3 posts tagged with "knowledge graph"

Real estate is inherently information‑dense: each property listing, zoning record, mortgage filing, or rental transaction embeds dozens of entities (people, places, organizations), relationships (ownership, financing, management), and events (sale, lease, foreclosure, renovation). Yet, most of this data is siloed in heterogeneous web pages, PDFs, portals, and APIs. A real estate knowledge graph (KG) aims to unify these signals into a structured, queryable representation that can support search, valuation, underwriting, risk analysis, and market intelligence.

Search engine optimization (SEO) is undergoing a structural shift from keyword-centric tactics to entity- and intent-centric strategies shaped by advances in knowledge graphs and machine learning. Rank tracking is no longer just about positions for a set of keywords; it now requires understanding how search engine result pages (SERPs), entities, and real‑time news or events interact in a dynamic ecosystem.

As organizations ingest ever-larger volumes of data from the web, they increasingly rely on knowledge graphs (KGs) to model entities (people, organizations, products, places) and their relationships in a structured way. However, web data is heterogeneous, noisy, and heavily duplicated. The same entity may appear thousands of times across sites, with different names, formats, partial data, or conflicting attributes. Without robust deduplication and canonicalization, a scraped knowledge graph quickly becomes fragmented, inaccurate, and operationally useless.