Knowledge graph reasoning (KGR) is a key technology that infers missing facts in knowledge graphs (KGs). Given that real-world scenarios typically encounter unseen KGs with new entities and new relations, researchers have begun to explore fully inductive KGR methods. This setting presents greater challenges and has not been fully explored. Current methods primarily construct relation graphs based on the original KG to facilitate message passing between relations. These models have made significant progress in achieving fully inductive reasoning. However, as relation graphs focus solely on the co-occurrence patterns between relations, they often fail to capture reasoning patterns in KGs, which causes the model to struggle in effectively distinguishing between different relations and entities. This limitation severely restrict the reasoning capabilities of existing methods. In light of this, we propose the Adaptive Rule Pattern Transfer model (AdaRPT) for KGR. It aims to leverage logical rules for each relation in the KG to learn more comprehensive and transferable knowledge representations for entities and relations. For entities, we design a non-parameter message passing model that aggregates path information from the query entity to other entities. The path information for each entity is then matched with rules to obtain the transferable feature of each entity. And for relations, we extract both reasoning and co-occurrence patterns from KGs as transferable relation features. Finally, a path-based graph neural network (GNN) is employed on the transferable features of entities and relations to perform reasoning on KGs. Extensive experimental evaluations on 43 datasets for both inductive and transductive reasoning demonstrate the effectiveness and generalization capability of AdaRPT.

As the field of machine reading comprehension (MRC) continues to evolve, it is unlocking enormous potential for its practical application. However, the currently well-performing models predominantly rely on massive pre-trained language models with at least several hundred million or even over one hundred billion parameters. These complex models not only require immense computational power but also extensive storage, presenting challenges for resource-limited environments such as online education.Current research indicates that specific capabilities of larger models can be transferred to smaller models through knowledge distillation. However, prior to our work, there were no small models specifically designed for MRC task with complex reasoning abilities. In light of this, we present a novel multi-teacher multi-stage distillation approach, MTMS. It facilitates the easier deployment of reasoning-based MRC task on resource-constrained devices, thereby enabling effective applications. In this method, we design a multi-teacher distillation framework that includes both a logical teacher and a semantic teacher. This framework allows MTMS to simultaneously extract features from different perspectives of the text, mitigating the limitations inherent in single-teacher information representations. Furthermore, we introduce a multi-stage contrastive learning strategy. Through this strategy, the student model can progressively align with the teacher models, effectively bridging the gap between them. Extensive experimental outcomes on two inference-based datasets from real-world scenarios demonstrate that MTMS requires nearly 10 times fewer parameters compared with the teacher model size while achieving the competitive performance.

The task of machine reading comprehension (MRC) is to enable machine to read and understand a piece of text and then answer the corresponding question correctly. This task requires machine to not only be able to perform semantic understanding but also possess logical reasoning capabilities. Just like human reading, it involves thinking about the text from two interacting perspectives of semantics and logic. However, previous methods based on reading comprehension either consider only the logical structure of the text or only the semantic structure of the text and cannot simultaneously balance semantic understanding and logical reasoning. This single form of reasoning cannot make the machine fully understand the meaning of the text. Additionally, the issue of sparsity in composition presents a significant challenge for models that rely on graph-based reasoning. To this end, a cross-graph knowledge propagation network (CGKPN) with adaptive connection is presented to address the above issues. The model first performs self-view node embedding on the constructed logical graph and semantic graph to update the representations of the graphs. Specifically, a relevance matrix between nodes is introduced to adaptively adjust node connections in response to the challenge posed by sparse graph. Subsequently, CGKPN conducts cross-graph knowledge propagation on nodes that are identical in both graphs, effectively resolving conflicts arising from identical nodes in different views, and enabling the model to better integrate the logical and semantic relationships of the text through efficient interaction. Experiments on the two MRC datasets ReClor and LogiQA indicate the superior performance of our proposed model CGKPN compared to other existing baselines.

