Search Results for author: Filip Ilievski

Found 29 papers, 8 papers with code

The Predicate Matrix and the Event and Implied Situation Ontology: Making More of Events

no code implementations GWC 2016 Roxane Segers, Egoitz Laparra, Marco Rospocher, Piek Vossen, German Rigau, Filip Ilievski

This paper presents the Event and Implied Situation Ontology (ESO), a resource which formalizes the pre and post situations of events and the roles of the entities affected by an event.

Numeracy enhances the Literacy of Language Models

no code implementations EMNLP 2021 Avijit Thawani, Jay Pujara, Filip Ilievski

This paper studies the effect of using six different number encoders on the task of masked word prediction (MWP), as a proxy for evaluating literacy.

ReferenceNet: a semantic-pragmatic network for capturing reference relations.

no code implementations GWC 2018 Piek Vossen, Filip Ilievski, Marten Postrma

In this paper, we present ReferenceNet: a semantic-pragmatic network of reference relations between synsets.

Word Embeddings

Story Generation with Commonsense Knowledge Graphs and Axioms

no code implementations AKBC Workshop CSKB 2021 Filip Ilievski, Jay Pujara, Hanzhi Zhang

Our method aligns story types with commonsense axioms, and queries to a commonsense knowledge graph, enabling the generation of hundreds of thousands of stories.

Common Sense Reasoning Knowledge Graphs +1

Analyzing Race and Country of Citizenship Bias in Wikidata

no code implementations11 Aug 2021 Zaina Shaik, Filip Ilievski, Fred Morstatter

Through this analysis, we discovered that there is an overrepresentation of white individuals and those with citizenship in Europe and North America; the rest of the groups are generally underrepresented.

User-friendly Comparison of Similarity Algorithms on Wikidata

1 code implementation11 Aug 2021 Filip Ilievski, Pedro Szekely, Gleb Satyukov, Amandeep Singh

While the similarity between two concept words has been evaluated and studied for decades, much less attention has been devoted to algorithms that can compute the similarity of nodes in very large knowledge graphs, like Wikidata.

Entity Linking Knowledge Graphs

Creating and Querying Personalized Versions of Wikidata on a Laptop

no code implementations6 Aug 2021 Hans Chalupsky, Pedro Szekely, Filip Ilievski, Daniel Garijo, Kartik Shenoy

Application developers today have three choices for exploiting the knowledge present in Wikidata: they can download the Wikidata dumps in JSON or RDF format, they can use the Wikidata API to get data about individual entities, or they can use the Wikidata SPARQL endpoint.

A Study of the Quality of Wikidata

1 code implementation1 Jul 2021 Kartik Shenoy, Filip Ilievski, Daniel Garijo, Daniel Schwabe, Pedro Szekely

Wikidata has been increasingly adopted by many communities for a wide variety of applications, which demand high-quality knowledge to deliver successful results.

Do Language Models Perform Generalizable Commonsense Inference?

1 code implementation Findings (ACL) 2021 Peifeng Wang, Filip Ilievski, Muhao Chen, Xiang Ren

Inspired by evidence that pretrained language models (LMs) encode commonsense knowledge, recent work has applied LMs to automatically populate commonsense knowledge graphs (CKGs).

Knowledge Graphs

CoreQuisite: Circumstantial Preconditions of Common Sense Knowledge

no code implementations18 Apr 2021 Ehsan Qasemi, Filip Ilievski, Muhao Chen, Pedro Szekely

The task of identifying and reasoning with circumstantial preconditions associated with everyday facts is natural to humans.

Common Sense Reasoning

Dimensions of Commonsense Knowledge

no code implementations12 Jan 2021 Filip Ilievski, Alessandro Oltramari, Kaixin Ma, Bin Zhang, Deborah L. McGuinness, Pedro Szekely

Recently, the focus has been on large text-based sources, which facilitate easier integration with neural (language) models and application to textual tasks, typically at the expense of the semantics of the sources and their harmonization.

CSKG: The CommonSense Knowledge Graph

1 code implementation21 Dec 2020 Filip Ilievski, Pedro Szekely, Bin Zhang

Sources of commonsense knowledge support applications in natural language understanding, computer vision, and knowledge graphs.

