From b3ac891975b60fc8e7022f597885b8334ac8a681 Mon Sep 17 00:00:00 2001 From: moreshud Date: Tue, 4 Nov 2025 13:48:17 +0100 Subject: [PATCH 1/2] update tdl dice.bib and profile.ttl --- data/bib/dice.bib | 1 + data/people/MoshoodYekini.ttl | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/data/bib/dice.bib b/data/bib/dice.bib index 725ba5b9..efa010e7 100644 --- a/data/bib/dice.bib +++ b/data/bib/dice.bib @@ -9149,6 +9149,7 @@ @InProceedings{demir2025tree doi = {10.1007/978-3-032-06066-2_29}, url = {https://rdcu.be/eKvoL}, bdsk-url-1 = {https://svn.dice-research.org/open/papers/2025/ECML_TDL/tdl-public.pdf}, + keywords = {dice enexa demir moshood roeder mahmood ngonga}, abstract = {Learning continuous vector representations for knowledge graphs has significantly improved state-of-the-art performances in many challenging tasks. Yet, deep-learning-based models are only post-hoc and locally explainable. In contrast, learning Web Ontology Language (OWL) class expressions in Description Logics (DLs) is ante-hoc and globally explainable. However, state-of-the-art learners have two well-known limitations: scaling to large knowledge graphs and handling missing information. Here, we present a decision-tree-based learner (tDL) to learn Web Ontology Languages (OWLs) class expressions over large knowledge graphs, while imputing missing triples. Given positive and negative example individuals, tDL firstly constructs unique OWL expressions in SHOIN from concise bounded descriptions of individuals. Each OWL class expression is used as a feature in a binary classification problem to represent input individuals. Thereafter, tDL fits a CART decision tree to learn Boolean decision rules distinguishing positive examples from negative examples. A final OWL expression in SHOIN is built by traversing the built CART decision tree from the root node to leaf nodes for each positive example. By this, tDL can learn OWL class expressions without exploration, i.e., the number of queries to a knowledge graph is bounded by the number of input individuals. Our empirical results show that tDL outperforms the current state-of-the-art models across datasets. Importantly, our experiments over a large knowledge graph (DBpedia with 1.1 billion triples) show that tDL can effectively learn accurate OWL class expressions, while the state-of-the-art models fail to return any results. Finally, expressions learned by tDL can be seamlessly translated into natural language explanations using a pre-trained large language model and a DL verbalizer.} } diff --git a/data/people/MoshoodYekini.ttl b/data/people/MoshoodYekini.ttl index 1866001d..661ba3cf 100644 --- a/data/people/MoshoodYekini.ttl +++ b/data/people/MoshoodYekini.ttl @@ -12,5 +12,5 @@ dice:MoshoodOlawaleYekini a schema:Person ; schema:office "FU.214" ; schema:photo "moshood.jpg" ; schema:project dice:ENEXA ; - schema:publicationTag "" ; + schema:publicationTag "moshood" ; schema:content """""" . From 9c8723eb53525b7420f19b77e1e1a8deefcc0e89 Mon Sep 17 00:00:00 2001 From: moreshud Date: Tue, 4 Nov 2025 14:02:11 +0100 Subject: [PATCH 2/2] add sailproject in keywords --- data/bib/dice.bib | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/data/bib/dice.bib b/data/bib/dice.bib index efa010e7..2145587e 100644 --- a/data/bib/dice.bib +++ b/data/bib/dice.bib @@ -9149,7 +9149,7 @@ @InProceedings{demir2025tree doi = {10.1007/978-3-032-06066-2_29}, url = {https://rdcu.be/eKvoL}, bdsk-url-1 = {https://svn.dice-research.org/open/papers/2025/ECML_TDL/tdl-public.pdf}, - keywords = {dice enexa demir moshood roeder mahmood ngonga}, + keywords = {dice enexa sailproject demir moshood roeder mahmood ngonga}, abstract = {Learning continuous vector representations for knowledge graphs has significantly improved state-of-the-art performances in many challenging tasks. Yet, deep-learning-based models are only post-hoc and locally explainable. In contrast, learning Web Ontology Language (OWL) class expressions in Description Logics (DLs) is ante-hoc and globally explainable. However, state-of-the-art learners have two well-known limitations: scaling to large knowledge graphs and handling missing information. Here, we present a decision-tree-based learner (tDL) to learn Web Ontology Languages (OWLs) class expressions over large knowledge graphs, while imputing missing triples. Given positive and negative example individuals, tDL firstly constructs unique OWL expressions in SHOIN from concise bounded descriptions of individuals. Each OWL class expression is used as a feature in a binary classification problem to represent input individuals. Thereafter, tDL fits a CART decision tree to learn Boolean decision rules distinguishing positive examples from negative examples. A final OWL expression in SHOIN is built by traversing the built CART decision tree from the root node to leaf nodes for each positive example. By this, tDL can learn OWL class expressions without exploration, i.e., the number of queries to a knowledge graph is bounded by the number of input individuals. Our empirical results show that tDL outperforms the current state-of-the-art models across datasets. Importantly, our experiments over a large knowledge graph (DBpedia with 1.1 billion triples) show that tDL can effectively learn accurate OWL class expressions, while the state-of-the-art models fail to return any results. Finally, expressions learned by tDL can be seamlessly translated into natural language explanations using a pre-trained large language model and a DL verbalizer.} }