with the DBpedia chatbot. With this demo, we will showcase our solutions to these four challenges.

An evaluation of models for runtime approximation in link discovery

Time-efficient link discovery is of central importance to implement the vision of the Semantic Web. Some of the most rapid Link Discovery approaches rely internally on planning to execute link specifications. In newer works, linear models have been used to estimate the runtime of the fastest planners. However, no other category of models has been studied for this purpose so far. In this paper, we study non-linear runtime estimation functions for runtime estimation. In particular, we study exponential and mixed models for the estimation of the runtimes of planners. To this end, we evaluate three different models for runtime on six datasets using 500 link specifications. We show that exponential and mixed models achieve better fits when trained but are only to be preferred in some cases. Our evaluation also shows that the use of better runtime approximation models has a positive impact on the overall execution of link specifications.

A systematic survey of point set distance measures for link discovery

Large amounts of geo-spatial information have been made available with the growth of the Web of Data. While discovering links between resources on the Web of Data has been shown to be a demanding task, discovering links between geo-spatial resources proves to be even more challenging. This is partly due to the resources being described by the means of vector geometry. Especially, discrepancies in granularity and error measurements across data sets render the selection of appropriate distance measures for geo-spatial resources difficult. In this paper, we survey existing literature for point-set measures that can be used to measure the similarity of vector geometries. We then present and evaluate the ten measures that we derived from literature. We evaluate these measures with respect to their time-efficiency and their robustness against discrepancies in measurement and in granularity. To this end, we use samples of real data sets of different granularity as input for our evaluation framework. The results obtained on three different data sets suggest that most distance approaches can be led to scale. Moreover, while some distance measures are significantly slower than other measures, distance measure based on means, surjections and sums of minimal distances are robust against the different types of discrepancies.

GENESIS: a generic RDF data access interface

The availability of billions of facts represented in RDF on the Web provides novel opportunities for data discovery and access. In particular, keyword search and question answering approaches enable even lay people to access this data. However, the interpretation of the results of these systems, as well as the navigation through these results, remains challenging. In this paper, we present Genesis, a generic RDF data access interface. Genesis can be deployed on top of any knowledge base and search engine with minimal effort and allows for the representation of RDF data in a layperson-friendly way. This is facilitated by the modular architecture for reusable components underlying our framework. Currently, these include a generic search back-end, together with corresponding interactive user interface components based on a service for similar and related entities as well as verbalization services to bridge between RDF and natural language.

CEDAL: time-efficient detection of erroneous links in large-scale link repositories

More than 500 million facts on the Linked Data Web are statements across knowledge bases. These links are of crucial importance for the Linked Data Web as they make a large number of tasks possible, including cross-ontology, question answering and federated queries. However, a large number of these links are erroneous and can thus lead to these applications producing absurd results. We present a time-efficient and complete approach for the detection of erroneous links for properties that are transitive. To this end, we make use of the semantics of URIs on the Data Web and combine it with an efficient graph partitioning algorithm. We then apply our algorithm to the LinkLion repository and show that we can analyze 19,200,114 links in 4.6 minutes. Our results show that at least 13% of the owl :sameAs links we considered are erroneous. In addition, our analysis of the provenance of links allows discovering agents and knowledge bases that commonly display poor linking. Our algorithm can be easily executed in parallel and on a GPU. We show that these implementations are up to two orders of magnitude faster than classical reasoners and a non-parallel implementation.

Efficiently Pinpointing SPARQL Query Containments

Query containment is a fundamental problem in database research, which is relevant for many tasks such as query optimisation, view maintenance and query rewriting. For example, recent SPARQL engines built on Big Data frameworks that precompute solutions to frequently requested query patterns, are conceptually an application of query containment. We present an approach for solving the query containment problem for SPARQL queries – the W3C standard query language for RDF datasets. Solving the query containment problem can be reduced to the problem of deciding whether a sub graph isomorphism exists between the normalized algebra expressions of two queries.

Dynamic Planning for Link Discovery

With the growth of the number and the size of RDF datasets comes an increasing need for scalable solutions to support the linking of resources. Most Link Discovery frameworks rely on complex link specifications for this purpose. We address the scalability of the execution of link specifications by presenting the first dynamic planning approach for Link Discovery dubbed Condor. In contrast to the state of the art, Condor can re-evaluate and reshape execution plans for link specifications during their execution. Thus, it achieves significantly better runtimes than existing planning solutions while retaining an F-measure of 100%. We quantify our improvement by evaluating our approach on 7 datasets and 700 link specifications. Our results suggest that Condor is up to 2 orders of magnitude faster than the state of the art and requires less than 0.1% of the total runtime of a given specification to generate the corresponding plan.

Where is My URI

One of the Semantic Web foundations is the possibility to dereference URIs to let applications negotiate their semantic content. However, this exploitation is often infeasible as the availability of such information depends on the reliability of networks, services, and human factors. Moreover, it has been shown that around 90% of the information published as Linked Open Data is available as data dumps and more than 60% of endpoints are offline. To this end, we propose a Web service called Where is my URI?. Our service aims at indexing URIs and their use in order to let Linked Data consumers find the respective RDF data source, in case such information cannot be retrieved from the URI alone. We rank the corresponding datasets by following the rationale upon which a dataset contributes to the definition of a URI proportionally to the number of literals. We finally describe potential use-cases of applications that can immediately benefit from our simple yet useful service.