Information retrieval (IR) methods for KGQA consist of two stages: subgraph extraction and answer reasoning. We argue that current subgraph extraction methods underestimate the importance of structural dependencies among evidence facts. We propose Evidence Pattern Retrieval (EPR) to explicitly model the structural dependencies during subgraph extraction. We implement EPR by indexing the atomic adjacency pattern formed by resource pairs. Given a question, we perform dense retrieval to obtain atomic patterns. We then enumerate their combinations to construct candidate evidence patterns. These evidence patterns are scored using a neural model, and the best one is selected to extract a subgraph for downstream answer reasoning. Experimental results demonstrate that the EPR-based approach has significantly improved the F1 scores of IR-KGQA methods by over 10 points on ComplexWebQuestions and achieves competitive performance on WebQuestionsSP.

Graph-structured data describing entities and their properties has become a notable component of the Web. With the increasing size of data graphs, an entity is often associated with too many property values to be entirely shown to the user, thereby requiring a compact but characteristic summary to present its most distinguishing features. This paper aims to automatically generate such characteristic entity summaries for human users. To achieve it, we exploit the informativeness of property values by analyzing the data graph using information theory. To improve the utility of information carried by a summary, we learn it from a text corpus. To reduce the information redundancy of a summary, we perform logical reasoning and measure similarity with statistical support. We formalize the entity summarization problem considering these factors as combinatorial optimization problems to solve. Experiments based on a real data graph and hand-crafted gold standards show that our approach improves on two state-of-the-art approaches in F-measure by 20.63%-38.79%.

Entity alignment—the discovery of identical entities across different knowledge graphs (KGs)—is a critical task in data fusion. In this paper, we revisit existing entity alignment methods in practical and challenging scenarios. Our empirical studies show that current work has a low level of robustness to long-tail entities and the lack of entity names or relation triples. We aim to develop a robust and adaptive entity alignment method, and the availability of relations, attributes, or names is not required. Our method consists of an attribute encoder and a relation encoder, representing an entity by aggregating its attributes or relational neighbors using the attention mechanisms that can highlight the useful attributes and relations in end-to-end learning. To let the encoders complement each other and produce a coherent representation space, we propose adaptive embedding fusion via a gating mechanism. We consider four evaluation settings, i.e., the conventional setting with both relation and attribute triples, as well as three challenging settings without attributes, without relations, without both relations and names, respectively. Results show that our method can achieve state-of-the-art performance. Even in the most challenging setting without relations and names, our method can still achieve promising results while existing methods fail.

The large volume of open data on the Web is expected to be reused and create value. Finding the right data to reuse is a non-trivial task addressed by the recent dataset search systems, which retrieve datasets relevant to a keyword query. An important component of such systems is snippet generation, extracting data from a retrieved dataset to exemplify its content and explain its relevance to the query. Snippet generation algorithms have emerged but were mainly evaluated by user studies. More efficient and reproducible evaluation methods are needed. To meet this challenge, in this article, we present a set of quality metrics for assessing the usefulness of a snippet from different perspectives, and we select and aggregate them into quality profiles for different stages of a dataset search process. Furthermore, we create a benchmark from thousands of collected real-world data needs and datasets, on which we apply the presented quality metrics and profiles to evaluate snippets generated by two existing algorithms and three adapted algorithms. The results, which are reproducible as they are automatically computed without human interaction, show the pros and cons of the tested algorithms and highlight directions for future research. The benchmark data is publicly available.

Given a large graph representing relations between entities, searching for complex relationships (called semantic associations, or SAs for short) between a set of entities is a common type of information needs in many domains. Further, numerous SAs are often abstracted into a few frequent high-level conceptual graph patterns (called SA patterns, or SAPs for short), which organize SAs into interpretable subgroups. Whereas the quality and usefulness of SAs and SAPs have been extensively studied in the literature, in this article we aim to develop faster algorithms for SA search and frequent SAP mining. For the former problem, we leverage distances to prune the search space, and implement a distance oracle to balance the time and space for distance calculation. For the latter problem, we exploit both graph structure and labels to induce fine-grained skeleton-based partitions of SAs, which may be pruned to reduce SAP enumeration. Besides, we generate canonical codes for SAs, which not only enable result deduplication but also are reused in SAP mining to improve the overall performance. We extensively evaluate the efficiency of our algorithms on four large graphs, using both random queries and simulated queries which reproduce the extreme case of finding numerous SAs.

