Table Retrieval (TR) has traditionally been formulated as an ad-hoc retrieval problem, where relevance is primarily determined by topical semantic similarity. With the growing adoption of LLM-based agentic systems, access to structured data is increasingly instruction-driven, where relevance is conditional on explicit content and schema constraints rather than topical similarity alone. We therefore formalize Instruction-Following Table Retrieval (IFTR), a new task that requires models to jointly satisfy topical relevance and fine-grained instruction constraints. We identify two core challenges in IFTR: (i) sensitivity to content scope, such as inclusion and exclusion constraints, and (ii) awareness of schema-grounded requirements, including column semantics and representation granularity--capabilities largely absent in existing retrievers. To support systematic evaluation, we introduce FollowTable, the first large-scale benchmark for IFTR, constructed via a taxonomy-driven annotation pipeline. We further propose a new metric, termed the Instruction Responsiveness Score, to evaluate whether retrieval rankings consistently adapt to user instructions relative to a topic-only baseline. Our results indicate that existing retrieval models struggle to follow fine-grained instructions over tabular data. In particular, they exhibit systematic biases toward surface-level semantic cues and remain limited in handling schema-grounded constraints, highlighting substantial room for future improvements.

Digital publishing’s exponential growth has created vast scholarly collections. Guiding researchers to relevant resources is crucial, and knowledge graphs (KGs) are key tools for unlocking hidden knowledge. However, current methods focus on external links between concepts, ignoring the rich information within individual papers. Challenges like insufficient multi-relational data, name ambiguity, and cold-start issues further limit existing KG-based methods, failing to capture the intricate attributes of diverse entities. To solve these issues, we propose GLAMOR, a robust KG framework encompassing entities e.g., authors, papers, fields of study, and concepts, along with their semantic interconnections. GLAMOR uses a novel random walk-based KG text generation method and then fine-tunes the language model using the generated text. Subsequently, the acquired context-preserving embeddings facilitate superior top@k predictions. Evaluation results on two public benchmark datasets demonstrate our GLAMOR’s superiority against state-of-the-art methods especially in solving the cold-start problem.

Recently, uncertain knowledge graphs (UKGs), where each relation between entities is associated with a confidence score, have gained much attention. Compared with traditional knowledge graphs, UKGs possess the capability of uncertainty knowledge expression, which facilitates more reliable and precise knowledge graph reasoning by not only completing missing triples but also predicting triple confidences. In this paper, we release unKR, the first open-source python library for uncertain Knowledge graph (UKG) Reasoning by representation learning. We design a unified framework to implement two types of representation learning models for UKG reasoning, i.e., normal and few-shot ones. Besides, we standardize the evaluation tasks and metrics for UKG reasoning to ensure fair comparisons, and report the detailed results of each model under the consistent test setting. With unKR, it is effortless for users to reproduce existing models, as well as efficiently customize their own models. The library, documentation, demo, and re-implementing results are all publicly released at https://github.com/seucoin/unKR.

Query graph construction aims to construct the correct executable SPARQL on the KG to answer natural language questions. Although recent methods have achieved good results using neural network-based query graph ranking, they suffer from three new challenges when handling more complex questions: 1) complicated SPARQL syntax, 2) huge search space, and 3) locally ambiguous query graphs. In this paper, we provide a new solution. As a preparation, we extend the query graph by treating each SPARQL clause as a subgraph consisting of vertices and edges and define a unified graph grammar called AQG to describe the structure of query graphs. Based on these concepts, we propose a novel end-to-end model that performs hierarchical autoregressive decoding to generate query graphs. The high-level decoding generates an AQG as a constraint to prune the search space and reduce the locally ambiguous query graph. The bottom-level decoding accomplishes the query graph construction by selecting appropriate instances from the preprepared candidates to fill the slots in the AQG. The experimental results show that our method greatly improves the SOTA performance on complex KGQA benchmarks. Equipped with pre-trained models, the performance of our method is further improved, achieving SOTA for all three datasets used.