Knowledge Graph Completion (KGC) has garnered massive research interest recently, and most existing methods are designed following a transductive setting where all entities are observed during training. Despite the great progress on the transductive KGC, these methods struggle to conduct reasoning on emerging KGs involving unseen entities. Thus, inductive KGC, which aims to deduce missing links among unseen entities, has become a new trend. Many existing studies transform inductive KGC as a graph classification problem by extracting enclosing subgraphs surrounding each candidate triple. Unfortunately, they still face certain challenges, such as the expensive time consumption caused by the repeat extraction of enclosing subgraphs, and the deficiency of entity-independent feature learning. To address these issues, we propose a global-local anchor representation (GLAR) learning method for inductive KGC. Unlike previous methods that utilize enclosing subgraphs, we extract a shared opening subgraph for all candidates and perform reasoning on it, enabling the model to perform reasoning more efficiently. Moreover, we design some transferable global and local anchors to learn rich entity-independent features for emerging entities. Finally, a global-local graph reasoning model is applied on the opening subgraph to rank all candidates. Extensive experiments show that our GLAR outperforms most existing state-of-the-art methods.

Cross-lingual entity alignment has attracted considerable attention in recent years. Past studies using conventional approaches to match entities share the common problem of missing important structural information beyond entities in the modeling process. This allows graph neural network models to step in. Most existing graph neural network approaches model individual knowledge graphs (KGs) separately with a small amount of pre-aligned entities served as anchors to connect different KG embedding spaces. However, this characteristic can cause several major problems, including performance restraint due to the insufficiency of available seed alignments and ignorance of pre-aligned links that are useful in contextual information in-between nodes. In this article, we propose DuGa-DIT, a dual gated graph attention network with dynamic iterative training, to address these problems in a unified model. The DuGa-DIT model captures neighborhood and cross-KG alignment features by using intra-KG attention and cross-KG attention layers. With the dynamic iterative process, we can dynamically update the cross-KG attention score matrices, which enables our model to capture more cross-KG information. We conduct extensive experiments on two benchmark datasets and a case study in cross-lingual personalized search. Our experimental results demonstrate that DuGa-DIT outperforms state-of-the-art methods.

Community question answering (cQA) has become an important issue due to the popularity of cQA archives on the Web. This paper focuses on addressing the lexical gap problem in question retrieval. Question retrieval in cQA archives aims to find the existing questions that are semantically equivalent or relevant to the queried questions. However, the lexical gap problem brings a new challenge for question retrieval in cQA. In this paper, we propose to model and learn distributed word representations with metadata of category information within cQA pages for question retrieval using two novel category powered models. One is a basic category powered model called MB-NET and the other one is an enhanced category powered model called ME-NET which can better learn the distributed word representations and alleviate the lexical gap problem. To deal with the variable size of word representation vectors, we employ the framework of fisher kernel to transform them into the fixed-length vectors. Experimental results on large-scale English and Chinese cQA data sets show that our proposed approaches can significantly outperform state-of-the-art retrieval models for question retrieval in cQA. Moreover, we further conduct our approaches on large-scale automatic evaluation experiments. The evaluation results show that promising and significant performance improvements can be achieved.

Sentiment classification aims to automatically predict sentiment polarity (e.g., positive or negative) of user generated sentiment data (e.g., reviews, blogs). In real applications, these user-generated sentiment data can span so many different domains that it is difficult to label the training data for all of them. Therefore, we study the problem of sentiment classification adaptation task in this article. That is, a system is trained to label reviews from one source domain but is meant to be used on the target domain. One of the biggest challenges for sentiment classification adaptation task is how to deal with the problem when two data distributions between the source domain and target domain are significantly different from one another. However, our observation is that there might exist some domain shared knowledge among certain input dimensions of different domains. In this article, we present a novel method for modeling and mining the domain shared knowledge from different sentiment review domains via a joint non-negative matrix factorization–based framework. In this proposed framework, we attempt to learn the domain shared knowledge and the domain-specific information from different sentiment review domains with several various regularization constraints. The advantage of the proposed method can promote the correspondence under the topic space between the source domain and the target domain, which can significantly reduce the data distribution gap across two domains. We conduct extensive experiments on two real-world balanced data sets from Amazon product reviews for sentence-level and document-level binary sentiment classification. Experimental results show that our proposed approach significantly outperforms several strong baselines and achieves an accuracy that is competitive with the most well-known methods for sentiment classification adaptation.

We present Q2P, a system that discovers query templates from search engines via their query autocompletion services. Q2P is distinct from the existing works in that it does not rely on query logs of search engines that are typically not readily available. Q2P is also unique in that it uses a trie to economically store queries sampled from a search engine and employs a beam-search strategy that focuses the expansion of the trie on its most promising nodes. Furthermore, Q2P leverages the trie-based storage of query sample to discover query templates using only two passes over the trie. Q2P is a key part of our ongoing project Deep2Q on a template-driven data integration on the Deep Web, where the templates learned by Q2P are used to guide the integration process in Deep2Q. Experimental results on four major search engines indicate that (1) Q2P sends only a moderate number of queries (ranging from 597 to 1,135) to the engines, while obtaining a significant number of completions per query (ranging from 4.2 to 8.5 on the average); (2) a significant number of templates (ranging from 8 to 32 when the minimum support for frequent templates is set to 1%) may be discovered from the samples.

