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Guohui Xiao

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11 papers
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11

KR Conference 2025 System Paper

Can LLMs Solve ASP Problems? Insights from a Benchmarking Study

  • Lin Ren
  • Guohui Xiao
  • Guilin Qi
  • Yishuai Geng
  • Haohan Xue

Answer Set Programming (ASP) is a powerful paradigm for non-monotonic reasoning. Recently, large language models (LLMs) have demonstrated promising capabilities in logical reasoning. Despite this potential, current evaluations of LLM capabilities in ASP are often limited. Existing works normally employ overly simplified ASP programs, do not support negation, disjunction, or multiple answer sets. Furthermore, there is a lack of benchmarks that introduce tasks specifically designed for ASP solving. To bridge this gap, we introduce ASPBench, a comprehensive ASP benchmark, including three ASP specific tasks: ASP entailment, answer set verification, and answer set computation. Our extensive evaluations on ASPBench reveal that while 14 state-of-the-art LLMs, including deepseek-r1, o4-mini, and gemini-2. 5-flash-thinking, perform relatively well on the first two simpler tasks, they struggle with answer set computation, which is the core of ASP solving. These findings offer insights into the current limitations of LLMs in ASP solving. This highlights the need for new approaches that integrate symbolic reasoning capabilities more effectively. The code and dataset are available at https: //github. com/HomuraT/ASPBench.

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IJCAI Conference 2025 Conference Paper

LLM4VKG: Leveraging Large Language Models for Virtual Knowledge Graph Construction

  • Guohui Xiao
  • Lin Ren
  • Guilin Qi
  • Haohan Xue
  • Marco Di Panfilo
  • Davide Lanti

Virtual Knowledge Graphs (VKGs) provide an effective solution for data integration but typically require significant expertise for their construction. This process, involving ontology development, schema analysis, and mapping creation, is often hindered by naming ambiguities and matching issues, which traditional rule-based methods struggle to address. Large language models (LLMs), with their ability to process and generate contextually relevant text, offer a potential solution. In this work, we introduce LLM4VKG, a novel framework that leverages LLMs to automatize VKG construction. Experimental evaluation on the RODI benchmark demonstrates that LLM4VKG surpasses state-of-the-art methods, achieving an average F1-score improvement of +17% and a peak gain of +39%. Moreover, LLM4VKG proves robust against incomplete ontologies and can handle complex mappings where current methods fail.

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AAAI Conference 2020 Conference Paper

Query Answering with Guarded Existential Rules under Stable Model Semantics

  • Hai Wan
  • Guohui Xiao
  • Chenglin Wang
  • Xianqiao Liu
  • Junhong Chen
  • Zhe Wang

In this paper, we study the problem of query answering with guarded existential rules (also called GNTGDs) under stable model semantics. Our goal is to use existing answer set programming (ASP) solvers. However, ASP solvers handle only finitely-ground logic programs while the program translated from GNTGDs by Skolemization is not in general. To address this challenge, we introduce two novel notions of (1) guarded instantiation forest to describe the instantiation of GNTGDs and (2) prime block to characterize the repeated infinitely-ground program translated from GNTGDs. Using these notions, we prove that the ground termination problem for GNTGDs is decidable. We also devise an algorithm for query answering with GNTGDs using ASP solvers. We have implemented our approach in a prototype system. The evaluation over a set of benchmarks shows encouraging results.

