Manufacturing industry relies heavily on technical documents that encapsulate specialized knowledge essential for optimizing production and maintenance processes. However, extracting meaningful insights from these documents is challenging due to their complex structure, domain-specific terminology, and multimodal content, which includes text, images, and tables. Furthermore, there is a contextual gap between the generic training data of pre-trained language models (PLMs) and the specialized knowledge required for manufacturing documents. To address these issues, a Task-Centric Ontology (TCO) is designed to describe fundamental manufacturing tasks, and develop OmEGa, an Ontology-based Information Extraction Framework for Task-Centric Knowledge Graphs . OmEGa leverages large language models (LLMs) to perform instance recognition and relation classification on multimodal documents. By utilizing spatial embedding and modality linking, OmEGa addresses structural challenges, while TCO-driven reasoning mitigates contextual challenges. Experimental results demonstrate the effectiveness of OmEGa, achieving strong performance on both proprietary and open-source datasets. Additionally, a Knowledge Graph Question Answering (KGQA) system built on the extracted task-centric knowledge shows promise in enhancing communication among domain experts in the manufacturing sector .

Unlike the traditional recommender systems that rely on historical data such as clicks or purchases, a conversational recommender system (CRS) aims to provide a personalized recommendation through a natural conversation. The conversational interaction facilitates capturing not only explicit preference from mentioned items but also implicit states, such as a user’s current situation and emotional states from a dialogue context. Nevertheless, existing CRSs fall short of fully exploiting a dialogue context since they primarily derive explicit user preferences from the items and item-attributes mentioned in a conversation. To address this limitation and attain a comprehensive understanding of a dialogue context, we proposeCoreSense, aconversationalrecommender system enhanced with social commonsenseknowledge. In other words, CoreSense exploits the social commonsense knowledge graph ATOMIC to capture the user’s implicit states, such as a user’s current situation and emotional states, from a dialogue context. Thus, the social commonsense knowledge-augmented CRS can provide a more appropriate recommendation from a given dialogue context. Furthermore, we enhance the collaborative filtering effect by utilizing the user’s states inferred from commonsense knowledge as an improved criterion for retrieving other dialogues of similar interests. Extensive experiments on CRS benchmark datasets show that CoreSense provides human-like recommendations and responses based on inferred user states, achieving significant performance improvements.

Numerous large-scale knowledge graphs (KGs) fundamentally represent two-view KGs: an ontology-view KG with abstract classes in ontology and an instance-view KG with specific collections of entities instantiated from ontology classes. Two-view KG embedding aims to jointly learn continuous vector representations of entities and relations in the aforementioned two-view KGs. In essence, an ontology schema exhibits a tree-like structure guided by class hierarchies, which leads classes to form inheritance hierarchies. However, existing two-view KG embedding models neglect those hierarchies, which provides the necessity to reflect class inheritance. On the other hand, KG is constructed based on a pre-defined ontology schema that includes heterogeneous relations between classes. Furthermore, these relations are defined within the scope of those among classes since instances inherit all the properties of their corresponding classes, which reveals structural similarity between two multi-relational networks. Despite the consideration to bridge the gap among two-view KG representations, existing methods ignore the existence of structural similarity between two-view KGs. To address these issues, we propose a novel two-view KG embedding model, CISS, considering Class Inheritance and Structural Similarity between two-view KGs. To deal with class inheritance, we utilize class sets, each of which is composed of sibling classes, to learn fine-grained class representations. In addition, we configure virtual instance-view KG from clustered instances and compare subgraph representations of two-view KGs to enhance structural similarity between them. Experimental results show our superior performance compared to existing models.

Conversational recommender systems (CRS) aim to acquire a user’s dynamic interests for a successful recommendation. By asking about his/her preferences, CRS explore current needs of a user and recommend items of interest. However, previous works may not determine a proper action in a timely manner which leads to the insufficient information gathering and the waste of conversation turns. Since they learn a single decision policy, it is difficult for them to address the general decision problems in CRS. Besides, existing methods do not distinguish whether the past behaviors inferred from the historical interactions are closely related to the user’s current preference. To address these issues, we propose a novel Hierarchical policy learning based Conversational Recommendation framework (HiCR). HiCR formulates the multi-round decision making process as a hierarchical policy learning scheme, which consists of both a high-level policy and a low-level policy. In detail, the high-level policy aims to determine what type of action to take, such as a recommendation or a query, by observing the comprehensive conversation information. According to the decided action type, the low-level policy selects a specific action, such as which attribute to ask or which item to recommend. The hierarchical conversation policy enables CRS to decide an optimal action, resulting in reducing the unnecessary consumption of conversation turns and the continuous failure of recommendations. Furthermore, in order to filter out the unnecessary historical information when enriching the current user preference, we extract and utilize the informative past behaviors that are attentive to the current needs. Empirical experiments on four real-world datasets show the superiority of our approach against the current state-of-the-art methods.