Community question answering (cQA) has become an important service due to the popularity of cQA archives on the web. This paper is concerned with the problem of question search. Question search in cQA aims to find the historical questions that are semantically equivalent or similar to the queried questions. In this paper, we propose a faster and better retrieval model for question search by leveraging user chosen category. After introducing the question category, we can filter certain amount of irrelevant historical questions under a wide range of leaf categories. Experimental results conducted on real cQA data set demonstrate that the proposed techniques are more effective and efficient than a variety of baseline methods.

This work investigates selecting concise labels for the newly-arising topics in community question answer. Previous methods of generating labels do not take the information of the existing category hierarchy into consideration. The main motivation of our paper is to utilize this information into the label generation process. We propose a general framework to address this problem. Firstly, we map the questions into Wikipedia concept sets, which are more meaningful than terms. Secondly, important concepts are identified to represent the main focus of the newly-arising topics. Thirdly, candidate labels are extracted from Wikipedia category graph. Finally, candidate labels are filtered and reranked by combination of structure information of existing category hierarchy and Wikipedia category graph. The experiments show that in our test collections, about 80% "correct" labels appear in the top ten labels recommended by our system.

In this paper, we address the problem of expert finding in community question answering (CQA). Most of the existing approaches attempt to find experts in CQA by means of link analysis techniques. However, these traditional techniques only consider the link structure while ignore the topical similarity among users (askers and answerers) and user expertise and user reputation. In this study, we propose a topic-sensitive probabilistic model, which is an extension of PageRank algorithm to find experts in CQA. Compared to the traditional link analysis techniques, our proposed method is more effective because it finds the experts by taking into account both the link structure and the topical similarity among users. We conduct experiments on real world data set from Yahoo! Answers. Experimental results show that our proposed method significantly outperforms the traditional link analysis techniques and achieves the state-of-the-art performance for expert finding in CQA.

Community question answering (cQA) has become a popular service for users to ask and answer questions. In recent years, the efficiency of cQA service is hindered by a sharp increase of questions in the community. This paper is concerned with the problem of question routing. Question routing in cQA aims to route new questions to the eligible answerers who can give high quality answers. However, the traditional methods suffer from the following two problems: (1) word mismatch between the new questions and the users' answering history; (2) high variance in perceived answer quality. To solve the above two problems, this paper proposes a novel joint learning method by taking both word mismatch and answer quality into a unified framework for question routing. We conduct experiments on large-scale real world data set from Yahoo! Answers. Experimental results show that our proposed method significantly outperforms the traditional query likelihood language model (QLLM) as well as state-of-the-art cluster-based language model (CBLM) and category-sensitive query likelihood language model (TCSLM).

With the flourishing of community-based question answering (cQA) services like Yahoo! Answers, more and more web users seek their information need from these sites. Understanding user's information need expressed through their search questions is crucial to information providers. Question classification in cQA is studied for this purpose. However, there are two main difficulties in applying traditional methods (question classification in TREC QA and text classification) to cQA: (1) Traditional methods confine themselves to classify a text or question into two or a few predefined categories. While in cQA, the number of categories is much larger, such as Yahoo! Answers, there contains 1,263 categories. Our empirical results show that with the increasing of the number of categories to moderate size, the performance of the classification accuracy dramatically decreases. (2) Unlike the normal texts, questions in cQA are very short, which cannot provide sufficient word co-occurrence or shared information for a good similarity measure due to the data sparseness. In this paper, we propose a two-stage approach for question classification in cQA that can tackle the difficulties of the traditional methods. In the first stage, we preform a search process to prune the large-scale categories to focus our classification effort on a small subset. In the second stage, we enrich questions by leveraging Wikipedia semantic knowledge to tackle the data sparseness. As a result, the classification model is trained on the enriched small subset. We demonstrate the performance of our proposed method on Yahoo! Answers with 1,263 categories. The experimental results show that our proposed method significantly outperforms the baseline method (with error reductions of 23.21%).