New Embedded Representations and Evaluation Protocols for Inferring Transitive Relations

Beyond word embeddings, continuous representations of knowledge graph (KG) components, such as entities, types and relations, are widely used for entity mention disambiguation, relation inference and deep question answering. Great strides have been made in modeling general, asymmetric or antisymmetric KG relations using Gaussian, holographic, and complex embeddings. None of these directly enforce transitivity inherent in the is-instance-of and is-subtype-of relations. A recent proposal, called order embedding (OE), demands that the vector representing a subtype elementwise dominates the vector representing a supertype. However, the manner in which such constraints are asserted and evaluated have some limitations. In this short research note, we make three contributions specific to representing and inferring transitive relations. First, we propose and justify a significant improvement to the OE loss objective. Second, we propose a new representation of types as hyper-rectangular regions, that generalize and improve on OE. Third, we show that some current protocols to evaluate transitive relation inference can be misleading, and offer a sound alternative. Rather than use black-box deep learning modules off-the-shelf, we develop our training networks using elementary geometric considerations.Comment: Accepted at SIGIR 201

Integrating the document object model with hyperlinks for enhanced topic distillation and information extraction

Topic distillation is the process of finding authoritative Web pages and comprehensive “hubs” which reciprocally endorse each other and are relevant to a given query. Hyperlink-based topic distillation has been traditionally applied to a macroscopic Web model where documents are nodes in a directed graph and hyperlinks are edges. Macroscopic models miss valuable clues such as banners, navigation panels, and template-based inclusions, which are embedded in HTML pages using markup tags. Consequently, results of macroscopic distillation algorithms have been deteriorating in quality as Web pages are becoming more complex. We propose a uniform fine-grained model for the Web in which pages are represented by their tag trees (also called their Document Object Models or DOMs) and these DOM trees are interconnected by ordinary hyperlinks. Surprisingly, macroscopic distillation algorithms do not work in the finegrained scenario. We present a new algorithm suitable for the fine-grained model. It can dis-aggregate hubs into coherent regions by segmenting their DOM trees. Mutual endorsement between hubs and authorities involve these regions, rather than single nodes representing complete hubs. Anecdotes and measurements using a 28-query, 366000-document benchmark suite, used in earlier topic distillation research, reveal two benefits from the new algorithm: distillation quality improves and a by-product of distillation is the ability to extract relevant snippets from hubs which are only partially relevant to the query

Discriminative Link Prediction using Local Links, Node Features and Community Structure

A link prediction (LP) algorithm is given a graph, and has to rank, for each node, other nodes that are candidates for new linkage. LP is strongly motivated by social search and recommendation applications. LP techniques often focus on global properties (graph conductance, hitting or commute times, Katz score) or local properties (Adamic-Adar and many variations, or node feature vectors), but rarely combine these signals. Furthermore, neither of these extremes exploit link densities at the intermediate level of communities. In this paper we describe a discriminative LP algorithm that exploits two new signals. First, a co-clustering algorithm provides community level link density estimates, which are used to qualify observed links with a surprise value. Second, links in the immediate neighborhood of the link to be predicted are not interpreted at face value, but through a local model of node feature similarities. These signals are combined into a discriminative link predictor. We evaluate the new predictor using five diverse data sets that are standard in the literature. We report on significant accuracy boosts compared to standard LP methods (including Adamic-Adar and random walk). Apart from the new predictor, another contribution is a rigorous protocol for benchmarking and reporting LP algorithms, which reveals the regions of strengths and weaknesses of all the predictors studied here, and establishes the new proposal as the most robust.Comment: 10 pages, 5 figure

Studies of dissipative standing shock waves around black holes

We investigate the dynamical structure of advective accretion flow around stationary as well as rotating black holes. For a suitable choice of input parameters, such as, accretion rate ($\dot {\cal M}$) and angular momentum ($\lambda$), global accretion solution may include a shock wave. The post shock flow is located at few tens of Schwarzchild radius and it is generally very hot and dense. This successfully mimics the so called Compton cloud which is believed to be responsible for emitting hard radiations. Due to the radiative loss, a significant energy from the accreting matter is removed and the shock moves forward towards the black hole in order to maintain the pressure balance across it. We identify the effective area of the parameter space ($\dot {\cal M} - \lambda$) which allows accretion flows to have some energy dissipation at the shock $(\Delta {\cal E})$. As the dissipation is increased, the parameter space is reduced and finally disappears when the dissipation is reached its critical value. The dissipation has a profound effect on the dynamics of post-shock flow. By moving forward, an unstable shock whose oscillation causes Quasi-Periodic Oscillations (QPOs) in the emitted radiation, will produce oscillations of high frequency. Such an evolution of QPOs has been observed in several black hole candidates during their outbursts.Comment: 13 pages, 5 figures, accepted by MNRA

Neural Architecture for Question Answering Using a Knowledge Graph and Web Corpus

In Web search, entity-seeking queries often trigger a special Question Answering (QA) system. It may use a parser to interpret the question to a structured query, execute that on a knowledge graph (KG), and return direct entity responses. QA systems based on precise parsing tend to be brittle: minor syntax variations may dramatically change the response. Moreover, KG coverage is patchy. At the other extreme, a large corpus may provide broader coverage, but in an unstructured, unreliable form. We present AQQUCN, a QA system that gracefully combines KG and corpus evidence. AQQUCN accepts a broad spectrum of query syntax, between well-formed questions to short `telegraphic' keyword sequences. In the face of inherent query ambiguities, AQQUCN aggregates signals from KGs and large corpora to directly rank KG entities, rather than commit to one semantic interpretation of the query. AQQUCN models the ideal interpretation as an unobservable or latent variable. Interpretations and candidate entity responses are scored as pairs, by combining signals from multiple convolutional networks that operate collectively on the query, KG and corpus. On four public query workloads, amounting to over 8,000 queries with diverse query syntax, we see 5--16% absolute improvement in mean average precision (MAP), compared to the entity ranking performance of recent systems. Our system is also competitive at entity set retrieval, almost doubling F1 scores for challenging short queries.Comment: Accepted to Information Retrieval Journa