Medication recommendation targets to provide a proper set of medicines according to patients’ diagnoses, which is a critical task in clinics. Currently, the recommendation is manually conducted by doctors. However, for complicated cases, like patients with multiple diseases at the same time, it’s difficult to propose a considerate recommendation even for experienced doctors. This urges the emergence of automatic medication recommendation which can help treat the diagnosed diseases without causing harmful drug-drug interactions. Due to the clinical value, medication recommendation has attracted growing research interests. Existing works mainly formulate medication recommendation as a multi-label classification task to predict the set of medicines. In this paper, we propose the Conditional Generation Net (COGNet) which introduces a novel copy-or-predict mechanism to generate the set of medicines. Given a patient, the proposed model first retrieves his or her historical diagnoses and medication recommendations and mines their relationship with current diagnoses. Then in predicting each medicine, the proposed model decides whether to copy a medicine from previous recommendations or to predict a new one. This process is quite similar to the decision process of human doctors. We validate the proposed model on the public MIMIC data set, and the experimental results show that the proposed model can outperform state-of-the-art approaches.

Distant supervision has advantages of generating training data automatically for relation extraction by aligning triples in Knowledge Graphs with large-scale corpora. Some recent methods attempt to incorporate extra information to enhance the performance of relation extraction. However, there still exist two major limitations. Firstly, these methods are tailored for a specific type of information which is not enough to cover most of the cases. Secondly, the introduced extra information may contain noise. To address these issues, we propose a novel hybrid graph model, which can incorporate heterogeneous background information in a unified framework, such as entity types and human-constructed triples. These various kinds of knowledge can be integrated efficiently even with several missing cases. In addition, we further employ an attention mechanism to identify the most confident information which can alleviate the side effect of noise. Experimental results demonstrate that our model outperforms the state-of-the-art methods significantly in various evaluation metrics.

Knowledge graph (KG) embedding techniques represent entities and relations as low-dimensional, continuous vectors, and thus enables machine learning models to be easily adapted to KG completion and querying tasks. However, learned dense vectors are inefficient for large-scale similarity computations. Learning-to-hash is to learn compact binary codes from high-dimensional input data and provides a promising way to accelerate efficiency by measuring Hamming distance instead of Euclidean distance or dot-product. Unfortunately, most of learning-to-hash methods cannot be directly applied to KG structure encoding. In this paper, we introduce a novel framework for encoding incomplete KGs and graph queries in Hamming space. To preserve KG structure information from embeddings to hash codes and address the ill-posed gradient issue in optimization, we utilize a continuation method with convergence guarantees to jointly encode queries and KG entities with geometric operations. The hashed embedding of a query can be utilized to discover target answers from incomplete KGs whilst the efficiency has been greatly improved.We compared our model with state-of-the-art methods on real-world KGs. Experimental results show that our framework not only significantly speeds up the searching process, but also provides good results for unanswerable queries caused by incomplete information.

Extracting knowledge from Wikipedia has attracted much attention in recent ten years. One of the most valuable kinds of knowledge is type information, which refers to the axioms stating that an instance is of a certain type. Current approaches for inferring the types of instances from Wikipedia mainly rely on some language-specific rules. Since these rules cannot catch the semantic associations between instances and classes (i.e. candidate types), it may lead to mistakes and omissions in the process of type inference. The authors propose a new approach leveraging attributes to perform language-independent type inference of the instances from Wikipedia. The proposed approach is applied to the whole English and Chinese Wikipedia, which results in the first version of MulType (Multilingual Type Information), a knowledge base describing the types of instances from multilingual Wikipedia. Experimental results show that not only the proposed approach outperforms the state-of-the-art comparison methods, but also MulType contains lots of new and high-quality type information.

Knowledge graph embedding, which aims to represent entities and relations in vector spaces, has shown outstanding performance on a few knowledge graph completion tasks. Most existing methods are based on the assumption that a knowledge graph is a set of separate triples, ignoring rich graph features, i.e., structural information in the graph. In this paper, we take advantages of structures in knowledge graphs, especially local structures around a triple, which we refer to as triple context. We then propose a Triple-Context-based knowledge Embedding model (TCE). For each triple, two kinds of structure information are considered as its context in the graph; one is the outgoing relations and neighboring entities of an entity and the other is relation paths between a pair of entities, both of which reflect various aspects of the triple. Triples along with their contexts are represented in a unified framework, in which way structural information in triple contexts can be embodied. The experimental results show that our model outperforms the state-of-the-art methods for link prediction.

