Entity Correspondence with Second-Order Markov Logic

Integrating the extracted facts with an existing knowledge base has raised an urgent need to address the problem of entity linking. Specifically, entity linking is the task to link the entity mention in text with the corresponding real world entity in the existing knowledge base. However, this task is challenging due to name ambiguity, textual inconsistency, and lack of world knowledge in the knowledge base. Several methods have been proposed to tackle this problem, but they are largely based on the co-occurrence statistics of terms between the text around the entity mention and the document associated with the entity. In this paper, we propose LINDEN, a novel framework to link named entities in text with a knowledge base unifying Wikipedia and WordNet, by leveraging the rich semantic knowledge embedded in the Wikipedia and the taxonomy of the knowledge base. We extensively evaluate the performance of our proposed LINDEN over two public data sets and empirical results show that LINDEN significantly outperforms the state-of-the-art methods in terms of accuracy.

A critical step in bridging the knowledge base with the huge corpus of semi-structured Web list data is to link the entity mentions that appear in the Web lists with the corresponding real world entities in the knowledge base, which we call list linking task. This task can facilitate many different tasks such as knowledge base population, entity search and table annotation. However, the list linking task is challenging because a Web list has almost no textual context, and the only input for this task is a list of entity mentions extracted from the Web pages. In this paper, we propose LIEGE, the first general framework to Link the entities in web lists with the knowledge base to the best of our knowledge. Our assumption is that entities mentioned in a Web list can be any collection of entities that have the same conceptual type that people have in mind. To annotate the list items in a Web list with entities that they likely mention, we leverage the prior probability of an entity being mentioned and the global coherence between the types of entities in the Web list. The interdependence between different entity assignments in a Web list makes the optimization of this list linking problem NP-hard. Accordingly, we propose a practical solution based on the iterative substitution to jointly optimize the identification of the mapping entities for the Web list items. We extensively evaluated the performance of our proposed framework over both manually annotated real Web lists extracted from the Web pages and two public data sets, and the experimental results show that our framework significantly outperforms the baseline method in terms of accuracy.

Duplicate detection is the process of detecting multiple representations of same real world's entities. Nowadays, data is known to be heterogeneous, and the larger it becomes, the pairwise comparisons number grows highly as well which makes the task more complex. In recent years, many approaches have been developed and attempted to reduce the number of record pair's comparisons in the process while maintaining high matching quality. There are two well-known algorithms, which are the Sorted Neighborhood Method (SNM) and the Blocking algorithms. Being inspired by both algorithms, we propose an Enhanced Duplicate Count Strategy which is a new hybrid approach that creates iterative blocks using windows with dynamic size. It is based on comparing next element with last duplicate found in the current window. Consequently, comparisons are saved, and similarity distance is minimized, which can lead to higher matching quality.

Record linkage deals with finding records that identify the same real world entity, such as an individual or a business, from a given file or set of files. Record linkage problem is also referred to as the entity resolution or record recognition problem. To locate those records identifying the same real world entity, in principle, pairwise record analyses have to be performed among all records. Analytical operations between two records vary from comparing corresponding fields to enhancing records through large knowledge bases and querying large databases. Hence, these operations are complex and take time. To reduce the number of pairwise record comparisons, blocking techniques are introduced to partition the records into blocks. After that records in each block are analyzed against one and another. One of the effective blocking methods is the closure approach, where a “related” equivalence relation is used to partition the records into equivalence classes. This paper introduces the closure problem and describes the design and implementation of a parallel and distributed closure prototype system running in an enterprise grid.

Transgressive Women, Transworld Women

Robotic systems operating in the real world would benefit from a clear semantic model to understand their interactions with the real world. Such semantics are typically captured in an ontology. Unfortunately, the underlying model of existing ontologies requires many work-arounds before it can be used to capture general knowledge about objects and interactions in the real physical world. To remove such work-arounds, this article adopts the richer hypergraph model. It is used to develop an ontology, which is further implemented as the knowledge base of an actual robotic system performing search operations. Also, actual information extracted from the robot's sensors is used to update its knowledge base logically and sensibly.

Entity-based searching has been introduced as a way of allowing users and applications to retrieve information about a specific real world object such as a person, an event, or a location. Recent advances in crawling, information extraction, and data exchange technologies have brought a new era in data management, typically referred to through the term Web 2.0. Entity searching over Web 2.0 data facilitates the retrieval of relevant information from the plethora of data available in semantic and social web applications. Effective entity searching over a variety of sources requires the integration of the different pieces of information that refer to the same real world entity. Entity-based aggregation of Web 2.0 data is an effective mechanism towards this direction. Adopting the suggestions of the Linked Data movement, aggregators are able to efficiently match and merge the data that refer to the same real world object.