Knowledge bases (KBs) store rich yet heterogeneous entities and facts. Entity resolution (ER) aims to identify entities in KBs which refer to the same real-world object. Recent studies have shown significant benefits of involving humans in the loop of ER. They often resolve entities with pairwise similarity measures over attribute values and resort to the crowds to label uncertain ones. However, existing methods still suffer from high labor costs and insufficient labeling to some extent. In this paper, we propose a novel approach called crowdsourced collective ER, which leverages the relationships between entities to infer matches jointly rather than independently. Specifically, it iteratively asks human workers to label picked entity pairs and propagates the labeling information to their neighbors in distance. During this process, we address the problems of candidate entity pruning, probabilistic propagation, optimal question selection and error-tolerant truth inference. Our experiments on real-world datasets demonstrate that, compared with state-of-the-art methods, our approach achieves superior accuracy with much less labeling.

Reusing published datasets on the Web is of great interest to researchers and developers. Their data needs may be met by submitting queries to a dataset search engine to retrieve relevant datasets. In this ongoing work towards developing a more usable dataset search engine, we characterize real data needs by annotating the semantics of 1,947 queries using a novel fine-grained scheme, to provide implications for enhancing dataset search. Based on the findings, we present a query-centered framework for dataset search, and explore the implementation of snippet generation and evaluate it with a preliminary user study.

Knowledge graphs (KGs) have been extensively used to annotate text, e.g., news articles, in order to enhance its comprehension by readers. This requires to map entities occurring in the news to the target entities of the KG and to extract a so-called relationship sub-graph (RSG) that spans these entities. RSG extraction is computationally demanding and cannot scale to large KGs. Existing approximation algorithms that focus on structurally compact RSGs are not satisfactory since they often return no answers. We address this problem and develop an efficient algorithm to find approximations that connect the most salient subset of the target entities. Moreover, we propose a context-aware method to rank RSGs by their relevance to the news and their semantic cohesion. In the demo we will present our approach and the attendees will be able to experience how our system MiCRon helps to make sense of news article by computing and presenting RSGs relevant to these articles.

Relevance search over a knowledge graph (KG) has gained much research attention. Given a query entity in a KG, the problem is to find its most relevant entities. However, the relevance function is hidden and dynamic. Different users for different queries may consider relevance from different angles of semantics. The ambiguity in a query is more noticeable in the presence of thousands of types of entities and relations in a schema-rich KG, which has challenged the effectiveness and scalability of existing methods. To meet the challenge, our approach called RelSUE requests a user to provide a small number of answer entities as examples, and then automatically learns the most likely relevance function from these examples. Specifically, we assume the intent of a query can be characterized by a set of meta-paths at the schema level. RelSUE searches a KG for diversified significant meta-paths that best characterize the relevance of the user-provided examples to the query entity. It reduces the large search space of a schema-rich KG using distance and degree-based heuristics, and performs reasoning to deduplicate meta-paths that represent equivalent query-specific semantics. Finally, a linear model is learned to predict meta-path based relevance. Extensive experiments demonstrate that RelSUE outperforms several state-of-the-art methods.

Triple-structured open data creates value in many ways. However, the reuse of datasets is still challenging. Users feel difficult to assess the usefulness of a large dataset containing thousands or millions of triples. To satisfy the needs, existing abstractive methods produce a concise high-level abstraction of data. Complementary to that, we adopt the extractive strategy and aim to select the optimum small subset of data from a dataset as a snippet to compactly illustrate the content of the dataset. This has been formulated as a combinatorial optimization problem in our previous work. In this article, we design a new algorithm for the problem, which is an order of magnitude faster than the previous one but has the same approximation ratio. We also develop an anytime algorithm that can generate empirically better solutions using additional time. To suit datasets that are partially accessible via online query services (e.g., SPARQL endpoints for RDF data), we adapt our algorithms to trade off quality of snippet for feasibility and efficiency in the Web environment. We carry out extensive experiments based on real RDF datasets and SPARQL endpoints for evaluating quality and running time. The results demonstrate the effectiveness and practicality of our proposed algorithms.