Knowledge Graphs Language understanding +1

Knowledge-driven Data Construction for Zero-shot Evaluation in Commonsense Question Answering

1 code implementation7 Nov 2020 Kaixin Ma, Filip Ilievski, Jonathan Francis, Yonatan Bisk, Eric Nyberg, Alessandro Oltramari

Guided by a set of hypotheses, the framework studies how to transform various pre-existing knowledge resources into a form that is most effective for pre-training models.

Language Modelling Question Answering

Commonsense Knowledge in Wikidata

no code implementations18 Aug 2020 Filip Ilievski, Pedro Szekely, Daniel Schwabe

Our experiments reveal that: 1) albeit Wikidata-CS represents a small portion of Wikidata, it is an indicator that Wikidata contains relevant commonsense knowledge, which can be mapped to 15 ConceptNet relations; 2) the overlap between Wikidata-CS and other commonsense sources is low, motivating the value of knowledge integration; 3) Wikidata-CS has been evolving over time at a slightly slower rate compared to the overall Wikidata, indicating a possible lack of focus on commonsense knowledge.

Common Sense Reasoning Question Answering

Consolidating Commonsense Knowledge

no code implementations10 Jun 2020 Filip Ilievski, Pedro Szekely, Jingwei Cheng, Fu Zhang, Ehsan Qasemi

Commonsense reasoning is an important aspect of building robust AI systems and is receiving significant attention in the natural language understanding, computer vision, and knowledge graphs communities.

Common Sense Reasoning Knowledge Graphs +2

KGTK: A Toolkit for Large Knowledge Graph Manipulation and Analysis

1 code implementation29 May 2020 Filip Ilievski, Daniel Garijo, Hans Chalupsky, Naren Teja Divvala, Yixiang Yao, Craig Rogers, Rongpeng Li, Jun Liu, Amandeep Singh, Daniel Schwabe, Pedro Szekely

Knowledge graphs (KGs) have become the preferred technology for representing, sharing and adding knowledge to modern AI applications.

Knowledge Graphs

Combining Conceptual and Referential Annotation to Study Variation in Framing

no code implementations LREC 2020 Marten Postma, Levi Remijnse, Filip Ilievski, Antske Fokkens, Sam Titarsolej, Piek Vossen

The user can apply two types of annotations: 1) mappings from expressions to frames and frame elements, 2) reference relations from mentions to events and participants of the structured data.

Large-scale Cross-lingual Language Resources for Referencing and Framing

no code implementations LREC 2020 Piek Vossen, Filip Ilievski, Marten Postma, Antske Fokkens, Gosse Minnema, Levi Remijnse

In this article, we lay out the basic ideas and principles of the project Framing Situations in the Dutch Language.

The Profiling Machine: Active Generalization over Knowledge

no code implementations1 Oct 2018 Filip Ilievski, Eduard Hovy, Qizhe Xie, Piek Vossen

The human mind is a powerful multifunctional knowledge storage and management system that performs generalization, type inference, anomaly detection, stereotyping, and other tasks.

Anomaly Detection

Systematic Study of Long Tail Phenomena in Entity Linking

no code implementations COLING 2018 Filip Ilievski, Piek Vossen, Stefan Schlobach

In this paper we report on a series of hypotheses regarding the long tail phenomena in entity linking datasets, their interaction, and their impact on system performance.

Entity Linking

SemEval-2018 Task 5: Counting Events and Participants in the Long Tail

no code implementations SEMEVAL 2018 Marten Postma, Filip Ilievski, Piek Vossen

This paper discusses SemEval-2018 Task 5: a referential quantification task of counting events and participants in local, long-tail news documents with high ambiguity.

Word Sense Disambiguation

Semantic overfitting: what `world' do we consider when evaluating disambiguation of text?

no code implementations COLING 2016 Filip Ilievski, Marten Postma, Piek Vossen

Semantic text processing faces the challenge of defining the relation between lexical expressions and the world to which they make reference within a period of time.

Cannot find the paper you are looking for? You can Submit a new open access paper.