Computing all ABox repairs is a key to cautious or brave reasoning over inconsistent description logic (DL) ontologies. However, the number of ABox repairs can be exponential in the number of assertions in the ABox even for very lightweight DLs. Hence we propose to compute a minimal representative set of ABox repairs. A set of ABox repairs is representative, if every assertion occurring in at least one ABox repair also occurs in at least one element of this set, while every assertion occurring in all ABox repairs occurs in all elements of this set. Cautious or brave reasoning then can be approximated by standard reasoning over a minimal representative set other than the complete set of ABox repairs. However, computing a minimal representative set of ABox repairs is still intractable in general. To guarantee the tractability in data complexity for computing a minimal representative set, we focus on a class of DL ontologies called the first-order rewritable class. We propose a tractable method for computing a minimal representative set of ABox repairs in an inconsistent first-order rewritable ontology. Experimental results demonstrate the high efficiency and scalability of the proposed method.

DL-Lite is an important family of description logics. Recently, there is an increasing interest in handling inconsistency in DL-Lite as the constraint imposed by a TBox can be easily violated by assertions in ABox in DL-Lite. In this paper, we present a distance-based paraconsistent semantics based on the notion of feature in DL-Lite, which provides a novel way to rationally draw meaningful conclusions even from an inconsistent knowledge base. Finally, we investigate several important logical properties of this entailment relation based on the new semantics and show its promising advantages in non-monotonic reasoning for DL-Lite.

Resolving incoherent terminologies is an important task in the maintenance of evolving OWL 2 DL ontologies. Existing approaches to this task are either semi-automatic or based on simple deletion of axioms. There is a need of fine-grained approaches to automatize this task. Since a fine-grained approach should consider multiple choices for modifying an axiom other than the deletion of axioms only, the primary challenges for developing such an approach lie in both the semantics of the repaired results and the efficiency in computing the repaired results. To tackle these challenges, we first introduce the notion of fine-grained repair based on modifying one axiom to zero or more axioms, then propose an efficient incremental method for computing all fine-grained repairs one by one. We also propose a modification function for axioms expressed in OWL 2 DL, which returns weaker axioms. Based on this modification function and the method for computing fine-grained repairs, we develop an automatic approach to resolving incoherent OWL 2 DL terminologies. Our extensive experimental results demonstrate that the proposed approach is efficient and practical.

Finding justifications for an entailment in an OWL ontology is an important reasoning service in the development and maintenance of OWL ontologies. In this paper, we consider the problem of finding all the justifications of an entailment in description logic EL+, which underpins OWL 2 EL. Our method for computing all the justifications utilizes the classification result of an EL+ ontology by an OWL EL reasoner. We first give an algorithm for finding all the justifications of an entailment by constructing an explanation dependency graph from the entailment and the classification result. We then propose some pruning strategies to optimize this algorithm. We implement a prototype system by using the multi-core technology. The experimental results show that in many cases, our optimized algorithm outperforms an existing algorithm for finding all the justifications for an entailment in EL+ that is optimized with the module extraction technology.

Possibilistic Description Logics (DLs) extend description logics with possibilistic semantics to reason with inconsistent and uncertain knowledge. In possibilistic DLs, a crucial reasoning task is to compute the inconsistency degree of a possibilistic DL knowledge base. In this work, we first point out a shortcoming of a previous tableaux algorithm for possibilistic DL ALC and propose a new tableaux algorithm. We then propose a tableaux algorithm for computing the inconsistency degree of a knowledge base in possibilistic DL ALCI(R+), which extends possibilistic DL ALC with inverse roles and transitive roles. A blocking condition is proposed to ensure the termination of the algorithm. Although the tableaux algorithm for possibilistic DL ALCI(R+) is easy to understand and to implement, it may need exponential space in the worst case. Therefore, we give another tableaux algorithm to improve it and show that the complexity of this algorithm is PSpace-complete. This shows that the flexibility in representing uncertain information is handled without extra computational costs.