Searching for associations between entities is needed in many domains. It has been facilitated by the emergence of graph-structured semantic data on the Web, which offers structured semantic associations more explicit than those hiding in unstructured text for computers to discover. The increasing volume of semantic data requires ranking techniques to identify the more important semantic associations for users. Considering a lack of comprehensive empirical evaluation of existing techniques, we carry out an extensive evaluation of eight techniques including two novel ones we propose. The practical effectiveness of these techniques is assessed based on 1,200 ground-truth rankings created by 30 human experts for real-life semantic associations and on the explanations given by the experts.

Entity resolution (ER), the process of identifying entities that refer to the same real-world object, has long been studied in the knowledge graph (KG) community, among many others. Humans, as a valuable source of background knowledge, are increasingly getting involved in this loop by crowdsourcing and active learning, where presenting condensed and easily-compared information is vital to help human intervene in an ER task. However, current methods for single entity or pairwise summarization cannot well support humans to observe and compare multiple entities simultaneously, which impairs the efficiency and accuracy of human intervention. In this paper, we propose an automated approach to select a few important properties and values for a set of entities, and assemble them by a comparative table. We formulate several optimization problems for generating an optimal comparative table according to intuitive goodness measures and various constraints. Our experiments on real-world datasets, comparison with related work and user study demonstrate the superior efficiency, precision and user satisfaction of our approach in multi-entity resolution (MER).

Properties are used to describe entities, and a part of them are likely to be clustered together to constitute an aspect. For example, first name, middle name and last name are usually gathered to describe a person's name. However, existing automated approaches to property clustering remain far from satisfactory for an open domain like Linked Data. In this paper, the authors firstly investigated the relatedness between properties using 13 different measures. Then, they employed seven clustering algorithms and two combination methods for property clustering. Based on a sample set of Linked Data, the authors empirically studied property clustering in Linked Data and found that a proper combination of different measures and clustering algorithms gave rise to the best result. Additionally, they reported how property clustering can improve user experience in an entity browsing system.

To embrace the open data movement, increasingly many datasets have been published on the Web to be reused. Users, when assessing the usefulness of an unfamiliar dataset, need means to quickly inspect its contents. To satisfy the needs, we propose to automatically extract an optimal small portion from a dataset, called a snippet, to concisely illustrate the contents of the dataset. We consider the quality of a snippet from three aspects: coverage, familiarity, and cohesion, which are jointly formulated in a new combinatorial optimization problem called the maximum-weight-and-coverage connected graph problem (MwcCG). We give a constant-factor approximation algorithm for this NP-hard problem, and experiment with our solution on real-world datasets. Our quantitative analysis and user study show that our approach outperforms a baseline approach.

Searching for associations between entities is needed in many domains like national security and bioinformatics. In recent years, it has been facilitated by the emergence of graph-structured semantic data on the Web, which offers structured semantic associations more explicit than those hiding in unstructured text for computers to discover. The increasing volume of semantic data often produces excessively many semantic associations, and requires ranking techniques to identify the more important ones for users. Despite the fruitful theoretical research on innovative ranking techniques, there is a lack of comprehensive empirical evaluation of these techniques. In this article, we carry out an extensive evaluation of eight techniques for ranking semantic associations, including two novel ones we propose. The practical effectiveness of these techniques is assessed based on 1,200 ground-truth rankings created by 30 human experts for real-life semantic associations and the explanations given by the experts. Our findings also suggest a number of directions in improving existing techniques and developing novel techniques for future work.

While the geographical domain has long been involved as an important part of the Linked Data, the small amount of Chinese linked geographical data impedes the integration and sharing of both Chinese and cross-lingual knowledge. In this paper, we contribute to the development of a new Chinese linked geographical dataset named Clinga, by obtaining data from the largest Chinese wiki encyclopedia. We manually design a new geography ontology to categorize a wide range of physical and human geographical entities, and carry out an automatic discovery of links to existing knowledge bases. The resulted dataset contains over half million Chinese geographical entities and is open access.

Entity linking connects the Web of documents with knowledge bases. It is the task of linking an entity mention in text to its corresponding entity in a knowledge base. Whereas a large body of work has been devoted to automatically generating candidate entities, or ranking and choosing from them, manual efforts are still needed, e.g., for defining gold-standard links for evaluating automatic approaches, and for improving the quality of links in crowdsourcing approaches. However, structured descriptions of entities in knowledge bases are sometimes very long. To avoid overloading human users with too much information and help them more efficiently choose an entity from candidates, we aim to substitute entire entity descriptions with compact, equally effective structured summaries that are automatically generated. To achieve it, our approach analyzes entity descriptions in the knowledge base and the context of entity mention from multiple perspectives, including characterizing and differentiating power, information overlap, and relevance to context. Extrinsic evaluation (where human users carry out entity linking tasks) and intrinsic evaluation (where human users rate summaries) demonstrate that summaries generated by our approach help human users carry out entity linking tasks more efficiently (22-23% faster), without significantly affecting the quality of links obtained; and our approach outperforms existing approaches to summarizing entity descriptions.