Both entity typing and relation extraction from text corpora are widely used to identify the semantic types of an entity and a relation in a knowledge graph (KG). Most existing approaches rely on a pre-defined set of entity types and relation types in a KG. They thus cannot map entity mentions (relation mentions) to unseen entity types (relation types). To fundamentally overcome the limitations, we should add new semantic types of entities and relations to a KG schema. However, schema expansion traditionally requires manual conceptualization through a user’s observation on the text corpus while assuming the existence of suitable target KG schemas. In this work, we propose anActive learning framework forKnowledge graphSchemaExpansion (AKSE), which can generate a new semantic type for KG schemas, without depending on a set of target schemas and human users’ observation. Specifically, a granularity based active learning algorithm determines whether a KG schema requires new semantic types or not. We also introduce a KG schema attention-based neural method which assigns semantic types to the entities and relationships extracted. To the best of our knowledge, our work is the first study to expand a KG schema with active learning.

News recommendation systems? purpose is to tackle the immense amount of news and offer personalized recommendations to users. A major issue in news recommendation is to capture the precise news representations for the efficacy of recommended items. Commonly, news contents are filled with well-known entities of different types. However, existing recommendation systems overlook exploiting external knowledge about entities and topical relatedness among the news. To cope with the above problem, in this paper, we propose Topic-Enriched Knowledge Graph Recommendation System(TEKGR). Three encoders in TEKGR handle news titles in two perspectives to obtain news representation embedding: (1) to extract meaning of news words without considering latent knowledge features in the news and (2) to extract semantic knowledge of news through topic information and contextual information from a knowledge graph. After obtaining news representation vectors, an attention network compares clicked news to the candidate news in order to get the user's final embedding. Our TEKGR model is superior to existing news recommendation methods by manipulating topical relations among entities and contextual features of entities. Experimental results on two public datasets show that our approach outperforms state-of-the-art deep recommendation approaches.

This paper presents an efficient method of extracting n-ary relations from multiple sentences which is called Entity-path and Discourse relation-centric Relation Extractor (EDCRE). Unlike previous approaches, the proposed method focuses on an entity link, which consists of dependency edges between entities, and discourse relations between sentences. Specifically, the proposed model consists of two main sub-models. The first one encodes sentences with a higher weight on the entity link while considering the other edges with an attention mechanism. To consider various latent discourse relations between sentences, the second sub-model encodes discourse relations between adjacent sentences considering the contents of each sentence. Experiment results on the cross-sentence relation extraction dataset, PubMed, and the document-level relation extraction dataset, DocRED, show that the proposed model outperforms state-of-the-art methods of extracting relations across sentences. Furthermore, ablation study proves that both the two main sub-models have noticeable effect on the relation extraction task.

A large number of spatial knowledge graphs (SKGs) are available from spatially enriched knowledge bases, e.g., DBpedia and YAGO2. This provides a great chance to understand valuable information about the regions surrounding us. However, it is hard to comprehend SKGs due to the explosively growing volume and the complication of the graph structures. Thus we study the problem of similar region search (SRS), which is an easy-to-use but effective way to explore spatial data. The effectiveness of SRS highly depends on how to measure the region similarity. However, existing approaches cannot make use of the rich information contained in SKGs thus may lead to incorrect results. In this paper, we propose a spatial knowledge representation learning method for region similarity, namely SKRL4RS. SKRL4RS firstly encodes the spatial entities of an SKG into a vector space to make it easier to extract useful features. Then regions are represented by 3-D tensors using the spatial entity embeddings together with geographical information. Finally, region tensors are fed into the conventional triplet network to learn the feature vectors of regions. The region similarity measure learned by SKRL4RS can capture the hierarchical types, semantic relatedness, and relative locations of spatial entities inside a region. Experimental results on two real-world datasets show that our SKRL4RS outperforms the state-of-the-art by a significant margin in terms of the accuracy of measuring region similarity.

The amount of RDF data being published on the Web is increasing at a massive rate. MapReduce-based distributed frameworks have become the general trend in processing SPARQL queries against RDF data. Currently, query processing systems that use MapReduce have not been able to keep up with the increase of semantic annotated data, resulting in non-interactive SPARQL query processing. The principal reason is that intermediate query results from join operations in a MapReduce framework are so massive that they consume all available network bandwidth. In this article, the authors present an efficient SPARQL processing system that uses MapReduce and HBase. The system runs a job optimized query plan using their proposed abstract RDF data to decrease the number of jobs and also decrease the amount of input data. The authors also present an efficient algorithm of using Map-side joins while also using the abstract RDF data to filter out unneeded RDF data. Experimental results show that the proposed approach demonstrates better performance when processing queries with a large amount of input data than those found in previous works.