In the last few years, RDF is becoming the dominating data model used in semantic web for knowledge representation and inference. In this paper, we revisit the problem of pattern matching query in RDF model, which is usually expensive in efficiency due to the huge cost on join operations. To alleviate the efficiency pain, view materialization techniques are usually deployed to accelerate the query processing. However, given an arbitrary view, it remains difficult to identify how to reuse the view for a particular query, because of the NP-hardness behind the algorithm matching patterns and views. To fully exploit the benefit of the materialized views, we propose a new paradigm to enhance the effectiveness of the materialized view. Instead of choosing materialized views in arbitrary form, our paradigm aims to select the views only if they are sortable. The property of sortability raises huge gains on the pattern-view matching, bringing down the cost to linear complexity in terms of the pattern size. On the other side, the costs on identifying sortable views and searching over the views using inverted index are affordable. Moreover, sortable views generally improve the overall performance of pattern matching, by means of a cost model used to optimize the query rewriting on the most appropriate views. Finally, we demonstrate extensive experimental results to verify the superiority of our proposal on both efficiency and effectiveness.

In order to directly reason over inconsistent OWL 2 DL ontologies, this paper considers linear order inference which comes from propositional logic. Consequences of this inference in an inconsistent ontology are defined as consequences in a certain consistent sub-ontology. This paper proposes a novel framework for compiling an OWL 2 DL ontology to a Horn propositional program so that the intended consistent sub-ontology for linear order inference can be approximated from the compiled result in polynomial time. A tractable method is proposed to realize this framework. It guarantees that the compiled result has a polynomial size. Experimental results show that the proposed method computes the exact intended sub-ontology for almost all test cases, while it is significantly more efficient and scalable than state-of-the-art exact methods.

ABox abduction is an important reasoning facility in Description Logics (DLs). It finds all minimal sets of ABox axioms, called abductive solutions, which should be added to a background ontology to enforce entailment of an observation which is a specified set of ABox axioms. However, ABox abduction is far from practical by now because there lack feasible methods working in finite time for expressive DLs. To pave a way to practical ABox abduction, this paper proposes a new problem for ABox abduction and a new method for computing abductive solutions accordingly. The proposed problem guarantees finite number of abductive solutions. The proposed method works in finite time for a very expressive DL, , which underpins the W3C standard language OWL 2, and guarantees soundness and conditional completeness of computed results. Experimental results on benchmark ontologies show that the method is feasible and can scale to large ABoxes.

Axiom pinpointing plays an important role in the development and maintenance of ontologies. It helps the user to comprehend an unwanted entailment of an ontology by presenting all minimal subsets of the ontology which are responsible for the entailment (called MinAs). In this paper, we consider the problem of axiom pinpointing in description logic EL+, which underpins OWL 2 EL, a profile of the latest version of Web Ontology Language (OWL). We propose a novel method to compute all MinAs that utilizes the hierarchy information obtained from the classification of an EL+ ontology. The advantage of our method over an existing labeled classification based method is that we do not attach labels to entailed subsumptions, which can be memory exhaustion for large scale ontologies. We further consider axiom pinpointing in EL+ when ontologies change. An incremental algorithm is given to compute all MinAs by reusing MinAs previously computed.

Finding all justifications of an OWL entailment is an important reasoning service for explaining logical inconsistencies. In this paper, we consider finding all justifications of an entailment in OWL pD* fragment, which is a fragment of OWL that makes possible decidable rule extensions of OWL. We first propose a novel approach to find all justifications of OWL pD* entailments using TMS and show the complexity of this approach. This approach is limited by the hardware capabilities of standalone systems. In order to improve its scalability to handle large scale semantic data, we optimize the proposed approach by exploiting the MapReduce technology. We implement our approach and the optimization, and do experiments on synthetic and real world data sets. Evaluation results show that our approach has the ability to scale to more than one billion triples.