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IJCAI Conference 2019 Conference Paper

Enriching Ontology-based Data Access with Provenance

  • Diego Calvanese
  • Davide Lanti
  • Ana Ozaki
  • Rafael Penaloza
  • Guohui Xiao

Ontology-based data access (OBDA) is a popular paradigm for querying heterogeneous data sources by connecting them through mappings to an ontology. In OBDA, it is often difficult to reconstruct why a tuple occurs in the answer of a query. We address this challenge by enriching OBDA with provenance semirings, taking inspiration from database theory. In particular, we investigate the problems of (i) deciding whether a provenance annotated OBDA instance entails a provenance annotated conjunctive query, and (ii) computing a polynomial representing the provenance of a query entailed by a provenance annotated OBDA instance. Differently from pure databases, in our case, these polynomials may be infinite. To regain finiteness, we consider idempotent semirings, and study the complexity in the case of DL-LiteR ontologies. We implement Task (ii) in a state-of-the-art OBDA system and show the practical feasibility of the approach through an extensive evaluation against two popular benchmarks.

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IJCAI Conference 2018 Conference Paper

Ontology-Based Data Access: A Survey

  • Guohui Xiao
  • Diego Calvanese
  • Roman Kontchakov
  • Domenico Lembo
  • Antonella Poggi
  • Riccardo Rosati
  • Michael Zakharyaschev

We present the framework of ontology-based data access, a semantic paradigm for providing a convenient and user-friendly access to data repositories, which has been actively developed and studied in the past decade. Focusing on relational data sources, we discuss the main ingredients of ontology-based data access, key theoretical results, techniques, applications and future challenges.

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JAIR Journal 2018 Journal Article

Querying Log Data with Metric Temporal Logic

  • Sebastian Brandt
  • Elem Güzel Kalaycı
  • Vladislav Ryzhikov
  • Guohui Xiao
  • Michael Zakharyaschev

We propose a novel framework for ontology-based access to temporal log data using a datalog extension datalogMTL of the Horn fragment of the metric temporal logic MTL. We show that datalogMTL is EXPSPACE-complete even with punctual intervals, in which case full MTL is known to be undecidable. We also prove that nonrecursive datalogMTL is PSPACE-complete for combined complexity and in AC0 for data complexity. We demonstrate by two real-world use cases that nonrecursive datalogMTL programs can express complex temporal concepts from typical user queries and thereby facilitate access to temporal log data. Our experiments with Siemens turbine data and MesoWest weather data show that datalogMTL ontology-mediated queries are efficient and scale on large datasets.

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AAAI Conference 2017 Conference Paper

Ontology-Based Data Access with a Horn Fragment of Metric Temporal Logic

  • Sebastian Brandt
  • Elem GŸzel Kalaycõ
  • Roman Kontchakov
  • Vladislav Ryzhikov
  • Guohui Xiao
  • Michael Zakharyaschev

We advocate datalogMTL, a datalog extension of a Horn fragment of the metric temporal logic MTL, as a language for ontology-based access to temporal log data. We show that datalogMTL is EXPSPACE-complete even with punctual intervals, in which case MTL is known to be undecidable. Nonrecursive datalogMTL turns out to be PSPACE-complete for combined complexity and in AC0 for data complexity. We demonstrate by two real-world use cases that nonrecursive datalogMTL programs can express complex temporal concepts from typical user queries and thereby facilitate access to log data. Our experiments with Siemens turbine data and MesoWest weather data show that datalogMTL ontologymediated queries are efficient and scale on large datasets of up to 11GB.

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AAAI Conference 2016 Conference Paper

Beyond OWL 2 QL in OBDA: Rewritings and Approximations

  • Elena Botoeva
  • Diego Calvanese
  • Valerio Santarelli
  • Domenico Savo
  • Alessandro Solimando
  • Guohui Xiao