The conventional works on spatial keyword queries for a knowledge base focus on finding a subtree to cover all the query keywords. The retrieved subtree is rooted at a place vertex, spatially close to a query location and compact in terms of the query keywords. However, user requirements may not be satisfied by a single subtree in some application scenarios. A group of subtrees should be combined together to collectively cover the query keywords. In this paper, we propose and study a novel way of searching on a spatial knowledge, namely collective spatial keyword query on a knowledge base (CoSKQ-KB). We formalize the problem of CoSKQ-KB and design a baseline method for CoSKQ-KB (BCK). To further speed up the query processing, an improved scalable method for CoSKQ-KB (iSCK) is proposed based on a set of efficient pruning and early termination techniques. In addition, we conduct empirical experiments on two real-world datasets to show the efficiency and effectiveness of our proposed algorithms.

The main focus of relational learning for knowledge graph completion (KGC) lies in exploiting rich contextual information for facts. Many state-of-the-art models incorporate fact sequences, entity types, and even textual information. Unfortunately, most of them do not fully take advantage of rich structural information in a KG, i.e., connectivity patterns around each entity. In this paper, we propose a context-aware convolutional learning (CACL) model which jointly learns from entities and their multi-hop neighborhoods. Since we directly utilize the connectivity patterns contained in each multi-hop neighborhood, the structural role similarity among entities can be better captured, resulting in more informative entity and relation embeddings. Specifically, CACL collects entities and relations from the multi-hop neighborhood as contextual information according to their relative importance and uniquely maps them to a linear vector space. Our convolutional architecture leverages a deep learning technique to represent each entity along with its linearly mapped contextual information. Thus, we can elaborately extract the features of key connectivity patterns from the context and incorporate them into a score function which evaluates the validity of facts. Experimental results on the newest datasets show that CACL outperforms existing approaches by successfully enriching embeddings with neighborhood information.

In the Internet of Things (IoT) environment, the use of sensors and sensor readings is significant in research and industry. The number of sensors is increasing exponentially, adding a tremendous amount of data to the Web. Therefore, the efficient management of sensors and observation data is becoming important. Especially, the location and time of observations are expected to play a vital role in IoT. However, existing researches mainly focus on the temporal properties of data stream. It is necessary to consider the spatial features in addition to the temporal ones. In this article, the authors propose a spatiotemporal query language which integrates spatial and temporal features. Also, they propose an efficient method of building a spatiotemporal index and processing the proposed query language. To evaluate the proposed method, the authors conduct experiments through implementation. The experimental results show that the proposed method deals with spatiotemporal queries within a reasonable time.

Most of the mobile platforms provide a keyword based full text search (FTS) for users to find what they want. However, FTS has difficulties in dealing with the cases where a user cannot remember the exact keywords about target data or the number of search results is too many. To overcome these limitations of FTS, we propose a semantically enhanced method of searching for data on mobile devices along with mobile ontology. Experimental results of the proposed method show that our method provides accurate search results and is suitable for a mobile environment.

SGML/XML are having a profound impact on data modeling and processing. We present an efficient algorithm to compute differences between old and new versions of an SGML/XML document. The difference between the two versions can be considered to be an edit script that transforms one document tree into another. The proposed algorithm is based on a hybridization of bottom-up and top-down methods: The matching relationships between nodes in the two versions are produced in a bottom-up manner and then the top-down breadth-first search computes an edit script. Faster matching is achieved because the algorithm does not need to investigate the possible existence of matchings for all nodes. Furthermore, it can detect structurally meaningful changes such as the movement and copy of a subtree as well as simple changes to the node itself like insertion, deletion, and update.

This paper presents a syntactic method for sophisticated logical structure analysis that transforms document images with multiple pages and hierarchical structure into an electronic document based on SGML/XML. To produce a logical structure more accurately and quickly than previous works of which the basic units are text lines, the proposed parsing method takes text regions with hierarchical structure as input. Furthermore, we define a document model that is able to describe geometric characteristics and logical structure information of documents efficiently and present its automated creation method. Experimental results with 372 images scanned from the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) show that the method has performed logical structure analysis successfully and generated a document model automatically. Particularly, the method generates SGML/XML documents as the result of structural analysis, so that it enhances the reusability of documents and independence of platform.