Simulation in robotics is often a love-hate relationship: while simulators do save us a lot of time and effort compared to regular deployment of complex software architectures on complex hardware, simulators are also known to evade many of the real issues that robots need to manage when they enter the real world. Because humans are the paragon of dynamic, unpredictable, complex, real world entities, simulation of human-robot interactions may look condemn to fail, or, in the best case, to be mostly useless. This collective article reports on five independent applications of the MORSE simulator in the field of human-robot interaction: It appears that simulation is already useful, if not essential, to successfully carry out research in the field of HRI, and sometimes in scenarios we do not anticipate.KeywordsReal RobotVirtual HumanObject ArrangementReal World EntityAutomate ExecutionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Recently, knowledge bases (KBs) have become more and more essential and helpful data source for various applications and researches. Although modern KBs have included thousands of millions of facts, they still suffer from incompleteness compared with the total amount of facts in real world. Furthermore, a lot of inaccurate and outdated facts may be contained in the KBs. Although there have been many studies dealing with incompleteness of the KBs, very few of works have taken into account detecting the errors in the KBs. Broadly speaking, there are three main challenges in detecting errors in the KBs. (1) Symbolic and logical form of the knowledge representations cannot detect the inconsistencies very well on large scale KBs. (2) It is hard to capture the correlations between relations. (3) There is no golden standard to learn or observe the patterns of inaccurate facts. In this work, we propose a Relation Sensitive Embedding Approach (RSEA) to detect the inconsistencies from KBs. We first design two correlation functions to measure the relatedness between two relations. Then, a dynamic cluster algorithm is presented to aggregate highly correlated relations into the same clusters. Finally, we encode discrete knowledge facts with effects of correlated relations into continuous vector space, which can effectively detect errors in KBs. We perform extensive experiments on two benchmark datasets, and the results show that our approach achieves high performance in detecting incorrect knowledge facts in these KBs.

Extracting and representing user interests on the Social Web is becoming an essential part of the Web for personalisation and recommendations. Such personalisation is required in order to provide an adaptive Web to users, where content fits their preferences, background and current interests, making the Web more social and relevant. Current techniques analyse user activities on social media systems and collect structured or unstructured sets of entities representing users' interests. These sets of entities, or user profiles of interest, are often missing the semantics of the entities in terms of: (i) popularity and temporal dynamics of the interests on the Social Web and (ii) abstractness of the entities in the real world. State of the art techniques to compute these values are using specific knowledge bases or taxonomies and need to analyse the dynamics of the entities over a period of time. Hence, we propose a real-time, computationally inexpensive, domain independent model for concepts of interest composed of: popularity, temporal dynamics and specificity. We describe and evaluate a novel algorithm for computing specificity leveraging the semantics of Linked Data and evaluate the impact of our model on user profiles of interests.

Spatial data from different geographic databases can show a high degree of diversity in terms of object modelling, thematic information, completeness, or currentness of data. Thus, between datasets, database objects representing, e.g., the same road of the real world can show strong differences. Integrating two or more spatial data sources requires a matching on the instance level to identify and link multiple representations of the same real-world entities. Spatial matching has to analyze complex objects for their geometry and other attributes, as well as the dataset topology in order to calculate a matching between geographic objects based on similarity.

Modern Information and Communication Technologies ICT require very accurate and up-to-date knowledge resources, such as databases and knowledge bases, providing information about real-world entities e.g. locations, persons, events which can guarantee that results of automatic processing can be trusted enough for decision-making processes. The solutions employed so far to guarantee their quality mainly rely on the automatic application of integrity constraints for databases and consistency checks for knowledge bases. In order to achieve a higher accuracy, there is also a recent trend in complementing automatic with manual checks, via crowdsourcing techniques. This paper presents a methodology and an evaluation framework, based on the definition and application of a semantic schema, which analyses the sometimes hidden semantics of the terms in the entity descriptions from a knowledge resource, and allows assessing its quality and the identification of those potentially faulty parts which would benefit from manual checks. The approach is particularly suited for schema-less resources, i.e. resources in which entities do not follow a unique and explicit schema. Our evaluation showed promising results.

Inconsistency and its handling are very important in the real world and in the fields of computer science and artificial intelligence. When dealing with inconsistency in a knowledge base (KB), there is a whole host of deeper issues we need to contend with in order to develop rational and robust intelligent systems. In this paper, we focus our attention on one of the issues in handling KB inconsistency: how to measure the information content and the significance of inconsistency in a KB. Our approach is based on a fixpoint semantics for KB. The approach reflects each inconsistent set of rules in the least fixpoint of a KB and then measures the inconsistency in the context of the least fixpoint for the KB. Compared with the existing results, our approach has some unique benefits.

Recent studies have shown that machine learning as a service is susceptible to reconstruction attacks that would expose users' privacy. When it comes to face recognition systems, privacy leakage could incur unpredictable effects on confidential or financial matters. This paper focuses on black-box reconstruction attacks on face recognition systems, including face verification and face identification applications. Considering a face recognition system in the real world, the adversary only has access to the similarity score of a query compared to a certain ID. We build our method based on a pretrained StyleGAN face generator and optimize its latent code to obtain a face image that maximizes the similarity score. Given the similarity score alone, we adopt zeroth-order optimization to estimate the gradients faithfully. Our method can attack 1:1 verification system with efficient and high-quality. To validate the effectiveness of the proposed method, we perform extensive experiments on face recognition systems, and reveal their risks of privacy leakage.

XML documents are becoming the most common approach to represent entities of the real world. Users need to select such entities based on their expectations in terms of both requirements and preferences. The selection process should deal with a potentially high number of documents representing similar entities and with the diversity of the perceived satisfactions of the users. In this paper, we present XMatch, a query language enabling the expression of users requests about the expected satisfaction over XML documents. This language improves the expressiveness of queries and supports aggregation of an high number of elementary satisfactions.