Ontology-based data access (OBDA) is a novel paradigm facilitating access to relational data, realized by linking data sources to an ontology by means of declarative mappings. DL-LiteR, which is the logic underpinning the W3C ontology language OWL 2 QL and the current language of choice for OBDA, has been designed with the goal of delegating query answering to the underlying database engine, and thus is restricted in expressive power. E. g. , it does not allow one to express disjunctive information, and any form of recursion on the data. The aim of this paper is to overcome these limitations of DL-LiteR, and extend OBDA to more expressive ontology languages, while still leveraging the underlying relational technology for query answering. We achieve this by relying on two well-known mechanisms, namely conservative rewriting and approximation, but significantly extend their practical impact by bringing into the picture the mapping, an essential component of OBDA. Specifically, we develop techniques to rewrite OBDA specifications with an expressive ontology to “equivalent” ones with a DL-LiteR ontology, if possible, and to approximate them otherwise. We do so by exploiting the high expressive power of the mapping layer to capture part of the domain semantics of rich ontology languages. We have implemented our techniques in the prototype system ONTOPROX, making use of the state-of-theart OBDA system ONTOP and the query answering system CLIPPER, and we have shown their feasibility and effectiveness with experiments on synthetic and real-world data.

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IJCAI Conference 2013 Conference Paper

Tractable Queries for Lightweight Description Logics

  • Meghyn Bienvenu
  • Magdalena Ortiz
  • Mantas Šimkus
  • Guohui Xiao

It is a classic result in database theory that conjunctive query (CQ) answering, which is NP-complete in general, is feasible in polynomial time when restricted to acyclic queries. Subsequent results identified more general structural properties of CQs (like bounded treewidth) which ensure tractable query evaluation. In this paper, we lift these tractability results to knowledge bases formulated in the lightweight description logics DL-Lite and ELH. The proof exploits known properties of query matches in these logics and involves a querydependent modification of the data. To obtain a more practical approach, we propose a concrete polynomial-time algorithm for answering acyclic CQs based on rewriting queries into datalog programs. A preliminary evaluation suggests the interest of our approach for handling large acyclic CQs.

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AAAI Conference 2012 Conference Paper

Query Rewriting for Horn-SHIQ Plus Rules

  • Thomas Eiter
  • Magdalena Ortiz
  • Mantas Simkus
  • Trung-Kien Tran
  • Guohui Xiao

Query answering over Description Logic (DL) ontologies has become a vibrant field of research. Efficient realizations often exploit database technology and rewrite a given query to an equivalent SQL or Datalog query over a database associated with the ontology. This approach has been intensively studied for conjunctive query answering in the DL-Lite and EL families, but is much less explored for more expressive DLs and queries. We present a rewriting-based algorithm for conjunctive query answering over Horn-SHIQ ontologies, possibly extended with recursive rules under limited recursion as in DL+log. This setting not only subsumes both DL-Lite and EL, but also yields an algorithm for answering (limited) recursive queries over Horn-SHIQ ontologies (an undecidable problem for full recursive queries). A prototype implementation shows its potential for applications, as experiments exhibit efficient query answering over full Horn-SHIQ ontologies and benign downscaling to DL-Lite, where it is competitive with comparable state of the art systems.

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KR Conference 2010 Conference Paper

Computing Inconsistency Measurements under Multi-Valued Semantics by Partial Max-SAT solvers

  • Guohui Xiao
  • Yue Ma
  • Guilin Qi
  • Zuoquan Lin

consistent subsets of formulas (Knight 2002) or minimal inMeasuring the inconsistency degree of a knowledge base can help us to deal with inconsistencies. Several inconsistency measures have been given under different multi-valued semantics, including 4-valued semantics, 3-valued semantics, LPm and Quasi Classical semantics. In this paper, we first carefully analyze the relationship between these inconsistency measures by showing that the inconsistency degrees under 4-valued semantics, 3-value semantics, LPm are the same, but different from the one based on Quasi Classical semantics. We then consider the computation of these inconsistency measures and show that computing inconsistency measurement under multi-valued semantics is usually intractable. To tackle this problem, we propose two novel algorithms that respectively encode the problems of computing inconsistency degrees under 4-valued semantics (3-valued semantics, LPm) and under Quasi Classical semantics into the partial MaxSAT problems. We implement these algorithms and do experiments on some benchmark data sets. The preliminary but encouraging experimental results show that our approach is efficient to handle large knowledge bases.