In this paper, we propose fuzzy pD* semantics which generalizes pD* semantics to reason over fuzzy RDF data using OWL vocabulary. We first define the notions of fuzzy RDF graph and fuzzy pD* interpretation. We then present a set of fuzzy pD*entailment rules and define the Best Degree Bound (BDB) of a triple derived from a fuzzy RDF graph. We show the existence of the BDB of an arbitrary triple. After that, we generalize the partial and full pD* closures to obtain the BDBs of derived triples. We show that the partial fuzzy closure exists and can be computed within polynomial time. Finally, we prove soundness and completeness results for the entailment relation. We also prove that the consistency checking is in P, the entailment is NP-complete, and in P if the target fuzzy RDF graph is ground. Therefore, extending the pD* semantics with fuzzy semantics does not increase the computational complexity.

Possibilistic logic provides a convenient tool for dealing with uncertainty and handling inconsistency. In this paper, we propose possibilistic description logics as an extension of description logics, which are a family of well-known ontology languages. We first give the syntax and semantics of possibilistic description logics and define several inference services in possibilistic description logics. We show that these inference serviced can be reduced to the task of computing the inconsistency degree of a knowledge base in possibilistic description logics. Since possibilistic inference services suffer from the drowning problem, that is, axioms whose confidence degrees are less than or equal to the inconsistency are not used, we consider a drowning-free variant of possibilistic inference, called linear order inference. We propose an algorithm for computing the inconsistency degree of a possibilistic description logic knowledge base and an algorithm for the linear order inference. We consider the impact of our possibilistic description logics on ontology learning and ontology merging. Finally, we implement these algorithms and provide some interesting evaluation results. © 2011 Wiley Periodicals, Inc.

Performance and scalability are two issues that are becoming increasingly pressing as RDF data model is applied to real-world applications. Because neither vertical nor flat structures of RDF storage can handle frequent schema updates and meanwhile avoid possible long-chain joins, there is no clear winner between these two typical structures. In this paper, we propose an alternative storage schema called open user schema. The open user schema consists of flat tables automatically extracted from RDF query streams. A query is divided into two parts and conquered,respectively, on the flat tables in the open user schema and on the vertical table stored in a backend storage. At the core of this divide and conquer architecture with open user schema, an efficient isomorphism decision algorithm is given to guide a query to related flat tables in the open user schema. Our proposal in essence departs from existing methods in that it can accommodate schema updates without possible long-chain joins. We implement our approach and provide empirical evaluations to demonstrate both efficiency and effectiveness of our approach in evaluating complex RDF queries.

Falconer is a semantic Web search engine enhanced SIOC (Semantically-Interlinked Online Communities) application, which is designed to demonstrate the ability of accelerating the creation and reuse process of semantic Web data with easy-to-use user interfaces. In this process, semantic Web search engines feed existing semantic data into the SIOC framework, where new semantic data are composed by the community and indexed again by those search engines. Compared to existing social (semantic) Web applications, Falconer inherently conforms to SIOC specification. It provides semantic search engine based user registration suggestion, friends auto-discovery, and semantic annotation for forum post content. Another distinctive feature is that it enables users to subscribe any resource having a URI as the topic they are interested in. The relationships among users, topics, and posts are further visualized for analyzing the topic trends in the community. As all semantic data are formatted in RDF and RDFa, they can be queried with SPARQL query language.

Measuring Inconsistency in ontologies is an important topic in ontology engineering as it can provide extra information for dealing with inconsistency. Many approaches have been proposed to deal with this issue. However, the main drawback of these algorithms is their high computational complexity. One of the main sources of the high complexity is the intractability of the underlying Description Logics (DLs). In this paper, we focus on an important tractable DL family, \emph{DL-Lite}. We define an inconsistency degree of a \emph{DL-Lite} ontology based on a three-valued semantics. We also present an algorithm to compute this inconsistency degree and show that its time-complexity is PTime in the size of ABox and TBox.