An object on the Semantic Web is likely to be denoted with multiple URIs by different parties. Object coreference resolution is to identify "equivalent" URIs that denote the same object. Driven by the Linking Open Data (LOD) initiative, millions of URIs have been explicitly linked with owl:sameAs statements, but potentially coreferent ones are still considerable. Existing approaches address the problem mainly from two directions: one is based upon equivalence inference mandated by OWL semantics, which finds semantically coreferent URIs but probably omits many potential ones; the other is via similarity computation between property-value pairs, which is not always accurate enough. In this paper, we propose a self-training approach for object coreference resolution on the Semantic Web, which leverages the two classes of approaches to bridge the gap between semantically coreferent URIs and potential candidates. For an object URI, we firstly establish a kernel that consists of semantically coreferent URIs based on owl:sameAs, (inverse) functional properties and (max-)cardinalities, and then extend such kernel iteratively in terms of discriminative property-value pairs in the descriptions of URIs. In particular, the discriminability is learnt with a statistical measurement, which not only exploits key characteristics for representing an object, but also takes into account the matchability between properties from pragmatics. In addition, frequent property combinations are mined to improve the accuracy of the resolution. We implement a scalable system and demonstrate that our approach achieves good precision and recall for resolving object coreference, on both benchmark and large-scale datasets.

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.

Along with the rapid growth of the data Web, searching linked objects for information needs and for reusing become emergent for ordinary Web users and developers, respectively. To meet the challenge, we present Falcons Object Search, a keyword-based search engine for linked objects. To serve various keyword queries, for each object the system constructs a comprehensive virtual document including not only associated literals but also the textual descriptions of associated links and linked objects. The resulting objects are ranked by considering both their relevance to the query and their popularity. For each resulting object, a query-relevant structured snippet is provided to show the associated literals and linked objects matched with the query. Besides, Web-scale class-inclusion reasoning is performed to discover implicit typing information, and users could navigate class hierarchies for incremental class-based results filtering. The results of a task-based experiment show the promising features of the system.

This paper introduces dynamic view, a novel semantic Web application, for users to query, browse and visualize distribution and evolution of research interests in temporal, spatial and ontological dimensions. We present motivation and requirements for a system like dynamic view, and discuss its architecture. We outline main algorithms for term extraction from Web pages of researchers and ontology learning from digital libraries, and sketch visualizations we used in the interface.

As of today, the amount of data on the Semantic Web has grown considerably. The services for searching and browsing entities on the Semantic Web are in demand. To provide such services, we developed the Falcons system. In this poster, we present the features of the Falcons system.

Ontology summarization is very important to quick understanding and selection of ontologies. In this paper, we study extractive summarization of ontology. We propose a notion of RDF sentence as the basic unit of summarization. An RDF Sentence Graph is proposed to characterize the links between RDF sentences derived from a given ontology. The salience of each RDF sentence is assessed in terms of its "centrality" in the graph. We propose to summarize an ontology by extracting a set of salient RDF sentences according to a re-ranking strategy. We compare several measurements in assessing the salience of RDF sentences and give an overall evaluation of experiment results, which shows that our approach to ontology summarization is feasible.

On the investigation of linguistic techniques used in ontology matching, we propose a new idea of virtual documents to pursue a cost-effective approach to linguistic matching in this paper. Basically, as a collection of weighted words, the virtual document of a URIref declared in an ontology contains not only the local descriptions but also the neighboring information to reflect the intended meaning of the URIref. Document similarity can be computed by traditional vector space techniques, and then be used in the similaritybased approaches to ontology matching. In particular, the RDF graph structure is exploited to define the description formulations and the neighboring operations. Experimental results show that linguistic matching based on the virtual documents is dominant in average F-Measure as compared to other three approaches. It is also demonstrated by our experiments that the virtual documents approach is cost-effective as compared to other linguistic matching approaches.